Prediction and optimization model of activated carbon double layer capacitors based on improved heuristic approach genetic algorithm neural network. Issue 4 (11th June 2018)

• Record Type: Journal Article
• Authors: Yang, Zhen; Lin, Yun; Gu, Xingsheng; Liang, Xiaoyi
• Is Part Of: Engineering computations. Volume 35:Issue 4(2018)
• Page Start: 1625
• Page End: 1638
• Publication Date: 2018-06-11
• Subjects: Optimization model -- Activated carbon double layer capacitors -- Back-propagation neural network -- Improved heuristic approach genetic algorithm -- Materials design; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2017-0105
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to study the electrochemical properties of electrode material on activated carbon double layer capacitors. It also tries to develop a prediction model to evaluate pore size value. Design/methodology/approach: Back-propagation neural network (BPNN) prediction model is used to evaluate pore size value. Also, an improved heuristic approach genetic algorithm (HAGA) is used to search for the optimal relationship between process parameters and electrochemical properties. Findings: A three-layer ANN is found to be optimum with the architecture of three and six neurons in the first and second hidden layer and one neuron in output layer. The simulation results show that the optimized design model based on HAGA can get the suitable process parameters. Originality/value: HAGA BPNN is proved to be a practical and efficient way for acquiring information and providing optimal parameters about the activated carbon double layer capacitor electrode material.