The effect of anode potential on current production from complex substrates in bioelectrochemical systems: a case study with glucose.

Anode potential can affect the degradation pathway of complex substrates in bioelectrochemical systems (BESs), thereby influencing current production and coulombic efficiency. However, the intricacies behind this interplay are poorly understood. This study used glucose as a model substrate to comprehensively investigate the effect of different anode potentials (- 150 mV, 0 mV and + 200 mV) on the relationship between current production, the electrogenic pathway and the abundance of the electrogenic microorganisms involved in batch mode fed BESs. Current production in glucose-acclimatized reactors was a function of the abundance of Geobacteraceae and of the availability of acetate and formate produced by glucose degradation. Current production was increased by high anode potentials during acclimation (0 mV and + 200 mV), likely due to more Geobacteraceae developing. However, this effect was much weaker than a stimulus from an artificial high acetate supply: acetate was the rate-limiting intermediate in these systems. The supply of acetate could not be influenced by anode potential; altering the flow regime, batch time and management of the upstream fermentation processes may be a greater engineering tool in BES. However, these findings suggest that if high current production is the focus, it will be extremely difficult to achieve success with complex waste streams such as domestic wastewater.

Optimising the Hydraulic Retention Time in a Pilot-Scale Microbial Electrolysis Cell to Achieve High Volumetric Treatment Rates Using Concentrated Domestic Wastewater.

Bioelectrochemical systems (BES) have the potential to deliver energy-neutral wastewater treatment. Pilot-scale tests have proven that they can operate at low temperatures with real wastewaters. However, volumetric treatment rates (VTRs) have been low, reducing the ability for this technology to compete with activated sludge (AS). This paper describes a pilot-scale microbial electrolysis cell (MEC) operated in continuous flow for 6 months. The reactor was fed return sludge liquor, the concentrated filtrate of anaerobic digestion sludge that has a high chemical oxygen demand (COD). The use of a wastewater with increased soluble organics, along with optimisation of the hydraulic retention time (HRT), resulted in the highest VTR achieved by a pilot-scale MEC treating real wastewater. Peak HRT was 0.5-days, resulting in an average VTR of 3.82 kgCOD/m3∙day and a 55% COD removal efficiency. Finally, using the data obtained, a direct analysis of the potential savings from the reduced loading on AS was then made. Theoretical calculation of the required tank size, with the estimated costs and savings, indicates that the use of an MEC as a return sludge liquor pre-treatment technique could result in an industrially viable system.

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.

Understanding the complexity of wastewater: The combined impacts of carbohydrates and sulphate on the performance of bioelectrochemical systems.

Bioelectrochemical systems (BES) have long been viewed as a promising wastewater treatment technology. However, in reality, the performance of bioelectrochemical systems fed with real (and therefore complex) wastewaters is often disappointing. We have sought to investigate the combined impacts of complex substrates and presence of electron acceptors. In particular, this study illustrates and systematically evaluates the disparity in performance between a BES acclimatised with acetate and those acclimatised with more complex carbohydrates (glucose, sucrose or starch) and in the presence and absence of sulphate. Relative to acetate only, operating with complex carbohydrates reduced current by 73%-87% and coulombic efficiency by 4%-50%. Acclimation with complex carbohydrates seriously impeded the colonisation anode by Geobacteraceae, resulting in substantially reduced capacity to produce current (60.2% on average). Combined acclimation with sulphate further reduced current by 35% on average, and resulted in a total reduction of 83%-93% relative to the acetate control. However, the presence of an electrogenic sulphide-sulphur shuttle meant sulphate had little effect on the coulombic efficiency of the BES. The results indicate that a reduction in current and coulombic efficiency is, at present, an unavoidable consequence of operating a BES fed with complex wastewater. Researchers, designers and policy makers should incorporate such losses in both their plans and their prognostications.

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. In the present study, 69 samples with three different substrate types (acetate, carbohydrates and wastewater) collected from various laboratory environments were evaluated to determine the ability to identify feed substrates from the resultant microbial communities. Six machine learning algorithms with four different input variables were trained and evaluated on their ability to predict feed substrate from genomic datasets. The highest accuracies of 93 ±â€¯6% and 92 ±â€¯5% were obtained using NNET trained on datasets classified at the phylum and family taxonomic level, respectively. These accuracies corresponded to kappa values of 0.87 ±â€¯0.10, 0.86 ±â€¯0.09, respectively. Four out of six of the algorithms used maintained accuracies above 80% and kappa values higher than 0.66. Different sequencing method (Roche 454 or Illumina sequencing) did not affect the accuracies of all algorithms, except SVM at the phylum level. All algorithms trained on NMDS-compressed datasets obtained accuracies over 80%, while models trained on PCoA-compressed datasets presented a 10-30% reduction in accuracy. These results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.

Quantification of effective exoelectrogens by most probable number (MPN) in a microbial fuel cell.

The objective of this work was to quantify the number of exoelectrogens in wastewater capable of producing current in a microbial fuel cell by adapting the classical most probable number (MPN) methodology using current production as end point. Inoculating a series of microbial fuel cells with various dilutions of domestic wastewater and with acetate as test substrate yielded an apparent number of exoelectrogens of 17perml. Using current as a proxy for activity the apparent exoelectrogen growth rate was 0.03h(-1). With starch or wastewater as more complex test substrates similar apparent growth rates were obtained, but the apparent MPN based numbers of exoelectrogens in wastewater were significantly lower, probably because in contrast to acetate, complex substrates require complex food chains to deliver the electrons to the electrodes. Consequently, the apparent MPN is a function of the combined probabilities of members of the food chain being present.

Performance of a pilot scale microbial electrolysis cell fed on domestic wastewater at ambient temperatures for a 12 month period.

A 100-L microbial electrolysis cell (MEC) was operated for a 12-month period fed on raw domestic wastewater at temperatures ranging from 1°C to 22°C, producing an average of 0.6 L/day of hydrogen. Gas production was continuous though decreased with time. An average 48.7% of the electrical energy input was recovered, with a Coulombic efficiency of 41.2%. COD removal was inconsistent and below the standards required. Limitations to the cell design, in particular the poor pumping system and large overpotential account for many of the problems. However these are surmountable hurdles that can be addressed in future cycles of pilot scale research. This research has established that the biological process of an MEC will to work at low temperatures with real wastewater for prolonged periods. Testing and demonstrating the robustness and durability of bioelectrochemical systems far beyond that in any previous study, the prospects for developing MEC at full scale are enhanced.