Integration of microalgae culturing within · Integration of microalgae culturing within the wastewater scheme Oscar Mauricio Martínez Avila Matr. 797677 Supervisor: PhD. Eng. Elena

Integration of microalgae culturing within the wastewater scheme

Oscar Mauricio Martínez Avila

Matr. 797677

Supervisor:

PhD. Eng. Elena Ficara

Politecnico di Milano

School of Industrial and Information Engineering

M.Sc. Energy Engineering

Piacenza, Italy

A.Y. 2013-2014

Acknowledgments

First of all I would like to thanks to Professor Elena Ficara for her valuable support, for the

time she devoted to supervising this project and all her opinions and suggestion, but

mainly for her kindness and comprehension with me during the development of this

project. Furthermore, I would like to acknowledge to Professor Valeria Mezzanote who

has also supported and advised any step of the project with her helpful indications. A

special thanks to Chiara, Enrica and Mattia, who have helped me in different ways, but

mainly thanks for holding me with my awful Italian.

Abstract V

Abstract This document provides an analysis of the potential improvements involved in the implementation of a microalgae culture integrated to the typical depurative cycle of a wastewater treatment plant. In particular, it is of interest the integration with the anaerobic digestion of the waste sludge, since the biogas produced from the anaerobic co-digestion of these feeds can be used to generate onsite electrical power or thermal heat to offset biomass processing and extraction processes. In fact, when both processes are integrated and operated simultaneously, the benefits to wastewater treatment are extended beyond the energy generation, including a considerably improvement of the effluent quality. For this purpose a Pilot plant scale culture was tested in order to define the operational parameter required for modeling this unit in an integrated scheme WWTP-PBR. From the simulations, it is evident the increase on produced biogas due to the co-digestion process, as well as the boost quality in the centrate stream. The outcomes confirm the potential of this scheme for improving the energy balance of the WWTP, but it is essential to go into deep in the optimization of the culture system, as well as the harvesting process, in order to avoid possible negative effects.

Keywords: Microalgae, Anaerobic Digestion, WWTP, Ma ss balance, Integration

Contents VII

Table of Contents

Pg.

Abstract........................................... ................................................................................. V

List of figures.................................... .............................................................................. IX

List of tables .................................... ............................................................................... XI

List of Symbols and abbreviations ................. ............................................................. XII

Introduction ...................................... ............................................................................... 1

1. State of the art .................................. ........................................................................ 5

1.1 Current expectations on microalgal biomass .................................................... 5

1.2 Microalgae characteristics ............................................................................... 7

1.2.1 Microalgae growth ................................................................................. 9 1.2.2 Culture systems .................................................................................. 15

1.2.3 Productivity of microalgae cultures ...................................................... 24

1.3 Anaerobic digestion process .......................................................................... 26

1.3.1 Biochemistry of the AD process .......................................................... 28

1.3.2 Factors affecting AD process .............................................................. 31

1.3.3 Potential of microalgae species for producing methane....................... 34

1.4 Integration of microalgae growth with AD ....................................................... 37

2. Mathematical models for algae growth .............. ................................................... 41

2.1 Mass and energy balances on PBR ............................................................... 42 2.2 Kinetic models for nutrients ............................................................................ 43

2.3 Kinetic models related to light intensity .......................................................... 46

2.4 Multiple factors kinetics .................................................................................. 51

3. Experimental phase ................................ ............................................................... 53

3.1 Algae growth in batch operation ..................................................................... 54

3.2 Algae growth in continuous operation ............................................................ 60

3.3 Anaerobic digestion tests ............................................................................... 65

3.4 Harvesting of produced algae ........................................................................ 70

4. Integrated Mass Balance ........................... ............................................................ 73

4.1 Wastewater section ....................................................................................... 76

4.2 Sludge treatment section ............................................................................... 80

4.3 Combined Heat and Power section ................................................................ 85 4.4 Main results of the model ............................................................................... 87

VIII Integration of microalgae culturing within the wastewater

5. Conclusions and Recommendations ................... .................................................91

A. Appendix: Raw operational data in Bresso WWTP 2012- 2013 .............................93

B. Appendix: Combustion reaction analysis in CHP unit .........................................99

C. Appendix: Description of the solution in Excel .... .............................................. 103

D. Appendix: Description of the experimental systems a nd related procedures . 107

Bibliography ...................................... ........................................................................... 113

Contents IX

List of figures Pg.

Figure 1-1. Energy feedstocks processing using microalgae. ............................................6

Figure 1-2. Photosynthetic activity behavior of microalgae. ...............................................9

Figure 1-3. Algae growth phases. ....................................................................................10

Figure 1-4. Biomass productivity as function of light intensity ..........................................12

Figure 1-5. Biomass productivity as a function of culture mixing ......................................12

Figure 1-6. Comparison of microalgal CO2 fixation ability. ...............................................14

Figure 1-7. Typical open system configurations. ..............................................................16

Figure 1-8. Typical raceway pond configuration. ..............................................................16

Figure 1-9. Tubular PBR of parallel runs..........................................................................18

Figure 1-10. Flat plate PBR configuration. .......................................................................18

Figure 1-11. Bubbling column PBR. .................................................................................20

Figure 1-12. Main interactions accounted for photobioreactors design. ...........................20

Figure 1-13. Light pathway and light-darkness cycles ......................................................21

Figure 1-14. CO2 Mass transfer coefficient between gas and liquid phases. ....................23

Figure 1-15. Volumetric productivity of some microalgae strains .....................................25

Figure 1-16. Mean Pvol and Pareal for different PBR configurations. ...................................26

Figure 1-17. Typical routes for processing anaerobic digestion products. ........................27

Figure 1-18. Biogas yield and methane content of different substrates. ...........................28

Figure 1-19. Stages of the anaerobic digestion process. .................................................29

Figure 1-20. Growth rate of microorganisms in each temperature range. ........................31

Figure 1-21. Theoretical Biological methane potential of some microalgae species .........36

Figure 1-22. Methane yield of some algae strains. ..........................................................36

Figure 1-23. Scheme of integration AD-microalgae-wastewater proposed by Uttley ........37

Figure 1-24. Scheme of integration AD-microalgae-wastewater by Yuang ......................38

Figure 1-25. Scheme of integration AD-microalgae-wastewater according Sahu .............39

Figure 1-26. Proposed scheme of integration in Bresso plant. .........................................40

Figure 2-1. Operating variables of a PBR for algae growth. .............................................41

Figure 2-2. Irradiance profile along a flat-plate PBR. .......................................................48

Figure 2-3. Incident radiation and its relationship with a given point inside the culture .....50

Figure 3-1. Methodology employed in the experimental part............................................53

Figure 3-2. Current location and appearance of the PBR pilot plant. ...............................55

Figure 3-3. Procedure for operating the pilot plant in batch mode. ...................................55

Figure 3-4. Evolution of TN and TP concentration during batch runs ...............................56

Figure 3-5. Evolution of N-NH4 concentration during batch runs. .....................................57

Figure 3-6. Evolution of solids content during batch test. .................................................57

X Integration of microalgae culturing within the wastewater

Figure 3-7. Absorbance of the medium during batch runs. .............................................. 58

Figure 3-8. N-NH4 removal on the different batch tests. .................................................. 58

Figure 3-9. TSS concentration change on the batch tests. .............................................. 59

Figure 3-10. Linearization of the kinetic growth using absorbance as reference variable. 60

Figure 3-11. Procedure for operating the pilot plant in continuous mode. ........................ 61

Figure 3-12. Solids content for the continuous operation. ................................................ 62

Figure 3-13. N-NH4 behavior during continuous operation. ............................................. 62

Figure 3-14. Absorbance of the culture medium during continuous operation ................. 63

Figure 3-15. TSS/Abs correspondence in the microalgae culture. ................................... 64

Figure 3-16. Estimated yield parameters for the continuous experiments. ....................... 65

Figure 3-17. Sketch of the bench scale anaerobic digestion system. .............................. 65

Figure 3-18. Procedure for operating the AD system in semi-batch mode. ...................... 66

Figure 3-19. Methane production during the AD tests. .................................................... 68

Figure 3-20. (a) Specific methane production and (b) organic loading rate of AD runs. ... 68

Figure 3-21. Feed and digestate solid contents. .............................................................. 69

Figure 3-22. Settleable solids of produced microalgae during continuous operation. ...... 71

Figure 3-23. Efficiency and solid concentrations after microalgal biomass harvesting. .... 72

Figure 4-1. Transformations included in the ASM1. ......................................................... 74

Figure 4-2. Transformations included in the ADM1 ......................................................... 75

Figure 4-3. Proposed scheme of integration inside Bresso plant ..................................... 76

Figure 4-4. Process units composing the wastewater section in Bresso plant. ................ 76

Figure 4-5. Streams of the wastewater section. ............................................................... 77

Figure 4-6. Units and streams composing the sludge treatment section. ......................... 80

Figure 4-7. Units of the section dedicated to sludge treatment. ....................................... 82

Figure 4-8. Units composing the subsection of microalgae culture. ................................. 83

Figure 4-9. Units and streams composing the CHP section. ............................................ 85

Figure 4-10. Effect of PBR implementation on the produced biogas. ............................... 88

Figure 4-11. Required area for the PBR implementation at different productivities .......... 90

Contents XI

List of tables Pg.

Table 1-1. Main design characteristics of microalgal bioreactor systems. ........................17

Table 1-2. Generic description of mixing methods in PBR´s. ...........................................22

Table 1-3. Typical composition of biogas. ........................................................................27

Table 2-1. Kinetic models for nitrogen dependent algae growth. .....................................44

Table 2-2. Kinetic models for phosphorus dependent algae growth. ................................45

Table 2-3. Kinetic models for carbon dependent algae growth. .......................................46

Table 2-4. Kinetic models related to light intensity and algae growth. ..............................47

Table 2-5. Summary of specific growth rates of previous studies.....................................52

Table 3-1. Estimation of anaerobic digestion efficiency. ..................................................70

Table 4-1. Summary of degree of freedom analysis in wastewater section. .....................79

Table 4-2. Summary of degree of freedom analysis in sludge treatment section. ............81

Table 4-3. Limiting nutrients and maximum expected biogas production. ........................89

Contents XII

List of Symbols and abbreviations

Symbols with Latin letters

Symbol Definition SI Unit A Area m2 Ci Concentration of component/species i Kg/m3; mg/L h Plank constant J.s I Irradiance W.m-2 I Light Intensity µmol.m-2s-1 kLa Global mass transfer coefficient in the liquid phase m.s-1 m Maintenance coefficient Jg-1s-1 mi Mass flow rate of stream i Kg.s-1 ni Molar flow rate of stream i Kmol.s-1

PArea Areal biomass productivity gb.m-2.day-1

PVol Volumetric biomass productivity gb.m-3.day-1

Pdirect Equivalent light path distance for direct irradiance m Pdisp Equivalent light path distance for disperse irradiance m Pel Electric power Kwh.s-1 Qi Volumetric Flow rate stream i m3. s-1

Ri Growth Rate Reduction factor i - rrk Recycle Ratio section k - V Volume m3 Xi Biomass concentration in the medium mg.L-1

Yi Yield coefficient component i - Symbols with Greek letters

Symbol Definition SI Unit ν Wavelength nm η Removal efficiency, Unit efficiency - µ Specific Growth Rate algae s-1

ρ density Kg.m-3 φ angle Rad ϕ Polar coordinate inside a tubular reactor Rad β Stoichiometric Air to fuel ratio - ε Excess fraction of air -

Contents XIII

Abbreviations

Abbreviation Definition AD Anaerobic Digestion ASM1 Activated Sludge Model 1 ADM1 Anaerobic Digestion Model 1 BOD Biological Oxygen Demand BMP Biological Methane Potential CHP Combined Heat and Power Plant COD Chemical Oxygen Demand CV Coefficient of variance DOF Degree Of Freedom HRT Hydraulic Retention Time LHV Low Heating Value OCD Optimal Cell Density OLR Organic Loading Rate PAR Photosynthetically Active Radiation PBR Photobioreactor SMP Specific Methane Production SRT Solid Retention Time STS Settleable solids TOE Tons of oil equivalent TS Total Solids TSS Total Suspended Solids TVS Total Volatile Solids VFA Volatile Fatty Acids WWTP Wastewater Treatment Plant

Introduction The effects of pollutants presented in water streams have wide and heterogeneous characteristics. They affect the surrounded environment, the human health and also increase the pressure over the resource. From this point of view, we realized that a proper treatment of the polluted effluents reduces the adverse effects of those waste products, and at the same time, we increase the chance of reusing these streams for valuable purposes. On the other hand, a suitable design and operation of the treatment stations should be guaranteed in order to fulfill the related environmental requirements while keeping an economical and reliable processing. In terms of wastewater treatment, a widespread amount of techniques have been developed, and they are well established according to the particular characteristics of influents and geographical location of the streams. Most of the technologies include a primary stage of physical separation, a secondary phase based on a biological degradation and a final step to further improve the effluent quality by means of physic-chemical operations. Nevertheless a further consideration emerges from the main by-product of this process, the sludge streams. This is commonly treated for stabilizing it and to reduce its negative effect on the environment by means of composting, aerobic or anaerobic digestion, generating a biosolid that is employed as feedstock in the production of the so-called organic fertilizers. From these treatments, municipal and industrial anaerobic digestion (AD) of organic wastes is broadly practiced and recognized as a mature technology that additionally to treat the sludge, allows producing biogas (Holm-Nielsen et al, 2009). Just in Europe during 2007 energy from sewage sludge derived biogas was 887.2 Ktoe and it is expected that in 2020 this will arrive to 3.4 Mtoe, representing 15% of the total biogas energy production in the region (AEBIOM, 2009). This successful diffusion can be attributable to the advantages AD presents, of which the broad diversity of feedstocks to be employed in the process has played an important role. AD can use a variety of organic materials to produce methane, such as food waste from both domestic and industrial sources, farm manures and slurries, sewage sludge and purpose grown crops for energy. However another AD feedstock largely investigated since the fifties (Golueke et al, 1957) is the algal and microalgal biomass. Microalgae have a high productivity and are able to produce large quantities of biomass in a more efficient way than terrestrial crops. Depending on the system, photosynthetic efficiency of these microorganisms can reach 4–5% of the solar energy compared to 1–2% for

2 Introduction

terrestrial plants (Ward et al, 2014). Furthermore, microalgae can grow in wastewater containing high nutrient concentrations, recovering a substantial portion of them, helping in this way with its treatment (Razzak et al, 2013). In general microalgal biomass production counts with a significant amount of advantages, reason why its culture has been adopted as source for third-generation biofuels. These are also called advanced biofuels and are sourced from non-food crops, but the resulting product is identical to its petroleum counterparts (Frigon et al, 2013). But, even if there is no formal definition of which the most effective process for biofuel production from microalgae is, AD carries out the simplest transformation process and according to some investigations (Collet et al, 2011; Harun et al, 2011; Park et al, 2011), anaerobic digestion is the most environmentally friendly and cost-effective method to get an energy product. Thanks to the synergy presented between these processes, integration of microalgae culture with anaerobic digestion has become a prominent alternative not just for wastewater treatment, but as renewable energy source too. Thus, it is convenient to assess the interactions between variables involved in the integration process, in order to maximize the energy production as well as to improve the wastewater treatment system (Alcántara et al, 2013). While investigations like that presented by (Wang et al, 2010) and (Ji et al, 2013) have focused on the effectiveness of microalgae for treating wastewater coming from different steps inside the process, other like (Schenk et al, 2008), (Sforza, 2012) and (Cho et al, 2013) have studied some routes to foster microalgae growth in wastewater in order to produce a biomass rich in lipids which can be further extracted for biodiesel processing. Another line of investigation emphasis is the establishment of microalgae potential for removing CO2 as suggested by (Martinez, 2009), (Ho et al, 2011) and (Arbib et al, 2014). Finally, the explicit integration of microalgae growth with anaerobic digestion has been explored in a minor degree by authors like (Uslenghi, 2011) and (Sahu et al, 2013) who have tested the feasibility of growing microalgae in this medium, as a source for the AD treatment. In this work, an assessment of the mass balance inside an integrated scheme “microalgae growth-wastewater treatment” has been developed taking as reference the operation of the Bresso (Milano) municipal wastewater treatment plant. For this purpose, the effectiveness of the growth was evaluated considering the culture of microalgae inside a pilot plant cylindrical photobioreactor (PBR) using as feed the centrate obtained after the anaerobic digestion process. On the other hand, the potential of microalgae for producing methane through AD by bench tests was established, and those results were used for developing a simple model for describing the interaction of each unit in the global wastewater process. In the first part of the document a review of the main characteristics of microalgae growth and anaerobic digestion is presented, as well as a discussion about the integration

Introduction 3

process and its possible effects over the treatment performance. In Chapter 2 a conceptual discussion about the growth rate kinetics was established, while in chapter 3 a description of the methodology, materials and results of the experimental part that complement the results of the mass balance are shown Afterward, the development of the mathematical modeling of the mass balances in WWTP is described in chapter 4. In the final fragment of the document, the conclusions derived from the performed analysis are presented as well as the recommendations for future works. This project has been developed as teamwork between Politecnico di Milano, Università Degli Studi di Milano Bicocca with collaboration of AMIAQUE and Bresso WWTP, looking for an improvement in the energy balance of the whole plant treatment. An additional aim was the evaluation of the mass balance and the effect of the integration over the outlet streams, particularly the biosolid stream after the anaerobic digestion.

1. State of the art

1.1 Current expectations on microalgal biomass

Our contemporary society has become an insatiable fossil fuels consumer, leaving behind, a trail of social, political and economic problems, as well as several unsolved pollution issues. According to (BP, 2013) we consumed around 16 TW of energy worldwide during 2012 of which 85% came from combusting fossil fuels. This implies human society is prone to three large threats: depleting of fossil fuel reserves, economic disruptions due to competition for energy resources and global climate change produced by the increment in CO2 levels in the atmosphere (Rittmann, 2008). A significant number of alternatives have been proposed in order to face those threats, being the development of renewable energy sources the most common solution to these problems. It is evident that this search of new energy sources, carbon neutral, cost-effective, sustainable and environmentally-friendly has become a current challenge for society, reason why nowadays a vast amount of investigations focus on this matter (Mata et al, 2010). In this sense, one of the main options extensively studied refers to the use of bioprocess for producing feedstocks for transportation, power generation, heat, among others. This alternative essentially includes the production of biomass, biofuels and biogas derived from a wide variety of sources (Benemann et al, 2011). Microalgae have become one of the preferred examples of those sources for renewable energy production. As it can be seen in Figure 1-1, microalgae can be processed by different routes to obtain a number of replacement products for fossil fuels. From the thermochemical transformation for producing syngas or biomass, passing by the extraction of oil for the further production of biodiesel, and concluding with the biochemical conversion of microalgae, in which ethanol, hydrogen and methane are the main products.

6 Integration of microalgae culturing within the wastewater

Figure 1-1. Energy feedstocks processing using microalgae. Particularly, the most well-known route for converting microalgae in energy feedstock is the biodiesel production. This process has been mainly developed thanks to several incentives schemes in which biofuels replace fossil fuels in the transportation sector (Oncel, 2013). Nowadays, biofuels based on vegetal oil and bioethanol are the most important source of liquid biofuels around the world, but microalgae have key advantages that make them a latent competitor for this purpose (Cai et al, 2013):

• Microalgae do not compete with crops for arable land and freshwater since they can be cultivated in brackish water and on non-arable land.

• Microalgae usually grow faster than a crop, and they generate a biomass with high oil contents (20–50% dry weight basis) without any need of using fertilizers.

• These microorganisms can fix carbon dioxide, thus reducing greenhouse gas emissions and improving air quality.

• Microalgae culture can be carried out in wastewaters consuming the nutrients presented in the medium, providing an alternative method for wastewater treatment.

• Byproducts of microalgae cultivation after lipid extraction, namely algae biomass residue, can be used as a nitrogen source, such as a protein-rich animal feed or fertilizer for crops

It has been shown from several studies like presented by (Jayed et al, 2009), (Kurki et al, 2010), (Fargione et al, 2010) or (Cavalett & Ortega, 2010) that the use of agro resources for generating biofuels generally reduces the negative effects over climate change potential, but they are highly susceptible to create secondary environmental issues (e.g.

State of the art 7

eutrophication, resource depletion, ecotoxicity). Moreover, the global warming effect caused by the production of bioenergy from biomass has to be correctly estimated since it can be sometimes higher than the ones induced by fossil fuel production (Collet et al, 2011). Besides biodiesel production, microalgal biomass has been employed for additional purposes, such as a source of valuable chemicals or health foods (Pulz & Gross, 2004); as source for anaerobic digestion processes (De Schamphelaire & Verstraete, 2009), (Mussgnug et al, 2010), (Ras et al, 2011), (Zamalloa et al, 2011); also as an efficient wastewater treatment (Alvarez & Tomás, 1989), (Wang et al, 2010), (Boonchai et al, 2012), (Ji et al, 2013), (Arbib et al, 2014). More recently, microalgal photosynthesis has been suggested to be an effective mean for reducing the CO2 concentration, major greenhouse gas in the atmosphere (Martinez, 2009), (Kumar et al, 2011). This great deal of attention over microalgae as a feedstock can be explained given the characteristics of these microorganisms. (Schenk et al, 2008) indicate that the main advantages of second-generation microalgal systems are:

• They have a higher photon conversion efficiency • Can be harvested batch-wise nearly all-year-round, providing a reliable and

continuous supply of biomass. • Can utilize salty and wastewater as the growth medium, thus reducing freshwater

use. • Can couple CO2-neutral fuel production with CO2 sequestration. • Produce non-toxic and highly biodegradable biofuels. Current limitations exist

mainly in the harvesting process and in the supply of CO2 for high efficiency production.

Therefore, it can be stated that microalgae constitute a feasible alternative for many processes, but the recent tendency is focused on processing microalgae for the production of renewable energy, capture of CO2 and wastewater treatment.

1.2 Microalgae characteristics

Microalgae and cyanobacteria are unicellular species that typically grow suspended in water performing the same photosynthetic processes than higher plants. Most microalgae are autotrophs which means they transform sunlight, CO2 and a few nutrients, including nitrogen and phosphorous, into biomass. Other algae are capable of growing in absence of light by using sugar or starch (heterotrophic growth) or combining both mechanisms (mixotrophic growth). Algae are very diverse and found almost everywhere on the planet. They play an important role in many ecosystems, including providing the foundation for the aquatic food chains supporting all fisheries in the oceans and inland (Masojídek et al, 2004).


Microalgae contain three principal components: proteins, lipids and carbohydrates. Depending on the specie and growth conditions, each of them can vary in a large range. For instance, cyanobacteria have as much as 20% of lipids, while prokaryotic algae can reach 50% of lipid content (Ruiz, 2011). In terms of the basic constituents, authors like (Grobbelar, 2004) have proposed an equivalent molecular formula:

��

The photoautotrophs microalgae are capable of synthesizing organic matter starting from inorganic constituents and energy from the light thanks to photosynthesis. In this process, water is decomposed in oxygen and a reducing agent able to transform CO2 into organic carbon. In fact, some energy is used to strip electrons from suitable substances such as water, producing oxygen, while two further compounds are generated: reduced nicotinamide adenine dinucleotide phosphate (NADPH) and adenosine triphosphate (ATP) (Caiazzo, 2007). NADPH acts as an energy carrier during the dark phase of photosynthesis (Calvin cycle) providing the energy to convert carbon dioxide and water into organic compounds, that is called carbon fixation (Masojídek et al, 2004). The global stoichiometry of photosynthesis can be seen as:

6�� + 6�� + ℎ� → �� + 6�� (1)

Where hν represent the luminous energy. One of the most important aspects in photosynthesis is their efficiency and the photoinhibition phenomenon. The first refers to the efficiency of CO2 conversion per each photon caught by the cell while the second is a physiological response of the organisms to high irradiance stress (Martinez, 2009). From the whole electromagnetic spectrum, just the visible zone (380-710 nm) can be used by the plants to carry out photosynthesis. This range of light is commonly known as photosynthetically active radiation (PAR) (Masojídek et al, 2004). During the absorption process, all photons are caught in the chlorophyll reaction center; here a maximum of eight photons per mol of CO2 can be storage. In this scenario, producing a carbohydrate as product of carbon fixation, the maximum photosynthetic efficiency reaches 34% (Martinez, 2009). However according (Benemann & Tillett, 1993) there are more aspects that reduce the photosynthetic efficiency like:

• Photons in the visible field, which promote photosynthesis, account for about 43% of the overall solar radiation at the ground level.

• The fraction of the incident radiation that is adsorbed by the surfaces capable of promoting photosynthesis can reach a maximum of about 80%.

State of the art 9

• Part of this radiation is used in processes different from photosynthesis (e.g. transpiration). The available fraction for photosynthesis is never higher than 75%.

• It is possible that cells require more than eight photons per mol of CO2. Accounting for all this effects, the maximum conversion of solar energy efficiency is 8.5%. Additionally, it is expected lower values since photons are processed at a lower velocity than the absorption velocity. This implies that the unmetabolized extra photons are rejected as heat and fluorescence. Once the number of rejected photons is such big that there is no enhance in the photosynthetic activity, photoinhibition phenomenon appears affecting the process. As it can be seen in Figure 1-2, 3 main zones are distinguished, a light limited zone (zone 1) in which activity increases with light intensity, a saturated zone (zone 2) that is almost independent of the amount of light, and a photoinhibition zone (zone 3) where photosynthetic activity decrease even when light intensity still grow. Constant Ik is characteristic of each specie and defines the point in which photosynthetic activity suffer a deceleration and start becoming a saturated system (Masojídek et al, 2004).

