Application of Artificial Neural Network Analysis in Predicting the Performance of Microbial Energy Cells

Objective: The objective of this study was to apply Artificial Neural Networks to evaluate the performance of Microbial Energy Cells, to identify the best network configuration for cell evaluation. Theoretical Framework: Although several of the widely used effluent treatment methods show results, most of them have a common disadvantage: they lose the chemical energy contained in the treated effluent and have high energy consumption for their conduction. Therefore, an increasing effort has been made to develop effluent treatment technologies capable of recovering part of the energy contained in the waste to be treated. In this scenario, microbial energy cells (CEM) emerge as a potential technology, as they are devices that simultaneously treat effluent biologically and generate electrical energy. Methodology: For the application and evaluation of ANNs in CEM, a feedforward neural network was used, with a Levenberg-Marquardt training algorithm, 1 or 2 hidden layers, with sigmoid and tansig activation functions, and an accuracy factor of 10-5. The data used for training and validation for the ANN were obtained through a literature search. Networks with 15, 30, 50, 90, 100, 130, 150, and 200 neurons were used for testing to evaluate the best performance. Results and Discussion: With the results obtained, it was observed that the best adjustment of the network occurred with the 2-layer configuration, one layer with 100 neurons and the other output layer, with 49 interactions and R2 of 0.91 in the training adjustment, 0 .78 in the validation fit and 0.90 in the fit with all experimental data evaluated, respectively. Originality/Value: This study contributes to the literature by evaluating the application of artificial neural networks, which are empirical modeling mechanisms, inspired by biological nervous systems, with processing abilities, in microbial energy cells.