Biodiesel has gained a significant amount of attention over the past decade as an environmentally friendly fuel that is capable of being utilized by a conventional diesel engine. However, the biodiesel production process generates glycerol-containing waste streams which have become a disposal issue for biodiesel plants and generated a surplus of glycerol. A value-added opportunity is needed in order to compensate for disposal-associated costs. Microbial conversions from glycerol to valuable chemicals performed by various bacteria, yeast, fungi, and microalgae are discussed in this review paper, as well as the possibility of extending these conversions to microbial electrochemical technologies.

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. The core members present in all high-power generating biofilms were Geobacter, Aminiphilus, Sedimentibacter, Acetoanaerobium, and Spirochaeta, accounting for 72 ± 9 % of all genera. Aminiphilus spp., member of the Synergistetes phylum was present at higher abundances than previously reported in any other ecological studies. Results suggest a stable core microbiome in acetate-fed MFCs on both phylogenetic and functional levels.

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. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

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