Modeling Framework for Biogenic Methane Formation from Coal

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Biofuels and Biomass

Modeling Framework for Biogenic Methane Formation from Coal Abhilash Sharma, ADITYA JAGARAPU, Christopher Micale, Daman Walia, Scott Jackson, and Prasad S. Dhurjati Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b01298 • Publication Date (Web): 11 Jun 2018 Downloaded from http://pubs.acs.org on June 12, 2018

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Modeling Framework for Biogenic Methane Formation from Coal Abhilash Sharma‡, Aditya Jagarapu¤, Christopher Micale‡, Daman Walia‡, Scott Jackson§, and Prasad S. Dhurjati‡* ‡ Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716 ¤ Department of Biomedical Engineering, University of Delaware, Newark, DE 19716 § Department of Chemical Engineering, Villanova University, Villanova, PA 19085

ABSTRACT: In situ biodegradation of coal to methane offers an innovative and attractive alternative to the prevailing methods of harnessing the energy potential of coal. This alternative approach employs a community of microorganisms to biologically generate methane from unmined coal and has the promise of causing less pollution, reducing environmental impact of mining, and being potentially less expensive.9,10,15-18 The goal of our work is to develop a novel, comprehensive, and quantitative mathematical modeling framework to better understand the process of biogenic methane formation from coal. Intermediate chemical species and metabolic pathways are identified using existing literature information and incorporated into a lumped kinetic model, referred to as the Coal to Methane (C2M) Kinetic Model. Several intermediate compounds have been lumped into Polyaromatics (PACs), Long Chain Fatty Acids (LCFAs) and Mid Chain Fatty Acids (MCFAs), etc. The simulations of the model and sensitivity analysis along with a metabolic pathway connectivity map are useful for guiding experimental design and establishing important intermediate metabolites and bottlenecks. Dependence of methane production on

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temperature and concentration of enzymes has been studied through sensitivity analysis of kinetic parameters, showing CO2 reduction and acetate cleavage play a significant role. Additionally, interdependence of MCFAs hydrolysis to acetate and conversion of acetate to methane has been identified, implying acetate regulation as a key factor of methane formation. The mathematical model compares favorably to a limited set of experimental data provided by ArcTech Inc. Model parameters can aid in understanding the impact of metabolic bottlenecks on the bioconversion process and can be useful for monitoring or control of the biogenic methane process.

INTRODUCTION Coal is a large source of pollution. When burned it emits substantially more CO2 for an equivalent amount energy than natural gas or oil.1 Electricity from coal is predominantly generated through combustion in power plants that are run at elevated temperatures, require high capital costs, and generate pollutants in the form of toxic particulate matter and gases. The pollutants include sulfur dioxide, nitrogen oxides, and particulates.2 Hazardous air pollutants such as arsenic, chromium, nickel, mercury, hydrogen chloride and hydrogen fluoride are also produced.3 Coal power generation facilities around the world are required to install expensive pollution control processes, limiting emissions of these pollutants but creating hazardous waste sludge from scrubbers.4 Injecting microbes into coal seams facilitates a biological conversion of coal to methane below the ground (in situ). The methane can then be pumped above ground, dried, and accumulated for use. For a given amount of energy production, burning this methane produces much less pollution than burning coal. Combustion of natural gas produces half as much carbon dioxide, less than a third as much as nitrogen oxides and one percent as much sulfur oxides when compared to the same pollutants generated at a coal fired power plants.5


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The depth to which human mining operations can be carried out limits the amount of coal that is “mineable.” Of all the U.S. coal resources (an estimated 6 trillion tons), only 237 billion tons are mineable and considered to be proved reserves; this means that approximately 95.7% of U.S. coal resources are un-mineable by existing economical and operating conditions.6,7 Microorganisms can be sent to much greater depths than human miners and may be the only way to access the energy potential of un-mineable coal resources. Coal-bed methane (CBM) has received much attention in the past two decades as an unconventional source of clean-burning energy. The CBM industry has been capital intensive over the twenty first century, particularly in Australia, China, India, and the United States. However, the share of CBM as a portion of total domestic natural gas production diminished from 8.1% in 2007 to 3.3% in 2016. Improvements in horizontal drilling and hydraulic fracturing techniques have increased shale gas production, decreasing the percentage of the total domestic natural gas production accounted for by CBM.8 CBM wells have a finite lifetime and become dormant when the gas production stops. Microbial Enhanced CBM would provide an alternative solution with de novo methane for these existing dormant CBM wells. These wells are already equipped with the necessary infrastructure to generate the trapped natural gas and therefore require lower investment. CBM is being increasingly explored as an environmentally conscious and economically efficient option, and recent research has aimed to enhance this natural resource. CBM processes often fail for a variety of reasons. In some cases, gas content is not high enough to overcome the upper bound of absorption within the coal, and no gas is released to the surface. In others, low levels of permeability in the coal bed resulting in no migration path or low reservoir pressure prevents gas from reaching production wells. Some of these problems can be


