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Microbial fuel cells (MFCs) are among the newest bioelectrical devices that have attracted significant attention because they convert biodegradable organic matter to electricity. MFC design can be improved by understanding and predicting the performance of MFC under different conditions and substrate concentrations. However, few mathematical models have been investigated due to problems caused by the high sensitivity of MFC systems. In this research, a multilayer neural network (NN) was used to predict the generated power of a cell with three inputs (concentration, time, and resistance). Response surface methodology with factors including the number of first layer neurons, number of second layer neurons, training epochs, validation check, and training percentage was used to obtain the optimum structure of the network, and mean squared error (MSE). NN had the minimum MSE when the Number of neurons in …