Indian Institute of Technology, Kharagpur, Kharagpur 721302, India.
Environ Sci Pollut Res Int. 2013 May;20(5):3322-30. doi: 10.1007/s11356-012-1245-x. Epub 2012 Oct 23.
The adsorption of Pb(II) onto the surface of microwave-assisted activated carbon was studied through a two-layer feedforward neural network. The activated carbon was developed by microwave activation of Acacia auriculiformis scrap wood char. The prepared adsorbent was characterized by using Brunauer-Emmett-Teller (BET) surface area analyzer, scanning electron microscope (SEM), and X-ray difractometer. In the present study, the input variables for the proposed network were solution pH, contact time, initial adsorbate concentration, adsorbent dose and temperature, whereas the output variable was the percent Pb(II) removal. The network had been trained by using different algorithms and based on the lowest mean squared error (MSE) value and validation error, resilient backpropagation algorithm with 12 neurons in the hidden layer was selected for the present investigation. The tan sigmoid and purelin transfer function were used in the hidden and the output layers of the proposed network, respectively. The model predicted and experimental values of the percent Pb(II) removal were also compared and both the values were found to be in reasonable agreement with each other. The performance of the developed network was further improved by normalizing the experimental data set and it was found that after normalization, the MSE and validation error were reduced significantly. The sensitivity analysis was also performed to determine the most significant input parameter.
采用两层前馈神经网络研究了 Pb(II)在微波辅助活性炭表面的吸附。通过微波活化相思木废料炭制备了活性炭。通过比表面积分析仪、扫描电子显微镜和 X 射线衍射仪对制备的吸附剂进行了表征。在本研究中，所提出的网络的输入变量为溶液 pH、接触时间、初始吸附质浓度、吸附剂剂量和温度，而输出变量为 Pb(II)去除率的百分比。该网络使用不同的算法进行了训练，并基于最低均方误差 (MSE) 值和验证误差，选择具有 12 个隐藏层神经元的弹性反向传播算法用于本研究。所提出网络的隐藏层和输出层分别使用正切 S 型和纯线性传递函数。还比较了模型预测的和实验的 Pb(II)去除率的百分比，发现两者彼此之间都非常吻合。通过对实验数据集进行归一化，进一步提高了开发网络的性能，并且发现归一化后，MSE 和验证误差显著降低。还进行了敏感性分析以确定最显著的输入参数。
