Energy for Sustainable Development 18 (2014) 75–82

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Energy for Sustainable Development

Performance analysis of an air-blownpilot fluidized bed gasifier for rice husk

Jhon Jairo Ramírez Behainne a, Juan Daniel Martinez b,⁎a Department of Mechanical Engineering, Federal University of Technology, Av. Monteiro Lobato, km 04, Ponta Grossa, PR, Brazilb Environmental Research Group, Institute of Energy, Materials and Environment, Pontificia Bolivariana University, Circular 1 No. 70-01, Bloque 11, Piso 2, Medellín, Colombia

Abbreviations: BFB, Bubbling fluidized bed; CCD, CEquivalence ratio; RSM, Response surfacemodel; RH, Rice⁎ Corresponding author. Tel./fax: +574 3544569.

E-mail address: juand.martinez@upb.edu.co (J.D. Mart

0973-0826/$ – see front matter. Crown Copyright © 2013http://dx.doi.org/10.1016/j.esd.2013.11.008

a b s t r a c t

a r t i c l e i n f o

Article history:Received 17 April 2013Revised 18 September 2013Accepted 28 November 2013Available online 27 December 2013

Keywords:Biomass gasificationFluidized bedRice huskRSM

In order to contribute to the agricultural residue recovery in a sustainable way in Colombia, an atmospheric bub-bling fluidized bed reactor fed with rice husk (with nominal capacity of 150 kWth) was evaluated under gasifica-tion conditions. This systemwas assessed using a statistical design of experiments based on the response surfacemethodology-RSM (central composite design—CCD) in order to obtain empirical correlations for describing theperformance behavior of the process in terms of the equivalence ratio (ER) and the normalized fluidization ve-locity (Unc). The lower heating value, the volumetric gas yield, the gas power and the cold gasification efficiencywere the output variables. Optimum conditionswith respect to both the lower heating value (3.78 MJ/Nm3) andthe gas power (73.82 kW)were obtained at ERof 0.24 andUnc of 0.19 m/s.However, the height of the initial fixedbed of inerts requires to be higher in order to increase themass and energy transfer conditions and hence the car-bon conversion. The statistical correlations obtained are considered acceptable taking into account the complex-ity of the gasification phenomena in fluidized bed. The results are considered as a base for designing small scaledecentralized power generation units using rice husk as feedstock.

Crown Copyright © 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Introduction

Currently, the utilization of agriculture residues (rice husk, ricestraw and sugar cane bagasse) for thermal energy or electricity genera-tion has received a lot of attention. These feedstock do not threaten foodsupply and is therefore not the source of “food or fuel” controversy (Limet al., 2012). According to Timmer et al. (2010) the worldwide rice con-sumption in 2020 will be around 450 million tons, a 6.6% growth ascompared to 422 million tons in 2007. China and India are the mostrice producing countries in the world, with both countries contributingaround 50% of total production. Asia region alone produces over 90% ofthe total global rice output.

The annual production of dry paddy rice in Colombia is around of 2 -

million tons (Fedearroz, 2013). In view of rice husk (RH) representingapproximately 20% of the gross weight of the grain (Natarajan et al.,1998a), the annual generation of this agricultural residue is around400,000 tons. Nowadays, only a small fraction of the RH generated inColombia is used for floor covering in farms, moisture retention incrops, and also as fuel in furnaces for drying of agricultural products asthe same rice grain. The rest of the produced volume is burned inopen fields without any type of control, generating environmental im-pacts to the neighboring communities. Therefore, the lack of alternativemechanismsor appropriate techniques for RHdisposal leads to a serious

entral composite design; ER,husk; LHV, Lower heating value.

inez).

International Energy Initiative. Publi

environmental problem as well as an important renewable energysquandering.

The fluidized bed technology for the energetic valorization of bio-mass residues i.e. rice husk, sugar cane bagasse and sawdust amongothers, has experienced a considerable development in recent years. Flu-idized bed reactors have been widely proved as an efficient technologyfor high specific capacities and it is considered as the best choice forconverting biomass to energy due to its fuel flexibility and for the possi-bility to achieve a clean operation (Singh et al., 2008). Some of the maincharacteristics of the fluidized bed technology are: homogenous tem-perature distribution in the reaction chamber, good gas–solid contact,wide tolerance to variations in fuel quality, easy starting and shutdown, quick heat-up, high carbon conversion efficiency and relativelow investment (Kunii and Levenspiel, 1991; Anthony, 1995; Basu,2006). Additionally, this type of reactors also offers important advan-tages when they are used for gasifying low bulk density, high ash con-tent and irregular/complex shape biomass such as RH. Conventionalreactors such as the fixed beds are not suitable for processing RH giventhose physico-chemical properties and the low melting point for theashes. In this sense, the gasification, besides enabling the production ofgaseous products for power and/or thermal generation or for the syn-thesis of chemical compounds, usually leads to a lower reaction temper-ature in comparison to combustion. Thus, the problems associated withsintering and agglomeration of the ash during combustion areprevented (Natarajan et al., 1998a; Mansaray et al., 1999).

