Optimization of an artificial neural network topology using response surface methodology for microbial fuel cell power prediction

Abstract

Abstract 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 the first and second hidden layers, the training epochs, validation check, training percentage were 28, 20, 1000, 100, and 70, respectively. This built network had an excellent ability to predict, and was 98%. According to the results, increasing COD concentration increases generated power and system utilization time. In addition, reducing the external resistance up to 100 Ω can lead to more power obtained.

Key findings

• ▸The optimal network structure had 28 neurons in the first hidden layer, 20 neurons in the second hidden layer, 1000 training epochs, 100 validation check, and 70 training percentage.
• ▸The optimized network had a mean squared error (MSE) of minimum value and was able to predict MFC power generation with an accuracy of 98%.
• ▸Increasing the number of neurons in the hidden layers and training epochs improved the network's performance and accuracy.

Keywords

Identifiers

DOI

10.1002/btpr.3258

Journal

Biotechnology Progress

Year

2022