Multi-population model of a microbial electrolysis cell

Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater

Microbial electrolysis cells (MECs) have demonstrated high-rate H₂ production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H₂ production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. We also provide perspectives on future development needed to transition MEC technology to the marketplace.

Model development of bioelectrochemical systems: A critical review from the perspective of physiochemical principles and mathematical methods

Bioelectrochemical systems (BESs) are promising devices for wastewater treatment and bio-energy production. Since various processes are interacted and affect the overall performance of the device, the development of theoretical modeling is an efficient approach to understand the fundamental mechanisms that govern the performance of the BES. This review aims to summarize the physiochemical principle and mathematical method in BES models, which is of great importance for the establishment of an accurate model while has received little attention in previous reviews. In this review, we begin with a classification of existing models including bioelectrochemical models, electronic models, and machine learning models. Subsequently, physiochemical principles and mathematical methods in models are discussed from two aspects: one is the description of methodology how to build a framework for models, and the other is to further review additional methods that can enrich model functions. Finally, the advantages/disadvantages, extended applications, and perspectives of models are discussed. It is expected that this review can provide a viewpoint from methodologies to understand BES models.

A Catalytic Effectiveness Factor for a Microbial Electrolysis Cell Biofilm Model

The aim of this work is to propose a methodology to obtain an effectiveness factor for biofilm in a microbial electrolysis cell (MEC) system and use it to reduce a partial differential equation (PDE) biofilm MEC model to an ordinary differential equation (ODE) MEC model. The biofilm mass balances of the different species are considered. In addition, it is considered that all the involved microorganisms are attached to the anodic biological film. Three effectiveness factors are obtained from partial differential equations describing the spatial distributions of potential and substrate in the biofilm. Then, a model reduction is carried out using the global mass balances of the different species in the system. The reduced model with three uncertain but bounded effectiveness factors is evaluated numerically and analyzed in the sense of stability and parametric sensibility to demonstrate its applicability. The reduced ODE model is compared with a validated model taken from the literature, and the results are in good agreement. The biofilm effectiveness factor in MEC systems can be extended to the reduction of PDE models to obtain ODE models that are commonly used in optimization and control problems.

A scalable model of fluid flow, substrate removal and current production in microbial fuel cells

Mathematical modelling can reduce the cost and time required to design complex systems, and is being increasingly used in microbial electrochemical technologies (METs). To be of value such models must be complex enough to reproduce important behaviour of MET, yet simple enough to provide insight into underlying causes of this behaviour. Ideally, models must also be scalable to future industrial applications, rather than limited to describing existing laboratory experiments. We present a scalable model for simulating both fluid flow and bioelectrochemical processes in microbial fuel cells (MFCs), benchmarking against an experimental pilot-scale bioreactor. The model describes substrate transport through a two-dimensional fluid domain, and biofilm growth on anode surfaces. Electron transfer is achieved by an intracellular redox mediator. We find significant spatial variations in both substrate concentration and current density. Simple changes to the reactor layout can greatly improve the overall efficiency, measured in terms of substrate removal and total current generated.

Modelling the cathodic reduction of 2,4-dichlorophenol in a microbial fuel cell

This work presents a simplified mathematical model able to predict the performance of a microbial fuel cell (MFC) for the cathodic dechlorination of 2,4-dichlorophenol (2,4-DCP) operating at different cathode pH values (7.0 and 5.0). Experimental data from previous work were utilized for the fitting of the model. The MFC modelled consisted of two chambers (bioanode and abiotic cathode), wherein the catholyte contained 300 mg L⁻¹ of 2,4-DCP and the anolyte 1000 mg L⁻¹ of sodium acetate. The model considered two mixed microbial populations in the anode compartment using sodium acetate as the carbon source for growth and maintenance: electrogenic and non-electrogenic biomass. 2,4-DCP, its intermediates of the reductive process (2-chlorophenol, 2-CP and 4-chlorophenol, 4-CP) and protons were considered in the model as electron acceptors in the electrogenic mechanism. The global process rate was assumed to be controlled by the biological mechanisms and modelled using multiplicative Monod-type equations. The formulation of a set of differential equations allowed to describe the simultaneous evolution of every component: concentration of sodium acetate in the anodic compartment; and concentration of 2,4-DCP, 2-CP, 4-CP, phenol and chloride in the cathode chamber. Current production and coulombic efficiencies were also estimated from the fitting. It was observed that most of the organic substrate was used by non-electrogenic mechanism. The influence of the Monod parameters was more important than the influence of the biomass yield coefficients. Finally, the model was employed to simulate different scenarios under distinct experimental conditions.