Figure 1-2. Photosynthetic activity behavior of microalgae.

1.2.1 Microalgae growth

Microalgae grow very quickly compared to terrestrial crops. They commonly double in size every 24 hours and during their peak growth phase, some microalgae can double every 3.5 hours (Chisti, 2007). According to authors like (Barbato, 2009) the growth process can be split into five steps as Figure 1-3 shows:


• Lag phase. Period in which algae population start adapting to the environmental

conditions of the medium with a negligible grow. • Exponential phase. During this period algae population grow with an exponential

rate, but only for a short time, due to the little amount of cell presented in the environment.

• Linear phase. In this step algae grow at an almost linear rate, reaching a high value at the end of the interval (optimal interval for algae culture).

• Stationary phase. After the linear phase, the growth rate diminishes rapidly until reaching the maximum value (Limit interval in which saturation occurs).

• Decline phase. In this point algae population suffer a significant reduction due to the high concentration of algae as well as the nutrient exhaust (Algae die because of the suspension of the division and metabolic phases).

Figure 1-3. Algae growth phases. Adapted from (Mata et al, 2010)

Notwithstanding, growth rate is highly affected by several abiotic, biotic and operative factors which define the optimal growth conditions for each particular specie. In this sense, each variable play an important role as can be stated as follows. Temperature Temperature affects the biosynthetic reactions involved in the algae growth increasing this velocity exponentially till the point of maximum growth rate. Even when each microalga specie have its range of growth, can be generalized that they growth properly between 16 and 27°C (Mata et al, 2010). At temperat ures below 16°C the growth rate becomes such slow that it is almost inhibited. On the other hand, at high temperatures algae tend to die and then it is necessary to supply less light to the culture or adapt a cooling system to reduce the negative effects of this phenomenon (Ruiz, 2011).

State of the art 11

pH The pH of the medium directly influences the equilibrium of several species like CO2 and also defines how they are presented in the medium. During photosynthetic process pH increases due to the accumulation of OH- ions, which end up with the nitrogen elimination by ammonia stripping and precipitation of orthophosphates species (Ruiz, 2011). According to this, most of algae culture systems operate in the range of pH 7-9 but optimal value lies typically in the range 8.2-8.7. If pH is not in the proper range, culture collapses and it is required a compensation by means of addition of CO2 or air depending on the particular needs. Oxygen High dissolved oxygen concentrations can inhibit the CO2 absorption in the enzyme RuBisCo. This effect is more significant at higher temperatures and radiation rates, causing the death of algae after 2 or 3 hours if supersaturating conditions (> 120%) are presented in the culture (Pulz & Gross, 2004). Another consideration regards oxygen is its effect over the mass transfer diffusion of CO2, where is required a lower concentration of dissolved oxygen in order to enhance the CO2 flow to the medium. In this sense some authors (Baquerisse et al, 1999) and (Carvalho et al, 2006) agree that for high cell density culture, 40 mgO2/L is an admissible limit in which CO2 can be diffused without substantial interference due to the presence of oxygen. Light availability The algae growth is highly influenced by the amount of light they receive, but differently fom nutrients and CO2, light needs to be added continuously in the culture since radiant energy cannot be stored (Martinez, 2009). It can be stated that photosynthetic microorganism’s growth is proportional to the light intensity they get. The optimal light intensity will vary depending on the specie but it ranges between 30-200 µEs-1m-2 (Uslenghi, 2011). One of the most important parameters to be taken into account in the design of the reactor is the light penetration distance, which depends on light intensity, dispersion on the reactor’s walls and attenuation in the culture. In this sense, a suitable reactor design includes the definition of the optimum culture density in such a way incident and transmitted light be good enough for the algae growth avoiding photoinhibition phenomenon (Ruiz, 2011). As Figure 1-4 shows, microalgal biomass productivity reaches an optimal value depending on the light intensity and the culture concentration. Taking this into account, in order to keep high biomass productivity a significant light intensity is needed, but the system has to distribute this light in a suitable way in order to avoid the photoinhibition phenomenon. This can be reduced to optimize the light diffusion inside the reactor, and maintaining a high frequency light-dark cycles by a proper mixing of the culture (Carvalho et al, 2006).


Figure 1-4. Biomass productivity as function of light intensity

Stirring A proper stirring mechanism is required in order to enhance the mass transfer between the gas and liquid phases, for avoiding algae settling inside the reactor and guarantee a suitable distribution of light and enhance the diffusion of the gaseous species. In terms of light penetration, a good stirring system allows algae to experience rapid changes between light and dark zones, promoting better photosynthetic performances since there is a lower risk of photoinhibition and photo limitation (Coral et al, 2003).

Figure 1-5. Biomass productivity as a function of culture mixing. (*Optimal cell density (g/L);

**air volume/liquid volume/minute).

State of the art 13

On the other hand, this parameter indirectly influences the biomass productivity as it can be seen in Figure 1-5. Most of the commercial systems employ air streams for this purpose and this result in an efficient way of providing agitation to the culture at the same time that CO2 is supplied. Usually a higher ratio Gas/Liquid is guarantee of a well-mixed culture, however it is known that most of algae species are prone to hydrodynamic stress, which implies a limitation that has to be considered. Some authors like (Eriksen, 2008) have suggested that addition of nonionic surfactants can help to microalgae cells to elude their adhesion to air bubbles and then become more resistance to this stress forces. Nutrients

• Carbon External CO2 contribution is practically mandatory when high biomass production rates are required since its concentration in the atmosphere is not enough for providing carbon to autotrophs algae (Grobbelar, 2004). Additionally, some species are able to use bicarbonate ions (HCO3

-) by means of the carbonic anhydrase enzyme but this carbon source is pH dependent (Martinez, 2009). On the other hand, algae tolerance to CO2 varies along the species, but in general concentration has to be kept in a narrow range. Authors like (Pulz, 2001) indicate that maximum concentrations of 12% are commonly used in commercial PBR’s, but as (Kumar et al, 2011) summarize, the tolerance can be as higher as 70-80 % (v/v) under specifically optimized conditions. CO2 supply is probably after light distribution, the most important aspect for algae growth since it directly influences the productivity and economy of the process. CO2 needs can be estimated base on the stoichiometry and algae composition, finding values between 1.8 – 2.0 g CO2/gbiomass (Lardon et al, 2009), (Ruiz, 2011) and (Alcántara et al, 2013). However, CO2 consumption is dependent on several factors, like CO2 concentration, temperature, reactor configuration, light intensity, mixing, and algae species. (Ho et al, 2011) Show in their study, a compilation of CO2 needs for several species, finding that it can vary between 200 and 600 mg CO2/L.day. As it can be seen in Figure 1-6 this range can be extended in a significant amount, but a higher efficiency fixation it is only reachable if all variable are properly managed during all steps of the process.

• Nitrogen After Carbon, Nitrogen is the second most important nutrient for algae. The nitrogen content inside algae biomass ranges between 7 and 10% and varies depending on nitrogen availability as well as the type of source (Martinez, 2009). Algae generally take nitrogen as nitrate (NO3

-) and ammonium (NH4+), but there are some species able to use

urea, nitrite (NO2-), nitrogen oxides (NOx) or molecular nitrogen. Notwithstanding

ammonium represent a latent problem for algae growth since at high levels it results toxic for photosynthetic organisms (Grobbelar, 2004). Ammonium uncouples the electric transport inside photosystem II and competes with water during oxidation reactions that produce free O2 (Ruiz, 2011). (Shi et al, 2000) have found that Chlorella protothecodis can grow with ammonium levels up to 84 mgNH4

+/L, while (Yuan et al, 2011) found that Spirulina is only inhibited with concentrations over 200 mgN-NH3/L.


Figure 1-6. Comparison of microalgal CO2 fixation ability. Adapted from (Ho et al, 2011)

In nitrogen-limited culture systems, some studies reveal that the biosynthesis and accumulation of lipids are enhanced. Nonetheless, a different behavior can be found in species like Dunaliella strains where an increment in carbohydrate rather than their lipid content is the result of nitrogen depletion conditions (Grobbelar, 2004).

• Phosphorus Even when phosphorus content in algae biomass is limited under 1%, this nutrient plays and important role during algae growth, since it is essential in cellular processes like the formation of nucleic acids. The algae usually take it as orthophosphate (H2PO4

-, HPO4-2,

PO4-3) and its presence in the culture medium is limited by the dependence of the

equilibrium on the pH. Extreme pH values and absence of ions like K, Na or Mg promote a lower phosphorus capture (Ruiz, 2011). (Sforza, 2012) suggest that the ratio N:P is crucial in order to improve the algae growth rate; they clearly indicates that it is always better to adjust this ratio as closer as possible to the actual composition of the microorganisms. This does not imply that algae are unable to adapt their selves in order to take a greater proportion of one of the nutrients. In fact, under absence of nutrients, algae are fostered to produce more lipids that can be beneficial for production of secondary products like biodiesel (Khozin-Goldberg & Cohen 2006), (Rodolfi et al, 2008). It can be stated that the symptoms of phosphorous depletion are similar to those observed in nitrogen-deficient cultures. Chlorophyll a contents tend to decrease while carbohydrates rise in eukaryotic and prokaryotic cells (Grobbelar, 2004).

State of the art 15

• Other macro and micronutrients

In addition to C, N and P, algae required macronutrients like Na, K, Mg, Fe and S as well as micronutrients like B, Cu, Mn, Zn and Se relevant for enzymatic reactions and biosynthesis of compounds involved in the metabolism (Grobbelar, 2004). Similarly to phosphorus, these ions are prone to precipitation by significant changes in pH, reason why EDTA is advised as chelating agent. Salinity The salts content in the medium is important for the productivity of lipids and some other derivate. In general microalgae can collect small molecules as osmoregulatory substances or osmoticants in response to an increase in salinity or osmotic pressure of the environment (Grobbelar, 2004). An increase in salinity will promote slight improvements in the total lipid content of the microorganism.

1.2.2 Culture systems

Photobioreactors (PBR) are a particular kind of reactor in which photoautotrophs organisms are cultured. It is important to remark that the term PBR even when is widely used for closed systems, also involves those open system used for the same purpose. In this sense, the main difference among those reactor designs can be established in the contact or isolation of the surrounded ambient with the culture. According to (Grobbelaar, 2000), in open systems culture is exposed to the atmosphere while closed system usually are not. Open systems These systems have been widespread since the 50’s and they still cover a considerable portion of the commercial reactors for algae growth because of their low cost and easy operation (Borowitzka, 1999). However, these systems are susceptible to several drawbacks that make them poorly efficient. High land availability requirements, losses due to evaporation, deficient temperature control, high risk of contamination and limited light penetration are the main factors that usually affect the algae production in open systems (Coral et al, 2003). Within the open system can be distinguished the natural and artificial arrangements, the first are composed by lakes, lagoons and natural ponds, while raceway ponds and circular systems constitute the most common artificial open systems. In general, the culture suspension, containing nutrients, is pumped around in a cycle, being directly illuminated from sunlight. This implies that the raceway depth should not be higher than 30 cm in order to avoid microalgae to remain into darkness, but also to increase the CO2 diffusion from the atmosphere (Caiazzo, 2007). This construction design is the simplest mode for promoting the growth of phototrophic organisms, but due to their characteristics, open systems only reach limited areal productivity rates (Barbato, 2009). Furthermore, the energy consumption for pumping is relatively high, as high amounts of


diluted culture have to be processed. Artificial ponds are commonly designed in a raceway configuration, in which a paddlewheel circulates and mixes the algal cells and nutrients as can be seen in Figure 1-8.

Figure 1-7. Typical open system configurations.

Usually raceways are made of concrete, steel or plastic materials depending on the size, or they are dug into the ground and covered with a plastic liner when this option is feasible. These systems work in a continuous mode, where the fresh feed is added in front of the paddlewheel, and algal broth is harvested behind the paddlewheel after it has circulated through the loop (Ruiz, 2011).

Figure 1-8. Typical raceway pond configuration. From (Chisti, 2007)

The other basic design of open systems consists in circular ponds where circulation is provided by rotating arms, and inclined systems where mixing is reached by means of a combination of pumping and gravity flow. In both cases, the main characteristics remain the same as the raceway ponds, except that it is expected that circular ponds can be at maximum 1000 m2 size in order to keep a suitable mixture of the medium (Caiazzo, 2007).

State of the art 17

Closed systems Closed PBR´s differ from the open system reactors in the fact that they improve all the drawbacks presented in the aforementioned, making possible higher algae production rates. In these systems there is null or negligible contact with the environment, evaporation losses and temperature can be controlled in an easier way, and CO2 diffusion is promoted by several mechanism related with the intrinsic reactor design (Pulz, 2001). One of the main characteristics of the closed photobioreactors is that they are built with transparent materials (plastic or glass) for providing a better light penetration. In general those systems are more productive since they use less area for the same feed rate, and collection of generated biomass result less expensive. Even that, total investment costs tend to be higher than open systems, as well as operation and maintenance costs (Martinez, 2009). Table 1-1 summarizes the main differences between open and closed system.

Table 1-1. Main design characteristics of microalgal bioreactor systems. Characteristic Open System Closed System

Area/Volume ratio High Low Population density Low High

Harvesting efficiency Low High Cultivation period Limited Extended Pollution control Significant Negligible

Water evaporation Possible Negligible Light utilization efficiency Poor/fair Fair/Excellent

Gas mass transfer Poor Fair/high Temperature control None Excellent Capital investment Low High

PBR´s can be classified according their design and operation mode. Respect to the design they can be split into cylindrical (vertical- inclined-horizontal), plane (vertical-inclined-horizontal) and coil type. Respect to the mode of operation they are air driven, single phase reactors (gas exchange occurs in a separate chamber) and double phase reactors (gas exchange is carried out along the entire reactor).

• Tubular PBR These photobioreactors are constituted for several groups of plastic or glass tubes arranged in parallel or series, located horizontally or vertically as Figure 1-9 shows. In this section of the reactor microalgae catch light for performing photosynthesis, while, in the degassing section column produced oxygen is removed in order to prevent high dissolved oxygen level along the tubes. Unfortunately, the mixing level and therefore the mass transfer inside these reactors is quite limited, fostering high O2 concentration levels. Additionally, some photoinhibition problems are common since the poor mixing avoid cells to reach suitable light-dark cycles (Martinez, 2009).


Figure 1-9. Tubular PBR of parallel runs. (a) Horizontal configuration; (b) Vertical

configuration. In this configuration, tubes are always oriented North–South and the ground beneath the solar collector is often painted white, or covered with white sheets of plastic to increase reflectance, or albedo. Higher reflectance will increase the total light got by the tubes (Chisti, 2007). Sedimentation in tubes is hinder by adjusting the turbulent flow using either a mechanical pump or an airlift pump. Mechanical pumps can damage the biomass (but are easy to design, install and operate, but airlift pumps have been used quite successfully more recently (Tredici, 2004).

• Flat Plate PBR The main advantage of this reactor configuration lies in the significant surface exposed to radiation. In this PBR culture is forced to pass through the channels made by the reactor’s walls, while CO2 is fed from the bottom and collected in the upper part of the reactor for recycling or disposal. In Figure 1-10 it can be seen a typical flat plate photobioreactor. Usually this configuration achieves high productivity levels (cell densities up to 80 g/L) keeping a simple operation and maintenance (Posten, 2009). However, this design makes difficult the temperature control and increases the probability of hydrodynamic stress of cells (Uslenghi, 2011).

Figure 1-10. Flat plate PBR configuration.

State of the art 19

Even when these kind of reactors have been deeply investigated for several authors (Tredici, 2004), (Hsieh & Wu, 2009), (Valiorgue et al, 2011), among others, in commercial applications is not wide used due to the great difficulty of scaling and the intensive material requirements. More recently, flat plate PBR´s have been redesign by using plastic bags located between two iron frames. In this way, a substantial cost reduction is achieved, and the system can be set periodically inside water pools in order to control the temperature (Sierra et al, 2008).

• Bubble columns This type of reactor is characterized for being compact, low cost and easily of scaling. As can be observed from Figure 1-11 construction of these reactors is simple and their geometry allow a good mass transfer between gas and liquid phases, as well as good controlling of operational conditions. Even when the surface exposed to solar radiation of column PBR is no the highest respect to another type of photobioreactors, can be consider an efficient light receptor (Martinez, 2009). For high volume units, diameter of columns can reach 20cm and more; this leads to a considerable large dark fraction in the middle of the cylinder. Evidently this becomes a drawback since this fraction does not contribute to productivity, negative affecting the growth of microorganisms (Posten, 2009). In order to overcome this effect, annular reactor configurations have been developed. In that configuration, a concentric tube is located in the middle of the column as Figure 1-11 (b) shows. The annular section can be used in different ways depending on the most favorable result over the culture system. The simplest mode is acting as a wrapped flat plate PBR in which the inner surface provides additional space for the catching radiation. Since this surface probably does not contribute significantly to the overall radiation, an external lamp can be fitted in this space for improving the light capture efficiency of the reactor like in Figure 1-11 (c). Finally, the annular section can be exploited as a mechanism of mixing the culture for providing suitable light-dark periods with a small hydrodynamic stress for cells. This is possible by means of an airlift configuration as shown in Figure 1-11 (d) where the downcomer is arranged in the coaxial zone, while the riser is composed by the annular region. As the riser remains most of the time in the dark, the cells flow through it thanks to the introduction of bubbles in the lower part of the reactor, and then cells go down receiving the radiation along the downcomer. In this way regular light-dark cycles in the range of 1–100 milliseconds are promoted, achieving high productivities (Posten, 2009).


Figure 1-11. Bubbling column PBR. (a) simple bubbling; (b) Annular; (c)Internally

illuminated; (d) Airlift. Design Considerations for PBR’s A photobioreactor can be considered a three phase system formed by the liquid phase who acts as a medium, the microalgae cells in solid phase and the gas phase providing CO2. In general, the design of a PBR requires understanding of the interaction between the environmental parameters and the biological response involved in the three phase system as Figure 1-12 depicts.

Figure 1-12. Main interactions accounted for photobioreactors design. From (Posten,

2009)

State of the art 21

Between the most relevant aspects to take into account in the design of any PBR can be mentioned:

• Light distribution In photoautotroph microorganisms, culture light availability determines the photosynthesis velocity and consequently their growth rate. Nevertheless in any culture system, cells that are closer to PBR walls block light, hindering its penetration to the center of the reactor. In fact, light cannot penetrate beyond few centimeters when there is a high density culture, producing a mutual shading effect in those cells placed far away from the surface (Coral et al, 2003). Clearly, the movement of cells along the PBR stimulates a periodical exposing of them to light and darkness. This period ranges between 1 millisecond to few seconds, and it is defined as the time a cell remains in the photic zone. In order to understand this phenomenon it is essential the definition of the light path length, referred as the distance a photon has to cover for passing through the PBR. As long as the light pathway length exceeds the wall thickness, an exponential growth can be found. Once biomass concentration gets higher values, only linear growth is presented. This situation does not imply that the efficiency decreases immediately because the mixing helps to achieve a proper light distribution. However, a fraction of the total volume remains in the dark not contributing to productivity but to energy cost (Posten, 2009). As it can be seen in Figure 1-13, the higher the light pathway length, the higher the volume fraction in dark, due to the mutual shading. This indicates that is convenient to keep short light pathway lengths (not higher than 3cm) guaranteeing a high frequency light-dark cycles (Coral et al, 2003).

Figure 1-13. Light pathway and light-darkness cycles. Adapted from (Coral et al, 2003)


• Mixing

Mixing is essential for microalgae growth since a proper culture mixing favors the gas-liquid mass exchange, prevents the cellular sedimentation, maintains a stable regime inside the reactor and it is crucial for providing a good light distribution. Actually, light-dark cycles have a considerable effect on algae growth, as (Posten, 2009) summarizes, benefits of mixing are not only in the improvement of mass transfer, but also in the increment of the light-dark cycles frequency. The major mixing/recirculation systems commercially used are described in Table 1-2. As it can be seen, they can be split into pumping systems (normally employed when more than one vessel is present), mechanical stirring (present when only one vessel is used) and gas mixing (where the injection of CO2 in the culture is exploited for promoting turbulent mixing and recirculation through the PBR). Even when mixing can be performed by means of several options, airlift configurations are preferred because of their simplicity and low mechanical damage caused to cells (Coral et al, 2003). Table 1-2. Generic description of mixing methods in PBR´s. From (Carvalho et al, 2006)

Process Type

reactor Mixing

efficiency Gas

transfer Hydrodynamic

stress Scale-

up Pumping mode

Centrifugal fair low medium Easy Positive

displacement fair low medium Easy

Peristaltic fair low medium Easy Diaphragm fair low medium Easy

Lobe fair low medium Easy Mechanical stirring mode

Stirring with blades

CSTR Uniform Fair/High High Medium

Gas mixing mode

Injection of gas Air-lift Uniform High Low Medium

Bubbling Fair Fair Low Medium

• CO2 supply and aeration Another important aspect that PBR improves is the feeding of CO2 for promoting microalgal photosynthesis, as well as a suitable removing of the produced oxygen. In this sense it is particularly important to have a reliable prediction of the CO2 mass transfer for accurate design, scale-up and operation (Carvalho et al, 2006). It is well known that flux rate from the gas to the liquid phase depends mainly on the diffusion kinetics, and in a minor degree in some reactions in the liquid phase occurring in the vicinity of the interface. In any case the diffusion of CO2 in the medium can be explained by means of the two-film theory as depicted in Figure 1-14. The mass transfer start from the bulk of the gas to the

State of the art 23

thin gaseous film at the immediate vicinity of the interface and then take place the diffusion through this gas film. After this, CO2 passes across the gas/liquid interface and then diffuses through the adjacent liquid film till arriving at the bulk of the liquid phase (Carvalho et al, 2006). In that point, carbon dioxide can be metabolized for microalgae. The overall resistance over the entire path distance can be calculated by adding up the aforementioned single resistances.

Figure 1-14. CO2 Mass transfer coefficient between gas and liquid phases.

At the same time, the mass transfer rate of this process is proportional to the driving force for diffusion and the available area for transfer. This proportionality corresponds to the so-called global volumetric mass transfer coefficient kLa or kGa which is the sum of the reciprocals of all resistances to transfer and can be established as Eq. (2):

�� ∗ � �� ∗ � (2)

Where NCO2: mass transfer rate kLa, kGa: Global mass transfer coefficient in liquid and gas phase C*

CO2, P*CO2: CO2 concentration and partial pressure in equilibrium from Henry’s law.

CCO2, PCO2: CO2 concentration and partial pressure measured in the bulk of the phases. In the case of microalgal culture some characteristics of the bubbles affect the value of kL indirectly, whereas others affect mainly a. Moreover, the amount of metabolites present in the medium will play an important role since they also affect kLa since metabolites modify


the surface tension of the medium and thus act as an extra barrier to mass transfer (Molina et al, 1999).

1.2.3 Productivity of microalgae cultures

As it can be seen until this point, there are numerous variables to be considered for carrying out a microalgal culture. Perhaps the first item to be solved is the selection of the microalgae strain in the case of monoculture, or the definition of the strains that could exist in a multiculture system. This selection can be inherent to the environmental conditions when culture is developed in mediums like natural open systems or wastewater, where it is advantageous to work with the presented indigenous microorganisms in the medium. On the other hand, strain selection can be also made based on the final purpose of the microalgal biomass, as well as the characteristics of each microorganism (Oncel, 2013). In any case, one indicator that collects the behavior of the whole system for a particular strain is the productivity, measured as volumetric biomass productivity (Pvol) or areal biomass productivity (pArea). These performance indexes are commonly used to define how much microalgal biomass is produced per unit of reactor volume per day (gbiomass/L day) and how much biomass is generated per unit of used area per day (gbiomass/m

2 day). This way of measuring the performance of a given culture system result helpful when comparing different reactor configurations, sizes, operational conditions and strains. This index is needed since all effects of those conditions are condensed in only one value. A summary of Pvol for 20 of the most investigated microalgae strains is shown in Figure 1-15. In the figure, the green section corresponds to the most common values for each strain, while the lines indicate the maximum deviation found within the processed data. As it can be observed, there are a significant group of microorganisms that independently from the operational conditions and reactor type, achieve a volumetric productivity always concentrated in narrow ranges (small green bars). On the contrary, other group like Spirulina platensis, Schizochytrium limacinum, Porphyridium cruentum, Phaeodactylum tricornutum and Chlorella protothecoides achieve productivities spread in a wide range. Evidently, those trends are not only due to the particularities of each strain, but also a consequence of selected operational conditions, and the type of reactor itself. Considering this, the summary is only a first compilation of productivities among the different found alternatives. On the other hand, it is remarkable to note how Pvol is lower than 1 gbio/L.day in 14 out of 20 strains, being the lowest productivities for Isochrysis (0.140), Haematococcus pluvialis (0.126), Chlorella vulgaris (0.1248) and chlorella emersonii (0.0982). Additionally, from those strains who reach higher productivities like Spirulina platensis, Schizochytrium limacinum, Chlorella protothecoides and Chlorella pyrenoidosa just the last one has a consistent Pvol around 3.385 gbio/L.day; the remaining 3 strains have a considerable variation from that ranges from 0.1 to 7.

