An ANN Model for Predicting the Quantity of Lead and Cadmium Ions in Industrial Wastewater

E. A. Olajubu, Gbemisola Ajayi, Isaiah Oke and Franklin Oladiipo Asahiah
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E. A. Olajubu: Department Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Gbemisola Ajayi: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Isaiah Oke: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Franklin Oladiipo Asahiah: Department Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

International Journal of Information Communication Technologies and Human Development (IJICTHD), 2017, vol. 9, issue 4, 32-44

Abstract: Rapid industrialization has contributed immensely to the discharge of heavy metals into receiving water bodies untreated. The quantity of heavy metals prediction in industrial wastewater is very essential before treatment so that the quantity is precisely removed. This article formulates, simulate and evaluate a predictive model that mimics electrochemical treatment of lead and cadmium ions present in paint industrial wastewater using artificial neural network. The predictive model was formulated using Fuzzy Logic toolbox in MATLAB and the simulation was done in the environment. The prediction of the model was evaluated by comparing the predicted quantity of lead ions and cadmium ions with the result of the experimental work in the laboratory. The article concludes that the developed prediction model demonstrated very high prediction accuracy in predicting the percentage of lead and cadmium ions present in paints wastewater.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jicthd:v:9:y:2017:i:4:p:32-44

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