Linking population dynamics to microbial kinetics for hybrid modeling of bioelectrochemical systems

Mechanistic and data-driven models have been developed to provide predictive insights into the design and optimization of engineered bioprocesses. These two modeling strategies can be combined to form hybrid models to address the issues of parameter identifiability and prediction interpretability. Herein, we developed a novel and robust hybrid modeling strategy by incorporating microbial population dynamics into model construction. The hybrid model was constructed using bioelectrochemical systems (BES) as a platform system. We collected 77 samples from 13 publications, in which the BES were operated under diverse conditions, and performed holistic processing of the 16S rRNA amplicon sequencing data. Community analysis revealed core populations composed of putative electroactive taxa Geobacter, Desulfovibrio, Pseudomonas, and Acinetobacter. Primary Bayesian networks were trained with the core populations and environmental parameters, and directed Bayesian networks were trained by defining the operating parameters to improve the prediction interpretability. Both networks were validated with Bray-Curtis similarly, relative root-mean-square error (RMSE), and a null model. A hybrid model was developed by first building a three-population mechanistic component and subsequently feeding the estimated microbial kinetic parameters into network training. The hybrid model generated a simulated community that shared a Bray-Curtis similarity of 72% with the actual microbial community at the genus level and an average relative RMSE of 7% for individual taxa. When examined with additional samples that were not included in network training, the hybrid model achieved accurate prediction of current production with a relative error-based RMSE of 0.8 and outperformed the data-driven models. The genomics-enabled hybrid modeling strategy represents a significant step toward robust simulation of a variety of engineered bioprocesses.

Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

Hydrogen production automatic control in continuous microbial electrolysis cells reactors used in wastewater treatment

In this paper, two control laws are proposed and applied in a model for a continuous Microbial Electrochemical Cells system. The used model is based on mass balances describing the behavior of substrate consumption, microbial growth, competition between anodophilic and methanogenic microorganisms for the carbon source in the anode, hydrogen generation, and electrical current production. The main control objective is to improve the electrical current generated and thus the production of bio-hydrogen gas in the reactor, using the dilution rate and the applied potential as individual control input variables. The control laws implemented are nonlinear adaptive type. In order to demonstrate its usefulness, numerical simulation runs involving multiple set-point changes and input perturbations were conducted for each control variable. The results of these simulations show that both control laws were able to respond adequately and efficiently to the disturbances and reach the reference value to which they were subjected. Moreover, it is possible to control both the electrical current produced and the hydrogen produced. Finally, these simulations also show that the highest rate of hydrogen production can be obtained using the applied potential as a control input, but such productivity is only attainable for a short period of time.

In situ groundwater remediation with bioelectrochemical systems: A critical review and future perspectives

Groundwater contamination is an ever-growing environmental issue that has attracted much and undiminished attention for the past half century. Groundwater contamination may originate from both anthropogenic (e.g., hydrocarbons) and natural compounds (e.g., nitrate and arsenic); to tackle the removal of these contaminants, different technologies have been developed and implemented. Recently, bioelectrochemical systems (BES) have emerged as a potential treatment for groundwater contamination, with reported in situ applications that showed promising results. Nitrate and hydrocarbons (toluene, phenanthrene, benzene, BTEX and light PAHs) have been successfully removed, due to the interaction of microbial metabolism with poised electrodes, in addition to physical migration due to the electric field generated in a BES. The selection of proper BESs relies on several factors and problems, such as the complexity of groundwater and subsoil environment, scale-up issues, and energy requirements that need to be accounted for. Modeling efforts could help predict case scenarios and select a proper design and approach, while BES-based biosensing could help monitoring remediation processes. In this review, we critically analyze in situ BES applications for groundwater remediation, focusing in particular on different proposed setups, and we identify and discuss the existing research gaps in the field.