State of the art 25

Figure 1-15. Volumetric productivity of some microalgae strains. Information extracted

from (Eriksen, 2008), (Mata et al, 2010), (Brennan & Owende, 2010), (Chen et al, 2011), (Ho et al, 2011), (Suali & Sarbatly, 2012), (Lam & Lee, 2012), (Rawat et al, 2013), (Oncel,

2013) and (Bahadar & Bilal Khan, 2013)

These results can be also seen from the reactor design standpoint. As Figure 1-16 shows, the productivity of different PBR configurations varies significantly. In terms of volumetric productivity, flat plate PBR’s are in general the most efficient far away from airlift and tubular configurations. This result is due to the high surface area available for light catching of the flat plate reactors. However, given the high amount of single units required for treating a given flow rate, the areal productivity of this reactor is no as higher as the volumetric one. In this aspect, the simple configuration of bubbling column PBR’s reach the higher values of Pareal.


Figure 1-16. Mean Pvol and Pareal for different PBR configurations. Information derived from

(Carlozzi, 2008), (Brennan & Owende, 2010), (Ho et al, 2011) and (Oncel, 2013)

1.3 Anaerobic digestion process

The anaerobic digestion (AD) is a complex biological process in which organic raw materials are transformed into biogas and digestate by means of microorganisms acting in absence of oxygen. Biogas is composed mainly by methane and carbon dioxide, but it contains traces of hydrogen, hydrogen sulfide, nitrogen, among others. Meanwhile, digestate is a sludge rich in nutrients like nitrogen, phosphorus, potassium, calcium, etc., characterized for concentrating the fraction of non-degraded organic materials. Since the biogas stream has a substantial amount of CH4 (see Table 1-3) it becomes a latent energy source for any application while digestate is commonly used as feedstock for fertilizer production (Holm-Nielsen et al, 2009). In effect, it can be stated that biogas covers a variety of markets that include electricity generation, heat production and vehicle fuels. As Figure 1-17 illustrates, the raw biogas can be treated by cooling, draining, drying and removing H2S in order to avoid its corrosive effects for its direct use in boilers. For obtaining a high quality gas, this can be either upgraded to natural gas standard and compressed (Biomethane 98% CH4), or it can be employed as feedstock in hydrogen production via methane reforming (AEBIOM, 2009). On the other hand, digestate is usually dried and used as fertilizer or filling material in construction and sometimes depending on the origin of the sludge, it can be directly returned to the field as a soil improver. More recently there is an aim for exploiting this

State of the art 27

fraction by means of physicochemical processes like hydrolysis for producing ethanol, and nutrient extraction for conditioning the remaining biomass as well as obtaining a nutrient rich biofertilizer (Rigby & Smith, 2011).

Table 1-3. Typical composition of biogas. From (AEBIOM, 2009) Compound Composition (% v/v)

Methane 50-75 Carbon dioxide 25-45

Water vapor 1-2 Carbon monoxide 0-0.3

Nitrogen 1-5 Hydrogen 0-3

Hydrogen sulfide 0.1-0.5 Oxygen traces

Anaerobic digestion can be carried out starting from a wide variety of feedstocks and its options for utilization can be equally versatile. A general distinction can be made between biomass from agriculture like byproducts (manure) or dedicated crops for biogas and various waste streams. Typical industries with wastewaters suitable for AD are agro food industries (sugar, potato, starch, yeast, pectin, citric acid, cannery, confectionery, fruit, vegetable, dairy, bakery), beverage industries (beer, malting, soft drink, fruit juice, wine), alcohol distilleries (sugarcane juice, sugarcane molasses, sugar beet molasses, grape wine, grain), pulp and paper industries (recycle paper, mechanical pulp, sulfite pulp, straw, bagasse), and other fields (chemical, pharmaceutical, sludge liquor, municipal sewage, landfill leachate, acid mine water, etc.).

Figure 1-17. Typical routes for processing anaerobic digestion products.


From these sources, those with high moisture contents are the ones that reach the lower yields per unit of treated feedstock, while dry organic feedstocks are commonly highly productive as can be observed in Figure 1-18. On the other hand, it can be seen how the methane content in the produced biogas ranges from 50% to 70% depending on the raw material, but as it will be discussed after, both biogas yield and methane content are also highly affected by the operational conditions of AD. In general it can be stated that methane production depends not only on the biodegradability of the organic waste, but also on other factors such as the operating temperature, particle size of the waste, pH of the medium, presence of toxic or inhibitory compounds in the substrate, and oxidation state of the carbon (Asam et al, 2011). In this sense, variations in feed composition, concentration, or temperature can lead to imbalances in microbial activity, resulting in changes in pH, gas production and composition, and efficiency of chemical oxygen demand (COD) removal. Stability is a primary concern in industrial digesters and may be controlled by addition of alkali as well as control of feedstock composition and feeding rate or hydraulic retention time (HRT) (Alvarez, 2003).

Figure 1-18. Biogas yield and methane content of different substrates. From

(Fachagentur, 2006)

1.3.1 Biochemistry of the AD process

Organic matter degradation during anaerobic digestion is based on diverse and complex interactions between several groups of microorganisms. As a result of the microorganism’s activity on organic matter, bacterial population grow exploiting the

State of the art 29

presence of an enormous amount of soluble macro and micronutrients derived from the organic matter decomposition. AD process has been deeply studied from the biochemical and microbiological standpoint finding that the process evolves in two main phases, a non-methanogenic phase and the methanogenic stage. Classification of these phases is commonly carried out based on the intrinsic characteristics of the microorganisms that develop each step of the process as shown in Figure 1-19.

Figure 1-19. Stages of the anaerobic digestion process.

These microorganisms population are characterized by different growth rates, variable sensitivity to inhibitory components, and their efficiency is pH dependent. This implies each step presents a different rate of reaction and the accumulation of intermediates compounds hinder the development of subsequent stages (Fachagentur, 2006). In general, the rate of the global process is defined by the limiting step the (slowest one) which depends on several operational variables as well as the raw organic material composition. For soluble substrates, the slowest phase is commonly the methanogenesis, while for solid substrates the limiting step is the hydrolysis. Hydrolysis During this step the complex molecules presented in the organic raw material, composed by proteins, lipids and carbohydrates are broken down into amino acids, free fatty acids and sugars respectively. This process is developed by exoenzymes released thanks to the presence of anaerobic facultative bacteria like Enterobacteriacea, Bacilus,


Bacteroides, Micrococcus, Peptostreptococcus, Clostridium, Propionibacterium (Taricksa et al, 2009). Acidogenesis This stage allows soluble organic molecules to be catabolized by a set of microorganisms like Butyrivibrio, Propionibacterium, Clostridium, Bacteroides, Ruminococcus, Acetivibrio, Bifidobacterium, Eubacterium, Peptostreptococcus, Peptococcus, Selenomonas, Lactobacillus, Streptococcus, and members of the Enterobacteriaceae, which are commonly presented in the medium. During this process microorganisms produce acetic and formic acid, hydrogen and carbon dioxide that are products able to be used by methanogens. Moreover, this fermentation step also produces intermediate compounds like propionic, butyric and valeric acids (VFA) that has to be further oxidized in order to be ready for the methanogenic step. Under ideal conditions it is expected that acetic acid, H2, and CO2 are the major constituents, but if those conditions are not achieved, volatile fatty acid accumulation and H2 production occur (Appels et al, 2008). Acetogenesis In this stage the intermediates compounds, mainly VFA and alcohols are converted into acetic acid, hydrogen and carbon dioxide. This step proceeds taking into account a division of the microorganisms present in the medium, those that are not obligately proton-reducing, that is, hydrogen-producing, species and those that do reduce protons to hydrogen obligately during acetogenesis (Appels et al, 2008). The first group is broad, including the homoacetogens and species that may direct their metabolisms to proton reduction in the presence of an efficient hydrogen-removing system. Some of the most common species are Acetobacterium, Acetoanaerobium, Acetogenium, Butyribacterium, Clostridium, Eubacterium, and Pelobacter (Massi, 2012). This facultative change in metabolism has been demonstrated in defined methanogenic co-cultures degrading alcohols, lactate, pyruvate, cellobiose, glucose, fructose, and cellulose. On the other hand, obligate proton-reducing acetogenic bacteria can only be grown in an efficient electron-removing environment, for instance, in monoxenic culture with a hydrogen-removing or formate-removing species. The simplest mixed culture involving this type of ‘mutualistic’ interaction is a culture containing the acetogen and a hydrogen-removing bacterium such as a methanogen. Other obligate proton-reducing acetogens have been described: Syntrophobacter wolinii degrades propionate, Syntrophomonas wolfei degrades butyrate, and Syntrophus buswellii degrades benzoate (Massi, 2012). Methanogenesis The final step consists in the transformation of acetic acid, hydrogen and carbon dioxide into methane via acetoclastic methanogenesis, and hydrogenotrophic methanogenesis. The first is responsible for the conversion of acetic acid into CH4 and CO2, while the second contribute to the oxidation of hydrogen produced by the secondary fermenting bacteria and reduction of carbon dioxide to methane (Taricksa et al, 2009). The most common species of methanogens that use H2 and CO2 as substrate found in anaerobic

State of the art 31

digesters are Methanobacterium bryantii, formicicum, wolfei, thermoautotrophicum, uliginosum, thermoalcaliphilum, thermoaggregans; Methanobrevibacter arboriphilus, ruminantium, smithii; Methanothermus fervidus; Methanococcus maripaludis, deltae, vannielii, voltae, jannaschii, halophilus, thermolithotrophicus, frisius; Methanomicrobium mobile, paynteri; Methanogenium cariaci, marisnigri, olentangyi, tatii, aggregans, thermophilicum, bourgense; Methanospirillum hungatei; Methanoplanus limicola among others. In addition, the most frequent methanogenic species that use acetate as substrate are Methanosarcina barkeri, mazei, acetivorans and Methanothrix soehngenii, concilii, among others (Fachagentur, 2006).

1.3.2 Factors affecting AD process

Methane production can be considered one of the most used indexes for measuring the efficiency of any AD process, but this index is prone to changes due to the variation of operational conditions like temperature, pH and alkalinity, presence of inhibitor or toxic component and the content of available nutrients as can be stated as follows. Temperature Temperature has an important effect on the physicochemical properties of the components found in the liquid phase. It also influences the growth rate and metabolisms of microorganisms and hence the population dynamics in the anaerobic reactor. 3 optimal ranges for operating an AD reactor can be distinguished which define the growth rate of the populations: Psychrophilic (0-20 °C), Mesophili c (20-40 °C) and Thermophilic (50-60 °C). As can be seen in Figure 1-20, higher temperat ures favor the development of the microorganisms, but selection of operative temperature will depend on additional considerations.

Figure 1-20. Growth rate of microorganisms in each temperature range.


Acetotrophic methanogens are one of the most sensitive groups to increasing temperatures. Moreover, the temperature has a significant effect on the partial pressure of H2 in digesters, hence influencing the kinetics of syntrophic metabolism (Taricksa et al, 2009). Thermodynamics show that endergonic reactions (under standard conditions), for instance, the breakdown of propionate into acetate, CO2, and H2 would become energetically more favorable at a higher temperature, while reactions that are exergonic (e.g., hydrogenotrophic methanogenesis) are favored at lower temperatures (Appels et al, 2008). Increasing temperature has several benefits including a better solubility of the organic compounds, enhanced biological and chemical reaction rates, and a higher death rate of pathogens (thermophilic conditions). However, the application of high temperatures (thermophilic) has counteracting effects: there will be an increase in the fraction of free ammonia, which plays an inhibiting role for the microorganisms at certain concentrations, the energy consumption is high and affects the balance between energy supplied to produced energy, and there is a trend of VFA accumulation even higher than in Mesophilic conditions (Alvarez, 2003). pH and alkalinity pH plays an important role in the monitoring and control of AD because of the inhibitory effects of low pH on the activity of methanogens. Anaerobic digesters operate optimally in a pH range of 6.8–7.4, this considering the effect over the acidogens and methanogens which grow best in a pH range from 5.5-6.5 in the first case and 7.8-8.2 in the second case (Asam et al, 2011). pH values lower than 6.2 considerably inhibit the methanogenic activity. In addition, pH can produce several side effects. It controls the fraction of undissociated VFAs that are thought to freely permeate the cellular membrane of microorganisms. After permeating the membrane, the fatty acids internally dissociate, thus lowering the cytoplasmic pH and affecting bacterial metabolism. Therefore, the occurrence of low pH is the result of a well-developed imbalance in the anaerobic biomass (Taricksa et al, 2009). A control measure is usual to reduce the loading rate and employ supplement chemicals to adjust the pH. In this sense alkaline chemicals such as NaHCO3, NaOH, Na2CO3, quick lime (CaO), slaked lime (Ca(OH)2), limestone (or softening sludge) CaCO3, and NH3 can be used. On the other hand, buffer capacity is often referred to as alkalinity in AD, which is the equilibrium of carbon dioxide and bicarbonate ions that provide resistance to significant and rapid changes in pH, and the buffering capacity is, therefore, proportional to the concentration of bicarbonate. Buffer capacity is a reliable method of measuring digester imbalance. Increasing a low buffering capacity is best accomplished by reducing the organic loading rate (OLR), although a more rapid approach is the addition of strong bases or carbonate salts to remove carbon dioxide from the gas space and convert it to bicarbonate, or alternatively bicarbonate can be added directly. A more sensitive

State of the art 33

parameter for monitoring digesters and measuring process stability is the VFA/alkalinity ratio: when this ratio is less than 0.35–0.40 (equiv. acetic acid/equiv. CaCO3), the process is considered to be operating favorably without acidification risk (Appels et al, 2008). Nutrient content It is evident that the cellular biomass responsible for the development of the AD process requires a certain amount of mineral nutrients, carbon and energy for metabolizing them. Among the most important nutrients, essential for the development of the process are nitrogen, sulfur, phosphorus, and some micronutrients like iron, nickel, Cobalt, Selenium, among others. In general, the presence of those nutrients is guaranteed since they are in a higher proportion than needed for the bacteria populations, and only a small fraction of the total COD (4-10%) is removed by conversion in biomass (Taricksa et al, 2009). Inhibitor and toxic substances A substance may be judged inhibitory when it causes an adverse shift in the microbial population or inhibition of bacterial growth. Inhibition is usually indicated by a decrease of the steady-state rate of methane gas production, accumulation of organic acids or deficiency in the organic matter removal. Those substances can be part of the feed material or can be generated as by-products in some metabolic conversions. In some cases microorganisms can overcome the inhibition once they adapt their biological mechanisms by means of acclimatization, antagonism or synergy. In the first case microorganism can adapt itself to the inhibitory substance if this latter is presented in the medium in small concentrations. Antagonism occurs when there exists another secondary substance that reduces the effect of the inhibitory compound, and synergy is the ability of microorganisms of exploiting the different conditions in the medium in order to overpass the limitations imposed by the inhibitor (Bohutskyi & Bouwer, 2013). Between the most important inhibitor compounds are the free ammonia, sulfur, and some organic hydrophobic toxics like chlorophenol, aliphatic halides and long chain fatty acids. HRT, SRT AND OLR The other set of operational parameters that defines the efficiency of the AD process are related to the hydraulic residence time (HRT), sludge residence time (SRT) and organic loading rate (OLR). HRT is related to the average time that a soluble molecule fed into the reactor remains inside it that is also the time available to microorganisms to degrade this organic matter. It is defined according Eq. (3) as:

�"# � $%&'()*(+,&-

./�012 (3)

On the other hand, SRT quantifies the average time that a particulate (molecule/particles) fed into the reactor spends inside it, that is also the time available to microorganisms to remain in the reactor.

3"# � 456678679:;:<;:=>6?>@4566A56?>@B>@;5C ./�012 (4)


Each time solids are withdrawn from digester, a fraction of the bacterial population is removed thus implying that the cell growth rate must at least compensate for the cell removal rate to ensure steady state and avoid process failure (bacteria wash‐out). When several microorganisms should coexist, the SRT is fixed on the basis of the slowest growing ones. The importance of these parameters can be seen from the fact that if HRT is high, the fraction of organic material degraded increases, but the biogas production will be below of the optimal value. On the contrary, if HRT is too little biodegradation is incomplete, and a significant amount of degradable substrate will remain in the digestate. At the same time HRT has to be fixed high enough for obtaining a proper SRT or the system has to be conditioned with a solid-liquid separation unit in order to recover the solid fraction for recirculating it inside the digester. In this point a third parameter has to be taken into account, the OLR that is defined as:

�E" � �F+'$%&'()*(+

G H=IJ;5C.LMN (5)

It quantifies the mass of substrate that enters the digester per unit of time and per unit of digester volume. If OLR is high, it means a large amount of organic material is fed into the digester and probably there is not neither enough microorganism population nor time for developing the complete degradation. In that scenario, a VFA accumulation occurs with a consequence pH drop, causing the stopping of the degradation in the acidogenic step. On the contrary, if ORL is low, biogas production is not optimal and can be improved.

1.3.3 Potential of microalgae species for producing methane

The preferred way of measuring the ability of a given organic material for producing biogas via anaerobic digestion is by means of the so call biochemical methane potential (BMP). This parameter is an index of how much methane can be produced for a given organic substrate during its disintegration in the AD process. The first approximation is the theoretical BMP (BMPth) which is a computation based on the stoichiometry of conversion of the organic compound to CH4, CO2 and NH3 as Eq. (6) states. In this estimation it is assumed that reaction is completed, there is no biomass synthesis and consequently, all COD is transformed into methane.

�5�O�P�; + Q�� → Q�� + QR�� + Q��R (6)

BMPth can be computed according to the Buswell and Boruff relationship, in which coefficients α1 to α4 can be estimated as:

State of the art 35

Q� � 4� � T � 2U + 3/4 ; Q� � 4� + T � 2U � 3/

8 QR � 4� � T + 2U + 3/

8 ; Q � /

Consequently the BMPth will be given by:

YZ�?[ � \�H=F+'.].

�^_`ab�cbM%

d��5eOe��Pe�; fL g

hLij^M

H=IJ k (7)

Where Vm is the molar volume of methane at the temperature and pressure conditions (e.i. 22.14L/mol at 0°C and 1 atm). By means of Eq. (7) it is possible to estimate the volume of CH4 depending on the amount of volatile solids (VS) available in the substrate being digested. Nevertheless Eq. (7) overestimates the gas production due to the assumption of 100% conversion of the volatile solids to biogas, and it does not take into account the carbon needs for bacterial cell maintenance and anabolism (Ward et al, 2014). Unfortunately, BMPth can be only used when the elemental composition of the organic material is known, reason why a second index, based on an experimental assay, is commonly used for that organic material in which is difficult to estimate their composition. The BMP experimental (BMPexp) is a laboratory test in which is assessed the potential of the substrate for producing methane controlling the conditions of the AD (Ward et al, 2014). In Figure 1-21 the BMP of some microalgae strains computed with Eq. (7) based on average compositions or values found in the literature can be seen. Besides the convenience of using BMP as guide for measuring the potential of a feedstock, BMP results an index that only predict the maximum amount of methane that a given organic material could generate through AD. Since BMP test is carried out under ideal conditions and considering high HRT, the results can be far away from the situation found under operative conditions of the anaerobic digester. This means that a better parameter for assessing the AD process is the methane yield at the operative conditions. As it can be observed from Figure 1-22, the methane yields for several microalgae strains was considered based on a compilation of different results. The first conclusion from this information is that a mean reduction of almost 50% in the methane production is found for the different species if compared with the BMP of Figure 1-21. Evidently this a case dependent result since yield can vary substantially depending on the particular operating conditions. This can be better perceived analyzing the variation that this parameter gets from 100 until nearly 500 mLCH4/gvs for most of the examined strains.


Figure 1-21. Theoretical Biological methane potential of some microalgae species.

Information derived from (Sialve et al, 2009), (Bohutskyi & Bouwer, 2013), (Ward et al, 2014)

Figure 1-22. Methane yield of some algae strains. Information extracted from (Mussgnug et al, 2010), (Frigon et al, 2013), (Bohutskyi & Bouwer, 2013), (Lakaniemi et al, 2013) and

(Ward et al, 2014)

State of the art 37

1.4 Integration of microalgae growth with AD

As detailed before, the potential of microalgae for supplying an AD process can be exploited in several ways. From the large range of configurations that can be used for integrating these two processes in a WWTP just a few have been studied as a single system. One of them correspond to the work of (Uttley et al, 2011) in which the modeling of an integrated process as illustrated in Figure 1-23 is developed. In this case the aim is the use of microalgae as a tertiary treatment for improving the effluent quality as well as to produce microalgal biomass able to be used in the AD process of the plant.

Figure 1-23. Scheme of integration AD-microalgae-wastewater proposed by (Uttley et al,

2011)

In this configuration the main characteristic is the use of the treated wastewater for feeding the PBR, which can be translated in a lower supply of nutrients, since it is expected that a significant amount of them have been recovered in the former steps of the treatment. Evidently this arrangement provides a high COD, Nitrogen and phosphorus removal, but a very low biomass productivity (0.2 g/d m3). A second alternative investigated by (Yuang et al, 2012) include the combination of the effluent stream with a portion of centrate derived from the sludge treatment as presented in Figure 1-24. In this way the algae growth system is fed with more substrates, increasing the potential of producing microalgal biomass since centrate contains around 15% to 20% of the total N load of the WWTP.


Figure 1-24. Scheme of integration AD-microalgae-wastewater by (Yuang et al, 2012)

One of the disadvantages of this configuration lies in the latent adverse effect of NH4

+ on algae growth, if ammonium concentration reaches levels in which inhibition is predominant. In that point, the difficulty of controlling conditions of the culture feed, put the microalgae growing in a unfavorable position. On the other hand, it is essential to assess the convenience of including all the wastewater effluent or a fraction of it in the algae culture for guaranteeing the quality of the latter. A possible negative effect due to the mixture with centrate is the loss of quality at the end of the process. A similar consideration has been proposed by (Sahu et al, 2013) but considering that nutrient sources for algae growth can also be extended beyond municipal wastewater. As it can be seen in Figure 1-25, integration of the processes can be carried out collecting nutrients from different sources and then use them for algae culture.

State of the art 39

Figure 1-25. Scheme of integration AD-microalgae-wastewater according (Sahu et al, 2013)

Depending on the availability, supplementary sources of nutrients can cover from cattle manure till fresh human urine which can be properly mixed to achieve a suitable substrate for the algae culture. This could represent many benefits for the whole process since uniformity of feed is nearly assured due to the different sources, but as a global result implementation of this configuration improves the quality of all influents producing more energy in the CHP unit. In the present work, the integration has been evaluated considering an intermediate configuration among the aforementioned. Figure 1-26 presents a block diagram of Bresso WWTP including the PBR for algae growth. As it can be seen, the integration is made by means of the use of the centrate stream derived from the dewatering unit. This stream contains an important load of N, and depending on the influent, amount of phosphorus can be enough for algae culturing. Furthermore, considering the positive effect of a PBR in the middle, final centrate is expected to be cleaner than the original one, improving the quality of the discharge.


Figure 1-26. Proposed scheme of integration in Bresso plant.

An additional consideration that can be assessed is the further integration of processes for algae harvesting. It can be expected that if solids content of the culture is such that it can be mixed with the main stream entering the primary settling, this unit can be enough for separating algae in the primary sludge. Clearly this condition is ideal since avoids the use of a new separation unit devoted to algae concentration, but it will depend on PBR’s efficiency of producing microalgal biomass.

2. Mathematical models for algae growth

Mathematical models are representations of physical, chemical, biological, etc., phenomena which are usually focused on a set of convenient properties and characteristics of the latter. Models are essential in almost all engineering process and they allow us to predict the system behavior based on three different approaches (Hermanto, 2009): • First-principles (or white-box) models, which are derived from well-known physical and

chemical relationships, reflecting the underlying principles that govern the process behavior.

• Data-driven (or black-box) models, which are of empirical nature (e.g. artificial neural networks, time series).

• Hybrid (gray-box) models: a combination of the above. In the case of describing a PBR through a mathematical model, the first step is the identification of the different components involved in the process as stated in Figure 2-1.

Figure 2-1. Operating variables of a PBR for algae growth.

In Figure 2-1, Q and Q0 are the incoming and outgoing liquid flow rate [Ls-1], X0 and X are the algae concentration [gL-1], Si0 and Si are the substrate concentration of nutrient i at the inlet and outlet [gL-1], V is the photobioreactor volume capacity [L], I0 the light intensity [µmol.m-2s-1] and RCo2 the input rate of CO2 from the gas stream [gs-1].


