Pretreatment of Cellulosic Biomass Through Bioleaching to

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Pretreatment of Cellulosic Biomass Through Bioleaching to Pretreatment of Cellulosic Biomass Through Bioleaching to

Reduce Inorganic Ingredients Reduce Inorganic Ingredients

Ning Zhang Clemson University, [email protected]

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i

PRETREATMENT OF CELLULOSIC BIOMASS THROUGH BIOLEACHING TO REDUCE INORGANIC INGREDIENTS

A Dissertation Presented to

the Graduate School of Clemson University

In Partial Fulfillment of the Requirements for the Degree

Doctor of Philosophy Biosystems Engineering

by Ning Zhang

December 2020

Accepted by:

Terry Walker, Committee Chair Yi Zheng

Caye Drapcho Julia Kerrigan

ii

ABSTRACT

Lignocellulosic biomass is one of the most abundant natural resources available,

though has not been fully exploited as a feedstock for fuel and chemicals.

Thermochemical conversions of this biomass face a range of issues as a result of the

inorganic elements present in the biomass, thus necessitating a pretreatment to remove

such elements. Bioleaching is one promising method to achieve this objective, by

utilizing microbial activities to extract and remove the inorganic components from the

biomass feedstock.

In this research three microbial species including two fungi (Fusarium oxysporum

and Aspergillus niger) and one bacterium (Burkholderia fungorum) were selected to

pretreat four lignocellulosic feedstocks – switchgrass, corn stover, wheat straw and

sorghum. Results demonstrated that among the three microbes, A. niger was the most

efficient in removing most elements by 80% after 48 hours, and sorghum was relatively

more amenable to bioleaching. With A. niger, the bioleaching with a water to feedstock

ratio (v/w) of 25 for 6 h was sufficient to leach K (85%), Cl (90%), Mg (60%), and P

(70%) from sorghum. Bioleaching was shown as more efficient than water leaching.

Studies on bioleaching mechanism indicated that the acidification resulted from organic

acids produced by A. niger during bioleaching might have contributed to the higher

leaching efficiency. Following that, the bioleaching process with A. niger was scaled up

to be carried out in custom built bioreactors. Three operating parameters were

investigated for their effects on leaching efficiency – fungal mass added to each reactor,

leaching time, and glucose concentration. Response surface methodology (RSM) was

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used for the experiment design and model regression. Results showed that after the

bioreactor leaching process, the residual ash percentage of the sorghum biomass was

significantly lower (3.63±0.19%, mean ± standard deviation) compared with the ash

content (4.72±0.13%) after water leaching (p<0.00001). The RSM model provided

directions for improving our bioleaching process.

iv

DEDICATION

I would like to dedicate this work to my parents Mr. X. Zhang and Ms. Liying

Gao for their support and encouragement throughout my exceptionally long period of

school life.

.

v

ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Terry Walker for his support and guidance

during my study at Clemson University. And I would also like to thank my committee

member, Dr. Yi Zheng for his patience and support during my PhD study. Meanwhile, I

owe many thanks to my committee member, Dr. Caye Drapcho for her encouragement

and help. And I would like to thank Dr. Julia Kerrigan for being in my committee and for

providing the fungal culture used in my research. I also thank Dr. William Bridges for

helping with data analysis.

I would like to thank Rodney Merck and Rodney Morgan for their help with the

bioreactor fabrications for my research. I want to thank David Lipscomb for his help in

my research. I would also like to thank my former fellow lab members Karthik

Gopalakrishnan, Xiang Li and Rui Xiao for their help. In addition, I want to thank Dr.

David Freedman, other faculty and staff members of the Department of Environmental

Engineering and Earth Science at Clemson, for their support. At last I want to thank my

mother, Liying Gao for her selfless support during my long period of school life. Without

her sharing my burden of taking care of the family while being a student, I cannot make it

here.

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TABLE OF CONTENTS

Page

TITLE PAGE .................................................................................................................... i ABSTRACT ..................................................................................................................... ii DEDICATION ................................................................................................................ iv ACKNOWLEDGMENTS ............................................................................................... v LIST OF TABLES ........................................................................................................ viii LIST OF FIGURES ........................................................................................................ ix CHAPTER I. LITERATURE REVIEW .............................................................................. 1 1.1 Introduciton ........................................................................................ 1 1.2 Lignocellulosic biomass ..................................................................... 2 1.2.1 Cellulose ................................................................................... 3 1.2.2 Hemicellulose ........................................................................... 3 1.2.3 Lignin ........................................................................................ 4 1.3 Pretreatment ....................................................................................... 4 1.3.1 Biochemical conversion pretreatments ..................................... 4 1.3.2 Thermochemical conversion pretreatments .............................. 5 1.4 Bioleaching ........................................................................................ 7 1.5 Conclusion ......................................................................................... 8 II. ASSESSMENT OF BIOLEACHING MICROORGANISMS .................... 10 2.1 Introduction ...................................................................................... 10 2.2 Materials and Methods ..................................................................... 13 2.2.1 Biomass feedstocks ................................................................. 13 2.2.2 Microbial culture preparation ................................................. 13 2.2.3 Bioleaching setup .................................................................... 15 2.2.4 Analytical methods ................................................................. 15 2.2.5 Data Analysis .......................................................................... 17 2.3 Results and Discussion .................................................................... 17 2.3.1 Biomass feedstock composition .............................................. 17 2.3.2 Microbial growth characteristics ............................................. 18

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2.3.3 Bioleaching with different microorganisms ............................ 19 2.4 Conclusion ....................................................................................... 21 III. STUDY OF FACTORS THAT AFFECT BIOLEACHING PROCESS ..... 22 3.1 Introduction ...................................................................................... 22 3.2 Materials & Methods ....................................................................... 22 3.2.1 Effect of leaching time ............................................................ 23 3.2.2 Effect of water loading ............................................................ 23 3.2.3 Study of bioleaching mechanisms .......................................... 24 3.3 Results & Discussion ....................................................................... 24 3.3.1 Effect of time on bioleaching .................................................. 24 3.3.2 Effect of water loading on bioleaching ................................... 25 3.3.3 Study of bioleaching mechanisms .......................................... 26 3.4 Conclusion ....................................................................................... 28 IV. THE BIOLEACHING PROCESS SCALING UP ....................................... 30 4.1 Introduction ...................................................................................... 30 4.2 Materials & Methods ....................................................................... 32 4.2.1 Lignocellulosic biomass.......................................................... 32 4.2.2 Aspergillus niger ..................................................................... 32 4.2.3 Bioleaching reactors................................................................ 33 4.2.4 Response surface methodology (RSM) .................................. 35 4.2.5 Analytical methods ................................................................. 36 4.3 Results & Discussion ....................................................................... 37 4.4 Conclusion ....................................................................................... 40 V. CONCLUSIONS AND RECOMMENDATIONS ...................................... 41 5.1 Conclusions ...................................................................................... 41 5.2 Recommendations ............................................................................ 43 TABLES ........................................................................................................................ 45 FIGURES ....................................................................................................................... 50 APPENDICES ............................................................................................................... 66 Appendix A .................................................................................................. 67 Appendix B .................................................................................................. ?? Appendix C .................................................................................................. ?? REFERENCES .............................................................................................................. 79

viii

LIST OF TABLES

Table Page 2.1 Detection limits for the ICP method ............................................................ 45 2.2 Chemical compositions of biomass feedstocks............................................ 45 2.3 Elemental compositions of biomass feedstocks ........................................... 46

2.4 Major chemical compositions (dry basis) of biomass feedstocks after 48 h of leaching with A. niger and water ........................................................... 46

4.1 Actual variable values and the coded values used in RSM ......................... 47 4.2 Box-Behnken design for the RSM study ..................................................... 47 4.3 Actual results and predicted responses based on the RSM model ............... 48 4.4 Analysis of variance (ANOVA) for the quadratic model ............................ 48 4.5 The ANOVA table for lack of fit of the model ............................................ 48 4.6 Estimates of parameter coefficients and the evaluation of significance ...... 49 S2.1 ICP-OES analysis of selected elements in leached biomass feedstocks ..........

................................................................................................................ 67

S3.1 ICP-OES analysis of selected elements in leached sorghum in time factor study ....................................................................................................... 71

S3.2 ICP-OES analysis of selected elements in leached sorghum in water ratio

factor study............................................................................................. 74 S3.3 ICP-OES analysis of selected elements in leached sorghum in the study of

leaching mechanisms ............................................................................. 76 S3.4 Major compositions (dry basis) of biomass feedstocks after 48 h of leaching

with A. niger and water .......................................................................... 77 S4.1 Residual ash percentage data ....................................................................... 78

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LIST OF FIGURES

Figure Page 2.1 Example of bioleaching setup ...................................................................... 59 2.2 Microbial growth curves .............................................................................. 59 2.3 Effect of bioleaching on different biomass feedstocks in 48h ..................... 60

3.1 Effect of leaching time on the removal of (A) = chloride, (B) = potassium and (C) = magnesium ................................................................................... 61

3.2 Effect of water loading on leaching efficiency of sorghum. ........................ 62

3.3 Bioleaching using different fractions of fungal culture compared with water

leaching. ................................................................................................. 63 4.1 Schematic of single reactor used for bioleaching ........................................ 64 4.2 Schematic of four bioleaching reactors set up ............................................. 65 4.3 Photograph of all four reactors set up for bioleaching ................................. 65 4.4 Comparison of ash content (based on dry weight) of sorghum straws after

bioleaching or water leaching and the untreated sorghum straws. ........ 66 4.5 Surface plot of the predicted response changing with variables X1 and X2 ..... ................................................................................................................ 66 4.6 Contour plot of the predicted response changing with variables X1 and X2 .... ................................................................................................................ 67 4.7 Surface plot of the predicted response changing with variables X1 and X3 ..... ................................................................................................................ 68 4.8 Contour plot of the predicted response changing with variables X1 and X3 .... ................................................................................................................ 69 4.9 Surface plot of the predicted response changing with variables X2 and X3 ..... ................................................................................................................ 70

x

List of Figures (Continued) Figure Page 4.10 Contour plot of the predicted response changing with variables X2 and X3 .... ................................................................................................................ 71 S4.1 Plot of Y (residual ash percentage) versus X1 (fungal loading). ...................... ................................................................................................................ 72 S4.2 Plot of residuals versus X1 (fungal loading) ................................................ 72 S4.3 Plot of Y (residual ash percentage) versus X2 (leaching time) .................... 73 S4.4 Plot of residuals versus X2 (leaching time) .................................................. 73 S4.5 Plot of Y (residual ash percentage) versus X3 (glucose concentration) ....... 74 S4.6 Plot of residuals versus X3 (glucose concentration) .................................... 74

1

CHAPTER I

LITERATURE REVIEW

1.1 Introduction

The history of mankind using biomass may date back to one million years ago,

with evidence of burned wood ash discovered from the Acheulien strata in a South

African cave (Berna, Goldberg et al. 2012). Since ancient times a major part of human

life has been closely entangled with biomass – fruit collection, crop cultivation, firewood

gathering, and beverage production from excess food. As the global society entered the

industrial age, the focus of human activities gradually shifted towards obtaining and

securing fossil fuels. However, the non-renewable nature of the fossil energy resources,

and the concomitant environmental issues have driven people to explore alternative

sources of fuels and raw materials that are eco-friendly. Also the current mainstream

practice of “take-make-dispose” or linear economy model has been based on the

presumption of abundant and inexpensive natural resources, whereas showing a variety of

disadvantages (Hassan, Williams et al. 2018). Thus, it is the right time to transition to an

economy with more emphasis on renewability – a circular economy that focuses on re-

use, recovery and recycle of resources (MacArthur 2013). To accomplish this goal,

biorefineries, instead of petroleum-based refineries should become the main engine

driving a sustainable economy. For that purpose, the vastly abundant and under-used

lignocellulosic biomass could be used in various conversion processes for fuel and

material production.

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Much attention has focused on lignocellulosic biomass biochemical conversion

into ethanol as a second-generation biofuel (Sims, Mabee et al. 2010). Due to the

structure and composition of the biomass, pretreatment is a necessity that would increase

the accessibility of its cellulose component to hydrolytic enzymes. Various types of

pretreatment technologies are being developed, which can be categorized into physical,

chemical and biological treatments, as well as any combination from these single types

(Kumar, A. K., Sharma 2017).

Thermo-chemical conversion (e.g., combustion, gasification, and pyrolysis) is

another application for lignocellulosic biomass, in which the biomass is directly

converted into fuels and other chemicals under heat, pressure and other physical

conditions. For such conversion processes, pretreatments are required due to the presence

of certain inorganic ingredients - more commonly known as ash (Davidsson, Korsgren et

al. 2002). To remove the inorganics from the biomass, a pretreatment called leaching has

been used, which could be carried out through rinsing of biomass by water (water

leaching), acidic or alkali reagents (chemical leaching), or microorganisms (bioleaching)

(Zhang, Wang et al. 2019a). This chapter reviews the pretreatment of the lignocellulosic

biomass, and the application of bioleaching.

1.2 Lignocellulosic biomass

In addition to inorganic ingredients, lignocellulosic biomass is mainly composed

of three biopolymers – cellulose, hemicellulose and lignin. Relative compositions of the

three vary considerably depending on the type, species and source of the biomass

(Agbor, Cicek et al. 2011).