Several works in literature have reported successful results for RHgasification using the fluidized bed technology (Hartiniati et al., 1989;Sanchez and Lora, 1994; Mansaray et al., 1999; Jiang et al., 2003;Fernandes, 2004; Andrade, 2007; Subramanian et al., 2011) and a

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Nomenclature

C Carbon in the rice husk, in wt.%GP Gas power, in kWH Hydrogen in the rice husk, in wt.%LHVCH4

Lower heating value of methane, in kJ/Nm3

LHVCO Lower heating value of carbon monoxide, in kJ/Nm3

LHVg Lower heating value of producer gas, in kJ/Nm3

LHVH2Lower heating value of hydrogen, in kJ/Nm3

LHVRH Lower heating value of rice husk, in kJ/kgmg Mass flow of producer gas, in kg/smRH Mass flow of rice husk, in kg/sN Nitrogen in the rice husk, in wt.%O Oxygen in the rice husk, in wt.%S Sulfur in the rice husk, in wt.%Tb Bed temperature, in °CUf Fluidization velocity, in m/sUnc Normalized fluidization velocity at 273.15 K and

101.325 kPa, in Nm/sV a Volumetric flow of air, in Nm3/sVGY Volumetric gas yield, in Nm3/kgyCH4

Volumetric gas concentration of methaneyCO Volumetric gas concentration of monoxide carbonyH2

Volumetric gas concentration of hydrogen

Greek lettersηc Cold gasification efficiency, in %ρg Producer gas density at 273.15 K and 101.325 kPa, in

kg/m3

βj Regression coefficientsϕa Air-to-fuel ratio actually used in the process, in Nm3/kgϕs Air-to-fuel ratio at stoichiometric combustion condi-

tions, in Nm3/kgε Error of the empirical model

Table 2RH ash analysis.

Oxide SiO2 K2O MgO CaO Al2O3 Fe2O3 Na2O TiO2

wt.% 84.36 1.93 0.48 0.29 0.27 0.13 0.06 0.02

76 J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

comprehensive review on RH combustion and gasification can also befound (Natarajan et al., 1998a). Roughly speaking, this review showsthat a lower heating value (LHV) between 4 and 6 MJ/Nm3 and coldgasification efficiencies around 60% can be expected when the processis carried out with air. Mansaray et al. (1999) showed the effects ofthe fluidization velocity (Uf) and equivalence ratio (ER) (see Eq. (1))using a dual distributor plate. At Uf of 0.22 m/s and ER of 0.25, the vol-umetric concentrations for the producer gaswere 4% of H2, 5% of hydro-carbons (CH4, C2H2, C2H4 and C2H6), 15% of CO2, 20% of CO and 57% ofN2. Similarly, Hartiniati et al. (1989) assessed the performance of a bub-bling fluidized bed (BFB)without any evidence of operational problemsin 36 h of continuous operation. The RH flow rate was between 75 and105 kg/h and the LHV was 6.3 MJ/Nm3 at 760 °C. Sanchez and Lora(1994) showed gasification results with different biomasses (sugarcane bagasse, sawdust and RH) in a BFB reactor of 200 mm internaldiameter. For RH, the authors found a LHV of 4 MJ/Nm3. In addition,

Table 1Ultimate and proximate analyses of RH.

Ultimate analysis (db) wt.% Proximate analysis (ar) wt.%

Carbon 36.60 Volatile 57.70Hydrogen 5.83 Residual moisture 9.30Nitrogen 3.31 Ash 17.60Oxygen (by difference) 34.85 Fixed carbon 15.40Sulfur 0.01 Lower heating value (MJ/kg) 11.81

Jiang et al. (2003) gasified RH in BFB reactor without inert additives.They found that low superficial velocities and low gasification tempera-tures (550–650 °C) were beneficial to the production of CO. The H2

composition was almost unchanged with the superficial velocity.Fernandes (2004) used a 200 kWth pilot plant with 400 mm of internaldiameter and 4.6 m of height coupled to a spark ignition engine. Hefound an average LHV of 4.2 MJ/Nm3 and a thermal efficiency in the en-gine around 26%. Also, Andrade (2007) using a pilot plant for gasifyngaround 150 kg/h of RH, observed volumetric concentrations of CO, H2

and CH4 around 15.4, 4.2 and 4.2% respectively, leading to a LHV of3.78 MJ/Nm3. In that study, the ER was 0.22 and the Uf was 0.6 m/s.

In another work, pine sawdust was used as feedstock in a BFB gasifi-er (Narváez et al., 1996). At 0.32 of ER, the LHV, gas power and gas yieldwere 6.3 MJ/Nm3, 2.49 kW and 2.1 Nm3/kg, respectively. The authorsgave special importance to the ER effect because its role in both bedand freeboard temperatures, as well as the gas composition, heatingvalue and tar yield. Similarly, they also developed a first-order statisticmodel to predict the variations of those variables from the H/C ratio, re-action temperature and ER. As expected, the results showed a significantinfluence of both the ER and theH/C ratio on theH2 concentration at 95%of confidence level. Nevertheless, no detailed results were shown for theother output process variables, such as LHV, gas yield, power and cold ef-ficiency. In addition, the model did not take into account possibilities ofinteraction among the input variables in the prediction of the outputvariables. Second-order statistical models are useful for optimizing pro-cess and products through a design of experiments because of they canconsider combined interactions of factors involved in the process. Its im-portance is widely known in researches and industrial applications(Schmidt and Launsby, 1992; Montgomery, 1997).

