a Department of Chemical and Process Engineering , Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia , UKM Bangi , Selangor , Malaysia.
b Department of Environmental Engineering , Faculty of Civil Engineering and Planning, Institut Teknologi Sepuluh Nopember (ITS), Keputih, Sukolilo , Surabaya , Indonesia.
Int J Phytoremediation. 2018 Jun 7;20(7):721-729. doi: 10.1080/15226514.2017.1413337.
In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.
在这项研究中，优化了植物水鳖去除砷（As）的效果。采用 Box-Behnken 设计，同时包括响应面法（RSM）和人工神经网络（ANN）的比较分析，以预测最大砷去除率。两种模型的适宜性函数预测的最佳条件为土壤中砷浓度 39mg/kg、历时 42 天（采样日）和曝气率 0.22L/min，RSM 和 ANN 预测的砷去除率分别为 72.6%和 71.4%。预测最佳点的验证表明实际砷去除率为 70.6%。这是通过将验证值与预测值之间的偏差控制在 3.49%（RSM）和 1.87%（ANN）以内来实现的。RSM 和 ANN 模型的性能评估表明，ANN 比 RSM 表现更好，R 值更高（接近 1.0），平均绝对偏差（AAD）和均方根误差（RMSE）值非常小（分别为 0.02 和 0.004），接近于零。两种模型都适用于砷去除的优化，ANN 表现出比 RSM 更高的预测和拟合能力。