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overcome with the use of methanogenic microbial communities.9 Since there is disagreement within the scientific community about whether CBM results from coal breakdown at elevated temperatures (thermogenic coal gasification), coal bed methane capture, or from the concerted activity of methanogenic communities in the coal bed (biogenic methane), the degradation process that coal undergoes is not well understood.10,11 While subsurface methane can accumulate through any of these methods, this study is focused on biogenic coal bed methane generation. Methanogenesis is the final process in the anaerobic degradation of organic matter in anoxic environments with low levels of terminal electron acceptors.12,13 Methanogenic communities including fermenting bacteria, acetogens, and methanogens can jointly convert heterogeneous compounds such as coal into a variety of smaller products, predominantly CH4 and CO2.14,15 While the microbial conversion of coal to CH4 has been observed in numerous laboratory settings and field studies10,16,17,18 and has even been successfully enhanced under certain conditions10, there is not yet a universally accepted description of the pathways by which the coal geopolymers decompose into organic intermediates and further into the product gases. Because coal structure varies by rank and is composed of recalcitrant organic compounds 19,20, the early portion of the process (corresponding to hydrolysis and initial fragmentation of coal) is difficult to elucidate and is dependent on geologic, chemical, and microbial factors.10,21 It has been suggested that this fragmentation and the subsequent activation of the coal components is the rate-limiting step in coal biodegradation.22 Recent laboratory experiments and pre-existing bodies of biochemical knowledge have yielded enough evidence to support several proposed connectivity models of the degradation process.11,14,18 However, despite the increasing interest in CBM enhancement there are very few


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models in the literature which attempt to describe explicitly how these chemical intermediates and end products are generated over both short (1 month) time scales.23,24 The development of such mathematical models can assist the growing field in understanding the factors which affect the microbial conversion of coal to methane and in identifying which parts of the degradation are being blocked when certain intermediates accumulate in a system. We propose a kinetic model with lumped chemical classes so that we may begin to assemble a more quantitative and predictive picture of this process, and we express these interactions as a connectivity model and a mathematical model. The models are formed and simulated using CytoScape25 and the MATLAB26 computing environment. The C2M Kinetic Model can aid in the design of new experiments by specifying which compounds to measure, and they can be refined in an iterative manner as more experimental data is collected. BACKGROUND FOR ASSUMPTIONS Hydrolysis and Fragmentation. The degradation of coal begins with the exoenzymatic hydrolysis of the geopolymers into long chain (C10 – C36) alkanes, long chain fatty acids (LCFAs), and single ring aromatics.11,16 The hydrolytic enzymes are secreted by the primary fermenting bacteria to facilitate the breakdown of organic polymers to monomers, which they can then ferment further.14 Further detail is given by Strapoć et al., who observed that coal first fragments into poly-aromatic compounds (PACs) in addition to LCFAs and alkanes. These are assumed to degrade into single-ring aromatic compounds, but the detailed mechanism is not yet fully understood.18 Under anaerobic conditions, the biodegradation of PACs such as benzopyrene, naphthalene, and phenanthrene proceeds via hydrogenation, hydration ring cleavage, and hydroxylation to give single-ring aromatics and organic acids.27 Single-ring aromatics degrade along a similar


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pathway, starting with a reductive phase of ring hydrogenation and then ring cleavage via hydration.15 These include benzoate, catechol, and phenols, all of which can be decomposed by methanogenic consortia.28,29 Long chain alkanes such as hexadecane can be decomposed into acetate and H2 by syntrophic bacteria in cooperation with acetoclastic (acetate-cleaving) and hydrogenotrophic (hydrogen-oxidizing) methanogens.30 Fumarate has been observed to be an important substrate in the catabolism of long chain alkanes, LCFAs and single-ring aromatics as an initial activation step31,32,33 In addition, it has been shown that long chain alkanes can be transformed into long chain fatty acids by sulfate-reducing bacteria or via aerobic respiration 11,34, but as the mechanism of anaerobic degradation of these monomers has yet to be fully elucidated, conclusions cannot be drawn about this particular pathway. Equation 1 shows a simple reaction pathway. 𝐶𝑜𝑎𝑙 (𝐿𝑖𝑔𝑛𝑖𝑛) + 𝐹𝑢𝑚𝑎𝑟𝑎𝑡𝑒 →

ℎ𝑦𝑑𝑟𝑜𝑙𝑦𝑠𝑖𝑠

𝐿𝐶𝐹𝐴𝑠 + 𝐿𝑜𝑛𝑔 𝐶ℎ𝑎𝑖𝑛 𝐴𝑙𝑘𝑎𝑛𝑒𝑠 + 𝑃𝐴𝐶𝑠

(1)

Acidogenesis. The acidogenesis steps of the biodegradation process, also termed the primary fermentation steps, include all reactions that convert the previous products, including LCFAs and single-ring aromatics, into organic acids. This grouping includes lactic acid and succinic acid, as well as various short chain fatty acids (SCFAs) or volatile fatty acids (VFAs). LCFAs are degraded via beta-oxidation by syntrophic bacteria to H2 and SCFAs, which include butyric acid, propionic acid, acetic acid, and formic acid.11,15 In addition to these acids, short alcohols such as methanol and ethanol also are formed in these reactions.14 Primary fermenting bacteria are responsible for these reactions in addition to the monomer formation from the initial fragmentation steps. Acidogenesis is not as well defined as the anaerobic degradation stages that come before and after it, but the reactions from this step bridge


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the hydrolysis and depolymerization of coal to the terminal processes of acetogenesis and methanogenesis. The acidogenesis steps of anaerobic degradation are featured prominently in the C2M Kinetic Model. LCFA and single-ring aromatics such as catechol degrade to butyric acid, succinic acid, and other similar organic compounds as shown in Equations 2 and 3: 𝐿𝐶𝐹𝐴 →

𝛽-𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛

𝐶6 𝐻6 𝑂2 →

𝐶𝐻3 𝐶𝐻2 𝐶𝐻2 𝐶𝑂𝑂𝐻 + 𝐻2

ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛𝑎𝑡𝑖𝑜𝑛, ℎ𝑦𝑑𝑟𝑎𝑡𝑖𝑜𝑛

𝐶2 𝐻6 𝑂4 + 𝐶𝑂2

(2) (3)