Fig. 1. Schematic representation of the BFB reactor.

Table 3Experimental combinations for the gasification tests according to CCD model.

Test Experimental combinations

ER Level Ucn (m/s) Level

1 0.240 −1 0.150 −12 0.320 +1 0.150 −13 0.240 −1 0.190 +14 0.320 +1 0.190 +15 0.280 0 0.142 −α6 0.280 0 0.198 +α7 0.223 −α 0.170 08 0.337 +α 0.170 09a 0.280 0 0.170 010a 0.280 0 0.170 011a 0.280 0 0.170 0

a Combinations corresponding to the central point of the CCD model.

Fig. 2. Response surface and predictive statistical model for the bed temperature.

77J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

In this work, a second-order statistical model is obtained from plan-ning experiments according to the rotatable central composite design(CCD), in order to predict the operational performance of a pilot-scaleRH fluidized bed gasifier in function of the normalized fluidizing veloc-ity (Ucn) and the ER.

Materials and methods

Feedstock characterization

The RHwas originated in the Tolima province, localized at the centerregion of Colombia. The feedstock did not suffer any pre-treatment pro-cess before being fed in the BFB. Table 1 presents the ultimate and prox-imate analyses. It is well-known that biomass contains less carbon andmore oxygen than other fossil fuels such as coal and petroleum-basedfuels. Likewise, the heating value is lower than these fossil fuels. TheRH shows certain differences respect to other types of biomasses. Forexample, the lower volatile content (63.61 wt.% on dry basis) as wellas the higher ash content (19.40 wt.% on dry basis) should be noted.Woody biomass has around 80–85wt.% (on dry basis) of volatilematterand less than 1% of ash. In addition, the higher silica content in the ash,as well as some potassium and phosphorus compounds can lead tosintering/agglomeration problems when the bed temperature (Tb) ishigher than 1000 °C (Natarajan et al., 1998b). The ash content of theRH depends on diverse factors, like ground, climate and fertilizers(Martínez et al., 2006) and hence, this temperature can decrease up to900 ºC depending on the concentration of those chemical compounds.The inert bedmaterial can also affect this temperature given its interac-tion with the high alkali content ashes (Schmidt and Launsby, 1992;Montgomery, 1997). Table 2 shows the main components of the RHash (obtained in an electronic oven at 700 ºC and 5 h) characterizedfrom X-ray fluorescence (XRF) analysis. Unlike other biomasses suchas wood, corn cobs or sunflower shells (Vassilev et al., 2013), the RH

Table 4Main results from experimental tests.

Test RH(kg/h)

Air(kg/h)

Tb(°C)

Uf

(m/s)Producer gas(kg/h)

1 44.2 49.2 790 0.65 67.862 33.2 49.2 812 0.67 66.573 56.0 62.4 828 0.84 83.754 42.0 62.4 866 0.86 82.255 35.9 46.6 781 0.63 61.776 50.0 65.0 874 0.91 84.317 53.9 55.8 784 0.72 76.058 35.7 55.8 864 0.78 69.889 43.0 55.8 846 0.77 74.4910 43.0 55.8 823 0.75 76.5511 43.0 55.8 821 0.75 78.01

ash is mainly composed of SiO2 (herein 84.36 wt.%) and for this reasonmany processes are being explored recently for the production ofsilicon-based products and alternative materials for concrete, cementand ceramics as reviewed by Sun and Gong (2001). Depending on theoxidation conditions, this silica can be found in a crystalline or amor-phous form (Natarajan et al., 1998b; Gómez-Barea et al., 2005). Besidesthis high SiO2 content, the RH ash is also composed of K2O (1.93 wt.%)andMgO (0.48 wt.%). The remainingmass (12.46 wt.%) can be attribut-ed to some unburnt carbon as well as some inorganic contained in theRH ash (P2O5, SO3, Cl) as showed elsewhere (Martínez et al., 2009).

Experimental setup

The equipment used for the present work is an atmospheric BFBreactor that uses air as a gasifying agent. Fig. 1 shows a schematic repre-sentation of the experimental facility. The reactor is a cylindrical refracto-ry chamber of 0.3 m internal diameter with expansion to 0.4 m in thefreeboard zone. The total height is 3 m. The heat necessary for preheatingthe bed in order to ensure the RH self-ignition inside the reactor is sup-plied from a natural gas burner. The combustion gases produced by theburner heat up the bed until 500 °C. The RH is fed to the reactor by an au-tomatic feeding screw systemwhich has both an external and an internalcooling chamber for avoiding the pyrolysis and carbonization of the feed-stock before entering the reactor. The air required for the process is sup-plied by a high-pressure air blower and a calibrated orifice plate is usedfor measuring the air flow rate. The particulate matter released duringthe gasification process is separated by a high efficiency cyclone. Detailedinformation of the system can be found elsewhere (Ramírez et al., 2007).

