Dépôt numérique
RECHERCHER

Artificial neural networks and genetic algorithms: An efficient modelling and optimization methodology for active chlorine production using the electrolysis process.

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

Gholami Shirkoohi, Majid, Tyagi, Rajeshwar Dayal, Vanrolleghem, Peter A. et Drogui, Patrick ORCID: https://orcid.org/0000-0002-3802-2729 (2021). Artificial neural networks and genetic algorithms: An efficient modelling and optimization methodology for active chlorine production using the electrolysis process. The Canadian Journal of Chemical Engineering , vol. 99 , nº S1. S389-S403. DOI: 10.1002/cjce.24036.

[thumbnail of P3861.pdf] PDF
Document sous embargo jusqu'à 17 janvier 2023.

Télécharger (2MB)

Résumé

This study evaluates the effectiveness of a modelling and optimization methodology based on artificial neural networks and genetic algorithms in the prediction of the behaviour of an electrolysis process of active chlorine production from a synthetic saline effluent. Multilayer perceptrons feedforward neural networks were developed for the active chlorine production and energy consumption based on the following inputs: electrolysis time, current intensity, hydrochloric acid concentration, and chloride ion concentration. In order to diagnose and prevent the over‐fitting problem during the learning process, learning curves and the regularization factor were utilized. The trained ANN models were able to successfully predict the active chlorine production and energy consumption of the process (R2=0.979 and MSE=3.826 for active chlorine production and R2=0.985 and MSE=6.952 for energy consumption). Multi‐objective optimization for maximizing active chlorine production and minimizing energy consumption was carried out by a genetic algorithm using the best derived ANN models. The Pareto front obtained led to multiple non‐dominated optimal points, which result in insights regarding the optimal operating conditions for the process.

Type de document: Article
Mots-clés libres: ANN‐GA; electrochemical processes; learning curves; multi‐objective optimization; response surface methodology
Centre: Centre Eau Terre Environnement
Date de dépôt: 03 févr. 2021 19:24
Dernière modification: 15 févr. 2022 16:43
URI: https://espace.inrs.ca/id/eprint/11225

Actions (Identification requise)

Modifier la notice Modifier la notice