2.1 Mass and energy balances on PBR

Considering the system presented in Figure 2-1 we can perform the mass balances for each species. In the case of microalgal biomass we can write:

l�11�UU�lmn�op�q � l�11rn�s:< �l�11rn�s7t? ±l�11v��/mUop�q/U�q1mlvop�q

/.fx2/o � y�x� � yx + "f

In which R represents the term of production of biomass (rate of reaction). Moreover, assuming that the PBR behaves like a perfectly stirred reactor, X can be stated as the concentration inside the reactor, which lead as to:

x /f/o + f/x/o � y�x� � yx + "f

The change in volume can be established as the difference of incoming and outgoing flow

/f/o � y� � y

Substituting we get:

f /x/o � y�x� � yx + "f � y�x + yx

If we consider there is no microalgal biomass in the incoming stream we conclude that:

/x/o � " � y�

f x

This result indicates that algae concentration depends on the so-called dilution factor (Q0/V) and the rate of reaction R, which can be expressed in terms of the specific growth rate µ as:

;z;? � x {| � ,}

$ ~ (8)

Similarly, we can develop the mass balances for Nitrogen and Phosphorus obtaining:

;��;? � ,}

$ �3��3h� � x�� (9)

;��;? �

,}$ �3��3�� x

�� (10)

Where YN and YP are the yield coefficient for nitrogen and phosphorus respectively. Finally, considering the mass transfer of CO2 given by Eq. (2) we can expect that the mass balance for this component will be:

Mathematical models for algae growth 43

;�c;? � ��

∗ − 3P� − ,}$ 3P − x ��c (11)

In which YC is the yield coefficient for carbon, kLa the mass transfer coefficient in the medium, and C*

CO2 the equilibrium concentration of CO2 derived from Henry’s law.

On the other hand, a global energy balance in the system can be written in terms of the main variables affecting the change of energy inside the PBR. According to (Lee & Hui 2004), neglecting the energy as biomass in the inlet stream, it can be expressed as:

��oUℎ�q��pq�q��0 = �q��0�T1��T�/T0Tp�l�11 − �q��0pq�morn�sTp�l�11 f ;�;? = �� − ,z� (12)

In which dE is the increase in energy content of the culture per unit of volume [JL-1], A is the illuminated surface area [m2] and Y is the overall growth yield of algae [gJ-1] determined in terms of the maximum growth yield YG [gJ-1] and a maintenance coefficient m [Jg-1s-1] as: �� = �� + L� (13)

Following the performed analysis of (Hulatt & Thomas 2011) the energy content of the culture can be written in terms of the low heating value of the microalgal biomass (LHVb) and its concentration in the medium (X), such that we can obtain from Eq. (12)

LHVb dXdt = I0AV - QXV { 1YG+mμ~ (14)

It is evident that the term dx/dt appears in both mass and energy balances, implying that final concentration of algae is dependent not just on the specific algae growth rate, but also on the incident energy over the PBR (I0). This interaction is more complex than expected, since µ is function of several variables that include nutrients, CO2, pH, light and temperature and should be properly articulated by means of kinetic models as follows.

2.2 Kinetic models for nutrients

The kinetic expression is the part of the model that relates the growth rate of algae to the substrate concentration in a culture media. These equations provide an understanding of biomass production and nutrient consumption rate, both essential for a proper PBR design as well as a tool for predicting process performance, optimization and control (Hermanto, 2009). The most commonly used kinetic models in biological, chemical, pharmacological, and medical processes to describe saturation phenomena are the Monod and Droop models (Molina et al, 1999). Many studies have been conducted for


finding those two model’s parameters for different species of algae like (Aslan & Kapdan 2006), (de Morais & Costa 2007), (Sasi, 2009), (Xin et al, 2010) and (Chojnacka & Zielińska 2012). In general the Monod model describes the relationship between microorganism growth and limiting factor as:

| = |L5� 6�)e6 (15)

Where µmax is the maximum specific growth rate achieved at high, non-limiting conditions and ks is the half-saturation constant defined as the factor concentration at which the specific growth rate is half of the maximum. On the contrary, Droop model relates growth rate to the internal nutrient content of a cell rather than the nutrient concentration around the medium. The Droop model can be written as:

| = |L5� {1 − �� ~ (16)

Where kq is the limiting cell quota for the limiting nutrient and q is the cell quota for the limiting substrate. Both kq and q are commonly expressed as the total amount of nutrient per cell, which is a better index of the nutritional status of the cells than global concentrations. Nevertheless, the cell quota of individual species cannot be measured easily under natural conditions, reason why this model results inappropriate for reactor design. In the case of kinetic models involving nitrogen effects, a significant amount of developments have been focus on the use of Monod equation or variations of this model based on a correction of the inhibition effect at higher substrate concentrations, like the Haldane model (Zhang et al, 1999). However can be found some other models that predict the experimental data under particular culture conditions or accounting for different parameter than µ as can be seen in Table 2-1.

Table 2-1. Kinetic models for nitrogen dependent algae growth. Model Description Reference

μ = μ ¡¢ shk¥,§ + sh

Simple Monod model µmax: Max. Specific growth rate ks,N: Half saturation coefficient sN: Nitrogen concentration

(Aslan & Kapdan 2006) (Xin et al, 2010)

R = "L5� shk¥,§ + sh

Modified Monod model Rmax: Max. nitrogen uptake rate ks,N: Half saturation coefficient sN: Nitrogen concentration

(Smith, 2002)


Table 2-1 (Continuation). Kinetic models for nitrogen dependent algae growth. Model Description Reference

μ = μ ¡¢ shk¥,§ + sh + 1h��:,h

Haldane model µmax: Max. Specific growth rate ks,N: Half saturation coefficient ki,N: Inhibition coefficient sN: Nitrogen concentration

(Zhang et al, 1999)

| = |L5� ©1 − ��,hq «

Simple Droop model µmax: Max. Specific growth rate Kq,N: Limiting cell quota Nitrogen q: Cell quota of nitrogen

(Goldman & Mccarthy 1978) (Yamaguchi et al, 2008)

Similarly, some of the developed models for Phosphorus focus on the basis of Monod

model and its modifications, but a significant concern over Droop model has been posed

from authors like (Flynn, 2002) and (Yao et al, 2011) who have established modification

that fit properly to the experimental data of their studies.

Table 2-2 Summarizes a list of models employed for describing the effect of phosphorus in algae growth.

Table 2-2. Kinetic models for phosphorus dependent algae growth.

Model Description Reference

μ = μ ¡¢ s�k¥,¬ + s�

Simple Monod model µmax: Max. Specific growth rate ks,P: Half saturation coefficient sP: Phosphorus concentration

(Aslan & Kapdan 2006)

(Xin et al, 2010)

μ = μ ¡¢ s�k¥,¬ + s� + 1��:,�

Haldane model µmax: Max. Specific growth rate ks,P: Half saturation coefficient ki,P: Inhibition coefficient sP: Phosphorus concentration

(Zhang et al, 1999)

μ = μ ¡¢ ©1 − k,¬q «

Simple Droop model µmax: Max. Specific growth rate Kq,P: Limiting cell quota Phosphorus q: Cell quota of phosphorus

(Sommer, 1991) (Yamaguchi et

al, 2008)

μ= μ ¡¢ �1 + KyB��Q¯ − Q �(Q± −Q )k +Q¯ −Q

Flynn-Droop model Qp, Qm, Qh: Actual, minimum and maximum phosphorus quota Kq: Limiting cell quota Phosphorus kq: Fitting parameter

(Flynn, 2002)


Table 2-2 (Continuation). Kinetic models for phosphorus dependent algae growth. Model Description Reference

T= μ ¡¢Q k¯ s¬s¬ + k {1 − Q´Q ~{1 − Q´Q ~ + k

Yao-Droop model T: Transport rate of phosphorus. km: Half saturation coefficient kp: Fitting coefficient kq: Constant to control the shape of the curve Qt, Qm: Total and maximum phosphorus quota

(Yao et al, 2011)

All kinetic models related to the inorganic carbon concentration have been developed based on Monod model. Previous work by (Caperon, 1978), (de Morais & Costa, 2007) or (Tang et al, 2011), were aimed at calculating Monod’s parameters and optimal carbon concentration for algae growth in carbon limiting cultures. In Table 2-3 is detailed the description of this model.

Table 2-3. Kinetic models for carbon dependent algae growth. Model Description Reference

μ = μ ¡¢ sµk¥,µ + sµ Simple Monod model µmax: Max. Specific growth rate ks,c: Half saturation coefficient sc: Carbon concentration

(Caperon, 1978) (de Morais & Costa, 2007)

(Tang et al, 2011)

2.3 Kinetic models related to light intensity

For most of the practical operations of photosynthetic cultures, availability and intensity of light are the most important aspects controlling their productivity (Molina et al, 1999). Usually, µ increases with increasing irradiance till a point in which µ is maximum (µmax), and then further increase in irradiance tend to inhibit algae growth (photoinhibition). In this sense Monod model has been widely used due to its simplicity but only when photoinhibition is null or neglected. Once this phenomenon becomes important, Monod model is not able to reproduce experimental results (Merchuk et al, 2007). Besides the aforementioned models, there exist another set of models that suggest that algae growth can be accurately expressed in terms of the average irradiance raised to some power greater than unity, fitting experimental observations with high accuracy (Martinez et al, 1997). Finally, a vast amount of models correspond to empirical correlations where the predominant factor is the use of experimental data for fitting


parameters. In Table 2-4 a summary of some of the models employed in the representation of algae growth by light intensity can be seen.

Table 2-4. Kinetic models related to light intensity and algae growth. Model Description Reference

| = |L5� Ik¶ + I Simple Monod model µmax: Max Specific growth rate kI: Half saturation coefficient I: Light intensity

(Martinez, 2009) (Hermanto, 2009)

(Sasi, 2009) (Chojnacka & Zielińska,

2012)

| = |L5� Ik¶ + I + ��

Haldane model µmax: Max Specific growth rate kI: Half saturation coefficient k2: Inhibition coefficient I: Light intensity

(Aiba, 1982) (Merchuk et al, 2007)

(Hermanto, 2009)

| = |L5� ©1 − �· ¸¸]_¹«

Exponential model µmax: Max Specific growth rate I: Light intensity Imax: Saturation light intensity

(Vanoorschot, 1995) (Martinez et al, 1997)

| = |L5� I<��< + I< Power law model I: Light intensity n: Fitting coefficient

(Molina et al, 1999)

|= 2|L5� {1 − I>�6~ {�5º�6 − I>�6~{1 − I>�6~� + {�5º�6 − I>�6~�

Muller-Feuga model Iav: Average light intensity Is: Saturation light intensity Ie: Minimum light intensity

(Muller-Feuga, 1999)

| = 2|L5� (1 + β) ��7{ ��7~� + 2¼ ��7 + 1

Peeters & Filer’s model I: Light intensity Io: Optimum Irradiance intensity β: Attenuation coefficient

(Bouterfas et al, 2002)

Notwithstanding, one the biggest concerns when using the previous relationships is the definition of the value for I to be used. It is well known that irradiance will change inside the PBR even if outdoor incident radiation remains constant, which means cells near reactor walls will receive more light than the ones in the interior. This situation implies the specific growth rate varies along position and time. According to some authors (Martinez


et al, 1997), (Fernández et al, 1997), (Masci et al, 2004), the use of an average irradiance Iav allows to determine µav for a given volume of reactor. Iav can be seen as the light received as a single cell randomly moving inside the culture, and it depends on the external irradiance on the surface of the PBR, the reactor geometry, morphology and concentration of the microalgae and the inherent absorption characteristic of the medium (Molina et al, 1999). A first and simple description of the light attenuation condition can be performed in terms of Beer-Lambert’s law considering that light intensity decreases along the path length it should cover. This analysis assumes that the direction of the incident radiation does not change as it crosses through the culture, radiation is monochromatic and scattering effect due to particles is negligible respect to absorption (Fernández et al, 1997). The simplest geometry to be analyzed is a flat-plate PBR as shown in Figure 2-2 in which the light gradient can be expressed with Eq. (17) as:

�7t? = �7�·�_z_½'� (17)

Where I0 is the irradiance at reactor surface [µmol.m-2.s-1], L is the total path length [m]; ka is the characteristic extinction coefficient of the medium [m2.kg-1], and Xalg the algae concentration in the medium [kg.m-3].

Figure 2-2. Irradiance profile along a flat-plate PBR.

In this case the average irradiance can be estimated by integrating along the path length of the reactor as:

I¡¾ = ¿ IÀe·ÂÃÄÃÅÆÇdyÉ� L − 0


Finding that:

�5º = ¸F�_z_½'� �1 − �·�_z_½'�� (18)

However, Beer-Lambert’s law approach fails in the sense of considering a monochromatic light, in which case a correction can be made for using polychromatic light source by computing an average ka to all wavelengths. Furthermore, the main disadvantage is that this approach is not appropriate for high biomass concentrations because of the scattering and selective absorption effects (Masci et al, 2004). There exist another set of models including scattering and absorption phenomena separately, like presented by (Aiba, 1982) or (Cornet et al, 1992). The latter, for instance, uses two fitting parameters Ea and Es for describing both effects independently assuming an isotropic light field across the culture. The main problem of this approach lies in the need of sophisticated numerical tools and long calculations. But if culture has a high concentration, Cornet’s model can be reduced to the monodimensional form, where scattering is neglected due to the concentration level, finding that:

¸̧} = \Ê>Ë�(�e\Ê)�·>bË�(�·\Ê)� (19)

Where:

Q� = Ì �5�5 + �6; Q� = x59=EQ�(�5 + �6) Another important consideration is the computation of the average irradiance in a more commonly used reactor geometry like a cylinder. In that case light distribution becomes a function of the total incident radiation at the surface I0, the optical properties of the culture and the distance from the surface to the point of interest inside the reactor. For any photobioreactor, the distance traveled by a direct ray from the tube’s surface to a point within the culture (Pdirect) is calculated based on the position of the Sun, which determines the point of incidence on the surface of the reactor, as well as the polar coordinates of the point (ri, ϕ) in a cross-section of the tube as indicated in Figure 2-3. If only the cross section of the cylinder is considered, some trigonometric relationships can be used for computing the transverse light path (ai) which is a projection of the real path Pdirect. Taking this into account we obtain:

�;:@>P? = 5& P76ÍP76{Î�·ÏÐÑ~ = Ò 6:< Ó·@& 6:< ÔP76{Î�·ÏÐÑ~ (20)

Where parameter ai can be stated as:


aÖ = �Ö cos Ù � " cos Úsin Ý � R sin ε � rÖ sin φ

cos°ω²

Figure 2-3. Incident radiation and its relationship with a given point inside the culture.

From (Molina et al, 1999) Moreover, by means of Pythagorean Theorem and using the information of Figure 2-3 (Top view) the path traveled for any disperse ray (Pdisp) can be estimated as:

�;:6B � â°�: 1pq Ù � " 1pq Ú²� + °�: U�1 Ù � " U�1 Ú²� (21)

At this point, local irradiance can be calculated by any of the aforementioned approaches (Eq. 17 and Eq. 19) considering contributions due to direct (IB(r,ϕ)) and disperse (ID(r,ϕ)) radiation in the light path. The last step is determining the average irradiance along the PBR volume, splitting the integration between the height and radius of the reactor in order to obtain the average irradiance of the system (Iav). For this purpose we can expect that:

�5º � 1f ã � /f � �

3� ã°�ä + �å²/1

And splitting the surface integral into two variables we obtain:

�5º � 1æ"� ã ã °�ä + �å²�/�/Ù

ÔÒ

Finally,


�5º = �çÒ� è¿ ¿ �ä(�, Ù)�/�/ÙÔÒ + ��ç¿ ¿ ¿ �å(�, Ù)�/�/Ù/ÚÓÔÒ é (22)

In this point, integration of the average irradiance can be performed numerically in such a way it can be obtained a radial profile of iso-irradiance values as suggested by (Molina et al, 1999), or simplifying to a single value that represents the average behavior of the reactor.

2.4 Multiple factors kinetics

Since µ is affected by all the aforementioned conditions, each of them contribute to the reduction of the maximum specific growth rate at optimal conditions µopt. Usually, the way in which this multiple relationship is expressed involves a product function composed by fractions representing each reduction due to each factor (Bouterfas et al, 2002). In general we can write that:

|(�, �, �, �, #, v�) = |7B?"¸"h"�"�"ê"Bë (23)

Where RI, RN, RP, RC, RT and RpH are the growth rate reductions by irradiance, nitrogen, phosphorus, carbon, temperature and pH. Evidently, each of the reductions will have their form according to the way they affect the specific growth rate as presented in sections 2.2 and 2.3. Typically, some of the reductions are neglected depending on the particular conditions of the culture. Factors like temperature and pH are prone to be controlled which always assure working at optimal conditions. In a lesser extent reductions associated with substrates are negligible since it depends on the availability of each of them, as well as their concentration variability on time. For this reason most of the previous works consider just the reductions that directly affect the analyzed culture like (Zhang et al, 1999) in which the specific growth rate has been defined in terms of the nutrients and pH effects as:

| = |L5�ì Sk¥ + S + 3��îì1k±� + �[�v� + v��[Rî

Or (Xin et al, 2010) in which the maximum population growth rate is just function of nitrogen and phosphorus concentration following a double Monod model:

R = "L5� © shk¥,§ + sh«© s�k¥,¬ + s�«


In Table 2-5 it can be seen some of the different values obtained by means of the aforesaid kinetics models, as well as the main characteristics taken into consideration during the experimental phase of each study.

Table 2-5. Summary of specific growth rates of previous studies.

µmax (day-1) Model Characteristics Reference

2.66 Exponential Batch culture, Chlorella p., I0 (2022

µmolm-2.s-1) (Martinez et al,

1997)

1.51 Power law modified

Outdoor culture, P. tricornutum, I0 (1000 µEm-2.s-1)

(Molina et al, 1999)

1.7 Modified Droop

Batch culture, Isochrisys g., I0 (200 µmolm-2.s-1)

(Masci et al, 2004)

1.59 Peeters &

Filers Batch culture, C. microporum, Iopt (390

µmolm-2.s-1) (Bouterfas et al,

2006)

0.62-0.99 Simple Monod Pilot Plant PBR, Euglena gracilis, I0

(150-550 µmolm-2.s-1) (Chae et al,

2006)

0.44 Direct

measurement Bench scale PBR, Spirulina sp, CO2

level (0-12%). (de Morais & Costa 2007)

0.70 Simple Droop Semi-continuous culture, Chatonella o., I0 (200 µmolm-2.s-1), NH4

+(18.µM), NaH2PO4(1.8.µM)

(Yamaguchi et al, 2008)

0.99 Direct

measurement Batch culture, Chlorella p, 10% CO2, I0

(180 µmolm-2.s-1) (Tang et al,

2011)

0.27-1.8 Modified Monod

Batch culture, Chlamydomonas r., I0 (200 µmolm-2.s-1), NH4

+(0-18.7mM) (Tevatia et al,

2012)

3. Experimental phase

In order to identify the main parameters which affect the performance of the microalgae culture a set of experimental runs has been developed in a pilot plant scale. The aim of those experiments is to obtain primary information about the behavior of the culturing in order to correlate it with the parameters that allow the analysis of a complete integrated mass balance. For this reason experiments were divided into four subsets, in such a way it was possible to collect information about the dynamic and stable behavior of the PBR, as well as the harvesting of the produced algae. On the other hand, for complementing the analysis, a couple of bench scale tests were performed in order to establish some of the operational parameters in the anaerobic digestion process. In these experiments a reproduction of the sludge digestion was considered as a first step, and also as a base for comparing with respect to the co-digestion with algae. Figure 3-1 contains a short description of the methodology and dependence of the experimentation process.

Figure 3-1. Methodology employed in the experimental part.


As it can be seen each of the four main experiments have a particular aim, starting with the batch test in which the establishment of HRT and µ are the main outcomes. After stabilization of the batch runs it is possible to continue with a steady state operation for a better understanding of the effects of the multiple variables involved in the culture. In this stage the removal efficiencies for nitrogen and the biomass yield, linked to the solid content, are the main results. Thanks to the microalgal biomass produced in the continuous operation of the PBR it is possible to assess two of the techniques associated to the concentration of microalgal biomass. In this set of experiments, the mass and volume efficiency derived of the separation process was determined. Finally, the concentrated fraction was used as feed for the co-digestion process in which the TVS efficiency and methane production were established.

3.1 Algae growth in batch operation

Batch runs were carried out as a first approach to the microalgae culturing since it is first required to determine the growth characteristics of the medium. Beside the information found in the literature, it is convenient to determine the time microalgae take to grow under the particular conditions presented in the pilot plant PBR. Additionally, information in dynamic operation helps to understand intrinsic characteristics of the medium like the specific growth rate, inhibition phenomenon, among others. These experiments were run in an 85L (effective volume) cylindrical plastic PBR located adjacent to Bresso’s CHP as it can be observed in Figure 3-2. A complete description of the reaction system can be seen in Appendix D, where the equipment diagrams are explained. Batch runs were performed during the period from March 3 to May 26 2014. For performing these batch runs the procedure described in Figure 3-3 was employed, considering as the main monitoring parameters the total and dissolved solid concentrations, nitrogen and phosphorus contents (nitrogen measured mainly as total nitrogen and ammoniacal nitrogen) and absorbance of the medium measured at 665nm following the procedure described by (Uslenghi, 2011). Furthermore, temperature, pH, conductivity and dissolved oxygen were measured as additional reference parameters. Total nitrogen and total phosphorus were measured using the persulfate method 4500-P J of (Standard-Methods, 2005) while N-NH4

+ was analyzed following the spectrophotometric-indophenol methodology of (IRSA-CNR, 2004). Temperature, pH, conductivity and D.O were measured directly on field by suitable probes, while solid contents were determined following the methods 2540 B, D and E of (Standard-Methods, 2005) techniques. These analyses were carried out directly by members of the research group on Bicocca University labs in Milan.

Experimental phase 55

Figure 3-2. Current location and appearance of the PBR pilot plant.

StartStart

Adjust the bubble system

putting it in the bottom

Adjust the bubble system

putting it in the bottom

Let empty the column-

wash it with water

Let empty the column-

wash it with water

Remove carefully top

covers and take 500 mL

sample

Remove carefully top

covers and take 500 mL

sample

NoNo

YesYes

NoNo YesYes

Fill the column until 90%

of max capacity with fresh centrate

Fill the column until 90%

of max capacity with fresh centrate

Use fresh microalgae sln

for filling the remaining

10% of the column

Use fresh microalgae sln

for filling the remaining

10% of the column

Qg adjusted and stable?

Qg adjusted and

stable?

Use the flowmeter for fixing the volumetric flow rate

Use the flowmeter for fixing the volumetric flow rate

Take a 500 mL sample for time zero characterization

Take a 500 mL sample for time zero characterization

Cover the top of column

with a plastic wrap and

protective mesh


with a plastic wrap and

protective mesh

Is tf achieved?Is tf achieved?

Take field measurements

of Tr, Tamb, Cond, pH, D.O. and PAR

Take field measurements

of Tr, Tamb, Cond, pH, D.O.

and PAR

Open the valve for

supplying the CO2 stream

Open the valve for supplying the CO2 stream


with a plastic wrap and protective mesh


with a plastic wrap and protective mesh

Is tmax

achieved?

Is tmax

achieved?

EndEnd YesYes

Figure 3-3. Procedure for operating the pilot plant in batch mode. In this first experimental stage, four tests were run letting the system to evolve for a period of 11-16 days in which culture was exposed to the climate changes of the period of


evaluation. As it can be observed in Figure 3-4 both TN and TP maintain a stable behavior remaining almost constant along the runs. It is evident that this condition is deviated from the expected since those substrates are consumed in the growing process for producing biomass, which lead us with a particular situation.

Figure 3-4. Evolution of TN and TP concentration during batch runs. (a) TN, (b) TP.

As observed, there is a parallel tendency in 3 out of 4 experiments, getting similar results in both substrates in the first 3 batch tests. Batch 4 could be affected by external factors that make it inappropriate for the analysis due to its deviated behavior, not just in the TN and TP behavior, but in all determined parameters. Then, considering only the common trend of the remaining 3 batch runs, the only conclusion we can state, is that there was something in the analytical procedure followed for determining those parameters that provide as a result the total content on the medium, including the produced biomass, remaining in this way a constant value for these parameters along the runs. The previous result can be better understood when ammonium concentration is analyzed. In this case, it was found a consistent reduction during the tests, a clear indicator of the consumption of the substrates. As shown by Figure 3-5, the behavior is the same in the first 3 batch runs, decreasing at a similar rate until levels of 50-80 mg/L. In this sense, it can be stated that N-NH4 results a more reliable monitoring parameter than the total nitrogen content since reflects the reduction of the substrate due to the microalgae growth. Furthermore, this reduction has to be correlated to the biomass production. Thus, the solid content helps us to identify how much biomass is being produced, since microorganism will be counted as the only source of solids in the medium. Evidently, it is important to determine which fraction of the total solids corresponds to dissolved and suspended solids, since each fraction affects in a different way the growing process. In average, during batch runs TS and TSS have grown at different rates, but there is no a


defined trend among the 4 runs as seen in Figure 3-6. Here, the effects of external variables like weather are clearer than in the substrate consumption.

Figure 3-5. Evolution of N-NH4 concentration during batch runs.

Figure 3-6. Evolution of solids content during batch test. (a) TSS, (b) TS

In any case the biomass production can also be correlated to the absorbance of the medium, which at the same time, has a strong link with the solids content. Similarly to was found with solids, absorbance tends to increase in all batch tests as detailed in Figure 3-7. This similitude between absorbance and TSS curves, confirming the direct relationship among these variables. Until this point, we can define thanks to the previous results that biomass seems to grow continuously till the 10th to 12th day, moment in which there are no clear trends, since we can observe both further increases and reductions. We can think


this point is a border in which phenomenons like self-shading start to be significant, and the consequent inhibition of the culture growth is present.

