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

Title
Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach
Type
Article in International Scientific Journal
Year
2021
Authors
Graca, NS
(Author)
Other
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Ana M. Ribeiro
(Author)
FEUP
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Rodrigues, AE
(Author)
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Journal
Vol. 22
Pages: 643-658
ISSN: 1606-9749
Publisher: IWA PUBLISHING
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Authenticus ID: P-00V-8ZK
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
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