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1.2.1 Cellulose

As the most abundant biomolecule on earth, cellulose is the main target for the

biological conversion of lignocellulosic biomass (Coffey, Bell et al. 1995). Being present

in the cell walls of most plants, cellulose provides structural support and protection to the

host species. Cellulose is a homogenous linear polymer consisting of only glucose

molecules connected by β-1,4 glycosidic linkages, with the disaccharide cellobiose as the

repeating unit. The number of glucose subunits in cellulose molecules ranges between

7000 – 15000. Multiple chains of polymerized glucose are bundled up by inter-chain

cross bridges – hydrogen bonds and Van der Waals forces to form microfibrils, which in

turn bond with other microfibrils, hemicellulose and lignin molecules to form

macrofibrils and other higher order structures (Brethauer, Studer 2015).

1.2.2 Hemicellulose

Hemicellulose molecules are either linear or branched heteropolymers that are

composed of pentoses (xylose and arabinose), hexoses (glucose, mannose, and galactose),

and the acidified form of sugars, such as glucuronic acid. Compared with cellulose,

hemicellulose has a shorter chain with 500 – 3000 subunits (Gibson 2012). Unlike the

homogenous cellulose, the composition of hemicellulose varies with different biomass

types. For instance, hemicellulose molecules in straw and grass biomass mainly consist of

xylan, while glucomannan is the major form in soft wood (Agbor, Cicek et al. 2011).

Compared with cellulose and lignin, hemicellulose presents the least difficulty

regarding the removal of it during the biomass pretreatment. However, some of its

degradation products after the pretreatment – furfurals and hydroxymethyl furfurals are

4

known to inhibit the following fermentation process. Therefore, the removal of

hemicellulose has to be carefully balanced with avoiding formation of these inhibitors

(Palmqvist, Hahn-Hägerdal 2000a, b).

1.2.3 Lignin

Lignin is the second most abundant biomolecule on earth, after the cellulose. As a

highly branched heteropolymer, lignin has an undefined structure (amorphous) and a

largely varying composition of three phenyl propane monomer subunits – p-coumaryl,

coniferyl and sinapyl alcohol. Lignin can be viewed as the “glue” that binds the cellulose

and hemicellulose together, which confers mechanical strength on the plant. And the

recalcitrance of the lignocellulosic biomass can also be attributed to the presence of

lignin. While being resistant to enzymatic hydrolysis, the degradation products of lignin

are also inhibitors of saccharification and fermentation processes (Brethauer, Studer

2015, Mood, Golfeshan et al. 2013).

1.3 Pretreatment

Common destinations of lignocellulosic biomass are: 1) biochemical conversion

into ethanol and other biochemicals; 2) thermo-chemical conversion into products such as

bio-oil, bio-gas and bio-char. Different pathways would require specific pretreatment that

facilitates biomass conversion.

1.3.1 Pretreatments for biochemical conversion

Successful biochemical conversion requires that the biomass be pretreated such

that its cellulose and hemicellulose components are more accessible to hydrolytic

enzymes that break down these polymers to their constituent monomers. Thereby the

5

fermentation can proceed with the hydrolysate as ready-to-use substrates. Pretreatments

include the following types: 1) physical, such as mechanical extrusion, milling,

microwave, ultrasound, etc.; 2) chemical, such as the use of dilute acids, alkaline

reagents, organosolv, ionic liquid, and ozonolysis, etc.; 3) biological, by using

microorganisms such as white rot fungi, soft rot fungi and brown rot fungi;

4) physiochemical, such as steam explosion that has real applications in the industry,

ammonia fiber explosion, CO2 explosion and liquid hot water (Kumar, A. K., Sharma

2017). In addition, any combination of the above types may also be considered so as to

incorporate the advantages of different pretreatments (Agbor, Cicek et al. 2011).

1.3.2 Pretreatments for thermo-chemical conversion

Thermo-chemical conversion refers to the thermal decomposition of

lignocellulosic biomass at elevated temperature and pressure inside certain special

purpose equipment. Different conversion processes include pyrolysis, gasification, direct

combustion, etc. Several products are generated by the conversion, such as the bio-oil,

bio-gas, syngas, and tar, that could either be directly burned for heat / energy generation

or upgraded into higher quality transportation fuels. Solid products generated are also

known as bio-char and can be used as fertilizers. Besides, the biomass can be blended

with coal in the co-firing process in power plants (Baxter, Miles et al. 1998).

The current thermo-chemical conversion processes are facing a range of issues

that are caused by inorganic compounds present in the lignocellulosic biomass. Alkali

metals (Na, K), alkali earth metals (Ca, Mg), Cl, S and Si are among those inorganic

elements that have been shown to lead to slagging, fouling, agglomeration, corrosion, etc.

6

These issues would reduce the heat transfer rate of the conversion equipment, increase

the maintenance cost of the process due to frequent shutting down and cleaning, and

eventually damage the equipment (Carrillo, Staggenborg et al. 2014, Dayton, Jenkins et

al. 1999).

To mitigate the above problems, pretreatments are required before thermo-

chemical conversion of lignocellulosic biomass to remove its inorganic ingredients. One

simple pretreatment is rinsing the biomass with water, also known as water leaching. In

other cases, the biomass would be left in the field for several days so that natural

precipitation would perform water leaching before collection. Therefore, water leaching

is a simple and inexpensive method of reducing the inorganic compounds in the biomass.

However, water leaching can only remove those elements that are water soluble, such as

Na, K, Cl, etc., while it cannot achieve the same degree of removal with elements that are

insoluble in water, such as Mg, Ca and Si. Another disadvantage of water leaching is that

it requires a high water to biomass ratio (water volume / biomass dry weight), thus

preventing large scale application in the industry (Liu, Bi 2011a, Zhang, Wang et al.

2019b).

To remove insoluble elements that are bound to the organic backbones of the

biomass, dilute acids including sulfuric acid, hydrochloric acid, acetic acid, or nitric acid

can be added in the pretreatment, which could remove almost all inorganic constituents

from the biomass. However, the added acids would bring additional cost to the process,

while the residual acid in the leachate would cause environmental concerns, and the

7

disposal would further increase the cost of the whole process (Liu, Bi 2011a, Davidsson,

Korsgren et al. 2002).

1.4 Bioleaching

Due to disadvantages of water / acid leaching, great challenges must be overcome

to attain objectives of removing water insoluble elements and improving the economic

sustainability of the leaching process. For such purposes, bioleaching might be the

solution, where microorganisms are used for their physiological activities that might

achieve the elemental removal under environmentally mild conditions.

Bioleaching, also known as biomining or biohydrometallurgy, has been widely

applied by the mining industry to recover metals from low grade ores. Large size mining

heaps have been established by copper mines around the world, where acidophilic

bacteria gradually solubilize copper, iron, and zinc from mining ores. Meanwhile, such

bioleaching is also carried out in production scale reactors (Acevedo 2000, Sasaki,

Nakamuta et al. 2009, Yang, T., Xu, Wen, and Yang 2009a).

In addition to application by the industry, bioleaching has been extensively

studied by researchers. Priha et al. used both pure and mixed acidophilic bacteria

(Acidithiobacillus sp. and indigenous strains related to Burkholderia fungorum) to extract

phosphorus from low grade fluorapatite, obtaining up to 97% phosphorus yield after 3

weeks, where the biogenic acids from the leaching bacteria were suggested to be the

major leaching agent (Priha, Sarlin et al. 2014). Wu et al. used a reactor to solubilize

phosphorus, fluorine and other elements from granite grains through B. fungorum

8

leaching, which achieved high recovery for all elements after 35 days (Wu, L., Jacobson,

and Hausner 2008a). Mulligan et al studied fungus such as Aspergillus niger for leaching

low grade ores. Different culturing medium compositions were tested and after 14 days of

leaching, 68% Cu, 46% Zn and 34% Ni were recovered (Mulligan, Kamali 2003).

Bosshard et al. also used A. niger to extract heavy metals from municipal solid waste

(MSW) incineration fly ash and achieved optimal metal removals with 81% Cd, 66% Zn,

57% Cu, and 52% Pb after 24 h (Bosshard, Bachofen et al. 1996). Both studies indicated

that organic acids produced by A. niger played a critical role in the leaching process.

While previous bioleaching studies have been mostly focused on bio-mining, bioleaching

could also provide an alternative solution to water leaching alone in achieving more

efficient element removal from lignocellulosic biomass. Bansal et al. used the fungus

Fusarium oxysporum to extract Si nanoparticles from rice husks and up to 96% Si was

solubilized after 24 h of co-incubation with the fungus (Bansal, Ahmad et al. 2006). This

research indicates that bioleaching would be a promising technology for lignocellulosic

biomass pretreatment before thermo-chemical conversion processes.

1.5 Conclusion

Lignocellulosic biomass is one of the most abundant resources that is inexpensive

yet has not been fully exploited. To incorporate biomass into the new circular shaped

bioeconomy, major technological hurdles need to be overcome, which include: 1)

appropriate pretreatments that would facilitate the enzymatic hydrolysis of the biomass so

the following steps in the biochemical conversion could proceed with efficiency, and 2)

proper leaching pretreatments that could significantly lower the content of inorganic

9

compounds in the biomass without considerable tradeoff in economics. Bioleaching is a

promising technology, though more research is necessary to reveal its capability in

lignocellulosic biomass pretreatment. Our hypothesis is that bioleaching could be used as

an eco-friendly and efficient pretreatment method for lignocellulosic biomass, which

would significantly reduce the inorganic ingredients in the biomass, thus improve the

quality of the biomass as the feedstock for thermochemical conversion processes. With

this research, we aim to accomplish these following objectives in the ensuing chapters: 1)

study and compare a number of selected microorganisms on their capabilities to leach

inorganic elements from a range of biomass feedstocks (Chapter II); 2) select the best

performing microbe and one type of lignocellulosic biomass that is more amenable to

bioleaching than others (Chapter II); 3) focus on the selected combination of bioleaching

microbe and feedstock biomass, investigate certain factors that might influence the

bioleaching process, and conduct a preliminary study on bioleaching mechanisms

(Chapter III); 4) scale up the bioleaching process, and evaluate the effect of different

variables on bioleaching on the new platform (Chapter IV).

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CHAPTER II

ASSESSMENT OF BIOLEACHING MICROORGANISMS

2.1 INTRODUCTION

Lignocellulosic biomass has been widely studied as a feedstock for next

generation biofuel production. It has three major components – cellulose, hemicellulose

and lignin, and can be converted into biofuels (e.g., syngas and bio-oil) through

thermochemical pathways such as direction combustion, gasification and pyrolysis (Liu,

Bi 2011b). However, the presence of certain inorganic elements such as alkali metals (K

and Na), alkali earth metals (Mg and Ca), Si, Cl, and S in the biomass, especially

herbaceous biomass would cause issues during the thermal conversion processes, which

include fouling, slagging, agglomeration, and corrosion. For example during the fluidized

bed combustion, K and/or Na content can cause low melting point of the ash, and the

partially molten ash can result in sintering and crystallization in the fluidized bed reactor

(Thy, Jenkins et al. 2010, Steenari, Lundberg et al. 2009). Eventually the issues due to

inorganic elements can reduce the heat transfer rate of the biomass conversion

equipment, even damaging the reactors, thus increase the maintenance cost of thermal

conversion reactors (Turn, Kinoshita et al. 1998, Vamvuka, Zografos et al. 2008a, Pîşă,

Rădulescu et al. 2009, Yu, Thy et al. 2014). Furthermore, elements such as Cl, N and S

could be emitted during the conversion process in the form of acidic vapors leading to

reactor corrosion, even air pollution. In addition, alkali metals might also lead to high

viscosity of bio-oil after fast pyrolysis (Liu, Bi 2011b). Therefore, a pretreatment step is

11

necessary to remove those inorganic elements before the subsequent thermal chemical

conversion of the lignocellulosic biomass.

Rinsing with water (water leaching) is a simple and common pretreatment to

improve the biomass quality. Water leaching can effectively remove most of the water-

soluble elements such as K, Na and Cl (Jenkins, Bakker et al. 1996, Deng, Zhang et al.

2010). However, it is not effective with water-insoluble elements such as Mg and Ca

(Davidsson, Korsgren et al. 2002). Besides, water leaching requires a large amount of

water, which severely limits its application at industrial scales. Meanwhile, bioleaching

could be an alternative pretreatment method and it has been extensively studied and used

by the mining industry (bio-mining). With bioleaching, researchers utilized the microbial

activities to recover metals from low-grade ore or retrieve hazardous elements from solid

waste materials (Hocheng, Su et al. 2014, Mishra, Kim et al. 2008a, Priha, Sarlin et al.