Figure 3-7. Absorbance of the medium during batch runs.

Moreover, based on the ammonium consumption, after the day 12, exhaustion of this substrate reaches more than the 70% of the initial content, making more difficult the growing since more microorganisms are presented in the medium, and the higher the population, the higher the nutrients consumption for a suitable growth. This is more evident when looking at Figure 3-8, where N-NH4 removal is determined for each batch test.

Figure 3-8. N-NH4 removal on the different batch tests.


Correlated with the last result, the increase in solid content is also recognizable as detailed in Figure 3-9. As observed, TSS concentration continuously grows and even doubles its value after a period of few days in accordance with the expected behavior.

Figure 3-9. TSS concentration change on the batch tests.

However, as explained before, a high biomass concentration inside the photobioreactor could be adverse for the performance of the culture since inhibitory phenomenons start to be more relevant. Additionally, if we consider that microalgae growth follows a first kinetic order, from Eq. (8) we can deduce that:

ï = ï��?�� ln ï = ln ï� + |o Moreover, taking into account that we can express the biomass concentration x in terms of any of the measured variables, like absorbance or TSS concentration, we can establish the mean value for µ in each of the performed runs by means of the previous linearization. In fact, using the absorbance values for the different batch tests we find the correlation shown in Figure 3-10, in which it is possible to observe a similar trend in runs N°1 and N°3 with µ values of 0.387d-1 and 0.43d-1 and good coefficients of correlation. On the contrary, for batches 2 and 4 the slope was lower (0.05d-1 and 0.109d-1 respectively) as well as the fitting performance. Considering the previous results, the expected hydraulic retention time for a continuous operation, based on batches 1 and 3, would be close to 2.5 days (HRT:1/µ). On the contrary, considering results of batches 2 and 4, this value would be between 10-20 days. Then, since there is no a conclusive result, it was decided to fix a ten days period of growing as HRT of the continuous test, expecting a subsequent reduction in order to analyze the effectiveness of the culture under different conditions.


Figure 3-10. Linearization of the kinetic growth using absorbance as reference variable.

The obtained values are in a similar range than those found in a previous investigation of the research group (Uslenghi, 2011) or by researchers like (Tevatia et al, 2012). Nevertheless, these values are below many other results, when compared to information reported in Table 2-5, indicating the need of improving the operative conditions of the culture. Since substrates seem not to be a limiting condition, the main factor to be exploited is the irradiance caught by the PBR considering that for the tested culture system, this parameter changes considerably along the day, and even more in accordance to climatic characteristics of the season. Particularly, for this study temperature was in the range from 16.2°C in the fir st batch to 23.9°C in the fourth batch, similar to the range of ambient temperatures achieved during the spring season. Moreover, the mean pH along the batch was 9.05, 7.64, 9.04 and 6.38 respectively.

3.2 Algae growth in continuous operation

Once the last batch test was concluded, the system was adjusted for changing the configuration to a continuous operation. For this purpose, the procedure depicted in Figure 3-11 was followed. Moreover, as stated before, HRT parameter was established initially in 10 days, which lead us the need of regulating the volumetric flow rate of centrate in 5.9 mL/min. Similarly to the batch case, collection of information was performed 2-3 times a week, considering the same monitoring parameters as in the first case. This set of experiments was carried out within the period from 26 May to 31 July 2014.


Figure 3-11. Procedure for operating the pilot plant in continuous mode.

By operating the PBR in a continuous mode, it was expected to establish the typical behavior of the culture as well as the productivity of biomass under steady state condition, correlating the biomass production with the change in the solids content. Considering the results on the batch experimentation for TN and TP, N-NH4 was taken into account as monitoring parameter for determining the substrates consumption. For this purpose, along with the samples collected on the PBR, the feed, composed only by the same centrate produced in the dewatering section of Bresso plant was similarly characterized. During the operation in continuous mode, it was possible to observe in a clearer way the transformation of nutrients and light into biomass. One of the parameters that better represent this behavior is the solid content as it can be seen in Figure 3-12. In this graph the difference between inlet (Centrate) and outlet (reactor) conditions is clearly remarked. Here the change in TS and TSS during the experiment was always positive, and variations in the second part of the test (After day 30) due to a drastic change in the added feed, show how important is this condition on the biomass production.


Figure 3-12. Solids content for the continuous operation.

However, the initial solids content was not the only factor that changed in this part of the experiment, as can be seen in Figure 3-13, a sudden increase of substrates accompanied the aforementioned rise. However, the final ammonium concentration was stable, following a similar path that TSS.

Figure 3-13. N-NH4 behavior during continuous operation.

On the other hand, as mentioned for the batch runs, absorbance also reflects the microalgae grow due to the existing link among TSS and this parameter. In Figure 3-14 we observe how absorbance seems to change erratically along the experiment, ranging from values close to 1 up to values higher than 2. Surely these changes are more evident


considering that Beer-Lambert law remains valid only in a narrow range from 0.1 to 1, point in which deviations of its linearity begin affecting the results. In this scenario, it is better to perform a calibration among absorbance and solid content, in such a way both parameters can be correlated. For sure, the effect of diluting a sample is less adverse than measuring a concentrated one, thus getting a more reliable result.

Figure 3-14. Absorbance of the culture medium during continuous operation

Notwithstanding, a simple determination can help us to analyze how deviated from the linear fitting are TSS and absorbance of the medium. Simply computing the ratio TSS/Abs we can expect that along the experiment this ratio should remain constant. In Figure 3-15 the correspondence of both parameters in the algae culture is represented. As it can be seen, we can state that inside the culture media this ratio moves in a narrow range, which clearly indicate us that even when the split data seem to be not correlated, in fact they really are. In conclusion, the continuous operation has lead us to produced microalgal biomass, but in order to perform the subsequent mass balances, it is important to define the main 3 performance indexes of the PBR. The first is related to the substrate consumption and can be seen as a change in the substrate concentration, referred to its initial condition. Thus, for instance, the ammonium yield (YN-NH4) will be a negative value given the reduction in this substrate content.


Figure 3-15. TSS/Abs correspondence in the microalgae culture.

Similarly, the solid yields (YTSS and YTS) represent the change in the TSS and TS concentrations respectively, and it is expected that those parameters are positive according to the expression:

0:�äÒ = �:(ñ})�:(�) − 1

In this sense the higher the solid yields, the higher the produced biomass, and consequently more efficient the PBR is. In Figure 3-16 the behavior of these parameters along the test is shown. As it can be seen, ammonium removal was considerably consistent reaching an average of 76.2%, a little higher than the value reached during the batch experiments. Regarding the solid yields, it can be seen that there are two regions according to the change in the feed after day 30. In the first part YTS has reached an average increase of 152.2%, while after the change in the centrate, this yield dropped almost half until 73.6%. A different scenario is found for YTSS, which has no a defined trend in any moment, showing extreme changes that range from 400% to 23000%.


Figure 3-16. Estimated yield parameters for the continuous experiments.

3.3 Anaerobic digestion tests

Anaerobic digestion runs were performed in a 2.4L batch reactor, equipped with a water bath and automatic stirring system. The system was conditioned for operating in a semi-continuous operation and collecting the produced biogas in a measurement unit fixing the working temperature at 32°C. The AD system shown in Figure 3-17 was operated following the procedure depicted in the diagram of Figure 3-18.

Figure 3-17. Sketch of the bench scale anaerobic digestion system.


This system allowed reproducing conditions of the actual AD reactor of Bresso plant. Information collected is important to verify and complement the operational parameters of the process. First, after a stabilization period of one month, the system was conditioned for feeding only the same sludge fed in Bresso’s AD reactor, looking for a line base in which the idea was to compute the actual TVS removal efficiency, and the methane production. After concluding this phase, the system was modified for handling the co-digestion of sludge-microalgal biomass adjusting the HRT, keeping constant the volumetric flow rate of sludge (30 mL/day).

Figure 3-18. Procedure for operating the AD system in semi-batch mode.

Since the system is naturally batch mode, a 3 days/week frequency for supplying new feed was implemented, while recovering the produced digestate in the same proportion. In this way a semi-continuous operation is put in practice, simulating the operation of the WWTP digester. Analysis made to the digestate consisted in the measurement of TS, TVS, pH, T, TKN, TN, following the recommended procedures of (Standard-Methods, 2005), and FOS:TAC ratio using the method described by (Lossie & Putz, 2008). The same parameters were used for characterizing the feed. On the other hand, produced biogas was conducted through a plastic tube into a graduated cylinder filled with 1M


NaOH solution, as detailed in Appendix D, in order to absorb the CO2 fraction along the cylinder length in such a way the displaced liquid volume mainly corresponds to CH4. Results of this experimental run can be analyzed from two standpoints; first the performance of the AD process when only sludge was fed, and once co-digestion was implemented. The easiest comparison can be made by means of the produced methane which was measured with the same frequency of feeding. Theoretically, the expected result is to achieve a bigger amount of CH4 by co-digestion than only feeding sludge, but this is not true if operational conditions in the AD do not remain optimized. It is evident that the co-digestion implies a change in the organic fraction, mainly measured as a change in OLR, but changes have to be compensated modifying other operative variables in order to assure a suitable degradation of the new feed. According to this, co-digestion was split in two phases in which not just the OLR was changed, but the HRT of the medium was also changed for analyzing the effects on the process. Then, from Figure 3-19 (a), when both runs are compared, it can be seen that methane production is higher with only sludge fed reaching a mean absolute production of 7.6mLCH4/h. These results are found working at HRT of 20 days with an effective volume of 1200 mL. From that plot is also evident that the rate of CH4 production seems to be quite variable in time due to the semi-continuous operation, but in part (b) when the complete period is evaluated, it is found the expected linear correlation between methane volume and time for all runs. This allows us to conclude the convenience of using this adapted system for reproducing the continuous operation, without losing accuracy. At the same time, co-digestion was carried out changing the provided load, keeping the ratio of fed sludge to microalgal biomass (Qs/Qalg) constant in a value of 7. This change implies that the new feed is composed by 87.5% of sludge and 12.5% of concentrated microalgae. During the first phase of co-digestion, digester was fed with 240 mL/week of sludge-microalgae mixture in such a way the expected HRT was 35 days. Under these conditions, CH4 production reached a mean of 5.8mLCH4/h as detailed in part b of Figure 3-19. After that, volume of feed supplied to digester was changed to 120 mL/week, reaching an HRT of 70 days that promoted a methane production of 3.1mLCH4/h. Besides this initial behavior in which it seems that co-digestion is not working as well as sludge digestion, it is important to state that the absolute methane production is not the most meaningful indicator, since these values change depending not just on operational conditions, but depending also on the characteristics of the feed and the provided load. In fact, we can determine the specific methane production (SMP) as:

3Z� g�lE�ë^�$� k = f�ë^l$�

Where VCH4 is the volume of produced methane in the evaluated interval of time, and mVS is the amount of volatile solids added in the same period.


Figure 3-19. Methane production during the AD tests. (a) Punctual values, (b) complete

period performance. As it can be seen in Figure 3-20 (a), the SMP shows a different behavior than the absolute methane production. In this case, digestion of only sludge allows an average of 179.2 NmLCH4/gVS (CV 0.3), while the co-digestion phases achieved 151.1NmLCH4/gVS (CV 0.36) and 217.4NmLCH4/gVS (CV 0.18) respectively. From this point of view, along with changing the load of the digester, the change of HRT has a substantial effect on the productivity of the process, altering not just the time available for degrading, but promoting or not the adaptation of the microorganism to the organic matter to be degraded.

Figure 3-20. (a) Specific methane production and (b) organic loading rate of AD runs.


Furthermore, once we observe the Figure 3-20 (b), we find that the lower absolute methane production is a direct consequence of the lower OLR in the co-digestion phases, but at the same time this allows us to conclude that the co-digestion process was able to almost equalize the sludge digestion SMP, adding a lower amount of organic feed. On the other hand, we can analyze the AD process from the digestate behavior standpoint. In that sense, we first find that for only sludge feeding, after a period of stabilization (almost one month), TVS removal remains in a narrow range surrounding the 27.3 %, considerably close to the mean value found for Bresso AD unit of 25.1%. As it can be seen in Figure 3-21 (a), TS and TVS in the digestate maintain a similar comportment with a mean TVS/TS ratio of 59%. On the contrary, once we analyzed the co-digestion run, we find a slight difference in the removal efficiency, which achieves an average of 38.2% obtaining a stream of approximately 10 g/L of TVS as Figure 3-21 (b) details.

Figure 3-21. Feed and digestate solid contents (a) Sludge fed; (b)Co-digestion.

Moreover, it was identified a slight increase in the nitrogen content when co-digestion was put in practice, changing from 244.9mgN-NH4/L in the sludge fed case to 265.5mgN-NH4/L. This set of results indicate us that the addition of microalgal biomass, at least in the previously defined quantities, could have a positive effect on the digestion since degradability seems to be enhanced by the co-digestion, but in any case, this is only verified once both processes are compared at similar OLR and HRT, which was not possible due to the time required for performing each run. By now, the only indicator that can be involved in this analysis is the individual behavior of the microalgal biomass for producing methane through a BMP test. This was accomplished once a set of 3 replicates of microalgal biomass sample were digested during 30 days at 35°C following the BMP test by pressure monitoring methodology. From the developed BMP test, it was found that concentrated microalgae have an average of 0.187Nm3/KgVS or 187NmL/gVS. Value


slightly higher than obtained by (Uslenghi, 2011) of 140NmL/gVS but significantly lower than main results found in other investigations as detailed in chapter 1, 1.3.3, that ranges from 250 to 350 NmL/gVS. At the same time, the obtained value is almost half of the sludge BMP that rounds 300 – 350 NmL/gVS. Finally, using the results of both tests, it was possible to estimate the anaerobic digestion efficiency as detailed in Table 3-1 considering the average values of each run, as well as the estimated BMP for the primary sludge and co-digestion feed. Efficiency was considered in this study as the ratio between the actual SMP, and the maximum expected methane production as: òñå = 3Z�>�BYZ�

In which the BMP for co-digestion phases was considered as a weighted average based on the content of the added feed. As observed, correlated to the higher methane production, the efficiency of the process is also higher in the sludge fed case, even when for microalgal biomass the estimated BMP is lower. This is a clear evidence of the need of optimizing AD parameters for co-digestion, since initial configuration, besides its effectiveness, results inadequate for the mixture digestion.

Table 3-1. Estimation of anaerobic digestion efficiency. Parameter (main value) Sludge fed Co-digestion P1 Co-digestion P2

BMP (NmL/gvs) 300.0 285.9 285.9

SMP (NmL/gVS) 179.2 151.1 217.4

HRT (days) 20 35 70 OLR (kgVS/m3d) 0.757 0.554 0.336

ηAD 0.597 0.528 0.760

3.4 Harvesting of produced algae

In order to identify the efficiency of two solid/liquid separation techniques for providing a more concentrated microalgal biomass, some experimental runs were developed using the same samples obtained in the continuous operation tests. It is important to collect and concentrate microalgae for their further use since they are diluted in the PBR, and those conditions are deleterious with the development of those further processes. Considering this, sedimentation by gravity and centrifugation were tested as mechanisms for obtaining the concentrated biomass. For the sedimentation the monitoring parameter is typically the settleable solids (STS) (mLSTS/L) measured in an Imhoff cone, but additionally it is required to determine how is distributed the solid content in the two generated phases, this is the efficiency of separation. Figure 3-22 shows the typical behavior of STS during continuous operation; values range from 5 to 13 but it stabilizes near 8mLSTS/L. This first parameter indicates us


that sedimentation has a volumetric efficiency (ηv=Vconc/Vinitial) below 1%, which lead us to just a fraction of the total processed volume. But it is also important to know the TS and TVS content in the concentrated phase since this is a key factor that affects the further AD process. We can establish here also the mass efficiency (ηm) as the fraction of TS that ends up in the concentrated phase, or

òL = lê�Plê�:<:?:59 = fP�ê�Pf?7?�ê�:<:?:59 Where Vtot and Vc are the total volume (initial condition) and volume of the concentrated phase, CTS

initial and CTSc are the TS concentration in the initial and concentrated solutions

respectively. Then, these two efficiencies clearly identify how much of the TS and TVS contents can be separated, and how much volume we can get after the process.

Figure 3-22. Settleable solids of produced microalgae during continuous operation.

In the case of centrifugation, samples were put for 10 min at 4000 rpm and then separated for measuring the same solid contents performed in the sedimentation case. Since the volume of a single sample (50 mL) was considerably small for providing a measurable concentrated phase, quantification of the concentrated volume was done only after the treatment of 1200 mL. This volume was computed as the difference between the total processed volume (Vtot) and the supernatant volume after separation (VSup):

fP = f?7? − f6tB


Figure 3-23 contains a summary with the results of the 7 run experiments. There, it can be seen the measured TS and TVS content of the concentrated biomass, as well as the computed mass and volumetric efficiencies. It is evident the better performance of centrifugation respect with sedimentation in the amount of collected solids, represented not only by higher ηm values, but for the concentration of solid itself. While sedimentation achieves a maximum of 15.1gTS/L (11.3gTVS/L), centrifugation process is able to achieve until 71gTS/L (56gTVS/L). However, the mass efficiency is not higher than 16% in sedimentation and 52% for centrifugation, which means there is still a significant amount of biomass remaining in the supernatant, which will be lost.

Figure 3-23. Efficiency and solid concentrations after microalgal biomass harvesting tests.

In terms of the volume collected, results evidence a remarkable reduction from Vtot to Vc with the consequent low volumetric efficiency. In the sedimentation, it is possible to achieve until 1.9%, but centrifugation allows just an average of 1.2%. This indicator surely can be enhanced if the initial concentration of microalgal biomass is higher than the values obtained in this study. Beyond the previous results, it is essential to evaluate the cost and energy consumption of these separation techniques, because they are factors that affect the performance of the global plant. Other solutions like flocculation or filtration can be tested and judged in order to find better results.

4. Integrated Mass Balance

Usually the complete analysis of a WWTP is made by using proper mathematical models that describe each of the units of the process. The main characteristic those models have to contemplate is to take into account the components of importance for the whole process in such a way the entire behavior of the plant can be analyzed (Wentzel et al, 2006). In terms of mass balances, a WWTP can be seen as a set of individual unit operations connected through a network of flows, which means outputs from upstream units become inputs for downstream units. Moreover, another big concern is the use of recycling from downstream to upstream in some of the units since it is commonly used and affect the behavior of the plant in a significant degree (Ekama, 2009). Simulation models for WWTP include a wide variety of options that depends on the detail of the information, as well as the computation mechanisms. In general we can expect mathematical model can be (Grau Gumbau, 2007):

• Holistic and Reductionist models: A holistic model tries to explain the phenomenon by means of global parameters and general principles, while a reductionist model describe the process with a great detail going from the elementary phenomenon to the global one.

• Internal or external models: The first describe the answer of the system based on the knowledge of the physical, chemical or biological phenomenon. The latter is also known as black box model, in which a set of empirical relationships determines the behavior of the system.

• Dynamic and stationary models: This classification talks about the time dependence of the model. Meanwhile, dynamics model include the description of the model during transient, stationary models only reproduce the responses of the system during steady state conditions.

• Deterministic and stochastic models: In a deterministic model the response of the system is known with precision given the condition at the inlet. On the other hand a stochastic model represents the response of the system as a function of probability, commonly expressed as a probability distribution.

The selection of the model should be strictly related to the aim of the investigation, and in accordance with the available information for validate the model. Currently the most


effective way of simulating a WWTP is through models derived from the ASM1 (Activated Sludge Model N°1) developed by (Henze et al, 1987) as well as the ADM1 (Anaerobic Digestion Model N°1) established by (Batstone et al , 2002). Those models have the advantage of including all materials that are relevant for the different operation units by using a reductionist approach, built over a combination of well-known physical, chemical and biological laws together with empirical relationships that make easier the computation in some units. In any case this approach implies the deep use of numerical tools due to the big amount of found interrelationships. For instance in the original ASM1 the model is able to simulate the organic matter elimination along with the nitrogen removal considering hydrolysis, organic matter biodegradation by denitrification and under aerobic conditions, and nitrification of ammonium species as shown in Figure 4-1.

Figure 4-1. Transformations included in the ASM1. From (Grau Gumbau, 2007)

Similarly, ADM 1 describes the anaerobic digestion through biochemical and physic-chemical transformations, including the appropriate kinetics expressions and mass balances according to Figure 4-2.

Integrated Mass Balance 75

Figure 4-2. Transformations included in the ADM1. From (Batstone et al, 2002)

Another set of simpler models for WWTP are based on the global and local mass balances, in which operations are defined as black boxes using as main parameters the removal efficiency or production yield of the different components. In this modeling approach there is no need of advanced numerical tools since almost all expressions are linear equations that can be solved by fixing some operative conditions (Spellman, 2004). At the same time, the success of these models lies on the availability of information about the operation of the real plant, since this information allows to determine the required efficiency parameters, as well as to fix the recycle ratios and productivities. Only by using real data from the analyzed WWTP this approach can be properly adjusted for simulating the system. Evidently beyond the great advantage of a lower computational effort, the main constraint is to validate the proposed modeling with experimental data and the lower degree of detail of the simulation, since only can be determined conditions on the streams, but no inside the process unit. For the purpose of this work, a black box approach based on efficiencies of each unit was used and it was adjusted and validated using the compilation of data of Bresso WWTP for the period 2012-2013. The following segments include the development of the mass balances in the different sections of Bresso plant and the inclusion of the PBR and its harvesting unit.


4.1 Wastewater section

From the block diagram of Figure 4-3 it is possible to conclude that Bresso plant can be split in sections according to the main components of the streams. For instance the section comprising the wastewater pretreatment, primary and secondary treatment can be considering a line of process devoted just to the water treatment as shown in Figure 4-5.

Figure 4-3. Proposed scheme of integration inside Bresso plant

As global outcome of this segment we obtain an effluent stream of known characteristics and a sludge that is usually called primary sludge, which has to be subsequently treated. Frequently, the characterization of the inlet wastewater is performed not at the very beginning of the process, but after the pretreatment section (from bar screen to grit removal) just before entering to primary settling tank. This is convenient since for municipal wastewater it is avoided accounting for big pieces or residues that are not representative of the liquid stream (Bonomo, 2008).

Bar Screen 1Bar Screen 1 Bar Screen 2Bar Screen 2 Grit removalGrit removal

Fat & grease

removal

Fat & grease

removal

Primary

settling

Primary

settling

Biological

oxidation

process

Biological

oxidation

process

Secondary

clarifier

Secondary

clarifier

Wastewater InWastewater In

Primary

sludge

Primary

sludge

Treated water

stream

Treated water

stream

Figure 4-4. Process units composing the wastewater section in Bresso plant.


Taking into account the aforementioned consideration the units commonly analyzed are the primary settler, the biological oxidation and the secondary clarifier. Since the aim of this section is the depuration of the wastewater, it is well known that the main parameters for monitoring the performance of the plant are the suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), ammoniacal nitrogen (NH4

+) and microbiological parameters like E coli (Bonomo, 2008). Those parameters, excluding TP and E. coli, are considered for the analysis of the section. Furthermore, an extra parameter has been included for accounting the total solids content of the streams (TS) which is an essential indicator for the behavior of the subsequent sections. The analysis of this segment can be started from a global mass balance that includes all units as can be seen in Figure 4-5. In this section the global mass balance (in which water is the main component) can be stated as:

l¸ó = l6ó + l�ó = ô¸y¸ = ô�y� + ô�y� (24)

In which mi represent each of the mass flow rates of the streams I (wastewater inlet), S (primary sludge) and E (Final Effluent), while Qi is the volumetric flow rate of the stream i. Since the main constituent of the streams is water, and the other component’s concentrations are always very small in comparison to water content, density of each stream changes very little, and can be always assumed constant during the process and equal to pure water density (ρI=ρS=ρE=ρ).

Figure 4-5. Streams of the wastewater section.