Acetogenesis. Syntrophic bacteria, also known as proton-reducing bacteria or secondary fermenting bacteria, are responsible for the synthesis of acetate from earlier fermentation products, including various organic acids and alcohols.14 Along with homoacetogenic bacteria, which reduce CO2 to acetate, these comprise the acetogens. Acetogenesis via the activity of syntrophs and methanogens is the primary pathway of acetate production in most environments. Homoacetogenesis from CO2 and H2 may or may not be prominent depending on the environment, but it can significantly affect the amount of CH4 generated in a system by competing with hydrogenotrophic methanogenesis for the same reactants.10 Acetate formation from longer SCFAs, such as butyrate (Eq. 3) and propionate (Eq. 4), proceed according to the following reactions:14 𝐶𝐻3 𝐶𝐻2 𝐶𝐻2 𝐶𝑂𝑂− + 2𝐻2 𝑂 → 2 𝐶𝐻3 𝐶𝑂𝑂− + 𝐻 + + 2𝐻2

(3)

𝐶𝐻3 𝐶𝐻2 𝐶𝑂𝑂− + 2𝐻2 𝑂 → 𝐶𝐻3 𝐶𝑂𝑂− + 𝐶𝑂2 + 3𝐻2

(4)

These reactions are endergonic, making them unfavorable for the syntrophic bacteria; however, when the products of the acetogens are later taken as substrates by the methanogens, the reactions become exergonic and all involved prokaryotes can gain energy.15 The three fatty acids mentioned therein are important intermediates to measure in methanogenic environments.


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Methanogenesis. There are two major types of biogenic methanogenesis encountered in anoxic environments. Acetoclastic methanogenesis (Eq. 5) proceeds by cleaving the molecule of acetate into CH4 and CO2, and hydrogenotrophic methanogenesis (Eq. 6) involves the reduction of CO2 with H2 as the electron donor. CH4 can also be produced from methanol (Eq. 7) and formate (Eq. 8), according to the following equations15: 𝐶𝐻3 𝐶𝑂𝑂𝐻 → 𝐶𝐻4 + 𝐶𝑂2

(5)

𝐶𝑂2 + 4𝐻2 → 𝐶𝐻4 + 2𝐻2 𝑂

(6)

4𝐶𝐻3 𝑂𝐻 → 3𝐶𝐻4 + 𝐶𝑂2 + 2𝐻2 𝑂

(7)

4𝐻𝐶𝑂𝑂𝐻 → 𝐶𝐻4 + 3𝐶𝑂2 + 2𝐻2 𝑂

(8)

CO2 reduction and acetoclastic methanogenesis are included as the major pathways of methanogenesis and H2 is made explicit in the C2M Kinetic Model. It is worth noting that the presence of H2 is important in the formation of biogenic CH4 because it impacts the thermodynamics of the degradation reactions14; however, we choose to exclude this relation, as the two models focus on the steps of chemical degradation only, and not on the thermodynamics underlying these reactions. In the following sections, we describe and apply two types of models for coal biodegradation: a connectivity model and a mathematical model. Connectivity models provide a visual perspective of the system while providing qualitative information about the interactions taking place. Mathematical models use a system of ODEs or PDEs describing the changes which, when solved simultaneously, simulate interactions between nodes.35 The purpose of this paper is to provide organized frameworks of the biochemical information of coal biodegradation using lumped kinetic models to use as a guide for design of new experiments with the goal of assisting in the analysis of experimental data and providing insight


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on the metabolic trends and bottlenecks occurring in the process of microbial coal to methane degradation. The accuracy of the assumptions made in the model development are validated through qualitative and quantitative comparison with experimental data. CONNECTIVITY MODEL OF COAL BIODEGRADATION Coal to Methane Kinetic Model Creation. An overview of the system was first formulated by considering the major observable chemical intermediates and focusing on these as the main components of the model. The theorized sequential steps in the pathway were coal degradation to polyaromatic compounds through hydrolysis, acidogenesis, acetogenesis and acetoclastic methanogenesis. Using this framework, we developed the C2M Kinetic Model for the biodegradation of coal as shown in Figure 1. Since several compounds were identified during the initial stages of hydrolysis and acidogenesis, lumping of similar chemical compounds has been carried out for modeling purposes. All the compounds obtained after hydrolysis have been lumped into several compartments such as Polyaromatics (PACs), Aromatics, Long Alkanes and Long Chain Fatty Acids (LCFAs). Acidogenesis of these compounds results in the formation of various short chain fatty acids and volatile fatty acids as described in the previous section. All these compounds were further lumped into a single compartment called as Mid Chain Fatty Acids (MCFAs). Further degradation of these MCFAs were carried out during the acetogenesis and methanogenesis steps resulting in production of various organic acids such as acetate, lactate, propionate, butyrate along with hydrogen, carbon dioxide and methane. Mid Chain Fatty Acids (MCFA) serves as the metabolic link between long chain alkanes, LCFAs, and the previously mentioned organic acids. The hydrogenotrophic mode of methanogenesis is integrated into the C2M Kinetic Model, and the production of H2 from propionic acid and butyric acid oxidation is explicitly modeled after


