Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Graca, NS (Author) Other; Ana M. Ribeiro (Author) FEUP; Rodrigues, AE (Author) Other

Journal

Title: Water Science and Technology: Water Supply

Vol. 22

Pages: 643-658

ISSN: 1606-9749

Publisher: IWA PUBLISHING

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Associated Projects

LA LSRE-LCM - Financiamento Plurianual 2020-2023 para a Unidade LA LSRE-LCM Laboratório de Processos de Separação e Reacção - Laboratório de Catálise e Materiais

Other information

Authenticus ID: P-00V-8ZK

DOI: 10.2166/ws.2021.249

Abstract (EN): An artificial neural network (ANN) with the topology 8-94-85-2 (input - hidden layer 1 - hidden layer 2 - output) was used to model the operation of the continuous electrocoagulation (CEC) process for the removal of fluoride from water. After the ANN training, the sum of the squared errors (MSE) and the determination coefficient (R-2) of the testing set model predictions were 0.0088 and 0.999, respectively, showing a good generalization and model's predictive capacity. The optimization of the process cost using the genetic algorithm (GA) showed that the optimal conditions are highly dependent on the feed concentration and the fluoride removal requirements. For a 5 L of water containing 10 mg/L of fluoride, the optimal conditions to reduce the fluoride concentration below the permissible limit (1.5 mg/L) are 88.3 mA of current intensity, a flow rate of 73.6 mL/min, and the use of a series monopolar (SM) electrode configuration, corresponding to a fluoride removal of 85% and an operating cost of 0.05 Euro/L.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16