2014, Yang, T., Xu, Wen, and Yang 2009b). Today in the mining industry, bacteria

mediated bio-mining has been a well-established process, such as being used for copper

recovery from low grade ores or bio-oxidation of refractory gold ores. The bio-mining

could be carried out in either heaps of ground ores, or specially designed, mechanically

agitated reactors (Acevedo 2000). So far a variety of microbes have been assessed for

their leaching capabilities on a wide range of raw materials. By using pure and mixed

cultures of acidophilic bacteria (e.g., Acidithiobacillus sp. and/or locally collected

bacterial strains related to Burkholderia fungorum), Priha et al. recovered phosphorus

from low grade fluorapatite, and obtained up to 97% yield after 21 days (Priha, Sarlin et

al. 2014). Wu et al. used B. fungorum to extract phosphorus, fluorine and several metal

12

elements from granite grains in a batch reactor, with a high releasing rate for all elements

after 35 days (Wu, L., Jacobson, and Hausner 2008b). Mulligan et al. studied Aspergillus

niger for leaching low grade ores, in which 68% Cu, 46% Zn and 34% Ni were removed

after 14 days of leaching (Mulligan, Kamali 2003). Bosshard et al. also used A. niger to

extract heavy metals from municipal solid waste (MSW) incineration fly ash and

recovered 81% Cd, 66% Zn, 57% Cu, and 52% Pb after 24 h of leaching (Bosshard,

Bachofen et al. 1996). Both studies with A. niger indicated that biogenic organic acids

played a crucial role in the leaching process. While most bioleaching studies have

focused on application in bio-mining, bioleaching is also expected to remove inorganic

elements from lignocellulosic biomass. In one research, Fusarium oxysporum was used to

extract Si in the form of nanoparticles from rice husks and up to 96% Si was recovered

co-incubation with the fungus for 24 hours (Bansal, Ahmad et al. 2006), suggesting that

bioleaching could be a promising technology for lignocellulosic biomass pretreatment

before thermochemical conversion processes.

In this chapter, three microorganisms were evaluated for their bioleaching

capabilities to remove inorganic elements from four biomass including switchgrass, corn

stover, sorghum, and wheat straw. The objective is to compare the leaching efficiency of

different micorbes on different biomass, and select one species to focus on in following

researches.

13

2.2 Materials and Methods

2.2.1 Biomass feedstocks

Switchgrass (cultivar: Alamo), sorghum (cultivar: hybrid E5200), wheat straw

(classic hard white wheat), and corn stover samples were obtained from the Idaho

National Laboratory (Idaho Falls, ID) in March 2016, all in the form of 1-inch long dry

particles weighing (as received) approximately 15 kg for each type of biomass. The

biomass was stored and sealed in plastic bags until used.

For biomass characterization (total solid analysis and ash content analysis) and

leaching studies, 200 g of secondary samples from each type of biomass were randomly

selected from primary samples as received. To ensure that secondary samples are

representative of the original samples, equal amounts of biomass were taken from the

surface, center, and bottom portions of primary sample bags. Then secondary samples

were individually mixed to homogenize the biomass that was from different portions of

primary sample bags, then was collected in self-sealing plastic bags. For analyses of

structural and non-structural carbohydrates, subsamples were selected in the same

manner as mentioned before from secondary sample bags. And subsamples were ground

through a 40-mesh screen by a knife mill (Wiley mini-mill, Thomas Scientific,

Swedesboro, NJ). All samples were sealed in plastic bags until used.

2.2.2 Microbial culture preparation

Fungal species F. oxysporum (ATCC 76255) and A. niger (ATCC 22343) were

provided by Dr. Guido Schnabel and Dr. Julia Kerrigan (Department of Plant and

Environmental Sciences, Clemson University), respectively, and bacterium B. fungorum

14

(ATCC BAA-463) was purchased from the American Type Culture Collection (ATCC).

The fungi and bacterium cultures were respectively maintained on the potato dextrose

agar and tryptic soy agar plates at 4 ℃. Cultures were transferred to fresh agar plates

periodically.

To prepare seed cultures for bioleaching experiments, colonies in agar plates were

picked and transferred to 250mL Erlenmeyer flasks containing 100mL fresh BD DifcoTM

broth media (potato dextrose broth for fungi and tryptic soy broth for B. fungorum) that

were incubated at 26 °C and 180 rpm for 48 h. Then these initial cultures were inoculated

into fresh broth media at the ratio of 1:10 (v/v) and incubated for another 48 h under the

same conditions as initial cultures. These secondary cultures were used to determine the

microbial growth curve or harvested for bioleaching experiments. For bioleaching

preparation, microbial biomass from secondary cultures was collected by strainer

filtration (fungal) or centrifugation (bacterial), then rinsed with deionized (DI) water for 3

times before being used for bioleaching. When a large amount of A. niger cell biomass

was needed for bioleaching, the seed culture was prepared in a 5.5 L fermenter (New

Brunswick Bioflo 110 bioreactor, Eppendorf North America, Hauppauge, NY) using the

aforementioned conditions and procedures for preparing small size seed cultures. The

microbial biomass from the fermenter was harvested by filtration with a 7-inch stainless

steel fine mesh strainer and rinsed with DI water for 3 times before being used for

bioleaching. During the seed culture preparation, all media were sterilized by

autoclaving, and all operations were conducted using aseptic techniques until the

microbial biomass was ready for harvesting before bioleaching experiments.

15

2.2.3 Bioleaching setup

As shown in Figure 2.1, approximately 2.5 g (dry weight) of each type of

feedstock biomass (as received) was packed in commercial nylon mesh tea bags with a

mesh size of approximately 160 μm (Lucklovely empty heat sealing tea bags, Amazon

item model number LL-TB001), which was sealed and soaked in 62.5 mL of DI water

contained in 250 mL Pyrex beakers. The prepared microbial biomass from Section 2.2.3

(0.9 g in dry weight fungal mass for fungal leaching, and 100 mL B. fungorum culture for

bacterial leaching) was added to the beaker to initiate the bioleaching process.

Meanwhile, water leaching was also carried out as a control under the same conditions as

the bioleaching without microbial biomass. All water leaching and bioleaching beakers

were incubated at 26 °C and 180 rpm for designated time. After leaching, the biomass

samples were retrieved from the nylon bags, dried and analyzed for dry weight, organic

matter and inorganic elements (Zhang, Wang et al. 2019a).

2.2.4 Analytical methods

Total solid (TS) of the feedstock biomass was analyzed by drying samples in an

oven at 105 °C until a constant weight was reached (Sluiter, A., Hames et al. 2008). Ash

content was measured by baking dry samples (after TS analysis) in the muffle furnace at

550 °C for 24 h and calculated as the percentage of ash on basis of TS (Sluiter, A.,

Hames, Ruiz et al. 2008). Using ground subsamples of 40 mesh particles as described in

section 2.2.1, non-structural carbohydrates of the feedstocks were extracted by Soxhlet

extraction with DI water and analyzed through the phenol-sulfuric acid assay (Sluiter,

A., Ruiz et al. 2008). After non-structural carbohydrates were extracted by Soxhlet

16

extraction, the post-extraction solid samples were air dried and digested by dilute sulfuric

acid and separated into acid soluble lignin, acid insoluble lignin and sugars. The lignin

portions and sugars were then analyzed following the Laboratory Analytical Procedures

of National Renewable Energy Laboratory (NREL) (Sluiter, Amie, Hames et al. 2008).

Elemental composition analyses were completed by Clemson University

Agricultural Service Laboratory. Total nitrogen content of the biomass feedstocks was

analyzed using the LECO FP528 Nitrogen Combustion Analyzer (LECO Corporation,

Saint Joseph, MI). Other selected elements in the biomass were analyzed using

inductively coupled plasma-optical emission spectrometry (ICP-OES). The samples

(0.5000±0.0005g dry weight) were ground, digested by 5mL nitric acid and 30%

hydrogen peroxide and then analyzed on an inductively coupled plasma-optical emission

spectrometer (Spectro Arcos ICP-OES; Spectro Analytical Instruments, Ametek Kleve,

Germany) equipped with a UV-Plus Purifier gas cleaning system with argon gas flow and

a Cetac autosampler. For the ICP-OES instrument, the radio frequency generator power

was set at 1425 Watts and other conditions included the coolant flow rate 13 L/min, the

auxiliary gas flow rate 1 L/min and the carrier gas flow rate 0.8-1.0 L/min. The

instrument was calibrated using commercially available ICP standard solutions

(Inorganic Ventures) every time when the analysis was conducted. A National Institute of

Standards and Technology (NIST) peach leave sample (NIST standard reference material

1547) with the same weight of the straw samples was ashed and then used as a check

standard, which was analyzed along with samples every 35 samples to ensure calibration.

Detection limits of the elements were shown in Table 2.1 (Zhang, Wang et al. 2019a).

17

The leaching efficiency was evaluated by the percentage of element removal,

which was calculated using Equation (1):

Elemental removal (%) = 𝑀𝑀1×𝐶𝐶1−𝑀𝑀2×𝐶𝐶2𝑀𝑀1×𝐶𝐶1

× 100 (1)

M1: dry mass of untreated feedstock biomass sample;

C1: concentration of specific element in untreated sample;

M2: dry mass of leached feedstock biomass sample;

C2: concentration of the same element in leached samples.

2.2.5. Data Analysis

Statistical significance was determined by analysis of variance (ANOVA) using

JMP Pro 12 (SAS Institute, Cary, NC, USA) with pcritical=0.05. Multiple comparisons

were done with Tukey’s test with α=0.05. Each experiment was carried out in duplicate

in this study unless stated otherwise.

2.3 Results and Discussion

2.3.1 Biomass feedstock composition

Major compositions of all four types of biomass feedstocks are shown in Table

2.2. Overall, carbohydrates and lignin contents are comparable to previous results while

the ash contents of the biomass are lower than the reported values (Tang, Lim et al.

2018). For instance, the reported ash contents of wheat straw, corn stover and switchgrass

are 8.3, 8.4 and 5.9%, respectively (Yu, Thy et al. 2014). This could be due to the

difference in harvesting season, location, and genotype, etc. Biomass harvested during

the winter season tends to be lower in ash content than fall-harvested biomass, most

18

likely due to the natural water leaching in the field by winter precipitations (Yu, Thy et

al. 2014). In addition, plants grown in saline irrigation regions could accumulate high ash

content in the resulting biomass (Bakker, Jenkins 2003).

Thirteen elements were analyzed, of which alkali metals (K and Na), alkali earth

metals (Mg and Ca), N, Cl, S, and Si (Table 2.3) had been demonstrated to cause fouling,

slagging and toxic gas emission issues during thermochemical conversion processes

(Bakker, Jenkins 2003). All samples had high nitrogen content that would be expected to

result in high ammonia generation during gasification and elevated NOx formation

during combustion (Yu, Thy et al. 2014). All samples had very low (<0.1% dry weight)

contents of sulfur, indicating that the SOx or H2S formation during the conversion may

not be a big concern. Switchgrass and wheat straw were higher in Cl than sorghum and

corn stover (approx. 0.5% vs. 0.1-0.2%), suggesting that the former biomass could form

more fouling, acid gas and/or aerosol upon thermochemical conversion (Baxter, Miles et

al. 1998, Dayton, Jenkins et al. 1999). Wheat straw was observed with the most K and Si

contents and the second most Na among all biomass feedstocks, thus slagging/fouling

issue would be most severe with wheat straw as the feedstock for thermochemical

conversion. In contrast, corn stover had the lowest Si, Na and K, suggesting that corn

stover might be relatively more suitable than others for thermochemical conversion.

2.3.2 Microbial growth characteristics

The growth characteristics of three microbial species were initially studied to

determine respective exponential stage when the microbes are physiologically the most

active and could be collected for bioleaching experiments. As shown in Figure 2.2, F.

19

oxysporum reached the exponential stage after 24 hours, while it took 36 hours for A.

niger to reach the middle of exponential phase. Thus, the active physiological condition

and sufficient amount of cell mass at such time points enabled the collection of microbial

biomass for bioleaching. For B. fungorum, the optical density at λ=600 nm (OD600) curve

suggested that B. fungorum reached the middle exponential stage at 15 h which was

selected as the cultivation time to prepare B. fungorum cell mass for bioleaching.

2.3.3 Bioleaching with different microorganisms

Using the three microbes, preliminary bioleaching studies were carried out on all

four biomass feedstocks (Table S2.1). For simplicity, 100 ml of B. fungorum culture from

one flask was used in bioleaching one tea bag of feedstock biomass (after collection and

rinsing). Meanwhile, due to the heterogeneity of the fungal culture, both fungal cultures

had to be pooled first, and then evenly distributed among beakers with tea bags

containing feedstock biomass, which resulted in each beaker having approximately 0.9 g

(dry weight) of A. niger or F. oxysporum. Different microbial species had quite different

leaching effect on the same biomass feedstock while the same microbe performed

differently on different biomass feedstocks (Figure 2.3). Furthermore, the removal

efficiency was distinct from each other among all analyzed elements. For switchgrass, A.

niger performed better than F. oxysporum, B. fungorum and water only in leaching of N

and Fe (maximum p-value=0.046) while A. niger removed more P, Mg and Zn from

sorghum than the other two microbes and water leaching (maximum p-value=0.014). For

wheat straw, higher removal of S and Cl were achieved by A. niger leaching compared to

the other two species and water leaching (maximum p-value=0.012) while higher

20

removal of K and Mg from corn stover was observed with A. niger leaching compared to

other methods (maximum p-value=0.023). F. oxysporum and B. fungorum did not

achieve any better results than water leaching of all measured elements except

bioleaching of Cl with F. oxysporum. The results also show that bioleaching with A.

niger is favorable to extract Mg, a water insoluble earth metal over water leaching among

all types of feedstock (p-value < 0.0001). The elements of Ca, S and Fe were not leached

well by either bioleaching or water leaching in any type of feedstock. For leaching of

switchgrass, it was found A. niger was better than or comparable to other leaching

methods. In sorghum bioleaching, A. niger can remove almost 80% P, over 90% Mg and

70% Zn, while these three elements were not easily removed using either water, F.

oxysporum, or B. fungorum leaching. In wheat straw bioleaching, all leaching methods

gave similar leaching results, except for the removal of Mg with A. niger. For corn

stover, only K, Cl and Zn were leached more than 50% whereas little Ca, S, Fe, and Na

were removed by any leaching methods. Overall, A. niger performed better than F.

oxysporum, B. fungorum and water leaching for most feedstocks. Therefore, A. niger was

selected for the further bioleaching studies.