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the balanced reactions given in Equations 3 and 4. Fumarate is featured prominently in this model, as it is shown to have a link to the degradation of higher compounds during the hydrolysis phase; the dotted line is meant to imply its relation as a chemical additive and not as a precursor to the larger compounds. Metabolites requiring the presence of environmental fumarate as an intermediate have been detected in groundwater collected from the San Juan Basin, suggesting its presence in abundance at the time of inoculation.36 Lastly, microorganisms are not made explicit in the C2M Kinetic Model, and more emphasis is placed on the compounds and pathways. Assumptions made in the C2M Kinetic Model are as follows: 1. Temperature and pH are constant and do not affect the degradation process 2. Microorganisms catalyze reactions throughout the model, but are not modeled explicitly. That is, the effect of changing microbial populations on the rates of reaction are not included in this model. 3. Sufficient water and nutrients available for the needs of microorganisms 4. Necessary microbial communities are evenly distributed across the coal bed, and concentrations of compounds in the coal bed represent the entire microenvironment 5. Certain intermediates may be present in some concentration at the initial time (succinate, fumarate) 6. Lumped quantities sufficiently represent the diverse chemical compounds in the coal bed Table 1 gives the kinetic parameters of this model system and the values of the parameters. The rate constants were estimated via qualitative analysis of the trends in the set of experimental data provided by Arctech Inc, shown in Figure 4. The parameter k23 is neglected since the dependence upon fumarate concentration is already associated with rate constants k5, k6, and k7. Table 1. List of kinetic parameters and their values for C2M Kinetic Model.


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Rate Rate Rate Valuea Valuea Valuea Constant Constant Constant k1 0.2857 k10 0.1429 k19 0.0714 k2 0.1429 k11 0.0143 k20 0.1000 k3 0.1429 k12 0.2857 k21 0.1143 k4 0.1429 * k13 0.3571 k22 0.1429 k5 0.1429* k14 0.4286 k23 -k6 0.0715 * k15 0.1429 k24 0.1429 k7 0.1429 k16 0.0071 k25 0.1143 k8 0.2857 k17 0.0429 k26 0.0571 * k9 0.2143 k18 0.0571 k27 0.0143 * a Units are in day-1 except where marked with asterisk (in which case they are in ncu-1 day-1). Visual Representation of Connectivity Model. Using the rate constants in Table 1, the model is implemented into Cytoscape to visually explain the interactions of the intermediates in the breakdown of coal. Figure 1 shows this interaction for the C2M Kinetic Model, based on the determined rate parameters in Table 1 and the law of mass action. Solid lines are used to represent intermediates from metabolism and the dashed line for fumarate represents the environmental availability of the metabolite. The relative reaction velocities, indicative of pathway fluxes, are represented by the thickness of the line.


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Figure 1. Reaction Velocity Map of C2M Kinetic Model Many metabolic bottlenecks result in accumulation of metabolites, represented in the model as a node with an inflow of thick arrows and an outflow of thin arrows. This aggregation occurs when the rate of production is much greater than the rate of consumption for a metabolite. The relatively slow rates of acetate conversion to carbon dioxide and methane suggest a build-up of acetate in the system. In addition to this, the thickness of the arrow labeled k26 shows that carbon dioxide metabolizes to methane at a faster rate than the other pathways of methane formation. The build-up of acetate suggests its use in regulation, and the rates relating to CO2 to CH4


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conversion suggest hydrogenotrophic methanogenesis is the primary pathway for methane production. Figure 1 also shows metabolites with a high consumption rate. Formate, lactate, and long alkanes all have outflows with thicker arrows than inflows, suggesting immediate consumption of the compound. The low relative velocity of lactate to acetate is expected for an anaerobic environment since energy for the cells would not be formed through oxidative phosphorylation. In the microorganisms, all lactate-formed acetate would first be oxidized to pyruvate in the cytoplasm and then transported to mitochondrion, where the Krebs Cycle occurs.37 Since this process requires utilizing an oxidizing agent (i.e. oxygen) as a method of ATP generation, it is not expected during anaerobic growth. These pathways have been seen in multiple organisms, such as Desulfovibrio vulgaris, CHO cells, and Lactococcus lactis.37,38,39 MATHEMATICAL MODEL OF COAL DEGRADATION C2M Kinetic Model ODEs. Table 2 shows all ODEs and initial concentrations for the C2M Kinetic Model. No assumptions are made pertaining to the order of kinetics in this model, as the many ODEs used to represent the reactions have various combinations of concentration terms. Concentrations are given in normalized concentration units (ncu). The ODE 45 solver in the MATLAB computing environment was used to determine solutions. The C2M Kinetic model was built using a system of ODEs (representing the conditions in a CSTR) rather than PDEs because the basic purpose of our model is to identify the reaction pathway bottlenecks rather than the spatial distribution of the various intermediates across an underground coal seam. Modeling the actual coal biodegradation in an underground coal seam using PDEs would add more complexity since one has to couple both heat and mass transfer in combination with chemical reactions across varying temperature, pressure, composition of the


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microbial populations, availability of nutrients for the microbes, composition of coal, and thickness of the coal bed. Modeling the above-mentioned variables would yield effective operating conditions of the coal biodegradation to yield maximum gas production in the most energy efficient ways. Table 2. List of differential equations and initial concentrations used in MATLAB implementation of the C2M Kinetic Model. Differential 𝑑[𝑐𝑜𝑎𝑙] 𝑑𝑡 𝑑[𝑝𝑜𝑙𝑦𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] 𝑑𝑡 𝑑[𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] 𝑑𝑡 𝑑[𝑙𝑜𝑛𝑔 𝑎𝑙𝑘𝑎𝑛𝑒𝑠] 𝑑𝑡 𝑑[𝐿𝐶𝐹𝐴] 𝑑𝑡 𝑑[𝑀𝐶𝐹𝐴] 𝑑𝑡 𝑑[𝑙𝑎𝑐𝑡𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝑓𝑜𝑟𝑚𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝑝𝑟𝑜𝑝𝑖𝑜𝑛𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝑏𝑢𝑡𝑦𝑟𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝑠𝑢𝑐𝑐𝑖𝑛𝑎𝑡𝑒] 𝑑𝑡