Producer gas composition (v%)

CO CH4 H2 N2 CO2 O2 H2O

13.88 4.09 5.15 53.62 12.05 0.45 10.7711.10 3.45 4.56 56.39 14.18 1.83 8.4914.07 3.93 5.58 54.37 10.69 0.45 10.9110.12 3.24 3.58 58.32 14.14 1.82 8.789.18 3.35 6.86 56.06 14.46 0.45 9.659.91 3.06 3.79 58.75 13.93 0.45 10.11

10.02 2.91 3.62 55.98 14.94 0.44 12.098.33 2.81 3.24 61.76 14.59 0.46 8.83

10.52 3.37 4.66 56.80 14.44 0.45 9.7712.06 3.77 4.29 55.42 14.48 0.45 9.5312.84 3.93 4.82 54.13 14.51 0.45 9.31

Table 5Experimental and estimated values for the output variables.

Test CO (v%) CH4 (v%) H2 (v%) Gas yield(Nm3/kg)

LHV (MJ/Nm3) Gas power (kW) Cold efficiency (%)

No ER Ucn

(m/s)VM VE EA VM VE EA VM VE EA VM VE EA VM VE EA VM VE EA VM VE EA

1 0.24 0.15 13.88 12.23 11.89 4.09 3.57 12.71 5.15 5.20 0.97 1.27 1.25 2.36 3.77 3.32 11.94 58.89 54.61 7.27 36.89 32.01 13.232 0.32 0.15 11.10 9.95 10.36 3.45 3.20 7.25 4.56 5.13 12.50 1.61 1.58 3.73 3.13 2.81 10.22 46.45 42.78 7.90 38.79 33.66 13.233 0.24 0.19 14.07 12.23 13.08 3.93 3.57 9.16 5.58 4.68 16.13 1.25 1.23 0.80 3.78 3.32 12.17 73.82 62.47 15.38 36.51 32.01 12.334 0.32 0.19 10.12 9.95 1.68 3.24 3.20 1.23 3.58 3.20 10.61 1.56 1.52 0.64 2.82 2.81 0.35 51.35 50.64 1.38 33.86 33.66 0.595 0.28 0.142 9.18 11.09 20.81 3.35 3.61 7.76 6.86 6.35 7.43 1.42 1.45 0.70 3.09 3.27 5.83 43.78 47.13 7.65 33.8 35.90 6.216 0.28 0.198 9.91 11.09 11.91 3.06 3.61 17.97 3.79 4.64 22.43 1.35 1.39 5.93 2.75 3.27 18.91 51.83 58.12 12.14 28.69 35.90 25.137 0.223 0.17 10.02 12.72 26.95 2.91 3.41 17.18 3.62 4.14 14.36 1.13 1.15 0.88 2.7 3.21 18.89 45.72 61.05 33.53 23.48 28.49 21.348 0.337 0.17 8.33 9.47 13.69 2.81 2.88 2.49 3.24 3.02 6.79 1.55 1.59 2.58 2.4 2.49 3.75 36.95 44.2 19.62 28.68 30.84 7.539 0.28 0.17 10.52 11.09 5.42 3.37 3.61 7.12 4.66 4.59 1.50 1.40 1.44 2.14 3.03 3.27 7.92 50.74 52.62 3.71 32.71 35.90 9.7510 0.28 0.17 12.06 11.09 8.04 3.77 3.61 4.24 4.29 4.59 6.99 1.44 1.44 0.69 3.33 3.27 1.80 57.13 52.62 7.89 36.84 35.90 2.5511 0.28 0.17 12.84 11.09 13.63 3.93 3.61 8.14 4.82 4.59 4.77 1.47 1.44 2.72 3.55 3.27 7.89 62.21 52.52 15.58 40.11 35.90 10.50

VM: Value measured experimentally.VE: Value estimated by response surface model.EA: Absolute error (%) of the model prediction.

78 J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

The reactor is able to process up to 50 kg/h of RH and this supposes anominal thermal inlet power of around 150 kWth.

Operational procedure

Gases leaving the reactor pass through a cyclone to separate and tocollect the entrained ashes. Temperatures were recorded by seventype K thermocouples placed along the reactor. The temperature signalwas collected by a data acquisition system. Pressures on bed weremea-sured from U tube manometers in order to check the fluidization re-gime. The bed material consisted of quartz sand with an averagediameter of 0.385 mm (Geldart group B particles (Geldart, 1973)) andthe static bed heightwas 0.3 m in all tests. The quartz sand particles en-sure a proper fluidization and lead to uniform temperature inside thebed. The gasification experiments started preheating the quartz sandbed by means of the burner, up to 400 °C. Once this temperature wasreached, RH was fed into the reactor and stoichiometric combustionconditions (air/fuel ratio around 4 Nm3/kg) were adjusted to reachquickly a bed temperature around 700 °C. After that, the gasificationcondition was adjusted. The fluidization velocity was controlled byusing a by-pass valve and anorifice platemeter, whichwas built accord-ing to the standard ISO 5167 (1991) in order to assurance anuncertainlyminor than 1%, while the ER was controlled from an automatic feedingscrew system that regulates themass flowof RH. Themass rate releasedby the feeding screw was calibrated before experiments by weightingan amount of discharged solids in a fixed period of time at different ro-tational velocities of the screw. In each test, the producer gas was col-lected once the process reached the steady state, which wasestablished by verifying negligible bed temperature variation.