At the same time, the global mass balance for each component can be written as:

ló :(¸) = ló :(�) +ló :(�) = y¸�:(¸) = y��:(�) + y��:(�) (25)

Where mi(j) is the mass flow rate of component i in stream j, and Ci

(j) is the concentration of component i in stream j. Similarly, the total and component mass balances in each of the units can be performed, recalling that global mass balance would be dependent on these unit mass balances, or


the same, that one of the unit balances is a linear combination of the remaining balances with the global one. Then for the primary settling we have that:

l¸ó + l@PMó = l6ó + l¸¸ó = y¸ +y@PM = y� + y¸¸ (26)

ló :(¸) +lõó (@PM) = ló :(�) +ló :(¸¸) = y¸�:(¸) + y@PM�:(@PM) = y��:(�) + y¸¸�:(¸¸) (27)

While for the biological treatment unit:

l¸¸ó + l@P�ó = l¸¸¸ó = y¸¸ + y@P� = y¸¸¸ (28)

ló :(¸¸) +lõó (@P�) = ló :(¸¸¸) ± "O?fO? = y¸¸�:(¸¸) + y@P��:(@P�) = y¸¸¸�:(¸¸¸) ± "O?fO? (29)

In which the component balance include the term RbtVbt that accounts for the extent of the reactions involved in the oxidation process. This means the amount of component that has reacted or has been produced during the biological oxidation. Finally, in the clarifier we find that:

l¸¸¸ó = l�ó + l@PÊó = y¸¸¸ = y� + y@PÊ (30)

ló :(¸¸¸) = ló :(�) +lõó (@PÊ) = y¸¸¸�:(¸¸¸) = y��:(�) + y@PÊ�:(@PÊ) (31) Additionally to the previous balances, and in order to know the response of the system under any variation in the inlet stream, we define some operative parameters that allow us to interconnect the units. The first type of operative parameters is the unit efficiency, or removal efficiency of the unit (ηu), where the component’s burden removal is evaluated considering information compiled in Appendix A. For the separation units like the primary settling and clarifier ηu is defined as:

ò�: � Lö°÷-²ó ·Lö°øù*²óLö

°÷-²ó � ,÷-�&°÷-²·,øù*�&

°øù*²

,÷-�&°÷-² (32)

In which ηki is the removal efficiency of the unit k for eliminating component i from the

main stream of the process (In). This means mi(Out) is the remaining load of component i in

the main stream. For the process involving reactions like the biological oxidation ηu can be defined analogously as:

òO?: � Lö°÷÷²ó ·Lö°÷÷÷²óLö°÷÷²ó � ,÷÷�&

°÷÷²·,÷÷÷�&°÷÷÷²

,÷÷�&°÷÷² (33)

On the other hand, the additional relationships employed in this section of the plant are the recycle ratios of the units. They can be specified in concordance to the fresh feed to the unit as:


�� = L56678@>PCP9>L566788@>6[8>>; (34)

In this point, it is convenient to use the degree of freedom analysis (D.O.F.) in the section for establishing a solving procedure as well as for defining the additional parameters needed for solving the mass balances. The first step is the definition of the total variables of the analyzed system, divided into mass variables (number of streams) and component variables (amount of compositions). As it can be seen in Figure 4-5 there are 8 streams and for each one we need to determine 7 compositions per stream, which means 56 component variables. From the total of 64 variables we can define 2 mass variables corresponding to streams I and S, and 7 component variables related to compositions in stream I. This means we have a total of 55 unknown variables. Then, we can state that for each process unit involved in the section we can write as much mass balances as components exist in each unit plus a global mass balance. There are 3 main units (Primary settling, biological treatment and secondary clarifier) which account for 24 equations and an extra fictitious unit in which stream rc1 is split into rc2 and rc3 from which we can only write 1 mass balance expression. Accounting for those units we can write 25 mass balances equations. Furthermore, starting from the assumption of modeling the units by removal efficiencies, we can specify one efficiency for each component in each unit, which lead us to 21 efficiencies equations. Until this point we have 46 independent equations for solving 55 unknowns, which means we need to establish 9 additional relationships. Two of those additional expressions are related to the recycle ratio of stream rc3 and the split ratio of stream rc1:

�@ = L+cMóL÷ó = ,+cM,÷ ; 1@ = L+c�óL+cÊó = ,+c�,+cÊ (35)

The remaining 7 additional expressions are related to the very principle of the splitting unit, in which we can establish that there is no change in concentration of the downstream respect to the upstream. In other words, concentrations of streams comprised in the splitting remain unchanged.

Table 4-1. Summary of degree of freedom analysis in wastewater section. Mass variables Component variables Total Variables

Total variables 8 56 64 Known variables 2 7 9

Unknown variables 6 49 55 Balance equations - - 25

Efficiencies - - 21 Additional rel. - - 9

Total equations - - 55 D.O.F. - - 0


This result indicates it is possible to solve the complete section with the proposed information. The procedure to achieve this goal can be summarized in few steps as follows:

• First solve the global mass balance of the section, finding QE. • By means of the additional relationships rr and sr solve Qrc3, Qrc2 and Qrc1. • By means of the proper unit mass balances find the remaining streams II and III. • By using the efficiencies of primary settling unit find the load of each component in

stream II. Similarly, using the efficiency of biological oxidation determine loads of stream III and then find the final load in the effluent E employing the efficiencies of the secondary clarifier.

• Solve the remaining component mass balances for determining the loads in the recycled streams.

• Using the component mass balance in the primary settling determine the composition in the stream S.

4.2 Sludge treatment section

Following the line of process that contains the primary sludge (S) we find the section devoted to the treatment of this sludge as seen in Figure 4-6. The main stage is the anaerobic digestion of the sludge, which produces a gas stream (G) containing mainly methane and carbon dioxide, and a liquid stream usually called digestate (D) composed by the non-digested substrates. Then in order to obtain a biosolid stream, with a concentrated fraction of solids, digestate is treated in the thickening and dewatering units obtaining streams bs (biosolid) and C (centrate). Due to the integration of the PBR, the clarified stream C is conducted as feed to the PBR, where it is converted into an algal diluted solution A0. That stream is going to be fed in the AD unit in co-digestion with the primary sludge, reason why there exists a mix point before this unit. The remaining process corresponds to a separation unit which aim is to concentrate A0 for providing a stream Ac with a higher biomass load than the original, producing as a by-product a secondary centrate Cf composed mainly by water and some traces of the components.

Figure 4-6. Units and streams composing the sludge treatment section.


This section can be split in 2 subsections containing the sludge treatment and the microalgae culture as detailed by dash lines in the diagram of Figure 4-6. Given the main characteristics of those processes, in this section only 4 components have been considered for monitoring the performance of the treatment, total solids (TS), suspended solids (TSS), volatile solids (TVS) and total nitrogen (TN). As done in the previous section, all units are described in terms of the removal efficiency in the case of separation units, and yields or productivities in the case of reactive units. Since there is a recycle of stream Ac to the inlet of the anaerobic digestion, there exists a close loop reference along the mass balances, reason why for solving the section it is convenient to start from the DOF analysis in order to define the operation parameters that make possible the solution. Table 4-2 contains a summary of DOF analysis of the section, recalling that streams FG and FGC are not considered since they are supposed to be treated independently in the next section.

Table 4-2. Summary of degree of freedom analysis in sludge treatment section. Mass variables Component variables Total Variables

Total variables 12 44 56 Known variables 1 4 5

Unknown variables 11 40 51 Balance equations - - 27

Efficiencies - - 21 Additional rel. - - 3

Total equations - - 51 D.O.F. - - 0

As it can be seen there are 12 streams in the section, 11 of which contains 4 components, and stream G whose composition is assumed to be known (See Appendix B). Under these circumstances, the amount of compositions in the system is 44, entailing 56 total variables. From the previous section, we can know the complete description of stream S, obtaining 51 unknown variables. On the other hand can be delineated 3 separation units and 2 mixing points in which can be performed 5 mass balances each one. Additionally, in both reactive units (AD and PBR) can be written 2 total unit mass balances completing 27 mass balance equations. For the main units (apart from mixing points) the description of the system has been defined in terms of efficiencies per each component, which means they account for 20 additional relationships. At the same time, another efficiency defines the gas production (stream G) completing a total of 21 new equations. This analysis gives us, as a result, the need of implementing 3 relationships in order to solve all the units. According to this, the final result is directly correlated with those 3 relationships, reason why it is essential to define them based on the information compiled in the real operation of the plant.


First of all, let us define the mass balance expression in the analyzed section. According to Figure 4-7, for the liquid streams we can expect that a global mass balance satisfies:

l�úó = låó = l�ó + lO6ó = y�ú = yå = y� + yO6 (36)

Where the central assumption is the negligible loss of water due to evaporation of dilution in the gas phase, considering the low content of organic matter.

Figure 4-7. Units of the section dedicated to sludge treatment.

In the meanwhile, for the thickening unit we can write the local and component balances:

låó = lå¹Êó + lå¹�ó = yå = yå¹Ê + yå¹� (37)

ló :(å) = ló :(å¹Ê) +lõó (å¹�) = yå�:(å) = yå¹Ê�:(å¹Ê) + yå¹��:(å¹�) (38)

Likewise, the dewatering section can be described by:

lå¹Êó = l�ó + lå¹Mó = yå¹Ê = y� + yå¹M (39)

ló :(å¹Ê) = ló :(�) +lõó (å¹M) = yå¹Ê�:(å¹Ê) = y��:(�) + yå¹M�:(å¹M) (40)

As explained before, the additional relationships than complete this sub-section are the efficiencies of each unit. In the case of the AD process we can define 2 different expressions, one that defines the efficiency of the process for producing biogas ηAD:

òñå = ñP?t59�ë^g�]Mij^û'IJ kä4�ü((%g�]Mij^û'IJ k (41)

Moreover, other which indicates the change in composition of the 4 constituents involved in the analysis:

ò@: = Lö(JÑ)ó ·Lö(ý)óLö(JÑ)ó = �&(JÑ)·�&(ý)�&(ý) (42)

From Eq. (41) we can expect that volumetric flow rate in stream G can be computed as:


y = y�ú�º6�ú �zij^ òñåYZ�8>>; (�þReê�(°�))�þR (43)

On the other hand, we have the subsequent sub-section in which the microalgae culture is developed. This portion is composed by two units as can be seen in Figure 4-8, a photobioreactor for algae growth and a separation unit for concentrating the produced biomass.

Figure 4-8. Units composing the subsection of microalgae culture.

Considering that flue gas flow through the PBR can be analyzed separately we can expect that globally:

l�ó = lñ}ó = lñcó + l�üó = y� = yñ} = yñc + y�ü (44)

Since the reaction unit can be defined in terms of the efficiency for transforming each of the components into biomass, we can establish as performance parameter the yield as the change between the outlet and inlet section of the reactor as previously defined in chapter 3:

0:�äÒ = Lö(�})ó ·Lö(i)óLö(i)ó = �&(�})�&(i) − 1 (45)

Where yiPBR is the change in load of the component i given the transformations inside the

PBR. This clearly implies that yiPBR can be positive, negative or zero depending on the

component and the efficiency of the process. It is expected that yTSPBR, for instance, would

be >1 since total solids is a direct indicator of the produced biomass while yTNPBR is

expected to be negative since nitrogen is absorbed for producing microalgal biomass. In addition, harvesting unit can be described in an analogous way as the previous separation units, but considering the aim of the unit, which is obtaining a concentrated stream. In that case we can write the component mass balance:

ló :(ñ}) = ló :(ñc) +lõó (�ü) = yñ}�:(ñ}) = yñc�:(ñc) +y�ü�:(�ü) (46)

And the efficiency of the harvesting unit as:

ò[: = Lö(�c)óLö(�})ó = ,�c�&(�c),�}�&(�}) (47)


Finally, for defining the 3 required relationships for solving the section we have to consider the available information of Bresso plant. One of the streams that is commonly characterized is the final digestate after dewatering (stream bs), particularly in terms of the total suspended solids (TSS) that has a strong correlation with the TSS load of treated sludge. According to information collected in Appendix A, it is possible to establish a linear relationship between stream D and its TSS concentration as:

�ê��(O6) = �ló ê��(å) + � (48)

Where γ and λ are constant derived from the plant performance as shown in Appendix A. Taking into account that there is no punctual information about the thickening unit, it is difficult to implement a relationship like Eq. (48), but it can be assumed the relative behavior of this unit with regards to the dewatering process. From the conceptual point of view, we know that dewatering allows most of the separation while thickening contributes in a minor degree. This comprises a latent correlation between TSS concentrations of streams Dx2 and bs. A first guess can be consider CTSS

(Dx2) as a fraction of CTSS(bs) or a

function of the concentration obtained in the dewatering section CTSS(Dx3) as:

�ê��(å¹�) = r��ê��(O6); �ê��(å¹�) = r��ê��(å¹M) (49)

The last parameter we need to fix is related to the harvesting unit, in which beside the mass efficiency previously defined, we have to consider the volumetric efficiency (ηv) that accounts for the fraction of the volume that has the concentrated microalgal biomass and it is defined as previously shown in chapter 3 as:

òº = ,�c,�} (50)

Even when the D.O.F. analysis indicates us that the solution of the section is possible through the definition of the above-mentioned parameters, it does not tells us nothing about the solving procedure since it is just a global analysis. For this reason, the easier way to solve the loop system is by means of a trial and error process. This implies an initial assumption of a set of values that have to be compared at the end of each iteration. The solving procedure for this section can be summarized as follows:

• Start the algorithm assuming the complete characterization of stream Ac, considering the experimental results presented in chapter 3.

• Compute the components load in the same stream, mi as Ci(Ac)QAc.

• Using the individual harvesting efficiency ηhi determine the mass load of each

component in stream A0, and by means of ηv estimate QA0. • Complete the mass balance in the harvesting unit determining the characterization

of stream Cf. • Using the information of stream S, now it is possible to complete the mass

balances in the mixing point for determining the characterization of stream S’.


• Using the efficiencies related to the AD unit, compute the mass load of each component in the digestate stream (D), as well as its volumetric flow rate through the global mass balance of the unit.

• Knowing the burden in the digestate now it is possible to determine the remaining fraction in stream Dx1 employing the removal efficiency associated to this unit.

• Completing the component mass balances it can be calculated the fraction that leaves the unit mi

(Dx2). • Similarly, with the mi

(Dx1) values it is possible to compute the mass load that will end up in stream C thanks to the removal efficiency in the dewatering unit.

• Using the mass balance in the dewatering unit now is easy to establish the removed load that composed stream Dx3, and completing the mass balances in the mixture point the final biosolid stream (bs) can be characterized.

• Since QD is already known, a global balance in the section covering both separation units allows us to correlate stream D, C and bs. Additionally, knowing that Qc is equal to QA0, the only unknown is the volumetric flow rate of biosolids (Qbs).

• The remaining flow rates QDx1, QDx2 and QDx3, are determined based on the additional relationships that involved streams Dx2 and bs.

• Once centrate stream (C) is completely characterized, it is possible to determine the concentrations after the PBR (stream A0).

• It is expected that concentrations computed directly by ηhi and ηv coincide with

calculated in the last step. • Iteration should continue until finding a set of values that make those

computations coincide.

4.3 Combined Heat and Power section

In this section the produced biogas (G) is conducted to a combined heat and power station for supplying the same WWTP’s needs. The flue gas produced in the combustion process is then used as a source of carbon in the microalgae culture, obtaining a stream with a reduced CO2 content as detailed in the diagram of Figure 4-9.

Figure 4-9. Units and streams composing the CHP section.


Since we cannot determine the real composition of biogas (stream G) as a function of the feed, because we are not considering the multiple effects of the AD process like HRT, OLR, temperature, pH, etc., we can assume the average composition of the stream according to the tests carried out directly on Bresso’s CHP as detailed in Appendix B. Then, by considering the combustion reaction with air in excess, we can also compute the composition of the flue gas stream and following the mass balances in the unit, we can determine the mass flow rate of each of them knowing that:

qñõ@ó = q ó ¼(1 + Ú) (51)

Where nAir is the total mole of air used for the combustion process, nG is the total mole of biogas produced, β is the molar stoichiometric Air/fuel ratio (molAir/molfuel) and ε is the excess of air defined for the combustion process. The total moles of biogas can be derived from the volumetric flow rate previously computed and taking into consideration the assumed composition of the stream, since:

q ó = L�ó4�� = ,��4�� (52)

In which mG is the total mass flow rate of G, MWG is the average molecular weight of G, QG the volumetric flow rate of G and ρG the density of stream G at the given temperature. In addition, we can state for the CHP unit that:

l ó + lñõ@ó = l� ó (53)

On the other hand, for modeling the response in the reaction unit, we use a single parameter that indicates how much CO2 is consumed in the process respect to the biomass produced as microalgae. That parameter has been defined according to the literature review (view chapter 1, section 1.2.1) and it can be established as:

0�� = H=iø�P7<6tL>;H=�JB@7;tP>; (54)

Considering the change in CO2 due to the absorption in the PBR we can compute both the mass flow rate of stream FGC and its composition that can be assumed to be only altered by the carbon dioxide content. In this point it is also assumed that the CO2 dissolved in the PBR’s effluent is negligible regards the consumption in the growing process, leading a simplification that only includes the change due to the microalgae growing. Concluding, based on the information provided of the CHP ordinary operation (see Appendix A), and considering a mean value for LHVG as detailed in Appendix B, the energy produced in the unit can be estimated thanks to the efficiency of the plant defined as:


ò>9 = �(½L�ó �ë$� (55)

4.4 Main results of the model

Excel was employed as tool for the implementation of the model. For doing this, the solution procedures explained in the previous sections were put into practice. A brief description of this implementation can be seen in Appendix C. Taking into account the main aim of the model, the analysis is focused on determining whether the inclusion of the PBR unit promotes or no any improvement in the performance of the plant. For this reason, it is appropriate to compare the operation of the WWTP with and without the section of microalgae culture, analyzing the parameters that better represent the outcomes of the plant. The first parameter that is expected to be changed is the volumetric flow rate of biogas (QG) which is a direct indicator of the AD performance. This change has to be directly related to the biomass production in the PBR, due to the invariable condition of the primary sludge (stream S). From a conceptual standpoint a simple analysis indicates that QG is a function of the organic load supplied to the reactor, the efficiency of the process and the biological methane potential of the feed (see Eq. (43)). We can start thinking in the new load fed to the digester, considering that it is a weighted average of both streams feeding the unit, which means that it will vary significantly as the microalgal biomass increases in volume (QAc increases) and organic content (CTVS

(Ac)). In other words, this factor depends on the productivity of the PBR, but also on the separation efficiency in the harvesting unit. From results of chapter 3 we can expect that volumetric efficiency of this concentration unit is as reduced as 10%; this clearly suggests that QAc is always a subtle fraction of the primary sludge. Looking at the BMP of the new feed, we could initially assume that there is no a significant variation considering the ratio Qs:QAc which value is mainly defined by the volumetric efficiency ηv and should be around 12:1. Notwithstanding, nature of both streams are quite different and influence synergistically the degradation process. In this point, a deeper analysis would be possible through experimental estimation of the mixture’s BMP. Similarly, efficiency of the process will vary depending on the type of load supplied if all other operative conditions remain unchanged, which lead us to a similar conclusion as before, AD efficiency can be consider constant when PBR is include in the model. This simplified scheme directly gives us as a result a linear dependency of QG with the additional organic load supplied to the unit (mTVS

Ac), which at the same time, is a linear function of the productivity in the PBR and the efficiency in the harvesting unit. In Figure 4-10 it can be seen this linear dependency, considering changes in PBR’s productivity at a given efficiency on the harvesting unit.


Figure 4-10. Effect of PBR implementation on the produced biogas.

As it can be observed, the increase in the TVS content of the feed, due to the co-digestion of microalgal biomass, produces a higher biogas production that changes between seasons mainly because of the intrinsic characteristics of the treated wastewater. While spring and autumn are characterized by high levels of wastewater (around 66000m3/day), summer presents the lower volumetric flow rate along the year (36400m3/day approx.), and winter is closer to 56000m3/day. Furthermore, in terms of the organic matter content, there is a subtle difference among seasons, but it is not so evident, such that efficiency parameters can be considered only affected by the first variable. However, the only part of the year in which there is no a clear tendency in this regard is during winter since is this season the least effective in terms of pollutant removal. This effect can be seen directly in the production of primary sludge, which varies from 382m3/d in spring, passing to an average of 337m3/d during summer and 330m3/d in winter, concluding in 320m3/d in autumn. In conclusion it is expected a lower production of organic matter available for the AD unit when there is a period in which the WWTP has to treat higher volumes of wastewater. In other words, the combined effects of dilution of components and decrease of the efficiency for high volumes, is the key factor that defines the performance of the plant along the year. Then, considering the aforementioned aspects, we observed from Figure 4-10 how is summer the period in which theoretically is expected a better benefit of using the PBR. In that case an increase of 1.8% in the TVS content of the feed produces a surplus of 1.3% of produced biogas (considering the main values of efficiency and performance of PBR


and harvesting units). At the same time, during winter the benefit is lower just considering that an increase of 0.4% of QG is only possible producing an extra of 1.05% of volatile solids. Evidently, these values are just a representation of the expected behavior under the conditions obtained in the pilot plant experiments, but PBR performance is still prone to be improved in several aspects. In that sense, through the developed mass balance it is possible to compute under ideal conditions, which should be the maximum biomass production given amount of nutrients supplied by the primary sludge, and the flue gas. For this purpose, it is convenient to think in the equivalent chemical formula of the produced microalgae (see chapter 1, section 1.2) and particularly the associated molar ratio N:C (0.151). This means that for each mol of carbon, we need 0.151 mol of nitrogen. Thus, once the content of nitrogen available for the culture is define (in the centrate stream), as well as the equivalent content of carbon in the flue gas (due to the CO2), it is possible to establish which of them is the limiting factor for the biomass growth. As it is detailed in Table 4-3, nitrogen results the limiting nutrient of the process, since the N:C ratio remains always below the theoretical ratio.

Table 4-3. Limiting nutrients and maximum expected biogas production.

Season N:C ratio (N:C)Act/(N:C)Th

mTVSA0

(kgTVS/d) mTVS

Ac (kgTVS/d)

QG-max (m3/d)

Increase in biogas

production (%) Winter 0,026 0,171 1093,2 294,6 3702,6 3,01 Spring 0,068 0,447 2130,1 574,1 2891,4 8,37

Summer 0,056 0,373 1357,2 365,8 2185,4 7,70 Autumn 0,017 0,111 647,7 168,3 3346,8 2,18 Then, in the case all available nitrogen is consumed for producing microalgal biomass, the system could produce a raw solution with an average of 1307kgtvs/d, able to produce a surplus of biogas that ranges from 2.2-8.4% respect to the initial scenario in which there is no PBR implemented. Those values are certainly higher that the experimental one, but they are a reference in order to optimize the operational parameters of the entire plant. Additionally, the same behavior exposed before is found in relation to the change of seasons. Spring and summer count with the most favorable performances almost doubling the biogas production increase respect to winter and autumn. Finally another important aspect that can be extracted from the mass balance is the land requirement for installing the PBR section. In this sense is important to define the productivity of the culture, as well as the relative dimension of the section based on the inhabitants which are favored by the WWTP. In the particular case of Bresso, the number of residents associated to the plant ranges 220113 for 2012. But, taking into account that productivity cannot be properly estimated based just on the performed experimental runs, since it only would represent an isolated reactor without any other interaction, it is better to analyze different scenarios in which productivity varies in range in accordance to results of previous results, as presented in chapter 1.


Figure 4-11 shows the trend of the required area, measured as the surface needed per inhabitant according to the expression:

�@>� l�pqℎ =lê��ñ9=ópqℎ�Tpo�qo1 ��ê�� /�pqℎ

�ñ@>59 è�ê�� l�/� é

As the diagram details, the higher the productivity, the lower the required area per inhabitant. The same behavior could be expected working at the different season conditions. In that case, if plant would work at summer conditions the required area would range from 0.042 to 0.084m2/inh depending on the productivity of the system. On the contrary, the higher requirements are needed when the plant operate at winter and autumn conditions, in which case the area would range from 0.087 to 0.17m2/inh.

Figure 4-11. Required area for the PBR implementation at different productivities

5. Conclusions and Recommendations

Conclusions

• It was possible to reproduce the behavior of the analyzed WWTP by means of simple mass balance modeling.

• The implementation of a PBR unit in the WWTP cycle was properly described by the definition of the associated performance parameters, experimentally determined.

• Through the experimental part, it was possible to assess the enhancement of the anaerobic digestion process when a co-digestion of primary sludge and microalgal biomass is employed.

• The integrated mass balance shows that there exists an improvement in the production of biogas when a microalgae culture is used as an additional source of organic matter inside the WWTP.

• From the modeling results, it is expected also an improvement of the quality of the liquid effluent, since a considerable load is removed by the PBR section, releasing the centrate mainly from the nitrogen burden.

• The proposed model indicates that under the intrinsic conditions of the Bresso plant, the nitrogen content is the limiting nutrient for the algae growth.

Recommendations

• It is needed to extend the experimental part related to the microalgae culturing, in order to involve some variables that critically affect the performance of the process, like the CO2 effects on the culture (gas diffusion-heat transfer, inhibition for high levels in the flue gas), and the effect of the irradiance at variable conditions like presented in this study.

• It would be interesting to design a set of batch test for computing the parameters that best fit a selected grow kinetic, in order to simulate the culturing behavior as a dynamic unit, instead of a static unit done in this work.

• It is convenient to complete the characterization of the produced algae for enhance the knowledge of the AD process. In this sense further BMP test are essential for understand the degradability of the co-digestion feed, as well as

92 A mass balance assessment approach…

identify the performance of mixtures of different composition on the production of methane.

• A further effort is needed for improving the harvesting of the biomass, since this will be a critical factor that will define the feasibility of the energetic and economic balance of the entire plant.

