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Math. Comput. Appl. 2017, 22(3), 38; doi:10.3390/mca22030038

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

Departamento de Control Automatico CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico
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Received: 28 June 2017 / Revised: 19 July 2017 / Accepted: 20 July 2017 / Published: 11 August 2017
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Abstract

This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2, and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme. View Full-Text
Keywords: artificial neural network; experimental data; modeling; particle swarm optimization; pollutant artificial neural network; experimental data; modeling; particle swarm optimization; pollutant
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Razvarz, S.; Jafari, R. ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater. Math. Comput. Appl. 2017, 22, 38.

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