Hydrogen production from lignocellulosic hydrolysate in an up-scaled microbial electrolysis cell with stacked bio-electrodes

Zero-discharge, self-sustained 3D-printed microbial electrolysis cell for biohydrogen production: a review

Microbial fuel cell (MFC) and microbial electrolysis cell (MEC) technologies have been used recently in bench-scale bioenergy (electricity) generation, biohydrogen (H2) production, biosensing, and wastewater treatment. There are still a lot of obstacles to overcome in terms of commercialization and industrial settling. These difficulties include lengthy start-up times, intricate reactor designs for managing large reaction volumes, and expensive and time-consuming large-scale system fabrication procedures. Interestingly, combining three-dimensional (3D) printing with MFC and MEC technology appears to be a workable and promising way to get past these obstacles. Moreover, a rapid start-up with no delays in the current generation using MFC and MEC is possible with 3D printed bio-anodes. Furthermore, H2 can be generated from wastewater by powering a stacked MFC and MEC-coupled with electrochemical capacitor (ECC) system using 3D printing technology. To the best of the author's knowledge, this review paper is the first to explicitly highlight the use of 3D printing in creating a stacked MFC-ECC-MEC system in conjunction with a photobioreactor (PBR) to produce significant quantities of H2 and carbon dioxide (CO2) can be utilized for algae production. A notable feature of 3D printing technology is its reliable production capabilities, enabling MFC-ECC-MEC-PBR systems to be expanded by setting up numerous stacks of MFC-ECC-MEC-PBR units devoid of material waste and human error. The present review attempts to provide an update on the current status of the 3D printing application, that is meant to propel the MFC-ECC-MEC-PBR system forward.

Leveraging microbial synergy: Predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN

Microbial Fuel Cells (MFCs) are a sophisticated and advanced system that uses exoelectrogenic microorganisms to generate bioenergy. Predicting performance outcomes under experimental settings is challenging due to the intricate interactions that occur in mixed-species bioelectrochemical reactors like MFCs. One of the key factors that limit the MFC's performance is the presence of a microbial consortium. Traditionally, multiple microbial consortia are implemented in MFCs to determine the best consortium. This approach is laborious, inefficient, and wasteful of time and resources. The increase in the availability of soft computational techniques has allowed for the development of alternative strategies like artificial intelligence (AI) despite the fact that a direct correlation between microbial strain, microbial consortium, and MFC performance has yet to be established. In this work, a novel generic AI model based on subspace k-Nearest Neighbour (SS-kNN) is developed to identify and forecast the best microbial consortium from the constituent microbes. The SS-kNN model is trained with thirty-five different microbial consortia sharing different effluent properties. Chemical oxygen demand (COD) reduction, voltage generation, exopolysaccharide (EPS) production, and standard deviation (SD) of voltage generation are used as input features to train the SS-kNN model. The proposed SS-kNN model offers an accuracy of 100% during training period and 85.71% when it is tested with the data obtained from existing literature. The implementation of selected consortium (as predicted by SS-kNN model) improves the COD reduction capability of MFC by 15.67% than that of its constituent microbes which is experimentally verified. In addition, to prevent the effects of climate change and mitigate water pollution, the implementation of MFC technology ensures clean and green electricity. Consequently, achieving sustainable development goals (SDG) 6, 7, and 13.

Hydrogen production in microbial electrolysis cells with biocathodes

Electroautotrophic microbes at biocathodes in microbial electrolysis cells (MECs) can catalyze the hydrogen evolution reaction with low energy demand, facilitating long-term stable performance through specific and renewable biocatalysts. However, MECs have not yet reached commercialization due to a lack of understanding of the optimal microbial strains and reactor configurations for achieving high performance. Here, we critically analyze the criteria for the inocula selection, with a focus on the effect of hydrogenase activity and microbe-electrode interactions. We also evaluate the impact of the reactor design and key parameters, such as membrane type, composition, and electrode surface area on internal resistance, mass transport, and pH imbalances within MECs. This analysis paves the way for advancements that could propel biocathode-assisted MECs toward scalable hydrogen gas production.

Potential application of bioelectrochemical systems in cold environments

Globally, more than half of the world's regions and populations inhabit psychrophilic and seasonally cold environments. Lower temperatures can inhibit the metabolic activity of microorganisms, thereby restricting the application of traditional biological treatment technologies. Bioelectrochemical systems (BES), which combine electrochemistry and biocatalysis, can enhance the resistance of microorganisms to unfavorable environments through electrical stimulation, thus showing promising applications in low-temperature environments. In this review, we focus on the potential application of BES in such environments, given the relatively limited research in this area due to temperature limitations. We select microbial fuel cells (MFC), microbial electrolytic cells (MEC), and microbial electrosynthesis cells (MES) as the objects of analysis and compare their operational mechanisms and application fields. MFC mainly utilizes the redox potential of microorganisms during substance metabolism to generate electricity, while MEC and MES promote the degradation of refractory substances by augmenting the electrode potential with an applied voltage. Subsequently, we summarize and discuss the application of these three types of BES in low-temperature environments. MFC can be employed for environmental remediation as well as for biosensors to monitor environmental quality, while MEC and MES are primarily intended for hydrogen and methane production. Additionally, we explore the influencing factors for the application of BES in low-temperature environments, including operational parameters, electrodes and membranes, external voltage, oxygen intervention, and reaction devices. Finally, the technical, economic, and environmental feasibility analyses reveal that the application of BES in low-temperature environments has great potential for development.

Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field.

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.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

Value addition through biohydrogen production and integrated processes from hydrothermal pretreatment of lignocellulosic biomass

Bioenergy production is the most sought-after topics at the crunch of energy demand, climate change and waste generation. In view of this, lignocellulosic biomass (LCB) rich in complex organic content has the potential to produce bioenergy in several forms following the pretreatment. Hydrothermal pretreatment that employs high temperatures and pressures is gaining momentum for organics recovery from LCB which can attain value-addition. Diverse bioprocesses such as dark fermentation, anaerobic digestion etc. can be utilized following the pretreatment of LCB which can result in biohydrogen and biomethane production. Besides, integration approaches for LCB utilization that enhance process efficiency and additional products such as biohythane production as well as application of solid residue obtained after LCB pretreatment were discussed. Importance of hydrothermal pretreatment as one of the suitable strategies for LCB utilization is emphasized suggesting its future potential in large scale energy recovery.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Wastewater-derived biohydrogen: Critical analysis of related enzymatic processes at the research and large scales

Organic-rich wastewater is a feasible feedstock for biohydrogen production. Numerous review on the performance of microorganisms and the diversity of their communities during a biohydrogen process were published. However, there is still no in-depth overview of enzymes for biohydrogen production from wastewater and their scale-up applications. This review aims at providing an insightful exploration of critical discussion in terms of: (i) the roles and applications of enzymes in wastewater-based biohydrogen fermentation; (ii) systematical introduction to the enzymatic processes of photo fermentation and dark fermentation; (iii) parameters that affect enzymatic performances and measures for enzyme activity/ability enhancement; (iv) biohydrogen production bioreactors; as well as (v) enzymatic biohydrogen production systems and their larger scales application. Furthermore, to assess the best applications of enzymes in biohydrogen production from wastewater, existing problems and feasible future studies on the development of low-cost enzyme production methods and immobilized enzymes, the construction of multiple enzyme cooperation systems, the study of biohydrogen production mechanisms, more effective bioreactor exploration, larger scales enzymatic biohydrogen production, and the enhancement of enzyme activity or ability are also addressed.

Boosting hydrogen production from fermentation effluent of biomass wastes in cylindrical single-chamber microbial electrolysis cell

Microbial electrolysis cells (MECs) are considered as green technologies for H₂ production with simultaneously wastewater treatment. Low H₂ recovery and production rate are two key bottlenecks of MECs fed with real H₂ fermentation effluent of biomass wastes. This work established a 1 L cylindrical single chamber MEC and enriched electroactive anodic biofilms from cow dung compost to overcome the bottlenecks. These MEC components (platinum-coated cylindrical titanium mesh cathode and cylindrical graphite felt anode) were arranged in a concentric configuration. A high H₂ production rate of 6.26 ± 0.23 L L^-1 day^-1 with H₂ yield of 5.75 ± 0.16 L H₂ L^-1 fermentation effluent was achieved at 0.8 V. The degradation of acetate and butyrate (main components of H₂ fermentation effluent) could reach to 95.3 ± 2.1% and 78.4 ± 3.6%, respectively. The microbial community analysis for anode showed the abundance of Geobacter (22.6%), Syntrophomonas (8.7%), and Dysgonomonas (6.3%) which are responsible to complex substrate oxidation, electrical current generation, and H₂ production. These results prove the feasibility of cylindrical single-chamber MEC for high H₂ production rate and high acetate and butyrate removal from H₂ fermentation effluent.

Scalability of microbial electrochemical technologies: Applications and challenges

During wastewater treatment, microbial electrochemical technologies (METs) are a promising means for in situ energy harvesting and resource recovery. The primary constraint for such systems is scaling them up from the laboratory to practical applications. Currently, most research (∼90%) has been limited to benchtop models because of bioelectrochemical, economic, and engineering design limitations. Field trials, i.e., 1.5 m³ bioelectric toilet, 1000 L microbial electrolysis cell and industrial applications of METs have been conducted, and their results serve as positive indicators of their readiness for practical applications. Multiple startup companies have invested in the pilot-scale demonstrations of METs for industrial effluent treatment. Recently, advances in membrane/electrode modification, understanding of microbe-electrode interaction, and feasibility of electrochemical redox reactions have provided new directions for realizing the practical application. This study reviews the scaling-up challenges, success stories for onsite use, and readiness level of METs for commercialization that is inexpensive and sustainable.