Optimal performance of COD removal during aqueous treatment of alazine and gesaprim commercial herbicides by direct and inverse neural network

• Published in: Desalination 2011-08, Vol.277 (1), p.325-337
• Main Authors: Hamzaoui, Y. El, Hernández, J.A., Silva-Martínez, S., Bassam, A., Álvarez, A., Lizama-Bahena, C.
• Format: Article
• Language: English
• Subjects: Applied sciences; Chemical engineering; Chemical oxygen demand; Crack opening displacement; Degradation; desalination; Exact sciences and technology; Herbicides; Inverse artificial neural network; light intensity; Mathematical analysis; Mathematical models; Neural networks; neurons; Optimal parameters; Optimization; oxidation; Pollution; Reaction time; Reactors; solar radiation; Sonophotocatalysis; ultrasonics; ultraviolet radiation
• ISSN: 0011-9164; 1873-4464
• DOI: 10.1016/j.desal.2011.04.060

A direct and inverse artificial neural network (ANN and ANNi) approach was developed to predict the chemical oxygen demand (COD) removal during the degradation of alazine and gesaprim commercial herbicides under various experimental conditions. The configuration 9–9–1 (9 inputs, 9 hidden and 1 output neurons) presented an excellent agreement (R 2 = 0.9913) between experimental and simulated COD value considering the hyperbolic tangent sigmoid and linear transfer function in the hidden layer and output layer. The sensitivity analysis showed that all studied input variables (reaction time, pH, herbicide concentration, contaminant, US ultrasound, UV light intensity, [TiO 2] o,[K 2S 2O 8] o, and SR solar radiation) have strong effect on the degradation of the commercial herbicide in terms of COD removal. In addition, reaction time is the most influential parameter with relative importance of 33.49%, followed by initial herbicide concentration. COD optimal performance was carried out by inverting artificial neural network. Now, ANNi could calculate the optimal unknown parameter (reaction time) to obtain a COD required. Very low percentage of error and short computing makes this methodology attractive to be applied to the on-line control of Advanced Oxidation Process (AOP) over the degradation of commercial herbicide. ► We predict the COD of alazine and gesaprim commercial herbicides by direct and inverse artificial neural network approach. ► We are able to obtain any unknown input variables on line using the artificial neural network inverse approach. ► The artificial neural network approach allows us to simulate the COD removal when the input parameters are known. ► We are able to obtain any unknown input variables using artificial neural network inverse approach. ► The inverse neural network methodology is useful for on-line optimal control in the framework of the advanced oxidation processes.