Experimental planning

The test program of the gasifier system was obtained from the re-sponse surface methodology (RSM) by means of the software Statistica6.0®. The RSM is a statistical tool that allows verifying the operationalperformance of processes, optimizing costs and time of execution. Theexperimental scheme is designed aiming to determine the influence of

Table 6Coefficients involved in the statistical model for predicting the CO, CH4 and H2 concentra-tions.

Component β0 β1 β2 β11 β22 β12

CO 19.074 −28.500 – – – –

CH4 −6.362 75.850 – 143.658 – –

H2 0.585 238.946 −299.766 −310.381 1154.514 −440.625

theprocess input variables (controllable factors) on the output variables(responses), and find an empirical model to predict the behavior of theprocess as well.

Input and output variable selectionAs stated by Gómez-Barea et al. (2005) for a non-externally heated

gasifier, there are only two important operating variables that can be in-dependently varied between a limited range: the biomass throughputand the air flow rate. These two parameters determine both the ERand Uf (or superficial gas velocity). As it is well-known, ER is one ofthe most important operational variables in biomass gasification withair. As showed in Eq. (1), the ER is defined as the air-to-fuel ratio actu-ally used in the process (in Nm3/kg, according to Eq. (2)) divided by theair-to-fuel ratio at stoichiometric combustion conditions (in Nm3/kg, asshown in Eq. (3)). In biomass gasification, ER usually varies from 0.20 to0.40 (Sanchez and Lora, 1994; Narváez et al., 1996). According toNatarajan et al. (1998a) the lower limit of ER is decided by the mini-mum quantity of air required to burn a fraction of the fuel, and thus torelease enough heat to support the endothermic reactions involved ingasification. On the other hand, the upper limit is determined by thecombined consideration of the reactor temperature (in order to avoidthe ash melting point), the fluidization quality, the gas heating valueand the tar content in the producer gas.

ER ¼ ϕa

ϕsð1Þ

ϕa ¼Va

mRHð2Þ

ϕs ¼ 0:0889 � C þ 0:375 � Sð Þ þ 0:265 � H−0:0333 � O: ð3Þ

The fluidization velocity is themain variable involved in fluidizationprocesses and it is linked with the apparent residence time of both charand volatiles inside the reactor, and alsowith themass and heat transferrates. In this work, the superficial air velocity in the dense bed zone at101.3 kPa and 273.1 K has been considered and named as normalizedfluidization velocity (Unc). It is worth to highlight that the proper fluid-ization velocity takes into account the contributions of the air and thevolatiles released in the process. In sum, the input variables selectedfor the performance analysis of the gasification process were ER andUcn. The output variables were the lower heating value, the volumetricgas yield, the gas power and the cold gasification efficiency, calculatedaccording to Eqs. (4)–(7), respectively.

LHVg ¼ yH2� LHVH2

þ yCO � LHVCO þ yCH4� LHVCH4

ð4Þ

Table 7Main reactions in biomass gasification process (Gómez-Barea and Leckner, 2010).

Reaction Heat of reaction (kJ/kmol) Name

Biomass → Char + Tar + H2O + (CO + CO2 + H2 + CH4 + …) N0 Devolatilization

Char combustionC + 1/2O2 → CO −111 Partial combustionC + O2 → CO2 −394 Complete combustion

Char gasificationC + CO2 → 2CO +173 Boudouard reactionC + H2O → CO + H2 +131 Steam gasification (heterogeneous water–gas shift reaction)C + 2H2 → CH4 −75 Hydrogen gasification (methane reaction)

Homogeneous volatile oxidationCO + 1/2O2 → CO2 −283 Carbon monoxide oxidationH2 + 1/2O2 → H2O −242 Hydrogen oxidationCH4 + 2O2 → CO2 + 2H2O −283 Methane oxidationCO + H2O → CO2 + H2 −41 Water gas shift reaction

Tar reactionsCnHm + (n/2)O2 → nCO + (m/2)H2 Highly endothermic + (200 to 300) Partial oxidationCnHm + nCO2 → (m/2)H2 + (2n)CO2 Dry reformingCnHm + nH2O → (m/2 + n)H2 + nCO2 Steam reformingCnHm + (2n − m/2)H2 → nCH4 HydrogenationCnHm → (m/4)CH4 + (n − m/4)C Thermal cracking

79J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

VGY ¼ mg

ρg �mRHð5Þ

GP ¼mRH � VGY � LHVg ð6Þ

ηc ¼VGY � LHVg

LVHRH� 100: ð7Þ

Selection of the statistical model for experimental tests

Due to the nonlinear behavior expected between the variables of theprocess, the central composite design (CCD) model was selected as themost appropriate for obtaining reliable results under a reasonable num-ber of experiments. For two factors, this model has a matrix composedby a complete factorial design with two levels (22), a central point andfour “star” points located in intermediate positions (Montgomery,

Fig. 3. Response surface and predictive statistical model for LHV.