Equation

Initial Concentration (ncu)

= −𝑘1 [𝑐𝑜𝑎𝑙]

10000

= 𝑘1 [𝑐𝑜𝑎𝑙] − [𝑝𝑜𝑙𝑦𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠](𝑘2 + 𝑘3 + 𝑘4 ) = 𝑘2 [𝑝𝑜𝑙𝑦𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] − 𝑘5 [𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠][𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒]

0 0

= 𝑘3 [𝑝𝑜𝑙𝑦𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] − 𝑘6 [𝑙𝑜𝑛𝑔 𝑎𝑙𝑘𝑎𝑛𝑒𝑠][𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒]

0

= 𝑘4 [𝑝𝑜𝑙𝑦𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] − 𝑘7 [𝐿𝐶𝐹𝐴][𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒]

0

= [𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒](𝑘 5 [𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] + 𝑘6 [𝑙𝑜𝑛𝑔 𝑎𝑙𝑘𝑎𝑛𝑒𝑠] 0 + 𝑘7 [𝐿𝐶𝐹𝐴] ) − [𝑀𝐶𝐹𝐴](𝑘8 + 𝑘9 + 𝑘10 ) = 𝑘9 [𝑀𝐶𝐹𝐴] − [𝑙𝑎𝑐𝑡𝑎𝑡𝑒](𝑘11 + 𝑘12 0 + 𝑘13 + 𝑘14 ) = 𝑘10 [𝑀𝐶𝐹𝐴] − 𝑘15 [𝑓𝑜𝑟𝑚𝑎𝑡𝑒] = 𝑘12 [𝑙𝑎𝑐𝑡𝑎𝑡𝑒] − [𝑝𝑟𝑜𝑝𝑖𝑜𝑛𝑎𝑡𝑒](𝑘16 + 𝑘17 + 𝑘18 ) = 𝑘13 [𝑙𝑎𝑐𝑡𝑎𝑡𝑒] − (𝑘19 + 𝑘20 + 𝑘21 )[𝑏𝑢𝑡𝑦𝑟𝑎𝑡𝑒] = 𝑘14 [𝑙𝑎𝑐𝑡𝑎𝑡𝑒] − 𝑘22 [𝑠𝑢𝑐𝑐𝑖𝑛𝑎𝑡𝑒]

0 0 0 0


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𝑑[𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝑎𝑐𝑒𝑡𝑎𝑡𝑒] 𝑑𝑡 𝑑[𝐶𝑂2 ] 𝑑𝑡 𝑑[𝐻2 ] 𝑑𝑡 𝑑[𝐶𝐻4 ] 𝑑𝑡

= 𝑘22 [𝑠𝑢𝑐𝑖𝑛𝑎𝑡𝑒] − [𝑓𝑢𝑚𝑎𝑟𝑎𝑡𝑒](𝑘5 [𝑎𝑟𝑜𝑚𝑎𝑡𝑖𝑐𝑠] + 𝑘6 [𝑙𝑜𝑛𝑔 𝑎𝑙𝑘𝑎𝑛𝑒𝑠] + 𝑘7 [𝐿𝐶𝐹𝐴] = 𝑘8 [𝑀𝐶𝐹𝐴] + 𝑘11 [𝑙𝑎𝑐𝑡𝑎𝑡𝑒] + 𝑘16 [𝑝𝑟𝑜𝑝𝑖𝑜𝑛𝑎𝑡𝑒] + 𝑘21 [𝑏𝑢𝑡𝑦𝑟𝑎𝑡𝑒] − [𝑎𝑐𝑒𝑡𝑎𝑡𝑒](𝑘24 + 𝑘25 ) = 𝑘15 [𝑓𝑜𝑟𝑚𝑎𝑡𝑒] + 𝑘18 [𝑝𝑟𝑜𝑝𝑖𝑜𝑛𝑎𝑡𝑒] + 𝑘19 [𝑏𝑢𝑡𝑦𝑟𝑎𝑡𝑒] + 𝑘24 [𝑎𝑐𝑒𝑡𝑎𝑡𝑒] − 𝑘26 [𝐶𝑂2 ][𝐻2 ] [𝑝𝑟𝑜𝑝𝑖𝑜𝑛𝑎𝑡𝑒] = 𝑘17 + 𝑘20 [𝑏𝑢𝑡𝑟𝑦𝑎𝑡𝑒] − 𝑘27 [𝐶𝑂2 ][𝐻2 ] = 𝑘25 [𝑎𝑐𝑒𝑡𝑎𝑡𝑒] + [𝐶𝑂2 ][𝐻2 ](𝑘26 + 𝑘27 )

10000

0

0

0 0

Mathematical Model Solution. Figure 2 shows a plot generated in MATLAB of the solution to the full system of ordinary differential equations in the C2M Kinetic Model, using the kinetic rate constants from Table 1 and the differential equations and initial conditions from Table 2. The code is provided as supplemental materials.


