Mathematical modeling of two-chamber batch microbial fuel cell with pure culture of Shewanella

Machine learning: an advancement in biochemical engineering

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

Energy generation from bioelectrochemical techniques: Concepts, reactor configurations and modeling approaches

The process industries play a significant role in boosting the economy of any nation. However, poor management in several industries has been posing worrisome threats to an environment that was previously immaculate. As a result, the untreated waste and wastewater discarded by many industries contain abundant organic matter and other toxic chemicals. It is more likely that they disrupt the proper functioning of the water bodies by perturbing the sustenance of many species of flora and fauna occupying the different trophic levels. The simultaneous threats to human health and the environment, as well as the global energy problem, have encouraged a number of nations to work on the development of renewable energy sources. Hence, bioelectrochemical systems (BESs) have attracted the attention of several stakeholders throughout the world on many counts. The bioelectricity generated from BESs has been recognized as a clean fuel. Besides, this technology has advantages such as the direct conversion of substrate to electricity, and efficient operation at ambient and even low temperatures. An overview of the BESs, its important operating parameters, bioremediation of industrial waste and wastewaters, biodegradation kinetics, and artificial neural network (ANN) modeling to describe substrate removal/elimination and energy production of the BESs are discussed. When considering the potential for use in the industrial sector, certain technical issues of BES design and the principal microorganisms/biocatalysts involved in the degradation of waste are also highlighted in this review.

Sustainable application of tubular photosynthesis microbial desalination cell for simultaneous desalination of seawater for potable water supply associated with sewage treatment and energy recovery

A sustainable approach for simultaneous desalination of actual seawater for potable water supply, and bioelectrochemical treatment of sewage associated with power generation was evaluated in a tubular photosynthesis desalination microbial fuel cell (PDMC) continually operated for 180 days. Anion exchange membrane (AEM) was used to separate the bioanode and desalination compartments, whereby, and cation exchange membrane (CEM) was used to separate the desalination and biocathode compartments. Mixed bacterial species and mixed microalgae were utilized for inoculation of the bioanode and biocathode, respectively. The results revealed that maximum and average desalination efficiencies of saline seawater fed to the desalination compartment were 80 ± 1 % and 72 ± 1.2 %, respectively. Maximum and average removal efficiencies of the sewage organic content in the anodic compartment were up to 99.3 ± 0.5 % and 91.0 ± 0.8 %, respectively associated with maximum power output of 430.7 ± 0.7 mW/m³. In spite of the heavy growth of the mixed bacterial species and microalgae as well, no fouling of AEM and CEM was observed during the entire period of operation. Kinetic study demonstrated that Blackman model described well the bacterial growth. Dense and healthy growth of biofilm and the microalgae in the anodic and cathodic compartments, respectively were clearly observed during the operation period. The promising outcomes of this investigation demonstrated that the suggested approach is a potential sustainable option for simultaneous desalination of saline seawater for potable water supply, biotreatment of sewage, and power generation.

Gluconobacter Oxydans-Based MFC with PEDOT:PSS/Graphene/Nafion Bioanode for Wastewater Treatment

Microbial fuel cells (MFCs) are a variety of bioelectrocatalytic devices that utilize the metabolism of microorganisms to generate electric energy from organic matter. This study investigates the possibility of using a novel PEDOT:PSS/graphene/Nafion composite in combination with acetic acid bacteria Gluconobacter oxydans to create a pure culture MFC capable of effective municipal wastewater treatment. The developed MFC was shown to maintain its activity for at least three weeks. The level of COD in municipal wastewater treatment was reduced by 32%; the generated power was up to 81 mW/m² with a Coulomb efficiency of 40%. Combining the MFC with a DC/DC boost converter increased the voltage generated by two series-connected MFCs from 0.55 mV to 3.2 V. A maximum efficiency was achieved on day 8 of MFC operation and was maintained for a week; capacitors of 6800 µF capacity were fully charged in ~7 min. Thus, G. oxydans cells can become an important part of microbial consortia in MFCs used for treatment of wastewaters with reduced pH.

Review on microbial fuel cells applications, developments and costs

The microbial fuel cell (MFC) technology has attracted significant attention in the last years due to its potential to recover energy in a wastewater treatment. The idea of using an MFC in industry is very attractive as the organic wastes can be converted into energy, reducing the waste disposal costs and the energy needs while increasing the company profit. However, taking aside these promising prospects, the attempts to apply MFCs in large-scale have not been succeeded so far since their lower performance and high costs remains challenging. This review intends to present the main applications of the MFC systems and its developments, particularly the advances on configuration and operating conditions. The diagnostic techniques used to evaluate the MFC performance as well as the different modeling approaches are described. Towards the introduction of the MFC in the market, a cost analysis is also included. The development of low-cost materials and more efficient systems, with high higher power outputs and durability, are crucial towards the application of MFCs in industrial/large scale. This work is a helpful tool for discovering new operation and design regimes.