In addition to element/ash removal, the remaining energy contents of biomass

should also be considered because leaching especially bioleaching could cause energy

component loss (i.e., carbohydrate and lignin) given that the leached biomass will be

subject to thermochemical conversion. The ideal bioleaching should be able to keep most

carbohydrates and lignin in biomass for the subsequent conversion. As a heterotrophic

fungus, A. niger would inevitably consume the carbohydrates in biomass to maintain its

21

physiological activities during bioleaching. The main chemical compositions of the

biomass after bioleaching were listed in Table 2.4. There was about 10% less

carbohydrates in bioleached sorghum than water leached, which necessitated the

improvement of bioleaching sorghum straw by A. niger in future studies to reduce the

carbohydrate loss. Since sorghum contains relatively high ash content and is regionally

available in Southeast U.S., further bioleaching studies were focused on sorghum.

2.4 Conclusions

For the first time, bioleaching is studied for removing inorganic elements from the

lignocellulosic biomass for thermochemical conversions. Three microbial species – F.

oxysporum, A. niger and B. fungorum were selected to perform bioleaching on four

biomass feedstocks including corn stover, wheat straw, switchgrass, and sorghum.

Results showed that different microbes had different leaching performance on the same

biomass while the same microbe performed differently on different biomass. In addition,

the removal efficiency was distinct from each other among all analyzed elements. Among

three microbes, A. niger was found to be the most efficient in removing most elements by

80% after 48 hours, and sorghum was relatively more amenable to bioleaching.

22

CHAPTER III

STUDY OF FACTORS THAT AFFECT THE BIOLEACHING PROCESS

3.1 Introduction

The presence of inorganic elements in lignocellulosic biomass necessitates the

development of pretreatment methods that can remove these elements before

thermochemical conversion of the biomass. Bioleaching has been widely used by the

mining industry to retrieve metals from low grade ores (Acevedo 2000, Brierley,

Brierley 2013). Multiple researches have also studied the potential of bioleaching in other

areas, such as recovering heavy metal from spent catalysts, treating municipal solid waste

(MSW) incineration fly ash, extracting Si nanoparticles from agriculture by-products

(Mishra, Kim et al. 2008b, Bansal, Ahmad et al. 2006, Wu, H., Ting 2006). Our previous

work has demonstrated that bioleaching could achieve higher elemental removal than

water leaching alone (Zhang, Wang et al. 2019a). Further studies would have to provide

more insights on the bioleaching process, by elucidating the variables that would affect

the bioleaching performance.

This chapter mainly studied two factors that might influence the bioleaching

efficiency – leaching time and water ratio (water volume in mL / feedstock biomass dry

weight in g). Then the mechanism of bioleaching was initially investigated.

23

3.2 Materials & Methods

From the bioleaching experiment results in Section 2.3.3, A. niger was found to

be the best among the selected microorganisms for bioleaching. Therefore, it was chosen

for further bioleaching studies in this chapter. Since sorghum contains relatively high ash

content and is regionally available in the southeast region of the U.S.A., further

bioleaching study was conducted on sorghum. The same sampling method for the

sorghum biomass was used as in Section 2.2.1. And the same method was used to prepare

the fungal biomass for leaching experiment as in Section 2.2.2.

3.2.1 Effect of leaching time on bioleaching

A series of beakers were set up for studying the time effect on bioleaching with

duplicate beakers for each time point. In each beaker, about 0.9 g (dry weight) of A. niger

biomass was added to 62.5 mL of DI water with 2.5 g (dry weight) sorghum particles

packed in nylon tea bags. Water leaching was carried out in parallel as controls. During

the leaching, duplicate beakers were withdrawn at each time point of 0.5, 1, 2, 3, 4, 5, 6,

12, 18, 24, 36, and 48 h, and the leached samples were collected and subject to analyses

for dry weight and element contents. The overall procedures, conditions and sample

analyses were the same as used in Sections 2.2.3 and 2.2.4.

3.2.2 Effect of water loading on bioleaching

A series of beakers were set up for different water loadings. In each beaker, about

0.9 g (dry weight) of A. niger biomass was mixed with 2.5 g (dry weight) sorghum which

was packed in nylon tea bags. All conditions were the same as stated in Section 2.2.3,

except that a series of water volumes including 62.5, 75, 100, 125, and 250 mL were used

24

in this study, corresponding to the water (mL)/sorghum biomass (g) ratios (v/w) of 25,

30, 40, 50, and 100, respectively. As controls, water leaching tests were also carried out

in parallel for each water-loading.

3.2.3 Study of bioleaching mechanisms

To investigate the possible bioleaching mechanisms, two alternative potential

leaching agents were tested on sorghum. A. niger can excrete certain chemical

compounds (e.g., organic acids and enzymes) into the cultivation medium that might be

the real leaching agent. Thus, supernatant from the seed culture were separated from the

fungal biomass and assessed for leaching capability. Among all potential compounds in

the supernatant, enzymes are of particular interest for their roles in biological activities.

Therefore, to evaluate the potential leaching capability of enzymes alone, crude enzyme

proteins were separated from supernatant by ultrafiltration using 10 kDa MWCO UF

membrane (Sterlitech, Kent, WA) in Amicon ultrafiltration cell (EMD Millipore,

Burlington, MA). This concentrated crude enzyme solution was also used in bioleaching.

In addition, normal bioleaching with fungal biomass and water leaching were carried out

in parallel as controls. In all, four leaching methods were tested in this study: raw

supernatant (without cells), filtered supernatant (i.e., crude enzyme solution), cell

biomass bioleaching, and water leaching.

25

3.3 Results & Discussion

3.3.1 Effect of time on bioleaching efficiency

Time is a key factor for determining cost-efficiency of leaching process.

Therefore, the temporal leaching efficiency for all elements was analyzed for sorghum

bioleaching by A. niger in parallel to water leaching (Table S3.1). Among all elements,

both bioleaching and water leaching achieved similar (approx. 80%) removal of Cl and K

in the first 30 minutes. After that, the Cl removal increased to 95 and 100% for water and

A. niger leaching, respectively until 6 h and afterwards, while K leaching did not vary

significantly until about 48 h (Figures 3.1 A and B). Meanwhile no recognizable pattern

was observed for all other elements except Mg, i.e., leaching result did not improve with

the increase of leaching time (data not shown). Water leaching was not effective for Mg

removal, i.e., the maximum removal efficacy is about 20% during the whole leaching

process, while time had a significant effect on bioleaching efficiency of Mg (Figure 3.1

C). The percentage of Mg removal by bioleaching initially started in the range of 26-36%

from 30 min to 12 h, then rapidly rose to around 56% at 18 h, 74% at 24 h, and reached

the peak of 80% at 48h. Previous research had established that Mg is a vital

macronutrient element for both plant and animal cells and involved in various cellular

functions, such as maintaining the stability of DNA and RNA, linking multiple

ribosomes, and associating with the adenosine triphosphate (ATP) metabolism (Wolf,

Trapani 2008), 29]. In addition, Mg acts as a co-factor for various cellular enzymes and is

inserted into the central position of the chlorophyll molecule (Wolf, Trapani 2008).

Therefore, the increasing removal of Mg after the 12 h in the bioleaching suggested an

26

increase in the fungal cell activity until 48 h. Since the removal of Mg already leveled off

at 74% at 24 h, the following bioleaching experiments were all conducted for 24 h.

3.3.2 Effect of water loading on the bioleaching efficiency

To study the effect of water loading on leaching efficiency, different water

loadings in volume were compared, which led to a series of ratios of water volume (mL)

to the dry weight of biomass feedstock (g) (Table S3.2). Bioleaching efficiency of alkali

and alkaline earth metals including K, Ca, and Mg was improved with high water loading

(Figure 3.2). For K removal, the improvement was about 8% when water ratio was

increased from 25 to 100, and the improvement was 9% and 19% in the case of Ca and

Mg, respectively. Meanwhile, no significant differences in bioleaching were observed in

all other elements among different water/biomass ratios. Therefore, water/biomass ratio

25 was selected for the next study on leaching mechanism.

3.3.3 Study of bioleaching mechanisms

While A. niger culture had been used in bioleaching, the real leaching agent could

be certain chemical compounds excreted by A. niger into the extracellular environment.

Previous research indicated that the bioleaching effect of A. niger might be based on

acidolysis as a result of the organic acids produced by the fungus (Bosshard, Bachofen et

al. 1996, Mulligan, Kamali 2003). Therefore, the supernatant from the A. niger culture

was studied for its leaching effect on sorghum, as plenty of organic acids have been

released to the medium during A. niger growth (Schuster, Dunn-Coleman et al. 2002,

Jadhav, Hocheng 2015). Also, A. niger has been known to produce a variety of enzymes,

which might influence the bioleaching process (Schuster, Dunn-Coleman et al. 2002). In

27

order to study whether enzymes alone in the extracellular environment could contribute

to bioleaching, we used ultrafiltration to separate enzymes from ions/small compounds in

the supernatant. Different leaching methods showed significant difference in element

removal efficiency on the same element, and the leaching efficiency of different element

varied significantly with the same method (Table S3.3 and Figure 3.3).

Almost all S, but little Ca and Fe were removed with all methods (data not

shown). The unfiltered supernatant achieved the highest leaching efficiency, except Cl

and Na, while biomass leaching had the lowest leaching efficiency on almost all elements

(except on Mg). The filtered supernatant was less efficient than or comparable to water

leaching. The industry has been using A. niger for producing a wide range of enzymes,

and researchers had indicated that enzymes could affect the leaching process (Jadhav,

Hocheng 2015). However, our data did not demonstrate much impact from the enzymes

on the leaching result, as the filtered supernatant containing crude enzymes did not

performed better than water leaching. The possible reason might be that ions had been

removed during the filtration, which hindered the normal functions of enzymes by

depriving them of essential co-factors. In addition, loss of ions/electrolytes could lead to

disruption of charges carried on the peptide molecules of which enzymes are composed

of, and this in turn caused incorrect folding of the enzyme molecule, eventually

denaturing. The role of enzymes will need further research in the future. The pH of the

unfiltered supernatant was quite acidic (average pH 2.9), while the pH values of other

three groups were nearly neutral (the cell only group pH 7.2 compared to around pH 7.6

in the other two groups). A. niger has been known to produce several organic acids, such

28

as acetic, butyric, citric, gluconic, lactic, oxalic acid, and others (De Windt, Devillers

2010). It has been widely used in the fermentation industry to produce these organic

acids, especially the citric acid (Schuster, Dunn-Coleman et al. 2002). As a result, in this

research, organic acids seem to be the critical leaching agents, and the main mechanism

could be the solubilization of elements from biomass through acidification (Bosshard,

Bachofen et al. 1996). Although acidification has contributed to the bioleaching

capabilities of A. niger, it is unclear which acids are the major contributors to the

bioleaching because organic acid profiles were different in previous researches, based on

medium composition, carbon sources and feedstock type in bioleaching (Bosshard,

Bachofen et al. 1996, Mulligan, Kamali 2003, Yang, J., Wang et al. 2008). Therefore,

further research will be needed to identify the different roles of various fungal

metabolites relevant to leaching efficiency.

3.4 Conclusion

The bioleaching potential of A. niger had been demonstrated by previous studies.

Based on the result from the last chapter, this fungus was selected as the main

bioleaching species to focus on. And a variety of factors would have to be investigated

regarding their effects on the bioleaching efficiency. Of the studied bioleaching

parameters, leaching time and water loading ratio did not affect leaching efficiency

significantly, according to results of this chapter. The possible mechanism of bioleaching

might be acidolysis related to biogenic organic acids produced by the fungus.

Bioleaching holds promise for lignocellulosic biomass pretreatment to provide high

quality feedstock for thermochemical conversions with mitigated ash-derived issues.

29

Future research is suggested to screen more microorganisms for bioleaching, elucidate

the bioleaching mechanisms and improve the bioleaching efficiency via process

optimization.