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Figure 2. Results of C2M Kinetic Model simulation run in MATLAB. Figure 2, generated from the simulation, indicates that butyrate and propionate are present in low concentrations throughout most of the process. It is further shown that H2 remains at an almost undetectable level for the duration of the bioconversion. This result agrees with the literature, which collectively states that hydrogen partial pressure is kept low by the activity of methanogens. Methanogenesis does not begin until after a lag period of about four days, after which the conversion of acetate to CH4 and CO2 occurs at the rate of acetate is availability. This result would seem to indicate that the accumulation of acetate in a methanogenic system like the coal bed implies the presence of an inhibitor acting on the methanogenic species. The methane concentration curve shape shows three distinctive regions: exponential growth, linear growth, plateauing. Each region correlates to the concentrations of various other metabolites, further explored in the discussion. Coal is readily consumed in the simulation, decreasing in concentration rapidly and leveling out around half way through the simulation.


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Energy & Fuels

This kinetic model supports several observations from experiments in the literature and is useful as a first step in analyzing the kinetics of coal bioconversion into methane gas. Nevertheless, certain portions of the C2M Kinetic Model may benefit in the future from further specificity. For example, the division of the poly-aromatics variable into separate quantities such as naphthalene, anthracene, and phenanthrene would give an opportunity to incorporate a larger diversity of degradation reactions. Similarly, the quantity representing single-ring aromatics could be replaced with an individual class for benzoate, catechol, and various phenols. Sensitivity Analysis. A model provides a means to analyze a great variety of cases for a given system. While these changes may not be realistic, they can provide cause-and-effect relationships in the network of chemical reactions. Kinetic parameters are dependent on changes in temperature and on the concentration of enzymes available for catalysis. Figure 3 shows the effects of altering kinetic parameters on the cumulative CH4 production by the simulated system in the mathematical model. The figure was generated by first converting the connectivity model into a visual web using the GUI for the MATLAB toolkit SimBiology.26 Kinetic parameters taken from Table 1 were linked to each reaction, a sensitivity analysis was run, and the values was generated by the program. The tornado plot was then generated using Excel.


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Figure 3. Tornado plot of kinetic parameters in C2M Kinetic Model. The parameter associated with acetate cleavage seems to have the most significant effect on the CH4 production. The combined parameters associated with CO2 and H2 reduction to methane closely trail, suggesting acetoclastic methanogenesis plays a significant role in methane formation and that hydrogen is readily converted to methane when made available. This finding is corroborated by Figure 2, in which the H2 levels are kept extremely low throughout the simulation. The amount of methane production predicted by the model is highly sensitive to the rate of MCFA hydrolysis. MCFA can only be converted to lactate, formate, or acetate, so a large fluctuation in k9 will be proportional to a fluctuation in the rate of fermentation of MCFA to acetate, k8. By keeping this in mind, the high sensitivity of methane to lactate formation can be considered a dependence of methane on acetate regulation, implying the use of acetate in the cellular regulatory network. It further suggests that the amount of methane from acetoclastic


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methanogenesis is largely affected by the metabolite concentrations during the acetogenesis stage, showing the importance of careful selection of syntrophic acetogenic/methanogenic bacteria for successful implementation of microbial C2M. MODEL VALIDATION Measuring the concentrations of the compounds and comparing experimental data against the predictions in this model will aid in validation. Arctech Inc has collected information on the concentration of various metabolites throughout the coal to methane conversion process. Source/Characterization of Coal Samples. Sub-bituminous coal samples were obtained from Penn State Coal bank and were crushed under anaerobic conditions to -100 mesh. Chemical characterization has been performed on the collected samples to estimate the contents of ash, volatile matter. Sulphur, Carbon and Hydrogen, shown in Table 3. Table 3. Analysis of dry sub-bituminous coal samples from Penn State Coal Bank. Sample

Ash %

Volatile Matter %

S%

C%

H2 %

Wyodak subbituminous, WY

7.6

48.1

0.43

76.2

6.2

Culture and Nutrient Medium. MicAN culture isolated from hind guts of wood eating termites as described in US Patent #5,854,032 was grown using standard culturing techniques and transferred to fresh nutrient medium amended with coal samples.40 These microbes have been deposited in the American Type Culture Collection. Enrichment process was carried out for the cultures every three weeks and the microbes could adapt and grow in the nutrient medium. The nutrient medium was prepared under anaerobic conditions as described by Huntgate et al.41 Experimental Setup. The experimental setup was conducted by first preparing a nutrient medium with coal samples and mixing the slurry with simulated groundwater samples.


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Simulated groundwater was prepared at Arctech based on element concentration data from waters in the Powder River Basin conducted by the United States Geological Survey, but the exact makeup was not provided. Nutrient medium was prepared under anaerobic conditions, dispensed in 100-mL vials, closed tightly, and then autoclaved. Core coal samples were crushed under anaerobic conditions and flushed with helium. Vials were inoculated with 10% of either concentrated adapted MicAN® culture, after which the ratio of coal to liquid was 2:1. Inoculated vials were then incubated at 37°C. During the incubation period, a GOW-MAC (Model 580) gas chromatograph fitted with a 10’ 1/8” OD stainless steel column was used to analyze samples. Experimental Analysis. Gas samples from the different test experiments were collected and analyzed for volume of total gas. For the laboratory tests, the total volume of the gas produced was measured by a pressure-tight syringe, which was made anaerobic by flushing it with helium at least three times. Gas composition was determined by gas chromatography. A GOW-MAC (Model 580) gas chromatograph fitted with a 10’ 1/8” OD stainless steel column packed with 100/120 mesh Carboseive S-II (Supelco Co.) was used to analyze gas samples for nitrogen (N2), carbon dioxide (CO2), and methane (CH4) content. Validity Analysis. Limited experimental data provided by Arctech Inc, shown in Figure 4a, was used for qualitative validation of the C2M Kinetic Model. The plot of the simulation has been simplified to show only the compounds given in the experimental data in Figure 4b.


