Firstly, the BP neural network model was established by artificial neural network in this paper and neural network procedures were compiled with Matlab language. Secondly, the experimental data were trained by the network. Thirdly, the relationship between adsorption time and unit adsorption amount was simulated by trained network and the simulated results and experimental data were compared. It was concluded that the mean square error (MSE) respectively were 0. 9719 (Zinc ions), 0. 2398 (Copper I-ons), 0. 9352 (Lead ions). The results showed that forecast of network on Na-bentonite adsorbed copper I-ons was best, followed by forecast for lead ions adsorption, the forecast of zinc ion adsorption was worst.%通过人工神经网络方法建立BP神经网络模型,采用Matlab语言编写神经网络程序,再以实验测得的数据对网络进行训练.然后,用训练好的网络对吸附时间和单位吸附量之间的关系进行仿真,并将仿真结果和实验数据加以比较,得出的均方误差(MSE)分别为0.9719(锌离子)、0.2398(铜离子)、0.9352(铅离子).结果表明:神经网络对钠基膨润土吸附铜离子的预测最好,其次是预测对铅离子的吸附,最差的是预测对锌离子的吸附.
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