1997). Unlike the complete factorial models, the CCD model allowsobtaining response surfaces of second-order, that make possible theidentification of optimal operational conditions according to the specificrange of the variables. Similarly, the CCD is a very efficient tool regard-ing the use of experimental resources and provides the capacity forobtaining a response equation of the evaluated process. From the CCDmodel, an experimental program with eleven tests was implemented,as shown in Table 3. Three runs at the central point (0 level for bothER andUcn)were considered,which are necessary for the determinationof the experimental error and the statistical significance test at the con-fidence level previously fixed of 80% (p b 0,2) in all cases analyzed.Looking forward to guarantee the rotatability property of the CCDscheme, the α value corresponded to

ffiffiffi

2p

.From the results of the CCD scheme a second-ordermodel can be ob-

tained, which is an empirical representation of the response variable yin terms of the factors or input variables x, according to Eq. (8).

y ¼ β0 þ β1x1 þ β2x2 þ β11x21 þ β22x

22 þ β12x1x2 þ ε ð8Þ

where, βj, j = 0, 1, 2 are the regression coefficients, and ε, is the error ofthe empirical model.

Results and discussion

The gas concentrations produced in the RH gasification processwereobtained through chromatographic analysis. For each gasification test,three gas samples were collected in order to obtain the mean value.The volumetric composition of the three samples did not deviate morethan 5% in relation to the each mean value. Table 4 shows the resultsfor the experimental conditions (mass flow for both RH and air, aswell as the resulting bed temperature and producer gas mass flow)

Table 8Coefficients involved in the statistical model for predicting the lower heating value, gasyield, gas power and cold efficiency.

Output variable β0 β1 β2 β11 β22 β12

Lower heating value −5.116 66.248 – −129.596 – –

Gas yield −1.139 14.439 – −18.842 – –

Gas power 60.646 −147.852 196.338 – – –

Cold efficiency −120.34 1095.39 – −1919.21 – –

Fig. 4. Response surface and predictive statistical model for the gas yield. Fig. 6. Response surface and predictive statistical model for the cold efficiency.

80 J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

and the mean gas concentrations obtained from field measurementsand laboratory analysis.

Fig. 2 illustrates the response surface obtained for estimating thegasification temperature from the two controlled factors. As alsofound by other authors in literature (Sanchez and Lora, 1994; Narváezet al., 1996; Fernandes, 2004; Subramanian et al., 2011), the gasificationtemperature is mainly influenced by the ER. In this work, both the ERandUnc had significant effect on the reaction temperature at 95% of con-fidence level, which confirm that more oxygen in fluidizing gas leads tointensify the carbon conversion and the heat released in the gasificationprocess. In addition, a higher fluidization gas velocity also contributes toincrease the temperature once the turbulence of the gas–solid flow isenhanced, promoting higher heat andmass transfer rates. The empiricalmodel suggested for estimating the temperature is given by Eq. (9).

Tb ¼ 436:306þ 541:053 � ER þ 1402:778 � Unc: ð9Þ

Fig. 5. Response surface and predictive statistical model for the gas power.

Combustible concentration in the producer gas

The results show that the ranges of ER and Unc used were appropri-ate for the gasification process in the pilot BFB since all of them agreewith those reported in literature as shown in the Introduction section(see Table 5). Variations in the gas concentrations are linked with theER and Unc. As it is well-known, the energy for heating the fuel to reac-tion temperature and for satisfying the endothermic reactions is provid-ed by combustion of part of the fuel (Gómez-Barea and Leckner, 2010).Although ER (determined by the air/fuel ratio) controls the reactiontemperature, as well as the heat released and the gasification productformation (CO, H2 and CH4), Unc can also affect the residence time ofboth particles and volatiles, and consequently the thermochemical con-version grade. Thus, equivalence ratios close to 0.223 (test 7) seem to bethe limit for keeping the endothermic reactions involved in ColombianRH gasification. Unlike fixed bed reactors, it is worth to note that the re-actions involved in biomass gasification (Table 7), which are led by thestages of drying, devolatilization (also known as pyrolysis), oxidationand reduction, occur at the same time in BFB and therefore, the perfor-mance analysis ismore complex. On the other hand, ER higher than 0.32(tests 2 and 4) leads to higher O2 concentration in producer gas, as re-sult of the higher combustion degree. In consequence, lower CO, CH4

