文献类型：期刊文献

英文题名：Prediction and optimization model of activated carbon double layer capacitors based on improved heuristic approach genetic algorithm neural network

作者：Yang, Zhen[1];Lin, Yun[1];Gu, Xingsheng[2];Liang, Xiaoyi[3]

机构：[1]East China Univ Sci & Technol, Dept Chem Engn, State Key Lab Chem Engn,Minist Educ, Key Lab Special Funct Polymer Mat & Their Related, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Inst Informat Sci, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai, Peoples R China

年份：2018

卷号：35

期号：4

起止页码：1625

外文期刊名：ENGINEERING COMPUTATIONS

收录：;EI(收录号：20182805540474);WOS:【SCI-EXPANDED(收录号:WOS:000439459700002)】；

基金：This work is partly supported by National Natural Science Foundation of China (21177038), China Scholarship Council Fund (201406745031) and Material Informatics for Engineering Design Research Group of Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. The authors would like to thank and the anonymous reviewers for their constructive suggestions that have improved the quality of this work.

语种：英文

外文关键词：Optimization model; Activated carbon double layer capacitors; Back-propagation neural network; Improved heuristic approach genetic algorithm; Materials design

摘要：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.