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Figure 4. a) Experimental data from Arctech. b) Simulated C2M Kinetic Model Our model is shown to be useful in representing the general trends in metabolic degradation of coal. However, the experimental data shows a longer latency period before methane produced, and the maximum amount of methane is greater. Also, our model shows acetate being converted to methane as it is created, whereas the experimental data shows an accumulation of acetate before methanogenesis occurs. This suggests the role of acetate as an inhibitor for methanogenesis is greater than initially assumed in our model, justifying the initial analysis of the connectivity model. A series of experiments would provide more information on the true kinetic parameters in this scheme of reactions. With this model, we could demonstrate the importance of certain intermediates and their interrelationships based on the individual concentration in the coal bed system; moreover, guidance is provided on potential intermediates to monitor during gasification experiments. Any further refining of this kinetic model should focus on increasing this latency period.


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DISCUSSION Coal degradation is a complex process and the goal of this research was to come up with an initial lumped kinetic model. The assumptions were made to simplify the coal environment and provide a starting point for the modeling of coal degradation pathways using microbes. A detailed kinetic model of coal degradation is difficult to formulate because the model must account for the production of a large number of intermediates that operate under numerous reaction mechanisms under variable operating conditions. Inclusion of all the metabolites and their reaction mechanisms will not serve the purpose of model reproducibility under various conditions and might not assist future experimental work. Hence major assumptions were made in terms of the number of lumped species and their reaction kinetics. When more experimental data is obtained, the assumptions can be appropriately modified based on the data and model complexity can be improved. However, complex models should be built depending on their scope of utilization and application to experimental designs. Further future models should follow a progressive approach that should be designed with the expectation of defining new parameters to test for in lab design, which would then lead to validate the experimental results for bottlenecks. The roles of the microorganisms in all steps of the CBM process must be determined so that methanogenic communities can be fully utilized in microbial-enhanced CBM operations. With the hypotheses for important chemical intermediates outlined and modeled in this paper, experiments can be designed to monitor the concentrations of these compounds and determine the validity of the model assertions. Such experiments would allow for the tuning of kinetic rate constants and might shed more light on the presence of bottlenecks in the coal conversion process. The development and representation of a kinetic model provides a useful resource for


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understanding the process of microbial coal conversion and for predicting potential bottlenecks in the pathway from coal to methane gas. Metabolic bottlenecks can be interpreted from the connectivity map and mathematical simulation of the C2M process. Figure 1 suggests propionate, acetate, and succinate are points of metabolite accumulation. The further conversion of these compounds occurs much slower than their production, resulting in a choke point. Figure 2 shows the time-dependent nature of metabolites in the C2M Model. As previously mentioned, the three regions in the methane concentration curve can be correlated to the concentration of other metabolites. The initial exponential growth phase halts when the lactate concentration is at a maximum. The linear growth phase occurs while propionate is at a plateau, and the methane concentration starts to level out when the propionate starts to deplete. These trends and results suggest a bottleneck occurs at acetate, propionate, and lactate. Monitoring of these metabolites during CBM experiments would provide information about and a useful method of monitoring the system. This research effort has focused on the creation of a lumped kinetic model, and on the development of a connectivity and a mathematical model that describes the process from the perspective of chemical pathways. These models are shown to be realistically useful with experimental data validation. Modeling can play an important part in other aspects of the microbial coal conversion problem as well. Fluid flow is an important aspect of CBM operations, and so a model describing fluid dynamics in conjunction with chemical kinetics would be very useful. Another example is a biological, stochastic model to describe the microorganisms which convert coal to methane. Both types of models offer an alternative approach to the kinetic models presented here. ASSOCIATED CONTENT


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Supporting Information The following files are available free of charge. Mathematical Model and Sensitivity Analysis MATLAB Code (PDF) AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] Author Contributions Abhilash Sharma enhanced the Cytoscape model, performed the sensitivity analysis, and wrote the final draft of the manuscript. Aditya Jagarapu assisted in theorizing the kinetic model and contributed to much of the material in the final manuscript. Christopher Micale developed the C2M Kinetic Model and wrote the initial draft of the manuscript. Daman Walia provided data integral to the model validation. Scott Jackson acted as a resource and a mentor for Sharma, Jagarapu, and Micale. Prasad Dhurjati was a research advisor for all three students, conceived the research projects, and guided the development of the models. Notes The authors declare no competing financial interest. ACKNOWLEDGEMENT This work benefited from the insight of Nathaniel Grande, Andrew DiPietro, Kenneth Loprete, Daniel Cook, and Juan Lucio-Vega. Matthew Wiatrowski contributed extensively to the initial literature review. We thank Dr. Julia Maresca for sharing her knowledge of microbiology with us (and her textbooks), and for allowing us to use her laboratory space to begin our experimental


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efforts. We thank Ryan Dudek for his substantial contribution to the literature review. We thank the various members of the Microbial C2M Group for all the work put forth in the collaboration of this project. ABBREVIATIONS CBM:

Coal-bed methane

CSTR: Continuous Stirred-Tank Reactor C2M:

Coal to methane

CHO:

Chinese Hamster Ovary

GUI:

Graphics user interface

LCFA: Long-chain fatty acid ncu:

normalized concentration unit

ODE:

Ordinary differential equation

PAC:

Poly-aromatic compound

PDE:

Partial differential equation

SCFA: Short-chain fatty acid VFA:

Volatile fatty acid

REFERENCES (1) National Energy Technology Laboratory (NETL). 2010. Cost and performance baseline for fossil energy plants, Volume 1: Bituminous coal and natural gas to electricity. Revision 2. November. DOE/NETL-2010/1397. United States Department of Energy.