30

CHAPTER IV

THE BIOLEACHING PROCESS SCALING UP

4.1 Introduction

Lignocellulosic biomass has now attracted worldwide attention from both the

academia and the industry as the most abundant plant material and a potential source of

future biofuels and biochemicals (Watkins, Nuruddin et al. 2015). So far most focus has

been on the biochemical conversion – production of bioethanol from the deconstructed

cellulose and hemicellulose, as well as the necessary pretreatment to tackle the

recalcitrance of the biomass before the conversion (Agbor, Cicek et al. 2011, Brethauer,

Studer 2015, Kumar, A. K., Sharma 2017, Kumar, R., Tabatabaei et al. 2016, Mood,

Golfeshan et al. 2013, Shirkavand, Baroutian et al. 2016). Meanwhile, conversion of

lignocellulosic biomass into energy through thermo-chemical processes has faced issues

as a result of the high ash content in the feedstock (Baxter, Miles et al. 1998). If burned

or combusted without pretreatment, the inorganic ingredients in the biomass could cause

serious issues to the equipment that carries out the thermo-chemical conversion, such as

slagging, fouling, agglomeration, corrosion, etc. (Baxter, Miles et al. 1998). These issues

not only reduce the heat transfer efficiency of the equipment, but might also exacerbate

the air pollution problem, considering the large quantities of biomass to be processed

(Vamvuka, Zografos et al. 2008b). To improve the quality of the lignocellulosic biomass

as a potential fuel, a pretreatment known as leaching has been extensively studied and

proven effective in decreasing some inorganic ingredients in the biomass (Yu, Thy et al.

31

2014). The most common pretreatment is the water leaching, which simply rinses the

biomass with adequate amount of water (Carrillo, Staggenborg et al. 2014). While this is

effective in removing certain inorganic ingredients that are water-soluble, supplemental

methods are necessary to reduce elements such as Ca, Mg, and Si that tend to form

insoluble compounds in the plant (Liu, Bi 2011a). Bioleaching might be an alternative

solution to this issue, as it has been successfully applied to extracting metals such as

copper from low grade ores by the mining industry (Hocheng, Su et al. 2014). Various

bacterial and fungal species have been demonstrated for their bioleaching capability to

treat a range of materials, from mining ores to municipal waste fly ash (Rohwerder,

Gehrke et al. 2003, Brierley, Brierley 2013).

In a previous study, we have demonstrated the potential of a small-scale

bioleaching process in pretreating lignocellulosic biomass for ash reduction with a focus

on Aspergillus niger as the bioleaching microorganism (Zhang, Wang et al. 2019b). As a

versatile industrial microbe, A. niger has been safe in standard industrial settings (van

Dijck, Selten et al. 2003). It has been the main producer of citric acid that is widely used

in food and pharmaceutical industries (Ward, Qin et al. 2005). And a variety of enzymes

can be derived from A. niger, such as amylase, cellulase, xylanase, pectinase, etc.

(Sundarram, Murthy 2014, Schuster, Dunn-Coleman et al. 2002, Guimaraes, Sorgatto et

al. 2013).Recently this microbe has received extensive attention in hydrometallurgical

and environmental investigations. Wu and Ting used A. niger to recover heavy metals

from municipal solid waste (MSW) incinerator fly ash in both one-step and two-step

leaching, and compared the bioleaching effects to chemical leaching, in which higher

32

recovery of Mn and Zn from bioleaching than chemical leaching was observed (Wu, H.,

Ting 2006). Santhyia treated the spent refinery catalyst with A. niger to recover Al, Ni

and Mo, in which the use of a buffer was found effective in stimulating oxalic acid

secretion of the fungus while reducing the leaching time compared to the bioleaching

process without the buffer addition (Santhiya, Ting 2005). Mulligan used A. niger in

extracting Cu and other metals from low-grade ores and achieved 68% and 46% for

solubilization of Cu and Zn (Mulligan, Kamali et al. 2004).

In this chapter, the bioleaching process is to be scaled up from the shake flask

level as used in previous chapters. Bioreactors would be designed and built as new

leaching platforms. Then certain bioreactor operating parameters would be studied

regarding their effects on bioleaching efficiency, which would also be part of the

optimization effort to achieve the minimal residual ash content of the sorghum biomass

after leaching.

4.2 Material & Methods

4.2.1 Lignocellulosic biomass

Sorghum straw was obtained from the Idaho National Laboratory (Idaho Falls,

ID) in the form of 1-inch long dry particles. The biomass was stored and sealed in plastic

bags until used. Sub-samples were taken by using the quartering method as in ASTM

C702 (ASTM International 2018).

4.2.2 Aspergillus niger

33

The same A. niger (ATCC 22343) culture was used in this study as in our

previous research. The fungus was maintained on potato dextrose agar, refrigerated for

about a month until transferred to fresh agar.

To prepare the seed culture for bioleaching sorghum straw, fungal spores were

scraped off the potato dextrose agar culture with an inoculation loop, added to 1mL of

sterilized 0.1% (w/v) tween 80, and centrifuged. The pellet was rinsed twice and re-

suspended with 0.1% tween solution. The concentration of fungal spores was measured

by hemocytometer counting. Then spores were added to fresh potato dextrose broth so

that each 100mL of medium was inoculated with ~ 0.9 × 107 spores. Up to this step, all

procedures were performed with aseptic techniques. The inoculated medium was

incubated at 26 ºC and 150 rpm until the culture reached the exponential stage.

To collect the fungal biomass for bioleaching, vacuum filtration was used to

separate the pellets from the culture medium. Then the fungal pellets were rinsed with de-

ionized water (DI) twice before loading into the reactors. The mass of fungal pellets

loaded to each reactor followed the experiment design in section 4.2.4.

4.2.3 Bioleaching reactors

In this study, bioleaching was carried out in four specially designed bioreactors,

and each single reactor was built according to the scheme as shown in Figure 4.1. The

main body of the reactor was a one-foot-long clear polyvinyl chloride (PVC) pipe with a

diameter of 4 inches, which was sealed on both ends with screwed-on white PVC caps.

The main body was separated into two halves by a round-shaped plastic mesh

plate with a pore size of 3mm. Sorghum straw was added to the upper half of the reactor.

34

The lignocellulosic biomass load – weight of sorghum straw added to each reactor was

determined by trial runs to be no more than 30 g (dry weight), while further increasing

the sorghum load could lead to issues as the wet straw could clog the venting port. One

layer of nylon mesh cloth (Component Supply Company, Fort Meade, FL) was placed

between the sorghum biomass and the mesh plate to keep the straw from falling to the

bottom half of the reactor. The nylon cloth was of the same mesh size (~160 µm) as used

in small-scale bioleaching in previous chapters. Fungal pellets were added to the bottom

half of the reactor, followed by the starting liquid phase containing glucose (glucose

concentration would be given in section 4.2.4). As our previous research found that the

ratio of liquid volume to straw biomass (mL / g dry wt.) could be maintained at 25, thus

750mL of liquid phase was added to each reactor before leaching began.

A port hole was drilled out in the center of the screwed-on cap on either end of

the reactor. The upper and bottom caps were connected by soft tubing (Masterflex

silicone tubing L/S 25, Cole-Parmer Instrument Co., Vernon Hills, IL), thus forming a

closed loop for liquid recirculation. The flow of liquid was driven by a peristaltic pump

(Model 13-310-662, Cole-Parmer Instrument Co., Vernon Hills, IL), and the flow

direction was from the bottom of the reactor to the top by pumping action, then from the

upper half of the reactor downwards by gravity. As one objective of bioleaching was to

reduce water usage, the flow rate was kept constant at 200 mL/min during this research,

and further increasing the flow rate could cause malfunctioning of the system.

Inside the upper cap, a sprinkler was attached to the center so that liquid could be

sprayed onto the sorghum straw for water distribution. To prevent the fungus from

35

clogging the liquid flow, a sandwich structure was placed in the bottom cap, which

contained a layer of nylon mesh cloth between two mesh plates. To enhance mixing of

the fungal culture, a magnetic stirrer was placed in the bottom cap, on one of the mesh

plates, and the bioreactor was placed on a stir plate.

Aeration was directed into the bottom half of the reactor to provide oxygen for

the fungus. Two venting ports were opened, with one port on the upper cap and the

second one on the lower half of the main body. For aeration adjustment, the air would

first pass through a rotameter before entering the reactor. The aeration originated from a

main pipe that led to the building’s compressed air supply system. Through one air

regulator (Norgren, Littleton, CO) and a self-built manifold, the air was separated into

five streams, one per reactor and a fifth stream left open to the atmosphere. Since we

observed that the aeration could not remain constant at low flow rates, all reactor air

flows were set at maximum capacity of the connected rotameter (1.2 L/min) to reduce the

effect of oxygen insufficiency on leaching.

The setup of all four reactors is shown in Figures 4.2 and 4.3. All parts of the

bioreactor are purchased from McMaster-Carr Supply Co. (Elmhurst, IL) except the

tubing and the pump.

4.2.4 Response surface methodology (RSM)

To optimize the bioleaching reactor operation parameters, the response surface

method was used. The objective was to achieve the minimum value for the response Y –

residual ash percentage on the basis of biomass dry weight (%). Three variables were

investigated in the research – the fungal mass in each reactor (g/L), the leaching time

36

(day) and the glucose concentration in the starting liquid phase (g/L). These variables

were shown in the response surface model as X1, X2 and X3 with coded levels set at -1, 0

and 1 for low, mid- and high, as shown in Table 4.1.

As for the experiment setup, a Box-Behnken design was adopted, with 15

experimental runs including 3 replicates at the center point. The actual run order was

randomized except the center points, as demonstrated by Table 4.2. Data were fitted into

the following second-order polynomial model as shown in Eq. (1):

𝑌𝑌 = 𝛽𝛽0 + ∑ 𝛽𝛽𝑖𝑖𝑋𝑋𝑖𝑖3𝑖𝑖=1 + ∑ 𝛽𝛽𝑖𝑖𝑖𝑖𝑋𝑋𝑖𝑖𝑖𝑖23

𝑖𝑖=1 + ∑ ∑ 𝛽𝛽𝑖𝑖𝑖𝑖𝑋𝑋𝑖𝑖𝑋𝑋𝑖𝑖3𝑖𝑖=2

2𝑖𝑖=1𝑖𝑖<𝑖𝑖

(1)

where X1, X2, X3 are independent variables (X1: fungal loading, X2: leaching time,

X3: glucose concentration) and Y is the response (residual ash percentage); βi (i = 1, 2, 3),

βii (i = 1, 2, 3) and βij (i = 1,2 and j = 2,3) are regression coefficients for the intercept,

linear, quadratic and interaction terms, respectively. And β0 is the intercept.

Water leaching experiment were also conducted in those bioreactors as control.

Same settings were used as in bioleaching, except that no fungal biomass was added, DI

water was added as starting liquid phase and the leaching time was 48 h.

4.2.5 Analytical methods

Total solids and ash content of the sorghum straw were determined following the

same procedure as in our previous research. The bioleaching effect was evaluated by the

residual ash percentage based on the biomass dry weight, which was calculated using Eq.

(2):

Residual ash percentage (%) = 𝑀𝑀2𝑀𝑀1

× 100 (2)

M1: dry mass of leached sorghum straw sample;

37

M2: ash mass of leached sorghum straw sample.

Statistical significance was determined by analysis of variance (ANOVA) using JMP Pro

14 (SAS Institute, Cary, NC, USA) with Pcritical = 0.05.

4.3 Results & Discussions

Before being fitted into the RSM model, the bioleaching ash data of all 15

experimental runs were pooled together, averaged and compared to the means of ash

percentage data of the sorghum biomass after water leaching (n=12) and the sorghum

biomass before leaching (n=12). As shown in Figure 4.4, the ash percentage after

bioleaching (3.63±0.19%, mean ± standard deviation) is significantly lower than that

after water leaching (4.72±0.13%) and the untreated (6.03±0.75%) (p-value < 0.0001 in

both cases).

Then the data were fitted to a quadratic polynomial model as in equation (3). The

model was then used to generate a series of predicted responses of the same parameter

combinations in the RSM design, as displayed in Table 4.3.

Y = 3.67 + 0.07X1 + 0.02 X2 – 0.15X3 + 0.14X1X2 + 0.08X1X3 – 0.02 X2X3 – 0.17X12 +

0.00 X22 + 0.10X32 (3)

As shown by the ANOVA in Tables 4.4 and 4.5, the quadratic model was

significant at 95% confidence level (p = 0.0394), while the lack of fit was not significant

(p = 0.8341). All the coefficients were displayed in the model expression in Eq. (3). The

coefficient of determination (R2) was 0.9062, indicating that 90.62% of the variability of

the observed data could be explained by this model.

38

All terms in equation (3) were evaluated for their significance in the model, as

displayed in Table 4.6. Besides the intercept, three terms were significant at 95%

confidence level – the linear effect of glucose concentration (X3), the interaction between

fungal mass and leaching time (X1X2) and the quadratic effect of fungal mass (X12).

Among these three, the glucose concentration (X3) had the largest effect on the response

with the smallest p-value at 0.0079.

By maintaining one variable at a constant level and varying the other two, surface

and contour plots were generated to assist the search for the optimal conditions that

generate the maximum or minimum response. Such optimal response is usually observed

at the center of an elliptical shape contour (Tanyildizi, Özer et al. 2005). Identifying the

conditions leading to the minimum response was one objective of this research, however

no such optimal value was observed with the current data set (Figures 4.5-4.10).

