Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms.

The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles.

Microbial Community Predicts Functional Stability of Microbial Fuel Cells.

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation.

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown feed substrate must be determined. The application of genomic data and well-developed data mining technologies can overcome these limitations and advance engineering development.

Conductive properties of methanogenic biofilms.

Extracellular electron transfer between syntrophic partners needs to be efficiently maintained in methanogenic environments. Direct extracellular electron transfer via electrical current is an alternative to indirect hydrogen transfer but requires construction of conductive extracellular structures. Conductive mechanisms and relationship between conductivity and the community composition in mixed-species methanogenic biofilms are not well understood.

Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters.

Stay connected: Electrical conductivity of microbial aggregates.

The discovery of direct extracellular electron transfer offers an alternative to the traditional understanding of diffusional electron exchange via small molecules. The establishment of electronic connections between electron donors and acceptors in microbial communities is critical to electron transfer via electrical currents. These connections are facilitated through conductivity associated with various microbial aggregates.

Millimeter scale electron conduction through exoelectrogenic mixed species biofilms.

The functioning of many natural and engineered environments is dependent on long distance electron transfer mediated through electrical currents. These currents have been observed in exoelectrogenic biofilms and it has been proposed that microbial biofilms can mediate electron transfer via electrical currents on the centimeter scale. However, direct evidence to confirm this hypothesis has not been demonstrated and the longest known electrical transfer distance for single species exoelectrogenic biofilms is limited to 100 μm.

Redox Conductivity of Current-Producing Mixed Species Biofilms.

While most biological materials are insulating in nature, efficient extracellular electron transfer is a critical property of biofilms associated with microbial electrochemical systems and several microorganisms are capable of establishing conductive aggregates and biofilms. Though construction of these conductive microbial networks is an intriguing and important phenomenon in both natural and engineered systems, few studies have been published related to conductive biofilms/aggregates and their conduction mechanisms, especially in mixed-species environments. In the present study, current-producing mixed species biofilms exhibited high conductivity across non-conductive gaps.

Suppression of methanogenesis for hydrogen production in single-chamber microbial electrolysis cells using various antibiotics.

Methanogens can utilize the hydrogen produced in microbial electrolysis cells (MECs), thereby decreasing the hydrogen generation efficiency. However, various antibiotics have previously been shown to inhibit methanogenesis. In the present study antibiotics, including neomycin sulfate, 2-bromoethane sulfonate, 2-chloroethane sulfonate, 8-aza-hypoxanthine, were examined to determine if hydrogen production could be improved through inhibition of methanogenesis but not hydrogen production in MECs.

Impact of tobramycin on the performance of microbial fuel cell.

Background: The release of antibiotics into aquatic environments has made the treatment of wastewater containing antibiotics a world-wide public health problem. The ability of microbial fuel cells (MFCs) to harvest electricity from organic waste and renewable biomass is attracting increased interest in wastewater treatment. In this paper we investigated the bioelectrochemical response of an electroactive mixed-culture biofilm in MFC to different tobramycin concentrations.

Enhanced power generation and energy conversion of sewage sludge by CEA-microbial fuel cells.

The production of methane from sewage sludge through the use of anaerobic digestion has been able to effectively offset energy costs for wastewater treatment. However, significant energy reserves are left unrecovered and effluent standards are not met necessitating secondary processes such as aeration. In the current study a novel cloth-electrode assembly microbial fuel cell (CEA-MFC) was used to generate electricity from sewage sludge.

Establishing a core microbiome in acetate-fed microbial fuel cells.

Establishing a core microbiome is the first step in understanding and subsequently optimizing microbial interactions in anodic biofilms of microbial fuel cells (MFCs) for increased power, efficiency, and decreased start-up times. In the present study, we used 454 pyrosequencing to demonstrate that a core anodic community would consistently emerge over a period of 4 years given similar conditions. The development and variation across reactor designs of these communities was also explored.