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

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.

Modeling of copper removal from aqueous solutions by using carbon-based adsorbents derived from hazelnut and walnut shells by artificial neural network

“…The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other [48]. Olajubu et al (2017) compared predicted removal quantity of lead ions and cadmium ions by ANN with the result of the experimental work in the laboratory and ANN showed very high prediction accuracy [49]. Allahkarami et al (2017) used ANN and nonlinear multi-variable regression (NLMR) models to predict Co(II) and Ni(II) ions removal.…”