Artificial Neural Network Approach for Modeling of Cr (VI) Adsorption from Waste Water by Lewatit MP64 and Dowex 1×8

2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE) Pub Date : 2019-03-01 DOI:10.1109/PICECE.2019.8747199

A. E. Tümer, Serpil Edebali

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Abstract

In this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1×8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1×8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.

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lewait MP64和Dowex吸附废水中Cr (VI)的人工神经网络建模1×8

在本研究中，建立了人工神经网络模型来估计lewait MP64和Dowex 1×8树脂对废水中Cr (VI)离子的去除效率。为此，在实验室批量研究中获得了36个实验数据。建立的模型以接触时间、吸附剂投加量、pH和浓度为输入参数，以lewait MP64和Dowex 1×8的去除效率为输出参数。模型的性能由均方误差和决定系数决定。采用Levenberg-Marquardt反向传播算法(TrainLM)的模型预测效果最好。该模型还具有一个隐藏层和15个神经元(4-15-1)。结果表明，lewait MP64和Dowex 1×8的去除率的决定系数分别为0.99和0.92。结果表明，人工神经网络可以成功地预测去除效率。

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2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)

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