Predicting the Sorption of Mn2+ ion from Wastewater onto Zinc Chloride Activated Sawdust using Artificial Neural Network (ANN)

Idowu Rudolph Ilaboya, Ebierin Akpoebidimiyen Otuaro


Activated carbon from sawdust was prepared and its textural properties was evaluated using Fourier transform infra-red (FTIR) and scanning electron microscope (SEM) to determine the presence of functional groups and visualize its microstructural arrangement in other to ascertain its potential for the removal of Mn2+ ion from wastewater. Batch experimental technique was then employed to evaluate the effects of adsorption variables, namely; initial metal ion concentration, adsorbent dose, pH, and contact time on the sorption of Mn2+ ion onto acid activated sawdust. Thereafter, statistical design of experiment (DOE) using central composite design was employed to randomized the levels of selected input parameters in other to produce an experimental design matrix. To model and predict the sorption of Mn2+ ion onto acid activated sawdust, artificial neural network (ANN) was employed. To apply the neural network, the input data were first normalized to avoid the problems of weight variation. Thereafter, different training algorithm and hidden neurons were selected and tested to ascertain the optimum number of hidden neuron and the best training algorithm that will produce the most accurate network for predicting the sorption of Mn2+ ion. Results obtained show that Levenberg Marquardt Back Propagation training algorithm with 10 hidden neurons in the input and output layer with tangent sigmoid transfer function produced the most accurate prediction network. In addition, artificial neural network gave a strong agreement between the experimental and predicted sorption efficiency of Mn2+ ion with coefficient of determination (R2) value of 0.9893.

Keywords: Activated sawdust, Artificial neural network, central composite design, Levenberg Marquardt Back Propagation training algorithm

DOI: 10.7176/JEES/9-10-15

Publication date:October 31st 2019

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ISSN (Paper)2224-3216 ISSN (Online)2225-0948

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