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(2) United States Energy Information Agency: Natural Gas 1998 Issues and Trends. http://webapp1.dlib.indiana.edu/virtual_disk_library/index.cgi/4265704/FID1578/pdf/gas/05609 8.pdf (Accessed March 15, 2018). (3) Emissions of Hazardous Air Pollutants from Coal-fired Power Plants, Paul Billings, Environmental Health & Engineering, March 2011. http://www.lung.org/assets/documents/healthy-air/coal-fired-plant-hazards.pdf (Accessed March 22, 2018). (4) Rubin, E. S., International pollution control costs of coal-fired power plants. Environmental Science & Technology 1983, 17 (8). (5) United States Environmental Protection Agency. Natural Gas Environmental Impacts. https://www.eia.gov/energyexplained/index.cfm?page=natural_gas_environment (Accessed March 18, 2018). (6) United States Department of Energy: Office of Fossil Energy. Carbon Storage Research and Development. http://energy.gov/fe/science-innovation/carbon-capture-and-storageresearch/carbon-storage-rd (Accessed Mar 18, 2018). (7) BP Statistical Review of World Energy 2014. http://large.stanford.edu/courses/2014/ph240/milic1/docs/bpreview.pdf (Accessed March 15, 2018). (8) United States Energy Information Agency: Natural Gas Gross Withdrawals and Production. http://www.eia.gov/dnav/ng/ng_prod_sum_dcu_nus_m.htm (Accessed Jan 1, 2018).


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(17) Jones, E.J.P.; Voytek, M.A.; Warwick, P.D.; Corum, M.D.; Cohn, A.; Bunnell, J.E.; Clark, A.C.; Orem, W.H. Bioassay for estimating the biogenic methane-generating potential of coal samples. Int. J. Coal Geol. 2008, 76, 138–150. (18) Strapoć, D.; Picardal, F.W.; Turich, C.; Schaperdoth, I.; Macalady, J.L.; Lipp, J.S.; Lin, Y.S.; Ertefai, T.F.; Schubotz, F.; Hinrichs, K.-U.; et al. Methane-Producing Microbial Community in a Coal Bed of the Illinois Basin. Appl. Environ. Microbiol. 2008, 74(8), 2424–2432. (19) Hofrichter, M.; Fakoussa, R. Microbial degradation and modification of coal. Biopolymers. 2001, 1, 393–429 (20) Fakoussa, R.M.; Hofrichter, M. Biotechnology and microbiology of coal degradation. Appl. Microbiol. Biotechnol. 1999, 52, 25–40. (21) Flores, R.M.; Rice, C.A.; Stricker, G.D.; Warden, A.; Ellis, M.S. Methanogenic pathways of coal-bed gas in the Powder River Basin, United States: The geologic factor. Int. J. Coal Geol. 2008, 76, 52–75. (22) Wawrik, B.; Mendivelso, M.; Parisi, V.A.; Suflita, J.M.; Davidova, I.A.; Marks, C.R.; Van Nostrand, J.D.; Liang, Y.; Zhou, J.; Huizinga, B.J.; et al. Field and laboratory studies on the bioconversion of coal to methane in the San Juan Basin. FEMS Microbiol. Ecol. 2012, 81, 26– 42. (23) Gouthami Senthamaraikkannan, Ian Gates, Vinay Prasad, Development of a multiscale microbial kinetics coupled gas transport model for the simulation of biogenic coalbed methane production, In Fuel, Volume 167, 2016, Pages 188-198, ISSN 0016-2361, https://doi.org/10.1016/j.fuel.2015.11.038.


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(32) Gieg, L.M.; Suflita, J.M. Detection of Anaerobic Metabolites of Saturated and Aromatic Hydrocarbons in Petroleum-Contaminated Aquifers. Environ. Sci. Technol. 2002, 36, 3755– 3762. (33) Meslé, M.; Dromart, G.; Oger, P. Microbial methanogenesis in subsurface oil and coal. Res. Microbiol. 2013, 164, 959–972. (34) So, C. M.; Phelps, C. D.; Young, L. Y. Anaerobic Transformation of Alkanes to Fatty Acids by a Sulfate-Reducing Bacterium, Strain Hxd3. Appl. Environ. Microbiol. 2003, 69(7), 3892– 3900 (35) Dhurjati, P. and Mahadevan, R., Systems Biology: The synergistic interplay between biology and mathematics. Can. J. Chem. Eng. 2008, 86: 127–141. doi:10.1002/cjce.20025 (36) Dariusz Strąpoć, Maria Mastalerz, Katherine Dawson, Jennifer Macalady, Amy V. Callaghan, Boris Wawrik, Courtney Turich, Matthew Ashby, Annual Review of Earth and Planetary Sciences 2011, 39:1, 617-656 (37) Hu, W.-S. Cell Culture Bioprocess Engineering; CRC Press, 2018. (38) Pankhania, I.P., Spormann, A.M., Hamilton, W.A. et al. Arch. Microbiol. (1988) 150: 26. https://doi.org/10.1007/BF00409713 (39) Walia, D. S.; Srivastava, K. C. Biological production of humic acid and clean fuels from coal, December 28, 1998. (40) Hungate, R. E. (1969). Chapter IV A roll tube method for cultivation of strict anaerobes. In Methods in microbiology (Vol. 3, pp. 117-132). Academic Press.


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Modeling Framework for Biogenic Methane Formation from Coal

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