Progress towards catalyzing electro-methanogenesis in anaerobic digestion process: Fundamentals, process optimization, design and scale-up considerations

Electro-methanogenesis represents an emerging bio-methane production pathway that can be achieved through integrating microbial electrolysis cell (MEC) with conventional anaerobic digester (AD). Since 2009, a significant number of publications have reported superior methane productivity and kinetics from MEC-AD integrated systems. The overall objective of this review is to communicate the recent advances towards promoting electro-methanogenesis in the anaerobic digestion process. Firstly, the electro-methanogenesis pathways and functional roles of key microbial members are summarized. Secondly, various extrinsic process parameters, such as applied voltage/potential, pH, and temperature are discussed with emphasis on process optimization. Moreover, available methods for the inoculation and start-up of MEC-AD process are critically reviewed. Finally, system design and scale-up considerations, such as the selection of electrode materials, surface area and surface chemistry of electrode materials, and electrode spacing are summarized.

A Dynamic Biofilm Model for a Microbial Electrolysis Cell

In this work, a mathematical description of a Microbial Electrolysis Cell (MEC) is proposed, taking into account the global mass balances of the different species in the system and considering that all the involved microorganisms are attached to the anodic biological film. Three main biological reactions are introduced, which were obtained from the solution of partial differential equations describing the spatial distribution of potential and substrate in the biofilm. The simulation of the model was carried out using numerical methods, and the results are discussed.

Understanding the interdependence of operating parameters in microbial electrosynthesis: a numerical investigation

A mathematical model to predict the influence of system parameters such as substrate concentrations and operation cycle time on MES performance.

Bioelectrochemical Systems for Removal of Selected Metals and Perchlorate from Groundwater: A Review

Groundwater contamination is a major issue for human health, due to its largely diffused exploitation for water supply. Several pollutants have been detected in groundwater; amongst them arsenic, cadmium, chromium, vanadium, and perchlorate. Various technologies have been applied for groundwater remediation, involving physical, chemical, and biological processes. Bioelectrochemical systems (BES) have emerged over the last 15 years as an alternative to conventional treatments for a wide variety of wastewater, and have been proposed as a feasible option for groundwater remediation due to the nature of the technology: the presence of two different redox environments, the use of electrodes as virtually inexhaustible electron acceptor/donor (anode and cathode, respectively), and the possibility of microbial catalysis enhance their possibility to achieve complete remediation of contaminants, even in combination. Arsenic and organic matter can be oxidized at the bioanode, while vanadium, perchlorate, chromium, and cadmium can be reduced at the cathode, which can be biotic or abiotic. Additionally, BES has been shown to produce bioenergy while performing organic contaminants removal, lowering the overall energy balance. This review examines the application of BES for groundwater remediation of arsenic, cadmium, chromium, vanadium, and perchlorate, focusing also on the perspectives of the technology in the groundwater treatment field.

Life cycle, techno-economic and dynamic simulation assessment of bioelectrochemical systems: A case of formic acid synthesis

A novel framework, integrating dynamic simulation (DS), life cycle assessment (LCA) and techno-economic assessment (TEA) of a bioelectrochemical system (BES), has been developed to study for the first time wastewater treatment by removal of chemical oxygen demand (COD) by oxidation in anode and thereby harvesting electron and proton for carbon dioxide reduction reaction or reuse to produce products in cathode. Increases in initial COD and applied potential increase COD removal and production (in this case formic acid) rates. DS correlations are used in LCA and TEA for holistic performance analyses. The cost of production of HCOOH is €0.015-0.005 g^-1 for its production rate of 0.094-0.26 kg yr^-1 and a COD removal rate of 0.038-0.106 kg yr^-1. The life cycle (LC) benefits by avoiding fossil-based formic acid production (93%) and electricity for wastewater treatment (12%) outweigh LC costs of operation and assemblage of BES (-5%), giving a net 61MJkg^-1 HCOOH saving.

A novel model for predicting bioelectrochemical performance of microsized-MFCs by incorporating bacterial chemotaxis parameters and simulation of biofilm formation

Bacterial transport parameters play a fundamental role in microbial population dynamics, biofilm formation and bacteria dispersion. In this study, the novel model was extended based on the capability of microsized microbial fuel cells (MFCs) as amperometric biosensors to predict the cells' chemotactic and bioelectrochemical properties. The model prediction results coincide with the experimental data of Shewanella oneidensis and chemotaxis mutant of P. aeruginosa bdlA and pilT strains, indicating the complementary role of numerical predictions for bioscreening applications of microsized MFCs. Considering the general mechanisms for electron transfer, substrate biodegradation, microbial growth and bacterial dispersion are the main features of the presented model. In addition, the genetic algorithm method was implemented by minimizing the objective function to estimate chemotaxis properties of the different strains. Microsized MFC performance was assessed by analyzing the microbial activity in the biofilm and the anolyte.