Though no optimal response was found in this study, the data did provide

directions for the future work that might eventually achieve this objective. As shown in

Figures 4.5 and 4.6, the model assumed a ridge shaped surface when the glucose

concentration was maintained constant at coded level 0 (5 g/L). And the contour plot in

Figure 4.6 revealed that the predicted response – ash percentage could be further

decreased by increasing X2 – leaching time while keeping the X1 – fungal mass at a low

level. This was in agreement with previous researches on bioleaching heavy metals with

Aspergillus niger from either fly ash or mining ores, whose leaching time durations

ranged from 12 to 70 days (Santhiya, Ting 2005, Wu, H., Ting 2006, Yang, J., Wang et

al. 2008).

39

The interaction between X1 (fungal mass) and X3 (glucose concentration) was not

significant as shown in Table 4.6. However the surface and contour plots (Figures 4.7 and

4.8, X2 = 0) did indicate an interesting area to explore, where X1 is lower beyond the

current range (<-1) while X3 is further increased (>1). In this study, the high level (+1) of

X3 was set at 10g/L with the consideration of reducing the process cost, meanwhile other

researchers were regularly using a sucrose-based medium with a sugar concentration of

100g/L for fungal cultivation and bioleaching (Rasoulnia, Mousavi 2016, Yang, J., Wang

et al. 2008). Therefore, higher carbon source concentration could be used in follow-up

studies to enhance the bioleaching effect.

Figures 4.9 and 4.10 demonstrated the effect on residual ash percentage from the

interaction of X2 and X3, when X1 was maintained at -1. The effect of X2 was visibly

insignificant on the response Y when X1 was set at 0 and higher, as the contour plot

showed that the response contours were approximately parallel to the X2 axis (data not

shown). However, when X1 was at the low level (-1), both plots (Figures 4.9 and 4.10)

revealed that the response Y could be further reduced when longer leaching time (larger

X2) and higher glucose concentration (larger X3) were used.

Fungal organic acids have been indicated by several researchers as the main

leaching agents. Yang and colleges claimed that gluconic acid was the major leaching

agent in their study using A. niger to remove heavy metals from municipal solid waste

(MSW) fly ash (Yang, J., Wang et al. 2008). De Windt used modelling to investigate the

fungal degradation of cement paste and suggested biogenic acids including acetic,

butyric, lactic and oxalic acids contributed to the degradation (De Windt, Devillers 2010).

40

Without using the fungus, Huang and colleges found citric acid, the most common

product from A. niger, was an effective leaching agent at room temperature for MSW fly

ash treatment (Huang, Inoue et al. 2011). And study by Umeda also confirmed the use of

citric acid in a chemical leaching of rice husk for metal impurities removal (Umeda,

Kondoh 2008). Our research has found that the leachate pH was continuously decreasing

from the first day of leaching for most experimental runs, and the final day pH (Table

4.3) was negatively correlated with X1 (fungal mass) and X3 (glucose concentration),

however no correlation was established between the pH and the model response –

residual ash percentage of the sorghum straw (data not shown). With further adjustment

on the current bioleaching process, such as more carbon source input and longer leaching

time, the correlation between fungal organic acids and bioleaching efficiency could

become more evident in our research.

4.4 Conclusion

This study reported on a pretreatment process for lignocellulosic biomass in

custom built bioreactors. Overall, bioleaching was more effective in ash content

reduction compared to water leaching. In an attempt to optimize the bioleaching process,

three variables were studied – fungal mass added to each reactor (g/L), leaching time

(day), and glucose concentration in the starting liquid phase (g/L). The main objective

was to search and find the optimal conditions of these three factors so as to minimize the

response – the biomass ash percentage. Response surface methodology was used to

generate a quadratic polynomial model that was useful in providing directions on how the

bioleaching process could be further improved.

41

CHAPTER V CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

Lignocellulosic biomass is one of the most abundant natural resources that might

be the key to a new model of development for human society mostly dependent on non-

renewable energy sources. A sustainable economy requires mass scale application of

lignocellulosic biomass for energy and raw materials. However, such an objective is still

much elusive due to the inherent properties of the biomass, which include: 1) resistance

to hydrolysis as a result of its lignin component, thus limiting the scale of current

biochemical conversion technologies, and 2) presence of inorganic elements that

frequently lead to maintenance issues for thermochemical conversion facilities. To

overcome these hurdles and expand lignocellulosic application in the industry, extensive

research efforts have been taken to develop: 1) pretreatment methods that would facilitate

the enzymatic hydrolysis of the biomass so later steps in the biochemical conversion

could proceed with enhanced efficiency. 2) proper leaching pretreatments that could

significantly lower the content of inorganic elements in the biomass without considerable

tradeoff in its fuel quality. Bioleaching has been widely used by the hydrometallurgy

industry to retrieve metals from low value mining ores. Researchers have demonstrated

bioleaching as a promising technology for recovering material from rocks, industrial

waste and fly ash, though more research is necessary to reveal its capability in

lignocellulosic biomass pretreatment.

For the first time, bioleaching was studied for removing inorganic elements from

the lignocellulosic biomass for thermochemical conversions. Three microbial species – F.

42

oxysporum, A. niger and B. fungorum were selected to be evaluated for their bioleaching

capabilities on four biomass feedstocks – switchgrass, sorghum, wheat straw and corn

stover. Results demonstrated that different microbes had different leaching efficiencies

on the same biomass feedstock, and the same microorganism performed differently on

different biomass feedstocks. In addition, the removal efficiency was distinct from each

other among all analyzed elements. After the preliminary study, A. niger was found to be

the most efficient in removing most elements by 80% after 48 hours, and sorghum was

relatively more amenable to bioleaching. Thus A. niger was selected as the main

bioleaching species to focus on.

Two factors were investigated regarding their effects on the bioleaching

efficiency of A. niger. Results showed that neither leaching time nor water loading ratio

could affect leaching efficiency significantly. Following that, a preliminary investigation

of the bioleaching mechanism was conducted with various parts of the fungal culture.

The result suggested that the possible mechanism of bioleaching might be the acidolysis

related to biogenic organic acids produced by the fungus. Future research is suggested to

screen more microorganisms for bioleaching, elucidate the bioleaching mechanisms and

improve the bioleaching efficiency through process optimization.

Based on previous results, the bioleaching process using A. niger was scaled up to

be carried out in custom built bioreactors. Overall, bioleaching was more effective in ash

content reduction compared to water leaching, demonstrated by the residual ash

percentage based on dry weight after leaching (3.63±0.19% vs. 4.72±0.13%). In an

attempt to optimize the bioleaching process, three variables were studied – fungal mass

43

added to each reactor (g), leaching time (day), and glucose concentration in the starting

liquid phase (g/L). The main objective was to search and find the optimal conditions of

these three factors so as to minimize the response – the biomass ash percentage.

Response surface methodology was used to generate a quadratic polynomial model that

was useful in providing directions on how the bioleaching process could be further

improved.

5.2 Recommendations

Bioleaching holds a promise as part of the ideal pretreatment that would promote

larger scale application of lignocellulosic biomass for fuels and chemicals. However,

several challenges are hindering the realization of that goal due to shortcomings of

current technologies. One disadvantage is that bioleaching or biological pretreatment is a

slow process, as demonstrated by researches that usually took time from 10 days to 3

months to reach the significant pretreatment results that were reported. Bioleaching

results of this research could have been more significant if the bioleaching process was

extended so as to reach a comparable time frame as other bioleaching studies.

Also, one single species of microorganism might only preferably consume or

remove limited species of inorganic elements. Future work could use a mixed culture of

microbes as in a microbial consortium, which may expand the scope of leaching

capabilities to include more elements. To do that, a preliminary study would be necessary

to determine whether the selected different microbial species could form a synergistical

relationship in regular cultures, and the characterization of individual species growing in

the mixed culture would have to be performed as an initial assessment.

44

Furthermore, bioleaching could be combined with other types of pretreatment,

such as physical pretreatment, or chemical pretreatment using reagents that are mild to

avoid inhibition of bioleaching microbes. For instance, steam explosion is currently being

used by the bioethanol industry as a pretreatment to increase the lignocellulosic biomass

accessibility to enzymatic hydrolysis. Thus, similar pretreatment could be carried out on

the biomass for thermochemical conversion, which would be followed by bioleaching,

and then evaluated for the elemental removal efficiency.

45

TABLES

Table 2.1. Detection limits for the ICP method.

Element Linear Range (ppm)

Detection limit (ppm)

Std Error (ppm)

Ca 0.200-48000 0.200 1.69x10-11 Cu 0.004-720 0.004 8.43x10-14 Fe 0.002-720 0.002 5.9x10-13 K 10.0-48000 10.0 1.31x10-10

Mg 0.100-12000 0.100 1.37x10-11 Mn 0.001-720 0.001 2.88x10-16 Na 0.03-7200 0.03 1.42x10-11 P 1.900-24000 1.900 8.43x10-12 S 2.00-7200 2.00 1.32x10-11

Zn 0.001-480 0.001 8.43x10-14 Si 0-24 0 0.115

Table 2.2. Chemical compositions of biomass feedstocks (dry basis)*

Feedstock Total solid a Ash content Non-structural carbohydrate

Structural carbohydrate Lignin Glucan Xylan

Sorghum 92.61±0.03 5.80±0.30 2.31±0.08 21.73±0.42 47.51±2.28 23.62±1.27 Wheat straw 92.63±0.02 7.43±0.26 4.51±0.10 21.03±0.20 42.75±3.84 20.49±2.31 Switchgrass 94.15±0.03 3.97±0.62 1.84±0.15 22.03±0.40 40.57±1.36 25.14±0.69 Corn stover 92.74±0.12 4.90±0.25 1.37±0.13 19.91±0.48 44.02±2.06 23.68±0.90

aBased on the weight as received *Values are shown as mean± standard deviation (n=3).

46

Table 2.3. Elemental compositions of biomass feedstocks (dry weight basis, all values are mean ± standard deviations, n=3)

Element Switchgrass Sorghum Wheat straw Corn stover N (%) 0.62±0.04 0.97±0.21 1.22±0.16 0.62±0.06 P (%) 0.05±0.01 0.06±0.01 0.07±0.04 0.02±0.00 S (%) 0.04±0.00 0.06±0.00 0.09±0.05 0.02±0.00 Cl (%) 0.48±0.01 0.18±0.00 0.49±0.03 0.12±0.02 K (%) 0.52±0.03 1.16±0.08 1.41±0.65 0.66±0.01 Ca (%) 0.15±0.01 0.15±0.03 0.51±0.31 0.22±0.01 Mg (%) 0.21±0.00 0.23±0.03 0.12±0.07 0.10±0.00 Si (ppm) 90±10 123±20 208±111 63±4 Zn (ppm) 9±1 13±3 14±6 7±1 Cu (ppm) 5±0 4±2 6±3 4±1 Mn (ppm) 46±2 30±7 21±12 35±3 Fe (ppm) 44±6 105±23 225±126 145±19 Na (ppm) 781±27 44±22 579±288 9±1

Table 2.4. Major chemical compositions (dry basis) of biomass feedstocks after 48 h of leaching with A. niger and water a

Feedstock Treatment Ash content (%) Lignin (%) Glucan (%) Xylan (%)

Sorghum Water leaching 4.15±0.17 20.97±0.19 39.38±6.13 23.61±3.62 Bioleaching 4.55±0.03 20.88±0.04 34.26±5.22 18.26±2.11

Wheat straw Water leaching 5.56±0.20 22.60±3.99 36.61±2.87 19.03±0.66 Bioleaching 7.53±0.55 20.18±0.82 32.82±3.19 14.77±2.26

Switchgrass Water leaching 3.48±0.37 21.49±0.54 37.48±2.35 24.73±1.24 Bioleaching 4.06±1.13 22.29±0.63 36.64±1.22 22.50±0.94

Corn stover Water leaching 3.84±1.07 19.55±0.50 39.18±0.38 22.42±0.69 Bioleaching 4.45±0.69 20.74±0.58 40.88±5.33 21.10±3.35

a All numbers were normalized based on the dry weight of feedstock biomass prior to leaching and are shown as mean± standard deviations (n=3). Each sample’s measurement was normalized first, then the mean and standard deviation were calculated.

47

Table 4.1. Actual variable values and the coded values used in RSM.

Variable Unit Symbol Coded levels -1 0 1

Fungal mass g/L X1 0.4 1.2 2 Leaching time d X2 1 3 5

Glucose concentration g/L X3 0 5 10

Table 4.2. Box-Behnken design for the RSM study.

Run# X1 X2 X3 1 1 1 0 2 0 -1 -1 3 1 -1 0 4 0 1 -1 5 -1 1 0 6 -1 0 1 7 0 0 0 8 1 0 1 9 0 0 0 10 -1 0 -1 11 1 0 -1 12 -1 -1 0 13 0 0 0 14 0 -1 1 15 0 1 1

Y- Ash percentage based on dry weight (%), X1- Fungal mass added to each reactor, X2- Leaching time, X3- Glucose concentration.

48

Table 4.3. Actual results and predicted responses based on the RSM model.