and H2 concentrations are obtained, suggesting that ER close to 0.32 isthe higher ER limit for conducting the RH gasification. Regarding theUnc, the lower CO and CH4 concentrations are linked with the lowerUnc since it leads to both lower mass and energy transfer rates in thedense-phase (Wen andMiller, 1961; Kunii and Levenspiel, 1991). How-ever, it is noteworthy that although these concentrations were relative-ly low, the H2 concentration showed higher values than those found athigher Unc. It is an established fact that those transfer rates increasewiththe Uf until an optimum velocity is reached. On increasing the velocityfurther, the heat transfer rate decreases (Gupta and Sathiyamoorthy,1999). Thus, the lower CO, H2 and CH4 concentrations found for thehigher Unc can be ascribed to this fact as well as the elutriation phenom-ena. Similarly, higher Unc decreases both the solid mixture and volatileresidence time, resulting in a poor RH conversion. The quality of fluidi-zation is one of the most important factors that affect the efficiency ofthermochemical conversion processes conducted in fluidized beds andit is controlled mainly by Uf. The final effect of both Unc and ER dependson the combined influence of these two variables since both time andtemperature reactions play an important role in the thermochemicalprocess. In addition, the central point in the statistical model (tests 9,10 and 11: 0.28 of ER and 0.17 of Unc) showed a considerable dispersion

81J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

among the combustible gas concentrations even though experimentaltests were carefully conducted.

From the experimental results shown in Table 5, Unc of 0.19 m/s andER of 0.24 (test 3) appeared to be the optimum conditions concerningthe combustible gas concentrations (14.07 v% CO, 5.58 v% H2 and3.93 v% CH4). Although these conditions for both Unc and ER are notstrictly similar to those found in literature, the CH4 and H2 concentra-tions are thoroughly comparable (Hartiniati et al., 1989; Sanchez andLora, 1994; Natarajan et al., 1998a; Mansaray et al., 1999; Jiang et al.,2003; Fernandes, 2004; Andrade, 2007; Subramanian et al., 2011).These variations in the process conditions can be attributed to manyreasons, such as the RH composition, the inert material properties thatlead to different fluidization conditions and the gasifier design (isola-tion, distributor plate, feeding point of the feedstock). Furthermore,the CO concentration was relatively smaller than those found byMansaray et al. (1999) and Fernandes (2004), both around 20 v%. Thisdeficiency can be explained by the smaller rate of carbon conversiondue to the use of a shallow static bed (0.3 m) compared to deeperones employed in other studies as reviewed by Natarajan et al.(1998b). Additionally, it is noteworthy that differences of fuel compo-nent concentrations in the producer gas can be explained by the designspecifications of the gasifier (Narváez et al., 1996). It is well-known thatan intimate gas–solid contact is affected by the self-induced mixingcaused by the bubbles. The characteristics of the bubbles and their pop-ulation density are influenced by several parameters, such as the phys-ical properties of both the fluidization agent and the mixture of solidinside the bed, as well as the operating velocity, the thermal conditions,and the gas distributor design. The bubbles emerging from the distribu-tor plate have a remarkable influence on the hydrodynamics of the bed(Gupta and Sathiyamoorthy, 1999). In this sense, Srinivasa and Venkat(2007) highlighted the effect of the distributor plate on the RH combus-tion in a BFB, affecting the temperature profile and consequently thethermal efficiency of the thermochemical process. Significant changesin temperature profiles in the fluidization vessel were observed by theauthors regarding the opening area of distributor’s injectors. In thepresent work, the distributor plate of the pilot BFB is a tuyer type andconsists of a plate with vertical nozzles with lateral perforationswhere the air is distributed into the reactor. This distributor type pre-sents convenience to be used at high temperatures. Also, it presentsthe advantage of reducing the backflow of the bed material towardthe plenum.

Table 5 also shows the absolute deviation between the experimentaldata (VM) and the predicted values (VE) obtained from each representa-tive statisticalmodel at 80% of confidence level (p b 0.20). The regressioncoefficients considered in the statistical models of the CO, CH4 and H2

concentrations are listed in Table 6. As observed, the maximum absoluteerrorwas found for the COprediction (27%),which is considered satisfac-tory taking into account the complexity of the gasification process in apilot scale fluidized bed reactor.

Lower heating value

The LHV of the producer gaswas calculated from theweighted com-bination of the LHV of each fuel component of the gas, as showed inEq. (4). From results shown in Table 5, Unc of 0.19 m/s and ER of 0.24(test 3) appeared to be the optimum conditions regarding the fuel com-ponent concentrations, as commented above, and consequently the LHV(3.78 MJ/Nm3). On the other hand, Fig. 3 shows the response surfacewhile Table 8 shows the coefficients involved in the statistical modelbased on the independent variables of the process, as shown inEq. (8). The behavior of the obtained surface was as expected, oncethere was a maximum value in the ER range studied. In experiments,the highest LHV was approximately 20% less than those reported byother authors (Mansaray et al., 1999; Fernandes, 2004). This result is at-tributed to the relatively less proportion of CO contained in the produc-er gas as previously explained. In addition, this heating value is slightly

lower to those found using downdraft (Jain and Goss, 2000; Yoon et al.,2012) entrained flow (Zhao et al., 2009) and circulating fluidized bedgasifiers (Li et al., 2002) (N4 MJ/Nm3) that used RH as a feedstock andsimilar ER (between 0.2 and 0.4). According to the statistical model, amaximum value of 3.32 MJ/Nm3 is obtained when the gasifier operateswith ER close to 0.24.