Computational and experimental analysis of organic degradation positively regulated by bioelectrochemistry in an anaerobic bioreactor system

Methane production was tested in membrane-less microbial electrolysis cells (MECs) under closed-circuit (R_CC) and open-circuit (R_OC) conditions, using glucose as a substrate, to understand the regulatory effects of bioelectrochemistry in anaerobic digestion systems. A dynamic model was built to simulate methane productions and microbial dynamics of functional populations, which were colonized in groups R_CC and R_OC during the start-up stage. The experiment results showed significantly greater methane production in R_CC than R_OC, the average methane production of R_CC was 0.131 m³/m³/d, which was 1.4 times higher than that of R_OC (0.055 m³/m³/d). The simulation results revealed that bioelectrochemistry had a significant influence on the abundance of microorganisms involved in acidogenesis and methanogenesis. The abundance of glucose-uptaking microorganisms was 87% of the total biomass in R_OC without applied voltage, which was 20% higher than that in R_CC (67%) when external voltages were applied between the anode and cathode. The abundance of hydrogenotrophic methanogens in R_CC was 6% higher than that in R_OC. The simulation results were verified through 16S rDNA high-throughput sequencing analysis. An electron balance analysis revealed that alteration of the acidogenesis type led to more acetate and hydrogen production from glucose fermentation, compared with the situation without bioelectrochemistry. An additional pathway from acetate to hydrogen was introduced by bioelectrolysis. These two factors resulted in significant enhancement of methane production in R_CC. Bioelectrolysis process directly contributed to 26% of the total methane production after the start-up stage. When the applied voltages were cut down or decreased, R_CC could maintain considerable methane productions, because the microbial communities and electron transfer pathways were already formed. Starting-up with high voltage, but operating under low voltage, could be an energy-favorable strategy for accelerating biogas production in bioelectro-anaerobic bioreactors.

A Multiple Reaction Modelling Framework for Microbial Electrochemical Technologies

A mathematical model for the theoretical evaluation of microbial electrochemical technologies (METs) is presented that incorporates a detailed physico-chemical framework, includes multiple reactions (both at the electrodes and in the bulk phase) and involves a variety of microbial functional groups. The model is applied to two theoretical case studies: (i) A microbial electrolysis cell (MEC) for continuous anodic volatile fatty acids (VFA) oxidation and cathodic VFA reduction to alcohols, for which the theoretical system response to changes in applied voltage and VFA feed ratio (anode-to-cathode) as well as membrane type are investigated. This case involves multiple parallel electrode reactions in both anode and cathode compartments; (ii) A microbial fuel cell (MFC) for cathodic perchlorate reduction, in which the theoretical impact of feed flow rates and concentrations on the overall system performance are investigated. This case involves multiple electrode reactions in series in the cathode compartment. The model structure captures interactions between important system variables based on first principles and provides a platform for the dynamic description of METs involving electrode reactions both in parallel and in series and in both MFC and MEC configurations. Such a theoretical modelling approach, largely based on first principles, appears promising in the development and testing of MET control and optimization strategies.

Performance evaluation of a continuous-flow bioanode microbial electrolysis cell fed with furanic and phenolic compounds

An MEC bioanode operated under different continuous-flow conditions converts problematic furanic and phenolic compounds to renewable hydrogen.

Bioelectrochemical production of hydrogen in an innovative pressure-retarded osmosis/microbial electrolysis cell system: experiments and modeling

BACKGROUND

While microbial electrolysis cells (MECs) can simultaneously produce bioelectrochemical hydrogen and treat wastewater, they consume considerable energy to overcome the unfavorable thermodynamics, which is not sustainable and economically feasible in practical applications. This study presents a proof-of-concept system in which hydrogen can be produced in an MEC powered by theoretically predicated energy from pressure-retarded osmosis (PRO). The system consists of a PRO unit that extracts high-quality water and generates electricity from water osmosis, and an MEC for organic removal and hydrogen production. The feasibility of the system was demonstrated using simulated PRO performance (in terms of energy production and effluent quality) and experimental MEC results (e.g., hydrogen production and organic removal).