Run# X1 X2 X3 pH Y Actual Y Predicted 1 2 5 5 2.1 3.76 3.71 2 1.2 1 0 3.5 3.93 3.92 3 2 1 5 3.5 3.36 3.43 4 1.2 5 0 1.9 3.95 3.92 5 0.4 5 5 2.7 3.36 3.29 6 0.4 3 10 3.1 3.32 3.30 7 1.2 3 5 2.5 3.65 3.67 8 2 3 10 2.1 3.62 3.59 9 1.2 3 5 2.8 3.55 3.67 10 0.4 3 1 3.7 3.73 3.74 11 2 3 1 3.6 3.72 3.74 12 0.4 1 5 3.6 3.52 3.57 13 1.2 3 5 2.8 3.81 3.67 14 1.2 1 10 3.5 3.63 3.62 15 1.2 5 10 2.5 3.56 3.62

Y Actual - Actual residual ash percentage based on dry weight (%), Y Predicted - Residual ash percentage based on dry weight (%) predicted by the RSM model, X1- Fungal mass added to each reactor (g dry weight), X2- Leaching time (day), X3- Glucose concentration (g/L).

Table 4.4. Analysis of variance (ANOVA) for the quadratic model.

Source DF* Sum of Squares Mean Square F Ratio Model 6 0.47 0.08 11.22 Error 8 0.06 0.01 Prob > F Total 14 0.53 0.0016

*Degree of freedom

Table 4.5. The ANOVA table for lack of fit of the model.

Source DF* Sum of Squares Mean Square F Ratio Lack of Fit 6 0.02 0.003 0.21 Pure Error 2 0.03 0.017 Prob > F Total Error 8 0.05 0.94

*Degree of freedom

49

Table 4.6. Estimates of parameter coefficients and the evaluation of significance.

Term Estimate Std Error t Ratio Prob>|t| Intercept 3.67 0.06 64.03 <.0001 X1 0.07 0.04 1.89 0.1177 X2 0.02 0.04 0.68 0.5286 X3 -0.15 0.04 -4.27 0.0079 X1X2 0.14 0.05 2.82 0.0371 X1X3 0.08 0.05 1.56 0.1792 X2X3 -0.02 0.05 -0.45 0.6693 X12 -0.17 0.05 -3.29 0.0217 X22 0.00 0.05 0.00 1.0000 X32 0.10 0.05 1.89 0.1178

50

FIGURES

Figure 2.1. Example of bioleaching setup (left: water leaching, right: bioleaching with A. niger culture)

Figure 2.2. Microbial growth curves. FO=F. oxysporum, AN=A. niger and BF=B. fungorum. FO and AN were based on biomass dry weight. BF was based on OD600. Error bars are standard deviations (n=3)

51

Figure 2.3. Effect of bioleaching on different biomass feedstocks in 48 h. FO=F. oxysporum, AN=A. niger

and BF=B. fungorum. Error bars are standard deviations (n=2)

52

Figure 3.1. Effect of leaching time on the removal of (A) = chloride, (B) = potassium and (C) =

magnesium. Error bars are standard deviations (n=2).

53

Figure 3.2. Effect of water loading on leaching efficiency of sorghum. Error bars are standard deviations

(n=3).

54

Figure 3.3. Bioleaching using different fractions of fungal culture compared with water leaching. Error

bars are standard deviations (n=3)

55

Figure 4.1. Schematic of single reactor used for bioleaching. 1- air, 2a- upper cap venting port, 2b- lower body venting port, 3- sprinkler, 4- rotameter, 5- nylon mesh membrane and plastic mesh plate, 6- liquid surface, 7- fungal pellets, 8- magnetic stirrer, 9- peristaltic pump.

56

Figure 4.2. Schematic of four bioleaching reactors set up.

Figure 4.3. Photograph of all four reactors set up for bioleaching.

57

Figure 4.4. Comparison of ash content (based on dry weight) of sorghum straws after bioleaching (n=15) and water leaching (n=12). Initial ash content of sorghum straws is 6.03±0.75%

Figure 4.5. Surface plot of the predicted response changing with variables X1 and X2. Y- Ash percentage based on biomass dry weight (%), X1- Fungal mass added, X2- Leaching time.

0

1

2

3

4

5

6

Bioleaching Water Leaching

Ash

%

58

Figure 4.6. Contour plot of predicted response changing with variables X1 and X2. X1:Fungal biomass, X2:Leaching time.

59

Figure 4.7. Surface plot of the predicted response changing with variables X1 and X3. Y- Ash percentage based on biomass dry weight (%), X1- Fungal mass added, X3- Glucose concentration.

60

Figure 4.8. Contour plot of predicted response changing with variables X1 and X3. X1:Fungal biomass, X3:Glucose concentration.

61

Figure 4.9. Surface plot of the predicted response changing with variables X2 and X3. Y- Ash percentage based on biomass dry weight (%), X2- Leaching time, X3- Glucose concentration.

62

Figure 4.10. Contour plot of predicted response changing with variables X2 and X3. X2:Leaching time, X3:Glucose concentration.

63

Figure S4.1 Plot of Y (residual ash percentage) versus X1 (fungal loading).

Figure S4.2 Plot of residuals versus X1 (fungal loading).

64

Figure S4.3 Plot of Y (residual ash percentage) versus X2 (leaching time).

Figure S4.4 Plot of residuals versus X2 (leaching time).

65

Figure S4.5 Plot of Y (residual ash percentage) versus X3 (glucose concentration).

Figure S4.6 Plot of residuals versus X3 (glucose concentration).

66

APPENDICES

67

Appendix A Raw data for Chapter II

Table S2.1. ICP-OES analysis of selected elements in leached biomass feedstocks.

aDI+SW DI+SW DI+SW bFO+SW FO+SW cAN+SW AN+SW AN+SW dBF+SW BF+SW BF+SW

Feedstock dry wt. (g)

Untreated 2.71 2.39 2.57 2.57 2.58 2.05 2.05 2.19 2.19 2.00 2.17

Leached 2.51 2.50 2.38 2.44 2.34 1.77 1.90 2.02 1.99 1.83 1.97

Elem

ent c

once

ntra

tion

N (%) 0.55 0.57 0.69 0.58 0.65 0.59 0.47 0.44 0.9 0.9 0.97

P (%) 0.03 0.03 0.04 0.03 0.04 0.01 0.01 0.01 0.1 0.08 0.07

K (%) 0.12 0.11 0.1 0.07 0.1 0.03 0.03 0.03 0.18 0.13 0.14

Ca (%) 0.18 0.18 0.26 0.15 0.29 0.18 0.12 0.12 0.33 0.34 0.29

Mg (%) 0.14 0.14 0.16 0.12 0.17 0.03 0.02 0.02 0.2 0.21 0.18

S (%) 0.04 0.04 0.04 0.03 0.05 0.04 0.03 0.03 0.07 0.06 0.06

Cl (%) 0.06 0.06 0.07 0 0 0 0 0 0.05 - 0.02

Si (ppm) 39 53 61 43 57 44 43 39 88 92 85

Zn (ppm) 7 8 8 7 10 3 3 3 15 13 15

Cu (ppm) 5 5 6 6 6 6 5 5 8 6 8

Mn (ppm) 48 47 60 40 66 11 9 8 75 78 68

Fe (ppm) 73 61 81 67 93 51 38 40 93 89 105

Na (ppm) 131 156 115 87 115 76 66 73 228 167 196

68

aDI: water leaching, SW: switchgrass. bFO: bioleaching by Fusarium oxysporum. cAN: bioleaching by Aspergillus niger. dBF: bioleaching by Burkholderia fungorum. Same column names indicate replicates. Table S2.1 continued.

eDI+SO DI+SO DI+SO FO+SO FO+SO AN+SO AN+SO AN+SO BF+SO BF+SO BF+SO


Untreated 2.36 2.50 2.39 2.28 2.46 1.78 1.88 2.03 1.92 1.90 2.23

Leached 2.15 2.22 2.20 2.13 2.28 1.54 1.65 1.77 1.80 1.74 2.02

Elem

ent c

once

ntra

tion

N (%) 0.84 0.96 0.98 0.70 0.93 0.63 0.88 0.80 0.86 0.84 0.96

P (%) 0.05 0.05 0.05 0.04 0.04 0.02 0.02 0.03 0.06 0.06 0.06

K (%) 0.36 0.39 0.38 0.28 0.27 0.06 0.07 0.10 0.19 0.24 0.25

Ca (%) 0.26 0.24 0.23 0.17 0.18 0.19 0.18 0.22 0.16 0.19 0.20

Mg (%) 0.28 0.26 0.25 0.21 0.22 0.03 0.03 0.03 0.21 0.25 0.25

S (%) 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.06

Cl (%) 0.07 0.05 0.06 0.00 0.00 - 0.08 0.07 0.07 0.06 0.05

Si (ppm) 62 59 62 57 49 54 51.7 58 145 123 112

Zn (ppm) 16 14 13 12 13 4 3 5 13 14 15

Cu (ppm) 5 4 4 5 5 5 4 4 5 5 4

Mn (ppm) 45 42 40 30 30 6 6 7 27 31 32

Fe (ppm) 236 215 234 184 195 160 134 167 177 168 192

Na (ppm) 31 25 19 21 24 29 33 45 42 45 54

eSO: sorghum. Same column names indicate replicates.

69

Table S2.1 continued.

fDI+WS DI+WS DI+WS FO+WS FO+WS AN+WS AN+WS AN+WS BF+WS BF+WS BF+WS


Untreated 1.99 1.95 2.44 2.30 2.38 1.90 1.95 1.96 1.93 1.98 1.97

Leached 1.76 1.71 2.06 2.06 2.18 1.68 1.76 1.74 1.67 1.73 1.74

Elem

ent c

once

ntra

tion

N (%) 0.85 0.78 0.95 0.89 0.79 0.86 0.66 0.98 0.79 0.87 0.96

P (%) 0.04 0.03 0.06 0.05 0.04 0.04 0.05 0.04 0.06 0.06 0.08

K (%) 0.20 0.21 0.24 0.28 0.28 0.14 0.20 0.29 0.21 0.23 0.26

Ca (%) 0.42 0.40 0.54 0.44 0.45 0.51 0.56 0.54 0.50 0.52 0.57

Mg (%) 0.07 0.06 0.07 0.07 0.07 0.02 0.02 0.03 0.08 0.08 0.09

S (%) 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.06

Cl (%) 0.07 0.04 0.05 0.00 0.00 0.15 0.13 0.18 0.06 0.11 0.08

Si (ppm) 97 65 112 48 51 89 119 128 140 176 149

Zn (ppm) 9 8 10 12 11 9 8 7 11 12 12

Cu (ppm) 4 4 5 6 6 4 4 3 5 4 5

Mn (ppm) 17 15 21 14 14 6 7 8 17 14 18

Fe (ppm) 233 206 289 239 224 186 226 241 255 289 286

Na (ppm) 68 71 88 116 105 102 138 195 90 153 113

fWS: wheat straw. Same column names indicate replicates.

70


gDI+CS DI+CS DI+CS FO+CS FO+CS AN+CS AN+CS AN+CS BF+CS BF+CS BF+CS


Untreated 2.01 1.85 1.97 2.01 2.10 1.81 1.91 1.86 1.97 1.92 1.82

Leached 1.91 1.76 1.84 1.93 2.08 1.69 1.75 1.70 1.89 1.84 1.75

Elem

ent c

once

ntra

tion

N (%) 0.62 0.59 0.64 0.43 0.49 0.68 0.55 0.74 0.77 0.72 0.75

P (%) 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.05 0.05 0.04

K (%) 0.11 0.11 0.16 0.15 0.15 0.07 0.05 0.07 0.18 0.17 0.17

Ca (%) 0.24 0.28 0.26 0.23 0.21 0.25 0.26 0.26 0.27 0.27 0.24

Mg (%) 0.07 0.09 0.09 0.08 0.07 0.02 0.02 0.02 0.09 0.09 0.09

S (%) 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.04 0.04 0.03

Cl (%) 0.03 0.04 0.04 0.00 0.00 0.11 0.06 0.05 0.03 0.01 0.02

Si (ppm) 51 52 54 65 47 67 68 72 95 126 138

Zn (ppm) 5 9 6 8 6 4 2 6 9 8 7

Cu (ppm) 4 5 4 6 5 4 3 3 4 5 4

Mn (ppm) 34 42 41 34 32 9 9 9 43 47 35

Fe (ppm) 177 239 208 236 201 159 176 165 245 233 199

Na (ppm) 11 11 20 16 28 43 35 51 35 37 38

gCS: corn stover. Same column names indicate replicates.

