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

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 builtnetwork had an excellent ability to predict, and R 2 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.

Optimization of constraint rules for power grid topological relationships based on knowledge graph techniques

“…With the increasing complexity of the power system, the optimization of grid topological relations has become an important task to ensure the safety of the grid and improve operation efficiency. In recent years, knowledge graphs, as an emerging data processing technology, have been gradually applied to the optimization of grid topological relationship constraint rules [1][2][3].…”