Fundamental understanding of microbial fuel cell technology: Recent development and challenges

The research on microbial fuel cells (MFCs) is rising tremendously but its commercialization is restricted by several microbiological, material, and economic constraints. Hence, a systematic assessment of the research articles published previously focusing on potential upcoming directions in this field is necessary. A detailed multi-perspective analysis of various techniques for enhancing the efficiency of MFC in terms of electric power production is presented in this paper. A brief discussion on the central aspects of different issues are preceded by an extensive analysis of the strategies that can be introduced to optimize power generation and reduce energy losses. Various applications of MFCs in a broad spectrum ranging from biomedical to underwater monitoring rather than electricity production and wastewater treatment are also presented followed by relevant possible case studies. Mathematical modeling is used to understand the concepts that cannot be understood experimentally. These methods relate electrode geometries to microbiological reactions occurring inside the MFC chamber, which explains the system's behavior and can be improved. Finally, directions for future research in the field of MFCs have been suggested. This article can be beneficial for engineers and researchers concerned about the challenges faced in the application of MFC.

Modelling of self-sustainable microbial fuel cell type oil sensors based on restricted oxygen transfer and two-population competition

Oil leaks during oil industrial chain pose threats to the ecosystem. The microbial fuel cell-type oil sensor has been developed for early warning of such issues. Oil contacting with the sensor restricts oxygen availability and triggers correlative signal anomaly which serves as indicative of the oil presence. To extend its application for the real world, modelling of the sensor is required to pre-describe the signal behavior under unknown conditions. Therefore, by integrating Butler-Volmer, restricted oxygen transfer (ROT) and Monod equations, a dynamic ROT-MFC model with sufficient substrate precondition was developed. The ROT-MFC model was trained on the experimental single-oil-shock test (R 2 = 0.996) and validated by the experimental sequential-shocktest (R 2 = 0.998). Numerical analysis of the trained ROT-MFC model indicates that the single-shock detection has higher sensitivity (≥40.6 mV/detection) and the sequential-shocks detection spends a shorter response time (≤2.2 h). Besides, the sequential-shocks detection with proper strategy is more applicable due to flexible options on detection limit and working range. The model was further evolved into the TPC-ROT-MFC model by introducing a two-population competition (TPC) theory to describe performance under limited substrate conditions. Results indicate a critical substrate concentration range (42.1 to 62.8 mg-COD/L) for dividing baseline steadiness, and that the impact of substrate concentration on anodic charge transfer coefficient soars when the substrate concentration lessens furtherly. This sensor model is relatively easy to implement and may enhance practical use for design and operation.

Statistical Modeling and Performance Optimization of a Two-Chamber Microbial Fuel Cell by Response Surface Methodology

Microbial fuel cell, as a promising technology for simultaneous power production and waste treatment, has received a great deal of attention in recent years; however, generation of a relatively low power density is the main limitation towards its commercial application. This study contributes toward the optimization, in terms of maximization, of the power density of a microbial fuel cell by employing response surface methodology, coupled with central composite design. For this optimization study, the interactive effect of three independent parameters, namely (i) acetate concentration in the influent of anodic chamber; (ii) fuel feed flow rate in anodic chamber; and (iii) oxygen concentration in the influent of cathodic chamber, have been analyzed for a two-chamber microbial fuel cell, and the optimum conditions have been identified. The optimum value of power density was observed at an acetate concentration, a fuel feed flow rate, and an oxygen concentration value of 2.60 mol m−3, 0.0 m3, and 1.00 mol m−3, respectively. The results show the achievement of a power density of 3.425 W m−2, which is significant considering the available literature. Additionally, a statistical model has also been developed that correlates the three independent factors to the power density. For this model, R2, adjusted R2, and predicted R2 were 0.839, 0.807, and 0.703, respectively. The fact that there is only a 3.8% error in the actual and adjusted R2 demonstrates that the proposed model is statistically significant.

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.

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters.

Shortcut Biological Nitrogen Removal (SBNR) in an MFC Anode Chamber under Microaerobic Conditions: The Effect of C/N Ratio and Kinetic Study

In this work, the feasibility of the Shortcut Biological Nitrogen Removal (SBNR) in the anodic chamber of a Microbial Fuel Cell (MFC) was investigated. Thirty day experiments were carried out using synthetic wastewaters with a Total Organic Carbon vs. nitrogen ratio (TOC/N) ranging from 0.1 to 1. Ammonium, nitrite, nitrate, pH, and TOC were daily monitored. Results showed that microaerobic conditions in the anodic chamber favored the development of nitritation reaction, due to oxygen transfer from the cathodic chamber through the membrane. Nitritation was found to depend on TOC/N ratio: at TOC/N equal to 0.1 an ammonium removal efficiency of up to 76% was observed. Once the oxygen supply to the cathodic chamber was stopped, denitritation occurred, favored by an increase of the TOC/N ratio: a nitrite removal of 80.3% was achieved at TOC/N equal to 0.75. The presence of nitrogen species strongly affected the potential of the electrochemical system: in the nitritation step, the Open Circuit Voltage (OCV) decreased from 180 mV to 21 mV with the decrease of the TOC/N ratio in the investigated range. Lower OCV values were observed in the denitritation steps since the organic carbon acted as the energy source for the conversion of nitrite to nitrogen gas. A kinetic analysis was also performed. Monod and Blackman models described the ammonium and the organic carbon removal processes well during the nitritation step, respectively, while Blackman-Blackman fitted experimental results of the denitritation step better.