RESULTS

The PRO and MEC models were proven to be valid. The model predicted that the PRO unit could produce 485 mL of clean water and 579 J of energy with 600 mL of draw solution (0.8 M of NaCl). The amount of the predicated energy was applied to the MEC by a power supply, which drove the MEC to remove 93.7 % of the organic compounds and produce 32.8 mL of H2 experimentally. Increasing the PRO influent volume and draw concentration could produce more energy for the MEC operation, and correspondingly increase the MEC hydraulic retention time (HRT) and total hydrogen production. The models predicted that at an external voltage of 0.9 V, the MEC energy consumption reached the maximum PRO energy production. With a higher external voltage, the MEC energy consumption would exceed the PRO energy production, leading to negative effects on both organic removal and hydrogen production.

CONCLUSIONS

The PRO-MEC system holds great promise in addressing water-energy nexus through organic removal, hydrogen production, and water recovery: (1) the PRO unit can reduce the volume of wastewater and extract clean water; (2) the PRO effluents can be further treated by the MEC; and (3) the osmotic energy harvested from the PRO unit can be applied to the MEC for sustainable bioelectrochemical hydrogen production.

Mathematical model of dynamic behavior of microbial desalination cells for simultaneous wastewater treatment and water desalination

Microbial desalination cells (MDCs) are an emerging concept for simultaneous wastewater treatment and water desalination. This work presents a mathematical model to simulate dynamic behavior of MDCs for the first time through evaluating multiple factors such as organic supply, salt loading, and current generation. Ordinary differential equations were applied to describe the substrate as well as bacterial concentrations in the anode compartment. Local sensitivity analysis was employed to select model parameters that needed to be re-estimated from the previous studies. This model was validated by experimental data from both a bench- and a large-scale MDC system. It could fit current generation fairly well and simulate the change of salt concentration. It was able to predict the response of the MDC with time under various conditions, and also provide information for analyzing the effects of different operating conditions. Furthermore, optimal operating conditions for the MDC used in this study were estimated to have an acetate flow rate of 0.8 mL·min(-1), influent salt concentration of 15 g·L(-1) and salt solution flow rate of 0.04 mL·min(-1), and to be operated with an external resistor less than 30 Ω. The MDC model will be helpful with determining operational parameters to achieve optimal desalination in MDCs.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance.

Separation of competitive microorganisms using anaerobic membrane bioreactors as pretreatment to microbial electrochemical cells

Anaerobic membrane bioreactors (AnMBRs) as pretreatment to microbial electrochemical cells (MECs) were first assessed for improving energy recovery. A dual-chamber MEC was operated at hydraulic retention time (HRT) ranging from 1 to 8d, while operating conditions for an AnMBR were fixed. Current density was increased from 7.5 ± 0 to 14 ± 1A/m(2) membrane with increasing HRT. MEC tests with AnMBR permeate (mainly propionate and acetate) and propionate medium confirmed that propionate was fermented to acetate and hydrogen gas, and anode-respiring bacteria (ARB) utilized these fermentation products as substrate. Membrane separation in the AnMBR excluded fermenters and methanogens from the MEC, and thus no methane production was found in the MEC. The lack of fermenters, however, slowed down propionate fermentation rate, which limited current density in the MEC. To symphonize fermenters, H2-consumers, and ARB in biofilm anode is essential for improving current density, and COD removal.

Nernst-ping-pong model for evaluating the effects of the substrate concentration and anode potential on the kinetic characteristics of bioanode

Understanding the electron-transfer mechanism and kinetic characteristics of bioanodes is greatly significant to enhance the electron-generating efficiencies in bioelectrochemical systems (BESs). A Nernst-ping-pong model is proposed here to investigate the kinetics and biochemical processes of bioanodes in a microbial electrolysis cell. This model can accurately describe the effects of the substrate (including substrate inhibition) and the anode potential on the current of bioanodes. Results show that the half-wave potential positively shifts as the substrate concentration increases, indicating that the rate-determining steps of anodic processes change from substrate oxidation to intracellular electron transport reaction. The anode potential has negligible effects on the enzymatic catalysis of anodic microbes in the range of -0.25 V to +0.1 V vs. a saturated calomel electrode. It turns out that to reduce the anodic energy loss caused by overpotential, higher substrate concentrations are preferred, if the substrate do not significantly and adversely affect the output current.