Taher Ahmadzadeh; Naser Mehrdadi; Mojtaba Ardestani; Akbar Baghvand
Volume 14, Issue 1 , April 2016, , Pages 1-8
Abstract
Intelligent model optimization is a key factor in water treatment improvement. In current study, we applied the artificial neural networks modelling for the optimization of coagulation and flocculation processes to get sufficient water quality control over the total organic carbon parameter. ANN network ...
Read More
Intelligent model optimization is a key factor in water treatment improvement. In current study, we applied the artificial neural networks modelling for the optimization of coagulation and flocculation processes to get sufficient water quality control over the total organic carbon parameter. ANN network consisted of a multilayer feed forward structure with backpropagation learning algorithm with the output layer of ferric chloride and cationic polymer dosages. The results were simultaneously compared with the nonlinear multiple regression model. Model validation phase performed using 94 unknown samples for which the prediction result was in good agreement with the observed values. Analysis of the results showed a determination coefficient of 0.85 for cationic polymer and 0.97 for ferric chloride models. Mean absolute percentage error and root mean square errors were calculated consequently as 5.8% and 0.96 for polymer and 3.1% and 1.97 for ferric chloride models. According to the results, artificial neural networks showed to be very promising for the optimization of water treatment processes.