A. Appendix: Raw operational data in Bresso WWTP 2012-2013

Since the main assumption of the employed modeling approach is the simulation of the units by efficiencies, it is required the acquisition of information that provide those efficiencies, based on the real operation of the units. For this reason, the operational parameters measured for the period January 2012 to August 2013 in Bresso plant have been used as a source for the mass balance modeling. Information provided during this 20 months period corresponds to the monthly reports Bresso´s staff has to perform for monitoring the performance of the WWTP. Each report reflects a summary of the monthly activity and a characterization of the streams computed from at least two measurements. Furthermore, the aforementioned period was chosen since August 2013 was the last month in which a complete and detailed report was elaborated. After this month, there is not enough information for describing the entire performance of the plant continuously. The most important information for make a good assessment is the characterization of the streams, components and volumetric flow rates, which are the final result we expect to determine by means of a mathematical model. As explained in chapter 3, components involved in the wastewater treatment are limited to 7, but 2 of them (TS and TVS) are not typically involved since are not clear indicator of the performance of the treatment. Tables A-1 to A-3 contain the measured concentration of the liquid streams in the section of waste water treatment for the analyzed period as well as the volumetric flow rate in the points in which there is an instrument for this purpose.


Table A-1. Inlet wastewater characterization (stream I).

Year Month QS (m3/month) CCOD (mg/L) CBOD (mg/L) CTSS (mg/L) CT-N (mg/L) CN-NH4 (mg/L)

2012

January N.A. 513 230 200 39,2 39,2

February N.A. 784 335 398 41,9 37,3

March N.A. 588 265 246 33,8 36,2

April N.A. 731 203 218 34,4 28,5

May N.A. 435 192 231 32,3 30

June N.A. 367 166 159 31,1 29,8

July N.A. 332 156 151 26,2 26

August N.A. 379 161 197 26,1 26,5

September N.A. 292 168 127 28,2 25,9

October N.A. 276 178 132 32,2 33,2

November N.A. 306 136 97 21,9 22,3

December N.A. 353 161 159 33,9 32,6

2013

January N.A. 450 141 149 32,4 31,6

February N.A. 282 152 146 27,7 27

March N.A. 381 183 161 33,6 31,9

April N.A. 365 174 153 34,5 35,5

May N.A. 344 168 151 34,3 33,8

June N.A. 315 154 156 29,1 30,2

July N.A. 318 139 158 29,7 29,4

August N.A. 281 98 154 25,8 22,4

Table A-2. 1st settling outlet characterization (stream II).

Year Month QS (m3/month) CCOD (mg/L) CBOD (mg/L) CTSS (mg/L) CT-N (mg/L) CN-NH4 (mg/L)

2012

January N.A. 330 194 115 45 46,6

February N.A. 207 140 98 37 39

March N.A. 225 162 101 47,5 42,8

April N.A. 245 149 139 42,1 35,8

May N.A. 217 134 104 40 34,9

June N.A. 200 148 87 42 42

July N.A. 196 130 64 34,6 34,6

August N.A. 165 102 51 28 32,6

September N.A. 145 100 54 32,1 32,1

October N.A. 183 140 89 44 35,7

November N.A. 230 145 91 39 40

December N.A. 205 138 93 38,7 38,7

2013

January N.A. 196 116 82 35,3 35,3

February N.A. 264 145 112 49 45

March N.A. 211 133 78 58 40,9

April N.A. 211 105 92 34 31

May N.A. 180 108 82 31,9 31,9

June N.A. 256 135 96 35,2 35,2

July N.A. 177 115 73 29 29

August N.A. 158 70 83 12 12

Appendix: Raw operational data in Bresso WWTP 2012-2013 95

Table A-3. Final effluent outlet characterization (stream E). Year Month QS (m

3/month) CCOD (mg/L) CBOD (mg/L) CTSS (mg/L) CT-N (mg/L) CN-NH4 (mg/L)

2012

January 1611910 26 7 7 27,9 2,3

February 1639072 34 7 12 27,1 0,7

March 1689158 30 6 8 26 0,9

April 1875052 17 5 7 30,9 1,4

May 1877550 22 5 4 23,5 2,2

June 1604532 24 5 6 19,5 2,6

July 1515384 21 5 4 25,7 5

August 1122128 20 5 5 24,6 0,5

September 1771283 24 5 3 24,3 0,5

October 1742726 16 5 3 28,5 0,5

November 1977068 37 6 6 24 0,5

December 1822241 14 5 3 26,9 0,5

2013

January 1791908 24 5 3 25,3 2

February 1671536 19 5 3 24,7 0,5

March 2135918 14 7 3 22,7 0,7

April 1964934 23 5 3 29,8 1,7

May 2407046 24 5 6 27,4 1,8

June 1997069 22 4 3 26,5 1,4

July 1884083 18 3 3 22,4 3,8

August 1597843 18 3 3 20,7 0,8

For complementing this section, it is usually determined for the primary sludge (stream S) and the secondary clarifier sludge (Stream rc1) the volumetric flow rate and the TSS and TVS concentrations as detailed in Table A-4. From this information can be computed a set of removal efficiencies for each component, considering negligible the change in the total mass flow rate between stream I and E (see how values of Qs are always less than 0.8% of QE). Evidently these removal efficiencies have a seasonal dependency, which can be seen from the characterization standpoint, and in terms of the mass of treated wastewater. From tables A-1 and A-3 we deduced that the global removal efficiency of any of the components has no a clear tendency between seasons, since the final effluent remain almost invariable along the year, and even more, the inlet wastewater does not change so much its characterization. Taking this into account, the main parameter that allows us to correlate the estimated efficiencies is the volumetric flow rate of treated wastewater, which changes in a predictive way from a high value during spring, then going down to the lowest point during summer, and finally increasing continuously in autumn and winter.


Table A-4. Primary sludge and Secondary sludge characterization (Streams S and rc1).

Primary Sludge Secondary Sludge

Year Month CTSS (mg/L) CVSS (mg/L) QS (m3/month) CTSS (mg/L) CVSS (mg/L) Qrc1 (m

3/month)

2012

January 28666,7 22818,7 10530 3333,3 2511,1 43963

February 24083,4 19001,7 10844 3000,0 2330,0 45984

March 20785,7 15957,0 12453 3285,7 2519,0 51711

April 21555,6 16406,2 11273 3400,0 2635,0 44142

May 22250,0 16539,1 12370 3333,3 2600,0 47274

June 21846,2 16149,4 10884 3250,0 2470,0 46348

July 23000,0 16928,0 10588 2444,5 1857,8 10309

August 18437,4 13980,3 7370 2750,0 2035,0 20406

September 22315,8 16337,5 7499 3250,0 2275,0 12049

October 28187,5 20939,3 9888 3714,3 2544,3 36242

November 24153,9 18765,6 9682 3777,8 2776,6 25310

December 24000,0 19265,5 9131 4833,3 3600,8 27445

2013

January 30076,9 24038,4 10515 4373,8 3456,3 31265

February 30857,1 24112,6 9120 4500,0 3555,0 27745

March 30545,5 23186,8 10767 3600,0 2808,0 34750

April 28272,7 20819,0 11477 4200,0 3108,0 36545

May 25700,0 18092,8 12073 3600,0 2592,0 39881

June 22555,5 15638,5 12543 3666,7 2640,0 34290

July 22166,7 14574,6 14897 4500,0 3082,5 46447

August 23400,0 15132,0 9295 3571,4 2571,4 26344

Over the section devoted to the sludge treatment, the monitoring is carried out over the biogas stream (G) and the final digestate after dewatering (stream bs). In the first case, the main measurements are related to the biogas production (QG) and the total power produced in the CHP unit (Pe) as it can be observed in Table A-5, while in the latter the characterization is the same as performed to the sludge streams of Table A-4. Once again, this set of data can be used for determining the behavior of the units in terms of efficiencies, particularly considering the relationships between those efficiencies and the change in the volatile content of the streams. On the other hand, by knowing the average BMP of the primary sludge as well as its volatile content, it can be determined the efficiency for the anaerobic digestion in terms of biogas production by means of Eq. (41) and (43).

Appendix: Raw operational data in Bresso WWTP 2012-2013 97

Table A-5. Biogas and final digestate sludge characterization (streams G and bs). Biosolid stream Biogas and energy production

Year Month CTSS (mg/L) CVSS (mg/L) Qbs (m3/month) QG (m

3/month) Pe (kwh/month)

2012

January 25750,0 16296,1 5124 98312 189680

February 23500,1 14315,4 6351 79051 191520

March 19100,0 12324,5 8763 86323 174800

April 20300,0 12890,5 6503 74353 166400

May 21235,3 13153,4 7705 76885 133040

June 23571,4 15164,3 6341 70232 162080

July 27941,3 17989,7 3898 84665 92510

August 27055,5 16871,0 3551 57896 92510

September 30143,0 17482,9 3077 69167 129120

October 32937,6 19191,7 4148 82635 119840

November 31428,6 17981,6 4669 87020 127120

December 35733,3 20892,1 3854 75560 189680

2013

January 29250,1 17944,8 4890 107650 116880

February 21800,1 13617,7 4222 81590 108265

March 18902,3 13663,1 5200 104775 109360

April 18017,6 13439,8 5684 104007 131500

May 18023,9 15327,8 5101 86573 148960

June 20319,2 15592,9 4780 75233 113120

July 19456,0 16054,6 7106 53286 72640

August 28454,6 16788,3 4567 29654 36880

Then, considering the effect of the total mass of wastewater treated, it was decided to determine an average value per efficiency for each season. Table A-6 and Table A-7 contain the computed values based on the aforesaid information.

Table A-6. Computed efficiencies for the wastewater section. Winter Spring

1

st settler Biological T. 2

nd settler 1

st settler Biological T. 2

nd settler

ηTSS 0,7710 0,8000 0,3150 0,5750 0,89000 0,30000

ηTS 0,8481 0,8000 0,3150 0,5750 0,89000 0,30000

ηT-N -0,1405 0,2940 0,2450 0,0430 0,08050 0,01000

ηNH4 -0,0100 0,9410 0,3450 0,1000 0,95500 0,35000

ηBOD 0,1620 0,9579 0,1620 0,3500 0,96500 0,48000

ηCOD 0,3609 0,8794 0,3609 0,4300 0,79560 0,60500

Summer Autumn

ηTSS 0,5350 0,8000 0,7428 0,8400 0,8000 0,2900

ηTS 0,5885 0,8000 0,7428 0,8400 0,8000 0,2900

ηT-N 0,0050 0,0250 0,0750 -0,0700 0,0950 0,1100

ηNH4 0,0150 0,9806 0,0350 0,0040 0,9852 0,0100

ηBOD 0,3706 0,9232 0,3706 0,2179 0,9551 0,2179


ηCOD 0,5675 0,7238 0,5675 0,3407 0,8696 0,3407

Table A-7. Computed efficiencies for the sludge section. Winter Spring Summer Autumn

Thickenin

g

Centrifug

e

Thickenin

g

Centrifug

e

Thickenin

g

Centrifug

e

Thickenin

g

Centrifug

e

ηTSS 0,050 0,970 0,050 0,970 0,050 0,970 0,050 0,970

ηTS 0,055 0,970 0,055 0,970 0,055 0,970 0,055 0,970

ηTN 0,350 0,080 0,350 0,080 0,350 0,080 0,350 0,080

ηTVS 0,055 0,970 0,055 0,970 0,055 0,970 0,055 0,970

Anaerobic Digestion Unit

Winter Spring Summer Autumn

ηVS 0,540 0,410 0,270 0,530

ηAD 0,755 0,721 0,764 0,833

BMP

(Nm3/kgvs)

0,350 0,350 0,360 0,355

ηTS 0,480 0,480 0,480 0,480

ηTSS 0,370 0,310 0,360 0,395

B. Appendix: Combustion reaction analysis in CHP unit

Parameters and assumptions employed in the examination of the CHP section are derived from the analysis made on the combustion of the produced biogas. In fact, the raw data available for starting this evaluation is the elemental analysis performed during 2012 to the flue gas at Bresso’s CHP outlet shown in Table B-1. As it can be seen, besides the composition of the stream we find the excess of air used in each case and the temperature in the point of measurement.

Table B-1. Elemental Analysis of flue gas stream in Bresso plant. Analysis 1 Analysis 2 Analysis 3

TFG (°C) 463,2 460,2 464,3

ε (%) 26 28 27

Xi (%)

O2 4,34 4,58 4,45

CO2 9,28 9,15 9,22

N2 70,18 70,29 70,24

H20 14,89 14,68 14,78

CO 0,0004810 0,0005210 0,0004790

NO2 0,0000532 0,0000584 0,0000257

NO 0,0010430 0,0007580 0,0010250

H2 0,0000460 0,0000430 0,0000380

We can state that this composition is the result of oxidizing a biogas whose components are methane, carbon dioxide, hydrogen, nitrogen, oxygen and hydrogen sulfide with an excess of air defined in terms of parameter ε. Taking this into account, we can go into deep in the reaction considering an equivalent model in which we analyze the transformation component by component, computing the atomic mass balance which has to be in accordance to the raw data of Table B-1. Therefore, we can first establish the equivalent oxidation reaction as:


�� + T�� + U�� + /�� + �� + r��3��ä:7=56 + ¼(1 + Ú)ì�� + ïh�ï�� + ïñ@ï�� + ï��ï�� ñ:@î

→ �� + �� + �R3�� + ��+ �� + �� + �þ��9t>=56

Where coefficients a to f are the molar fraction of the corresponding component in the biogas stream, β is the stoichiometric air need, ε is the excess air fraction, xi the molar fraction of the component i in the air stream and parameters z1 to z7 the mol produce of the corresponding component in the flue gas stream. Definition of each of the aforementioned coefficients depends essentially on the characteristics of the biogas, since for air we can expect the typical well-known characterization. In fact, they are correlated each other thanks to the set of individual atomic balances of the presented species. Thus, writing each of the balances based on the equivalent reaction we obtain that: ��T�q:� + T + ¼(1 + Ú) ï��ï�� = �� 0/��q: 4� + 2U + 2r = 2�� 3mnrm�:r = �R �po��q:2/ + 2¼(1 + Ú) ïh�ï�� = � + 2�

��q:¼(1+ Ú) ïñ@ï�� = ��

�ï0��q:2T + 2� + ¼(1 + Ú) ©1 + 2ï��ï�� « = 2�� + �� + 2�R + � + �þ Moreover, completing the set of equations we add a particular condition in which it is assumed that the only fraction of N2 that is oxidized correspond to the one derived from the biogas, not from the air. This condition can be written as:

2¼(1+ Ú) ïh�ï�� = 2� Molar fractions in flue gases can be directly estimated from z coefficients considering that:

ï�� = ��∑ �:þ:��

In a typical analysis, the composition of fuel is known and it is the set of 7 flue gas molar fractions the result of solving the system of equations. In this case we use the information of elemental analysis of flue gas for fitting the main biogas composition, considering some constraints shown in Table B-2 and minimizing the difference between computed and real molar fractions in flue gas stream. In other words we expect that:

Appendix: Combustion reaction analysis in CHP unit 101

�7O� = Zpq� ï:� ��Ò>59 − x:� ��7LBt?>;þ:��

1. o.ï� > ï�,L:< ∀� ï� < ï�,L5� ∀�

Table B-2. Constraints for fitting the biogas composition. Biogas Constrains Air Characteristics

Component ximax ximin Component xi

CH4 0,650 0,69 O2 0,2097

C02 0,200 0,28 N2 0,7808

H2 0,005 0,025 Ar 0,0094

N2 0,010 0,05 C02 1E-04

O2 0,001 0,007 - -

H2S 0,005 0,02 - -

Once this optimization is performed, we find that biogas composition that fits better to the experimental results is the one presented in Table B-3.

Table B-3. Biogas composition after fitting process. Component xi

CH4 0,69

C02 0,254

H2 0,025

N2 0,01

O2 0,001

H2S 0,02

Once biogas is characterized, it is possible to define some additional aspects that are important for the analysis of the CHP unit. First it is needed to compute both the mean molecular weight MWG and the gas density considering that:

Z� =� ï:Z�:�

��

And

ô � � Z� "#

Where PG is the absolute pressure of the biogas, initially assume close to ambient pressure, R is the universal constant of gases and TG the temperature of the produced biogas, estimated to be around 40°C.


Another important parameter to be computed is the low heating value (LHV) of the biogas, which allows us to determine the efficiency of the CHP unit. LHVG can be calculated considering the combustion reaction of biogas with stoichiometric air, and recalling that the normal enthalpy of reaction will be given by:

∆�Ò� =��!:Δ�8,:� �B@7;tP?6 −��!:Δ�8,:� �@>5P?5<?6 = E�f #

And the per-mass based LHV will be: E�f = Δ�Ò�Z�

Alternatively, considering we are working with a gas stream, the per-volume based LHV will be:

E�f º = E�f ô

From this expression we obtain that produced biogas has a MW of 23,29 Kg/Kmol, a density of 0,907 Kg/m3 at 40°C and a LHV of 568,5 KJ/mol, equivalent to 2 4,41 MJ/Kg and 25,4 MJ/m3.

C. Appendix: Description of the solution in Excel

Complete mass balance solution of Bresso plant was implemented in Excel due to the simplicity of expression as well as for the versatility of this software in terms of computation and graphical visualizations. File containing the mass balances is composed for 2 main sheets which visualized the main results, and 4 additional sheets that are devoted to storage the information of appendix A and B, and the performed sensitivity analysis. In the sheet “PBR Diag” can be found a brief description of the plant and its units. Graphically are represented each of the involved streams and the main characteristics of each one. This is the sheet in which the information for running the model is introduced. The required information is mainly associated to the characterization of the wastewater at the inlet and the operational parameters of the PBR section. As observed in Figure C-1, here is compiled the most representative information of the plant, expecting to provide to the user a general view of the process trying to use a friendly visual interface. In Figure C-1 can be also seen the conditions of the plant using the PBR in the winter season as an example of the generated summary. On the contrary, Figure C-2 contains the same sketch, but including the results when the WTTP operates without PBR in the same season. This sheet is connected with the one called “MB PBR” in which all mass balances computations are developed. This part of the solution was organized in such a way all columns represent the streams associated to the plant, while the rows contain the different parameters and variables related to the streams, like the volumetric flow rates, concentrations, mass flow rates, etc. In this part, the solution of the section is implemented, and results are linked to the previous sheet. Figure C-3 shows a part of this section, which additionally encloses all performance parameters computed in appendix A.


Figure C-1. Sketch of the main page. Results correspond to the use of the PBR unit under

winter conditions.

Figure C-2. Sketch of the main page. Results correspond to the plant operation without

PBR during winter.

Appendix: Description of the solution in Excel 105

Figure C-3. Sketch of the mass balance page inside the software.

Solution of the integrated Sludge treatment-PBR section was implemented by means of the solver tool of excel. Using this tool it is easy to carry out an iterative process as described in chapter 4 and determine the values that match constraints instantaneously. Inside the sheet the parameter to be validated is the set of characteristics of stream A0, which has to be the same using both computation approaches according to the explanation in chapter 4.2. Figure C-4 is an example of the appearance of this part of the solution inside the sheet “MB PBR”. Remaining sheets are just auxiliary tools for the main purpose of performing the mass balances. They contain tables with the real data of Bresso plant (sheet “Bresso Data”), the calculated parameters based on the aforementioned information (sheet “Efficiencies”) and the implementation of the analysis developed in appendix B (sheet “Flue Gas”). The last auxiliary sheet is “Sensitivity Analysis” in which a set of data generated with the model can be used for any purpose, as detailed in chapter 4.4.


Figure C-4. Sketch of the solution with solver for the integrated section.

D. Appendix: Description of the experimental systems and related procedures

The following is a brief description of the equipment and procedures employed during the experimental phase. Figure D-1 represents the equipment diagram for the pilot plant microalgae culturing. As can be seen beside the reactor, the system is properly connected to the auxiliary equipment required for storage, supply and control.

Figure D-1. Equipment diagram of the culturing system.

The system consist in a plastic column (150 cm height, 28.9 internal diameter, Vmax 98.4L, Voperative 85.1L), connected in the inlet section to a feed tank (150L approx.) through a peristaltic pump (Qmax 100 mL/min at 100% power, 30 mL/min at 20% power). The outlet section is connected to a storage tank (150L approx.) were produced biomass ends up. The column is also connected to the outlet line of flue gases coming from the CHP unit, and after a conditioning system (for avoiding high acid gases) the gas stream is conducted through a flowmeter before it enters to the bottom of the reactor.


Operation of this system can be split in batch and continuous modes. The following is the detailed description of both procedures, previously mentioned in chapter 3. Operation of PBR in batch

• Let empty the column and remove any residual by washing it with abundant water. • Adjust the bubbling system and put the exit of the gas tube (porous stones) in the

bottom of the column assuring a good distribution of the bubbles. • By using a bucket fill the column at 90% of its capacity with fresh centrate solution.

Be sure of proceed with caution and using suitable protection element (Wear lab-coat, glasses, and gloves).

• Proceeding in a similar way fill the remaining 10% of the column volume with fresh microalgae solution.

• Open the valve that controls the gas flow for supplying the column and adjust the volumetric flow rate of gas in the desired value.

• After a short period of time, enough for a suitable mixing, take a 500 mL sample of the mixture for its characterization in terms of T, TS, TSS, TN, N-NH4, N-NO3, TP, Conductivity, pH, D.O. and absorbance. This would represent conditions at time zero (t=0).

• Cover the top of the column for isolating it from the surroundings with a plastic wrap and complete the operation putting a protective mesh above the plastic cover.

• Define the time period for monitoring the system tf and the maximum time for performing the run test tmax.

Each tf it is necessary to perform: • Retire the covers on the top of the column carefully and take a 500 mL sample for

subsequent characterization. • If necessary perform a cleaning of the column taking care of avoiding any mass

losses. • Using a suitable probe take the measurements of temperature of the column Tr,

ambient temperature Tamb, conductivity, D.O. and PAR directly in the column. • Cover the top of the column for isolating it from the surroundings with a plastic

wrap and complete the operation putting a protective mesh above the plastic cover.

• Verify the volumetric flow rate of gas and if necessary adjust according to the defined value.

• Once tmax has been achieved conclude the run test. For continuous operation

• Define the HRT according to the total volume of the column and the maximum capacity of the liquid pump.

• Operate in batch mode following the aforementioned instructions until reaching the HRT.

• Use a barrel for storage the centrate to be fed into the column, be sure of always keeping a minimum level for supplying the column.

Appendix: Description of the experimental systems and related procedures 109

• Connect the inlet section of the liquid pump to the centrate storage using a proper plastic tube. Don´t forget to put a filter in the section that remains inside the centrate barrel for avoiding any obstruction.

• Connect the outlet section of the liquid pump to the inlet of the column. Be sure of letting the end of the tube in the bottom of the column, far away from the exit valve.

• Check for any leak and if needed solve it properly before the next step. • Using a 25 or 50 mL graduated cylinder filled with centrate solution, and a

chronometer, adjust the volumetric flow rate supplied to the PBR to the computed value based on the defined HRT. Don´t forget to avoid any air bubble inside the tubes, since they cause wrong values and possible malfunctions of the pump.

• Connect the outlet section of the column with a second barrel for storing the produced microalgae solution. Be sure of cleaning this container before any use.

• Open the outlet valve at the top of the column allowing the flow from the column to the microalgae barrel.

• Keep the pump and compressor systems clean and always covered. • Define the time period for monitoring the system tf and the maximum time for

performing the run test tmax. Each tf it is necessary to perform:

• Retire the covers on the top of the column carefully and take a 500 mL sample for subsequent characterization.

• If necessary perform a cleaning of the column taking care of avoiding any mass losses.

• Using a suitable probe take the measurements of temperature of the column Tr, ambient temperature Tamb, conductivity, D.O. and PAR directly in the column.

• Cover the top of the column for isolating it from the surroundings with a plastic wrap and complete the operation putting a protective mesh above the plastic cover.

• Verify the volumetric flow rate of gas and if necessary adjust according to the defined value.

• Verify the volumetric flow rate of centrate and if necessary adjust according to the computed value.

• Take a 5-10 L sample of the collected microalgae for further tests or as a backup at least once a week. Be sure of mixing well before taking the sample and preserve the sample in a refrigerator at 4°C.

• Once tmax has been achieved conclude the run test. On the other hand, we find in Figure D-2 the diagram for the used AD system. As can be seen, the system is composed by a 2.4L digester, connected to an automatic stirrer activated by a circuit breaker. The system is posed inside a water bath in which a couple of resistances provide the energy for keeping constant the temperature of the medium. Additionally, one of the outlets of the digester is connected to the system for measuring


the produced methane. This latter comprise a plastic graduated cylinder (1000mL) turned down inside a container full of a 1M NaOH sln.

Figure D-2. Sketch of the semi-continuous AD system.

The following is the detailed description of the operation procedures required for driving the AD system. For batch operation

• Prepare the reaction system by closing In and Out cover, as well as heating the water bath till 32 + 1°C.

• Add the inoculum into digester doing it in the biggest opening of the reactor. • Close the system by adjusting the stirring system. • Using a 1M NaOH solution, completely fill a 1000 mL graduated cylinder. Take

care of using suitable protection elements when manipulating corrosive substances like NaOH (Wear lab-coat, glasses and gloves).

• Put the cylinder upside down inside a container and fill it with more NaOH solution in such a way liquid remains inside the cylinder. It is important to keep constant the liquid level in this container in order to maintain constant the pressure inside the system. It is easier to perform this operation by using a seal of plastic wrap over the graduated cylinder.

• Put the gas out stream of digester into the graduated cylinder and fix it always at the same depth

• Once digester temperature is stable, add the substrate using a syringe according to the defined proportions using a syringe. Use the In cover for this purpose.

