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- Title
Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm, and particle swarm optimization.
- Authors
BANGHAI LIU; CHUNJI JIN; JITENG WAN; PENGFANG LI; HUANXI YAN
- Abstract
This study proposes a novel hybrid of artificial neural network(ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize pertinent parameters of an electrochemical oxidation (EO) Acid Black 2 process. Back propagation neural network (BPNN) was used as a modeling 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 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.
- Subjects
ARTIFICIAL neural networks; PARTICLE swarm optimization; COMPUTER algorithms; ELECTROCHEMICAL analysis; GENETIC algorithms
- Publication
Journal of the Serbian Chemical Society, 2018, Vol 83, Issue 1, p1
- ISSN
0352-5139
- Publication type
Article
- DOI
10.2298/JSC170721101L