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.