Open AccessModeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization
Author(s) -
Banghai Liu,
Jin Chen,
Jiteng Wan,
Pengfang Li,
Huan-Xi Yan
Publication year - 2018
Publication title -
journal of the serbian chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 45
eISSN - 1820-7421
pISSN - 0352-5139
DOI - 10.2298/jsc170721101l
Subject(s) - particle swarm optimization , artificial neural network , genetic algorithm , computer science , process (computing) , biological system , algorithm , artificial intelligence , machine learning , biology , operating system
This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process.