Volumetric gas yield and gas power

Volumetric gas yield is a performance parameter of the gasifier thatrelates the amount of gas volume generated by unit of fuel mass fed tothe system (see Eq. (5)). The generated gas volume was determinedthrough a nitrogen balance, based on the hypothesis that the absoluteamount of nitrogen entering the system remains fixed during the gasifi-cation process. The experimental test carried out at Unc of 0.19 m/s andER of 0.24 (test 3), which appeared to be the optimum conditions re-garding the fuel gas concentrations and the LHV, led to a volumetricgas yield of 1.25 Nm3/kg. Even so, the higher volumetric gas yield(1.61 Nm3/kg) is reached when ER and Ucn are 0.32 and 0.15 m/s,respectively. It isworth to note that from this ER condition a higher com-bustion degree is favored, as commented before. Mansaray et al. (1999)reported volumetric gas yields between 1.30 and 1.98 Nm3/kg for ERvarying from 0.25 to 0.35. So, the volumetric gas yield values reportedby the authorswere slightly higher than those found in thiswork.More-over, Fig. 4 presents the response surface and Table 8 shows the coeffi-cients for the empirical representation of this variable according toEq. (8). As observed, when ER increases, the volumetric gas yieldincreases almost linearly. This observation agrees with the trend report-ed by other studies on RH gasification (Hartiniati et al., 1989; Narváez etal., 1996; Natarajan et al., 1998a; Mansaray et al., 1999; Fernandes,2004). Likewise, in the studied operation range, a maximum value of1.61 Nm3/kg can be obtained when the ER approaches to 0.32.

Furthermore, the gas powerwas calculated from LHVof theproducergas and volumetric gas flow (see Eq. (6)). According to the experimentalresults depicted in Table 5, the maximum gas power (73.82 kW) isachieved when ER and Unc are 0.24 and 0.19 m/s, respectively. Theseoperational conditions are found to be the same for both the highestfuel concentrations in producer gas and LHV. Fig. 5 shows the responsesurface for the gas power, where a maximum value in the ER range isevident, as well as, a diminution of the gas power while increasing Unc.Results showed a maximum value around 62.5 kW when the ER isclose to 0.24 and the Unc tends to 0.19 m/s. Table 8 shows the coeffi-cients involved in the statistical model as shown in Eq. (8).

Cold efficiency

The gasification efficiency was determined on the LHV basis and asfired. The cold efficiency calculations account only for the heatingvalue of the producer gas and neglect the value of the sensible heat, asshown in Eq. (7). In order to compare the gasification efficiencies, thisefficiency avoids the uncertainty related to the calculations of the sensi-ble heat of producer gas discharged from the reactor since the high tem-perature of this gas is very often not the objective in the gasificationprocess (Martínez et al., 2012). Natarajan et al. (1998a) reported thatcold gas efficiencies higher than 60% could be achieved in the RH gasifi-cation process using BFB with carbon conversion efficiencies in theorder of 90%. Fernandes (2004) found a cold gas efficiency around 40%for the air-blown RH gasification using a BFB pilot-plant of 200 kW.From the experimental tests shown in Table 5, the higher cold gas effi-ciency (40.1%) is achieved at 0.28 of ER and 0.17 m/s of Unc given thelower RH feeding (43 kg/h) compared to the conditions observed forboth the greatest LHV and gas power (56 kg/h). The lower cold gas effi-ciencies found in this work are ascribed to the lower carbon conversionefficiencies, as commented before. Likewise, these efficiencies are lowerthan those found in others works using different types of reactors (Jainand Goss, 2000; Zhao et al., 2009; Yoon et al., 2012) as reported for the

82 J.J.R. Behainne, J.D. Martinez / Energy for Sustainable Development 18 (2014) 75–82

LHV. Moreover, Fig. 6 shows the response surface based on the studiedindependent variables while Table 8 shows the coefficients for the em-pirical representation of this variable (according to Eq. (8)). The resultsobtained showed that the cold gasification efficiency presents a maxi-mum value close to 36% at ER of 0.28.

Conclusions

Colombian RH has been gasified in an atmospheric BFB reactor usingair as gasifying agent. The process is a feasible alternative to contributeto the solutionof the agricultural residue disposalwith important benefitsin non-interconnected zones. Experimental tests suggest that Unc of0.19 m/s and ER of 0.24 are the optimum conditions for obtaining maxi-mumLHV (3.78 MJ/Nm3) of the producer gas and gas power (73.82 kW).The height of inerts is required to be higher than 0.3 to increase the massand energy transfer conditions and hence the carbon conversion. Fromthis strategy an increase in CO concentration in the producer gas isexpected. The statistical correlations were only significant at 80% of con-fidence level, and the absolute error obtained between the values mea-sured experimentally and those estimated by the response surfacemodel were considered satisfactory since they were lower than 25% inmost of cases.

Acknowledgments

The authors thank the Universidad Pontificia Bolivariana (UPB) andSena-Colciencias for the financial support (Contract No. 577-2002).Also, to the Universidad Pontificia Bolivariana for the internal projectNo. 65A-04/10-24. In addition, to the Premac S.A. company and all theadministrative and research staff of the Environmental ResearchGroup (GIA) at UPB.

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