Appendix: Description of the experimental systems and related procedures 111

• If necessary, use the stirring system at 80 rpm. • Take this point as time zero (t0). • Take measurements of the cylinder volume displacements with a defined

frequency. • Once volume displacements are not significant over time, the run test can be

considered finished. Semi-continuous operation

• Prepare the reaction system by closing In and Out cover, as well as heating the water bath till 32 + 1°C.

• Add the inoculum into digester doing it in the biggest opening of the reactor. • Close the system by adjusting the stirring system. • Using a 1M NaOH solution, completely fill a 1000 mL graduated cylinder. Take

care of using suitable protection elements when manipulating corrosive substances like NaOH (Wear lab-coat, glasses and gloves).

• Put the cylinder upside down inside a container and fill it with more NaOH solution in such a way liquid remains inside the cylinder. It is important to keep constant the liquid level in this container in order to maintain constant the pressure inside the system. It is easier to perform this operation by using a seal of plastic wrap over the graduated cylinder.

• It is easier to perform this operation by using a seal of plastic wrap over the graduated cylinder.

• Put the gas out stream of digester into the graduated cylinder and fix it always at the same depth cylinder

• Once digester temperature is stable, add the substrate using a syringe according to the defined proportions using a syringe. Use the In cover for this purpose.

• If necessary, use the stirring system at 80 rpm. • Define the operation parameters HRT (days) and weekly frequency of feeding (tf). • Establish the effective feed rate Q’in (mL/day) taking into account that:

y:< = f@�"#y:<ú = y:< /�01pq�s��(7)o8 (/�01/s��)

• Wait a suitable time for the stabilization of the system (temperature, gas production).

• Depending on the displaced volume in the graduated cylinder, refill it with new NaOH solution and adjust the liquid level to the starting point. You can use a vacuum pump directly connected to a plastic tube for this purpose. Be sure of adjusting the initial volume level V0 in such a way the NaOH Sln displacement can be handle in the tf period.

Once the tf period has passed carry out the next procedure:

• Take note of the cylinder displaced volume Vf.


• Put out the cylinder the gas outlet stream, you can leave it bubbling water in the

meantime. • Once stabilized the system (temperature, gas production) collect Q’in mL of the

outlet digestate for further analysis in an Erlenmeyer from Out cover. For this purpose, you can use a vacuum pump coupled with the Erlenmeyer and the Out cover. After sampling do not forget to close the outlet section.

• Take a 50 mL sample in a beaker for measuring the temperature and pH of the medium. Be sure of performing this analysis as faster as possible in order to be accurate with the temperature of the medium.

• If pH of the medium is out of limits (6.8-7.4) use a basic or acid diluted solution for adjusting the pH.

• By using a syringe add the same amount previously collected Q’in mL of new sludge into the inlet section of the digester (In cover). After feeding don’t forget to close the inlet section.

• Once again adjust the initial volume of NaOH Sln, according to the previous explanation.

• Take note of the time spent (hours) between each new measurement • At the same time, collect the outlet sludge in a beaker for analysis collecting it

from Out cover. • Depending on the gas cylinder collector volume, refill the cylinder with new NaOH

solution. • Determine de produced methane by means of the variation of volume inside the

graduated cylinder. • Take note of the time (hours) spent between each new measurement • Repeat the feeding procedure each tf.

Bibliography AEBIOM, 2009. A Biogas Road Map for Europe, Brussels, Belgium.

Aiba, S., 1982. Growth kinetics of photosynthetic microorganisms. In Microbial reactions.

Springer Berlin Heidelberg, pp. 86–154.

Alcántara, C., García-Encina, P. a. & Muñoz, R., 2013. Evaluation of mass and energy

balances in the integrated microalgae growth-anaerobic digestion process. Chemical

Engineering Journal, 221, pp.238–246.

Alvarez, J.A., 2003. Tratamiento anaerobio de aguas residuales urbanas en planta piloto.

Universidade Da Coruña.

Alvarez, M. & Tomás, C., 1989. Dinámica temporal de las microalgas de un tratamiento

terciario de aguas residuales urbanas a escala de laboratorio. Botanica Complutensis, 14,

pp.65–74.

Appels, L. et al., 2008. Principles and potential of the anaerobic digestion of waste-

activated sludge. Progress in Energy and Combustion Science, 34(6), pp.755–781.

Arbib, Z. et al., 2014. Capability of different microalgae species for phytoremediation

processes: wastewater tertiary treatment, CO2 bio-fixation and low cost biofuels

production. Water research, 49, pp.465–474.

Asam, Z.-Z. et al., 2011. How can we improve biomethane production per unit of

feedstock in biogas plants? Applied Energy, 88(6), pp.2013–2018.

Aslan, S. & Kapdan, I.K., 2006. Batch kinetics of nitrogen and phosphorus removal from

synthetic wastewater by algae. Ecological Engineering, 28(1), pp.64–70.

Bahadar, A. & Bilal Khan, M., 2013. Progress in energy from microalgae: A review.

Renewable and Sustainable Energy Reviews, 27, pp.128–148.

Baquerisse, D. et al., 1999. Modelling of a continuous pilot photobioreactor for microalgae

production. Journal od Biotechnology, 70, pp.335–342.

Barbato, F., 2009. Tecniche di Coltura di Microalghe. TechnologyBriefs ENEA, (Gennaio),

pp.1–5.


Batstone, D.J. et al., 2002. The IWA Anaerobic Digestion Model No 1 (ADM1). Water

science and technology, 45(10), pp.65–73.

Benemann, J. et al., 2011. Algae as a Feedstock for Biofuels,

Benemann, J.R. & Tillett, D.M., 1993. Utilization of Carbon Dioxide from fossil fuel-burning

power plants with biological systems. Energy Conversion and Management, 34(9-11),

pp.999–1004.

Bohutskyi, P. & Bouwer, E., 2013. Biogas production from algae and cyanobacteria

through anaerobic digestion. In J. Weifu Lee, ed. Advanced Biofuels and Bioproducts.

New York: Springer New York, p. 1110.

Bonomo, L., 2008. Tratamenti delle acque reflue Prima ediz. P. Roncoroni, ed., Milano:

Mc Graw Hill.

Boonchai, R. et al., 2012. Microalgae Photobioreactor for Nitrogen and Phosphorus

Removal from Wastewater of Sewage Treatment Plant. International Journal of

Bioscience, Biochemistry and Bioinformatics, 2(6), pp.407–410.

Borowitzka, M. a., 1999. Commercial production of microalgae: ponds, tanks, tubes and

fermenters. Journal of Biotechnology, 70(1-3), pp.313–321.

Bouterfas, R. et al., 2006. The effects of irradiance and photoperiod on the growth rate of

three freshwater green algae isolated from a eutrophic lake Source of the organisms

isolated from the eutrophic Takerkoust barrage ’ s. Limnetica, 25(3), pp.647–656.

Bouterfas, R., Belkoura, M. & Dauta, A., 2002. Light and temperature effects on the

growth rate of three freshwater algae isolated from a eutrophic lake. Hydrobiologia, (1),

pp.207–217.

BP, 2013. Statistical Review of World Energy 2013 BP, London.

Brennan, L. & Owende, P., 2010. Biofuels from microalgae—A review of technologies for

production, processing, and extractions of biofuels and co-products. Renewable and

Sustainable Energy Reviews, 14(2), pp.557–577.

Cai, T., Park, S.Y. & Li, Y., 2013. Nutrient recovery from wastewater streams by

microalgae: Status and prospects. Renewable and Sustainable Energy Reviews, 19,

pp.360–369.

Caiazzo, M., 2007. Studio delle caratteristiche di crescita di microalghe in impianti indoor

ed outdoor per la produzione di biomassa algale pregiata. Università degli studi Napoli

Federico II.

Bibliography 115

Caperon, J., 1978. Photosynthetic rates of marine algae as a function of inorganic carbon

concentration. Journal of American society of Limnology and Oceanopgraphy, 23(4),

pp.704–708.

Carlozzi, P., 2008. Closed Photobioreactor Assessments to Grow , Intensively , Light

Dependent Microorganisms : A Twenty-Year Italian Outdoor Investigation. The Open

Biotechnology Journal, 2, pp.63–72.

Carvalho, A.P., Meireles, L. a & Malcata, F.X., 2006. Microalgal reactors: A review of

enclosed system designs and performances. Biotechnology progress, 22(6), pp.1490–

1506.

Cavalett, O. & Ortega, E., 2010. Integrated environmental assessment of biodiesel

production from soybean in Brazil. Journal of Cleaner Production, 18(1), pp.55–70.

Chae, S.R., Hwang, E.J. & Shin, H.S., 2006. Single cell protein production of Euglena

gracilis and carbon dioxide fixation in an innovative photo-bioreactor. Bioresource

technology, 97(2), pp.322–329.

Chen, C.-Y. et al., 2011. Cultivation, photobioreactor design and harvesting of microalgae

for biodiesel production: a critical review. Bioresource technology, 102(1), pp.71–81.

Chisti, Y., 2007. Biodiesel from microalgae. Biotechnology advances, 25(3), pp.294–306.

Cho, S. et al., 2013. Microalgae cultivation for bioenergy production using wastewaters

from a municipal WWTP as nutritional sources. Bioresource technology, 131, pp.515–

520.

Chojnacka, K. & Zielińska, A., 2012. Evaluation of growth yield of Spirulina (Arthrospira)

sp. in photoautotrophic, heterotrophic and mixotrophic cultures. World journal of

microbiology & biotechnology, 28(2), pp.437–445.

Collet, P. et al., 2011. Life-cycle assessment of microalgae culture coupled to biogas

production. Bioresource technology, 102(1), pp.207–214.

Coral, C. et al., 2003. Avances en el diseño conceptual de fotobiorreactores para el

cultivo de microalgas. Interciencia, 28(8), pp.450–456.

Cornet, J.F., Dussap, C.P. & Dubertret, G., 1992. A structured model for simulation of

cultures of the cyanobacterium Spirulina platensis in photobioreactors, I. Coupling

between light transfer and growth kinetics. Biotechnology and bioengineering, 40(7),

pp.817–825.

Ekama, G. a, 2009. Using bioprocess stoichiometry to build a plant-wide mass balance

based steady-state WWTP model. Water research, 43(8), pp.2101–2120.


Eriksen, N.T., 2008. The technology of microalgal culturing. Biotechnology letters, 30(9),

pp.1525–1536.

Fachagentur, N., 2006. Handreichung: Biogasgewinnung und -nutzung, Leipzig.

Fargione, J.E., Plevin, R.J. & Hill, J.D., 2010. The Ecological Impact of Biofuels. Annual

Review of Ecology, Evolution, and Systematics, 41(1), pp.351–377.

Fernández, F.G. et al., 1997. A model for light distribution and average solar irradiance

inside outdoor tubular photobioreactors for the microalgal mass culture. Biotechnology

and bioengineering, 55(5), pp.701–714.

Flynn, K.J., 2002. How critical is the critical N/ P ratio? Phycology, 35(5), pp.961–970.

Frigon, J.-C. et al., 2013. Screening microalgae strains for their productivity in methane

following anaerobic digestion. Applied Energy, 108, pp.100–107.

Goldman, J.C. & Mccarthy, J.J., 1978. Steady state growth and ammonium uptake of a

marine diatom. Journal of the American Society of Limnology and Ocenography, 23(4),

pp.695–703.

Golueke, C.G., Oswald, W.J. & Gotaas, H.B., 1957. Anaerobic digestion of Algae. Applied

microbiology, 5(1), pp.47–55.

Grau Gumbau, P., 2007. Nueva metodología de modelado matemático integral de las

edar. Universidad de Navarra.

Grobbelaar, J.U., 2000. Physiological and technological considerations for optimising

mass algal cultures. Journal of Applied Phycology, 12, pp.201–206.

Grobbelar, J., 2004. Algal Nutrition. In A. Richmond, ed. Handbook of microalgal culture.

Oxford, UK: Blackwell Science, p. 577.

Harun, R. et al., 2011. Technoeconomic analysis of an integrated microalgae

photobioreactor, biodiesel and biogas production facility. Biomass and Bioenergy, 35(1),

pp.741–747.

Henze, M. et al., 1987. Activated Sludge Model N°1, London.

Hermanto, M.B., 2009. Identification of algae growth kinetics. Wageningen University.

Ho, S.-H. et al., 2011. Perspectives on microalgal CO2-emission mitigation systems-A

review. Biotechnology advances, 29(2), pp.189–198.

Holm-Nielsen, J.B., Al Seadi, T. & Oleskowicz-Popiel, P., 2009. The future of anaerobic

digestion and biogas utilization. Bioresource technology, 100(22), pp.5478–84.

Bibliography 117

Hsieh, C.-H. & Wu, W.-T., 2009. A novel photobioreactor with transparent rectangular

chambers for cultivation of microalgae. Biochemical Engineering Journal, 46(3), pp.300–

305.

Hulatt, C.J. & Thomas, D.N., 2011. Productivity, carbon dioxide uptake and net energy

return of microalgal bubble column photobioreactors. Bioresource technology, 102(10),

pp.5775–5787.

IRSA-CNR, 2004. Costituenti Inorganici Non Metallici. In Metodi Analitici per le acqua.

Roma, p. 16.

Jayed, M.H. et al., 2009. Environmental aspects and challenges of oilseed produced

biodiesel in Southeast Asia. Renewable and Sustainable Energy Reviews, 13(9),

pp.2452–2462.

Ji, M.-K. et al., 2013. Cultivation of microalgae species in tertiary municipal wastewater

supplemented with CO2 for nutrient removal and biomass production. Ecological

Engineering, 58, pp.142–148.

Khozin-Goldberg, I. & Cohen, Z., 2006. The effect of phosphate starvation on the lipid and

fatty acid composition of the fresh water eustigmatophyte Monodus subterraneus.

Phytochemistry, 67(7), pp.696–701.

Kumar, K. et al., 2011. Development of suitable photobioreactors for CO2 sequestration

addressing global warming using green algae and cyanobacteria. Bioresource

technology, 102(8), pp.4945–4953.

Kurki, A., Hill, A. & Morris, M., 2010. Biodiesel : The Sustainability Dimension. ATTRA

Journal, pp.1–12.

Lakaniemi, A.-M., Tuovinen, O.H. & Puhakka, J. a, 2013. Anaerobic conversion of

microalgal biomass to sustainable energy carriers--a review. Bioresource technology,

135, pp.222–231.

Lam, M.K. & Lee, K.T., 2012. Microalgae biofuels: A critical review of issues, problems

and the way forward. Biotechnology Advances, 30(3), pp.673–690.

Lardon, L. et al., 2009. Life-cycle assessment of biodiesel production from microalgae.

Environmental science & technology, 43(17), pp.6475–6481.

Lee, Y. & Hui, S., 2004. Basic Culturing Teachniques. In A. Richmond, ed. Handbook of

microalgal culture. Oxford, UK: Blackwell Science, p. 577.

Lossie, U. & Putz, P., 2008. Targeted control of biogas plants with the help of FOS/TAC,

Manchester.


Martinez, E. et al., 1997. Influence of light intensity on the kinetic and yield parameters of

Chlorella pyrenoidosa mixotrophic growth. Process Biochemistry, 32(2), pp.93–98.

Martinez, L., 2009. Eliminación de CO2 con microalgas autóctonas. Universidad de Leon.

Masci, P., Bernard, O. & Grognard, F., 2004. Microalgal biomass surface productivity

optimization based on a photobioreactor model. In Proceedings of the 11th CAB

conference. Leuven, Belgium, p. 6.

Masojídek, J., Koblízek, M. & Torzillo, G., 2004. Photosynthesis in microalgae. In A.

Richmond, ed. Handbook of microalgal culture. Oxford, UK: Blackwell Science, p. 577.

Massi, E., 2012. Anaerobic Digestion. In S. McPhail, V. Cigolotti, & A. Moreno, eds. Fuel

cells in the waste to energy chain. London: Springer-Verlag, p. 228.

Mata, T.M., Martins, A. a. & Caetano, N.S., 2010. Microalgae for biodiesel production and

other applications: A review. Renewable and Sustainable Energy Reviews, 14(1), pp.217–

232.

Merchuk, J.C., Garcia-Camacho, F. & Molina, E., 2007. Photobioreactor Design and Fluid

Dynamics. Chemical and Biochemical Engineering, 21(4), pp.345–355.

Molina, E. et al., 1999. Photobioreactors: light regime , mass transfer , and scaleup.

Journal of bioscience and bioingineering, 70, pp.231–247.

De Morais, M.G. & Costa, J.A.V., 2007. Biofixation of carbon dioxide by Spirulina sp. and

Scenedesmus obliquus cultivated in a three-stage serial tubular photobioreactor. Journal

of biotechnology, 129(3), pp.439–445.

Muller-Feuga, A., 1999. Growth as a function of rationing: a model applicable to fish and

microalgae (1999). Journal of Experimental Marine Biology and Ecology, 236, pp.1–13.

Mussgnug, J.H. et al., 2010. Microalgae as substrates for fermentative biogas production

in a combined biorefinery concept. Journal of biotechnology, 150(1), pp.51–56.

Oncel, S.S., 2013. Microalgae for a macroenergy world. Renewable and Sustainable

Energy Reviews, 26, pp.241–264.

Park, K.C. et al., 2011. Mixotrophic and photoautotrophic cultivation of 14 microalgae

isolates from Saskatchewan, Canada: potential applications for wastewater remediation

for biofuel production. Journal of Applied Phycology, 24(3), pp.339–348.

Posten, C., 2009. Design principles of photo-bioreactors for cultivation of microalgae.

Engineering in Life Sciences, 9(3), pp.165–177.

Pulz, O., 2001. Photobioreactors: production systems for phototrophic microorganisms.

Applied Microbiology and Biotechnology, 57(3), pp.287–293.

Bibliography 119

Pulz, O. & Gross, W., 2004. Valuable products from biotechnology of microalgae. Applied

microbiology and biotechnology, 65(6), pp.635–648.

Ras, M. et al., 2011. Experimental study on a coupled process of production and

anaerobic digestion of Chlorella vulgaris. Bioresource technology, 102(1), pp.200–206.

Rawat, I. et al., 2013. Biodiesel from microalgae: A critical evaluation from laboratory to

large scale production. Applied Energy, 103, pp.444–467.

Razzak, S. a. et al., 2013. Integrated CO2 capture, wastewater treatment and biofuel

production by microalgae culturing—A review. Renewable and Sustainable Energy

Reviews, 27, pp.622–653.

Rigby, H. & Smith, S., 2011. New Markets for Digestate from Anaerobic Digestion,

Banbury, Oxon, UK.

Rittmann, B.E., 2008. Opportunities for renewable bioenergy using microorganisms.

Biotechnology and bioengineering, 100(2), pp.203–212.

Rodolfi, L. et al., 2008. Microalgae for oil: Strain selection, induction of lipid synthesis and

outdoor mass cultivation in a low-cost photobioreactor. Biotechnology and bioengineering,

102(1), pp.100–112.

Ruiz, A., 2011. Puesta en marcha de un cultivo de microalgas para la eliminación de

nutrientes de un agua residual urbana previamente tratada anaeróbicamente.

Universidad Politécnica de Valencia.

Sahu, A.K. et al., 2013. Utilisation of wastewater nutrients for microalgae growth for

anaerobic co-digestion. Journal of environmental management, 122, pp.113–120.

Sasi, D., 2009. biokinetic behavior of chlorella vulgaris in a continuously stirred bioreactor

and a circulating loop photobioreactor. University of Saskatcewan.

De Schamphelaire, L. & Verstraete, W., 2009. Revival of the biological sunlight-to-biogas

energy conversion system. Biotechnology and bioengineering, 103(2), pp.296–304.

Schenk, P.M. et al., 2008. Second Generation Biofuels: High-Efficiency Microalgae for

Biodiesel Production. Bioenergy Research, 1(1), pp.20–43.

Sforza, E., 2012. Oil from microalgae: Species selection, photobioreactor design and

process optimization. Università degli studi di Padova.

Shi, X., Zhang, X. & Cheng, F., 2000. Heterotrophic production of biomass and lutein by

Chlorella protothecoides on various nitrogen sources. Enzime and microbial technology,

27(3-5), pp.312–318.


Sialve, B., Bernet, N. & Bernard, O., 2009. Anaerobic digestion of microalgae as a

necessary step to make microalgal biodiesel sustainable. Biotechnology advances, 27(4),

pp.409–16.

Sierra, E. et al., 2008. Characterization of a flat plate photobioreactor for the production of

microalgae. Chemical Engineering Journal, 138(1-3), pp.136–147.

Smith, A., 2002. Nitrogen Uptake by Gracilaria gracilis (Rhodophyta): Adaptations to a

Temporally Variable Nitrogen Environment. Botanica Marina, 42(2), pp.196–209.

Sommer, U., 1991. A comparison of the Droop and the Monod models of nutrient limited

growth applied to natural populations of phytoplankton. Functional Ecology, 5(4), pp.535–

544.

Spellman, F., 2004. Mathematical manuela for water and wastewater treatment plant

operators First Edit., New York: CRC Press.

Standard-Methods, 2005. Standard Methods for the examination of water ans wastewater

21th editi., Washington D.C.: American Public Healt Association/American Water Works

Association/Water Environment Federation.

Suali, E. & Sarbatly, R., 2012. Conversion of microalgae to biofuel. Renewable and

Sustainable Energy Reviews, 16(6), pp.4316–4342.

Tang, D. et al., 2011. CO2 biofixation and fatty acid composition of Scenedesmus

obliquus and Chlorella pyrenoidosa in response to different CO2 levels. Bioresource

technology, 102(3), pp.3071–3076.

Taricksa, J. et al., 2009. Anaerobic Digestion. In L. Wang, N. Pereira, & Y.-T. Hung, eds.

Biological Treatment Processes. New York: Humana press, p. 833.

Tevatia, R., Demirel, Y. & Blum, P., 2012. Kinetic modeling of photoautotropic growth and

neutral lipid accumulation in terms of ammonium concentration in Chlamydomonas

reinhardtii. Bioresource technology, 119, pp.419–424.

Tredici, M., 2004. Mass production of microalgae: Photobioreactors. In A. Richmond, ed.

Handbook of microalgal culture. Oxford, UK: Blackwell Science, p. 577.

Uslenghi, A., 2011. Integrazione della produzione di microalghe nel ciclo depurativo delle

acque. Politecnico di Milano.

Uttley, P. et al., 2011. Algae for Reewable Energy Doctoral Training Centre Symposium.

In Integration of biological wastewater treatment and algal growth for biofuels. Bath:

University of Bath, p. 1.

Bibliography 121

Valiorgue, P. et al., 2011. Elongated gas bubble dissolution under a turbulent liquid flow.

Chemical Engineering and Processing: Process Intensification, 50(8), pp.854–858.

Vanoorschot, J.L.P., 1995. CONVERSION OF LIGHT ENERGY IN ALGAL CULTURE.

University of Wageningen.

Wang, L. et al., 2010. Cultivation of green algae Chlorella sp. in different wastewaters

from municipal wastewater treatment plant. Applied biochemistry and biotechnology,

162(4), pp.1174–1186.

Ward, A.J., Lewis, D.M. & Green, F.B., 2014. Anaerobic digestion of algae biomass: A

review. Algal Research, pp.1–11.

Wentzel, M., Ekama, G. & Sotemann, S., 2006. Mass balance-based plant-wide

wastewater treatment plant models – Part 1 : Biodegradability of wastewater organics

under anaerobic conditions. Water SA, 32(3), pp.269–275.

Xin, L. et al., 2010. Effects of different nitrogen and phosphorus concentrations on the

growth, nutrient uptake, and lipid accumulation of a freshwater microalga Scenedesmus

sp. Bioresource technology, 101(14), pp.5494–5500.

Yamaguchi, H., Sakamoto, S. & Yamaguchi, M., 2008. Nutrition and growth kinetics in

nitrogen- and phosphorus-limited cultures of the novel red tide flagellate Chattonella ovata

(Raphidophyceae). Harmful Algae, 7(1), pp.26–32.

Yao, B. et al., 2011. A model and experimental study of phosphate uptake kinetics in

algae: Considering surface adsorption and P-stress. Journal of environmental sciences,

23(2), pp.189–198.

Yuan, X. et al., 2011. Impact of ammonia concentration on Spirulina platensis growth in

an airlift photobioreactor. Bioresource technology, 102(3), pp.3234–3239.

Yuang, X. et al., 2012. Microalgae growth using high-strength wastewater followed by

anaerobic co-digestion. Water environment research, 84(5), pp.396–404.

Zamalloa, C., Nico, B. & Verstraete, W., 2011. Lab-scale production and anaerobic

digestion of freshwater microalgae Scenedesmus obliquus and marine microalgae

Phaeodactylum tricornutum. Communications in Agricultural and Applied Biological

Sciences, 76(1).

Zhang, X., Chen, F. & Johns, M.R., 1999. Kinetic models for heterotrophic growth of

Chlamydomonas reinhardtii in batch and fed-batch cultures. Process Biochemistry, 35,

pp.385–389.

Documents

Integration of microalgae culturing within · Integration of microalgae culturing within the wastewater scheme Oscar Mauricio Martínez Avila Matr. 797677 Supervisor: PhD. Eng. Elena