71

Appendix B Raw data for Chapter III

Table S3.1. ICP-OES analysis of selected elements in leached sorghum in time factor study.

aW 0.5h

W 0.5h

bA 0.5h

A 0.5h

W 1h

W 1h

A 1h

A 1h

W 2h

W 2h

A 2h

A 2h

W 3h

W 3h

A 3h

A 3h

W 4h

W 4h

A 4h

A 4h

W 5h

W 5h

A 5h

A 5h

Feed

stoc

k dr

y w

t. (g

) Untreated

2.50

2.54

2.52

2.49

2.53

2.51

2.50

2.50

2.50

2.53

2.50

2.55

2.52

2.51

2.47

2.51

2.51

2.53

2.51

2.54

2.55

2.52

2.54

2.52

Leached

2.24

2.26

2.26

2.24

2.26

2.27

2.24

2.22

2.21

2.28

2.26

2.29

2.24

2.16

2.16

2.24

2.24

2.24

2.29

2.32

2.25

2.22

2.28

2.21

Elem

ent c

once

ntra

tion

(p

pm)

N (%) 0.72

0.69

0.54

0.61

0.6

0.54

0.69

0.67

0.77

0.67

0.7

0.77

0.68

0.69

0.71

0.6

0.58

0.64

0.61

0.9

0.72

0.73

0.73

0.88

K (%) 0.34

0.24

0.22

0.22

0.28

0.27

0.2

0.19

0.21

0.38

0.21

0.19

0.23

0.25

0.18

0.2

0.25

0.26

0.16

0.36

0.22

0.22

0.23

0.25

Ca (%) 0.2 0.1

5 0.15

0.15

0.16

0.16

0.16

0.18

0.18

0.17

0.15

0.16

0.17

0.18

0.18

0.18

0.17

0.19

0.14

0.2

0.14

0.17

0.21

0.25

Mg (%)

0.26

0.21

0.19

0.19

0.2

0.2

0.18

0.17

0.22

0.21

0.16

0.17

0.2

0.22

0.16

0.17

0.21

0.23

0.14

0.19

0.16

0.21

0.18

0.2

P (%) 0.04

0.03

0.03

0.03

0.03

0.03

0.02

0.03

0.02

0.03

0.03

0.02

0.02

0.03

0.03

0.03

0.03

0.03

0.02

0.03

0.02

0.02

0.03

0.03

S (%) 0.05

0.03

0.03

0.03

0.03

0.03

0.03

0.03

0.03

0.04

0.04

0.03

0.03

0.03

0.04

0.04

0.03

0.04

0.03

0.04

0.02

0.03

0.04

0.05

Cl (%) 0.04

0.04

0.03

0.03

0.03

0.03

0.04

0.04

0.03

0.05

0.03

0.02

0.02

0.03

0.02

0.01

0.02

0.02

0.03

0.03

0.02

0.01

0.01

0.04

Si (ppm) 99 101 87 13

1 72 74 99 78 87 70 99 97 90 123 77 78 13

8 139 77 94 65 66 10

2 166

Zn (ppm) 14 12 10 11 12 10 9 10 12 14 10 8 12 12 9 9 12 13 7 8 10 12 8 9

Cu (ppm) 5 4 5 4 4 4 4 4 4 4 6 4 5 4 4 4 4 4 4 5 3 3 4 4

Mn (ppm) 37 24 26 25 27 25 25 25 30 27 23 24 27 29 29 30 28 32 21 30 21 30 31 35

72

Fe (ppm) 188 84 11

8 101

108

120

132

117

110

129

136

117

124

124

165

192

139

159

125

168 86 13

0 166

219

Na (ppm) 24 17 26 27 16 13 19 34 14 31 60 21 14 24 32 30 19 22 22 40 11 8 28 38

aW: water leaching. bA: bioleaching by Aspergillus niger.

73


W 6h

W 6h

A 6h

A 6h

W 12h

W 12h

A 12h

A 12h

W 18h

W 18h

A 18h

A 18h

W 24h

W 24h

A 24h

A 24h

W 36h

W 36h

A 36h

A 36h

W 48h

W 48h

A 48h

A 48h

Feed

stoc

k dr

y w

t (g

)

Untreated

1.95

2.04

1.96

2.01

1.99

2.05

2.04

2.03

2.05

2.05

2.01

1.99

2.01

2.08

1.94

2.03

2.04

2.17

1.97

2.05

2.00

2.09

2.07

2.00

Leached

1.79

1.82

1.83

1.90

1.84

1.90

1.90

1.89

1.88

1.89

1.85

1.83

1.83

1.89

1.77

1.85

1.84

1.95

1.77

1.84

1.76

1.84

1.82

1.76

Elem

ent c

once

ntra

tion

N (%) 0.58

0.52

0.52

0.48

0.57

0.74

0.51

0.6

0.54

0.59

0.67

0.6

0.55

0.71

0.63

0.55

0.69 0.5

0.86

0.66

0.83

0.67

0.83

0.52

K (%) 0.03

0.03

0.02

0.03

0.03

0.04

0.02

0.02

0.04

0.04

0.03

0.03

0.04

0.05

0.03

0.02

0.05

0.04

0.04

0.04

0.06

0.05

0.04

0.05

Ca (%) 0.26

0.23

0.16

0.22

0.24

0.24

0.19

0.16

0.26

0.26

0.13

0.17

0.26

0.31

0.11

0.09

0.31

0.34

0.09

0.14

0.29 0.3

0.12

0.22

Mg (%)

0.21

0.2

0.16

0.21 0.2

0.26

0.17

0.18

0.23

0.21

0.24

0.2

0.23

0.25

0.2

0.17

0.28

0.24

0.26

0.28

0.26

0.27

0.23

0.22

P (%) 0.23

0.24

0.17

0.2

0.23

0.28

0.17

0.16

0.26

0.24

0.11

0.12

0.26

0.27

0.06

0.08

0.31

0.27

0.05

0.08

0.31

0.28

0.05

0.06

S (%) 0.04

0.05

0.04

0.05

0.04

0.06

0.04

0.04

0.05

0.05

0.05

0.05

0.05

0.06

0.05

0.04

0.06

0.05

0.06

0.06

0.06

0.06

0.05

0.06

Cl (%) 0.02 0 0 0

0.01 0 0 0 0 0 0

0.01 0

0.01 0 0 0 0 0

0.01 0 0 0 0

Si (ppm) 73 65

68

75 67 81 61 73 77 64 74 62 76 60 70 44

151 87 68 73

122 78 64 72

Zn (ppm) 20 17 8

12 13 18 8 6 15 17 10 7 19 19 21 5 27 16 10 11 28 24 7 8

Cu (ppm) 9 8 5 7 4 6 6 4 5 5 5 6 5 6 5 4 4 9 5 18 4 12 4 24

74

Mn (ppm) 37 38

29

34 38 45 27 28 39 37 19 20 38 41 12 13 44 40 10 19 46 41 9 12

Fe (ppm)

164

127

127

151

134

202

113

138

268

175

160

146

211

220

167

126

328

230

179

202

276

302

150

154

Na (ppm) 18 16

31

35 14 12 30 23 9 12 20 26 23 56 52 16 21 13 24 71 24 18 26 43

Table S3.2. ICP-OES analysis of selected elements in leached sorghum in water ratio factor study

aW 1:25

W 1:25

bA 1:25

A 1:25

W 1:30

W 1:30

A 1:30

A 1:30

W 1:40

W 1:40

A 1:40

A 1:40

W 1:50

W 1:50

A 1:50

A 1:50

W 1:10

0

W 1:10

0

A 1:10

0

A 1:10

0

Feed

stoc

k dr

y w

t. (g

) Untreated

2.51

2.36

2.34 2.46 2.5

1 2.30

2.55

2.44

2.37

2.30

2.43

2.53

2.48

2.29

2.36

2.44 2.32 2.39 2.49 2.39

Leached

1.87

2.11

2.06 1.88 2.3

3 2.06

2.38

2.04

2.42

2.06

2.34

2.11

2.38

2.04

1.97

2.05 2.06 1.93 2.05 2.09

Elem

ent c

once

ntra

tion

N (%) 0.6 0.6

5 0.64 0.58 0.6 0.5

8 0.59

0.59

0.61 0.6 0.5

4 0.61

0.61

0.59

0.65

0.59 0.58 0.61 0.71 0.56

K (%) 0.04

0.07

0.03 0.02 0.0

3 0.03

0.03

0.02

0.03

0.03

0.02

0.02

0.03

0.03

0.03

0.02 0.03 0.02 0.01 0.01

Ca (%)

0.26

0.27

0.19 0.17 0.2

2 0.24

0.19

0.18

0.17 0.2 0.1

3 0.14

0.17

0.16

0.13 0.1 0.09 0.11 0.06 0.06

Mg (%)

0.18

0.21

0.15 0.15 0.1

5 0.16

0.15

0.16

0.13

0.17

0.14

0.15

0.16

0.17

0.16

0.16 0.15 0.2 0.14 0.11

P (%) 0.24

0.22

0.15 0.15 0.1

9 0.21

0.16

0.16

0.18

0.23

0.14

0.15

0.21

0.21

0.15

0.13 0.17 0.2 0.1 0.08

S (%) 0.05

0.05

0.04 0.04 0.0

4 0.04

0.04

0.04

0.04

0.05

0.04

0.04

0.04

0.05

0.04

0.04 0.04 0.04 0.04 0.03

75

Cl (%)

0.02

0.01

0.01 0.01 0.0

1 0 0.01

0.02

0.01

0.02

0.03 0 0 0 0 0 0 0 0 0

Si (ppm) 78 76 62 63 44 59 95 71 64 82 60 75 58 96 79 89 92 74 69 61

Zn (ppm) 13 25 8 5 9 11 4 4 9 12 4 3 12 15 4 3 10 10 3 3

Cu (ppm) 9 7 5 4 4 5 3 3 3 4 3 4 4 3 4 3 4 3 4 3

Mn (ppm) 31 40 23 25 22 24 21 22 22 28 21 22 25 29 23 21 29 30 17 12

Fe (ppm) 187 138

3 433 134 139 143 14

7 132 149 172 18

9 128 147 166 18

2 138 193 164 120 130

Na (ppm) 15 24 30 42 14 16 31 29 10 10 20 23 44 13 21 16 13 8 12 11

aW: water leaching. bA: bioleaching by Aspergillus niger.

76

Table S3.3. ICP-OES analysis of selected elements in leached sorghum in the study of leaching mechanisms

Water leaching Biomass leaching Unfiltered supernatant Filtered supernatant


Untreated 2.34 2.35 2.38 2.41 2.45 2.42 2.34 2.37

Leached 2.23 2.28 2.26 2.37 2.34 2.29 2.25 2.26 El

emen

t con

cent

ratio

n

N (%) 0.77 0.7 0.94 0.89 0.73 0.54 0.68 0.74 K (%) 0.03 0.03 0.03 0.03 0.02 0.02 0.04 0.03 Ca (%) 0.18 0.2 0.23 0.22 0.06 0.05 0.23 0.24 Mg (%) 0.14 0.15 0.17 0.16 0.15 0.16 0.19 0.17 P (%) 0.18 0.19 0.14 0.16 0.02 0.02 0.22 0.2 S (%) 0 0 0 0 0 0 0 0 Cl (%) 0 0 0.03 0.02 0 0 0.02 0.03

Si (ppm) 65 56 81 61 60 53 57 74 Zn (ppm) 11 11 9 11 5 5 13 12 Cu (ppm) 3 3 13 13 6 6 3 3 Mn (ppm) 25 25 22 21 8 8 31 28 Fe (ppm) 136 139 129 113 110 114 183 138 Na (ppm) 0 0 8 4 117 102 0 0

77

Table S3.4. Major compositions (dry basis) of biomass feedstocks after 48 h of leaching with A. niger and water a.

Feedstock Treatment Ash content (%) Lignin (%) Glucan (%) Xylan (%)

Sorghum Water leaching 4.50±0.18 22.74±0.21 42.70±6.65 25.60±3.93 Bioleaching 5.00±0.03 22.94±0.04 37.64±5.73 20.06±2.32

Wheat straw Water leaching 6.24±0.22 25.38±4.48 41.11±3.22 21.37±0.74 Bioleaching 8.60±0.63 23.04±0.94 37.47±3.64 16.86±2.58

Switchgrass Water leaching 3.80±0.40 23.48±0.59 40.95±2.57 27.02±1.35 Bioleaching 4.30±1.20 23.60±0.67 38.79±1.29 23.80±1.00

Corn stover Water leaching 4.03±1.12 20.52±0.52 41.12±0.40 23.53±0.72 Bioleaching 4.74±0.74 22.09±0.62 43.55±5.68 22.48±3.57

a All numbers based on the dry weight of feedstock biomass after leaching and are shown as mean± standard deviations (n=3).

78

Appendix C

Raw data for Chapter IV

Table S4.1 Residual ash percentage data. (X1: fungal mass loading, X2: leaching time, X3: glucose concentration, all shown as coded levels)

X1 X2 X3 Leaching Time (day) 1 2 3 4 5

1 1 0 4.13 3.96 3.90 4.35 3.76 0 -1 -1 3.93 4.18 4.12 3.82 3.49 0 0 0 3.75 3.24 3.65 3.98 3.77 0 1 -1 3.34 3.87 4.45 5.14 4.15 1 -1 0 3.36 4.09 3.45 3.82 3.49 0 0 0 3.64 3.93 3.55 3.75 3.52 -1 0 1 3.45 4.25 3.32 3.40 3.51 1 0 1 3.55 3.26 3.62 3.60 3.46 -1 0 -1 3.80 3.56 3.73 4.07 4.09 0 -1 1 3.63 3.71 3.70 3.52 4.29 0 0 0 3.47 3.58 3.81 4.63 4.07 -1 1 0 3.93 3.37 3.36 3.47 3.36 -1 -1 0 3.52 3.53 3.53 3.97 3.41 1 0 -1 3.65 3.45 3.72 3.70 3.35 0 1 1 3.64 3.46 2.99 4.22 3.56

79

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