Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor | Zendy

Alain R. Picos-Benítez | Zendy; Juan M. PeraltaHernández | Zendy

Open Access

Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor

Author(s) -

Alain R. Picos-Benítez,

Juan M. PeraltaHernández

Publication year - 2018

Publication title -

water science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.406

H-Index - 137

eISSN - 1996-9732

pISSN - 0273-1223

DOI - 10.2166/wst.2018.370

Subject(s) - artificial neural network , genetic algorithm , wastewater , process (computing) , biological system , algorithm , chemical engineering , engineering , materials science , computer science , artificial intelligence , environmental engineering , machine learning , biology , operating system

This study evaluates the effectiveness of an artificial neural network-genetic algorithm (ANN-GA) artificial intelligence (AI) model in the prediction of behavior and optimization of an electro-oxidation pilot press-type reactor, which treats a synthetic wastewater prepared with a dye. The ANN was built from real experimental data using as input the following variables: time, flow, j, dye concentration, and as output discoloration. The performance of the ANN was measured with MAPE (8.3868%), calculated from real and predicted values. The coupled AI model was used to find the best operational conditions: discoloration efficiency (above 90%) at j = 27 mA/cm 2 and dye concentration of 230 mg/L.

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