Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Chemosphere. 2019 Dec;237:124486. doi: 10.1016/j.chemosphere.2019.124486. Epub 2019 Jul 30.
This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole acetic acid (IAA) and pseudomonas aeruginosa bacteria on increasing pyrene removal efficiency by phytoremediation process was studied. The experimental design was done using the Box-Behnken Design (BBD) technique. In the RSM model, the non-linear second-order model was in good agreement with the laboratory results. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one. Existence of eight neurons in the hidden layer leads to the highest R and lowest MSE and MAE. The results of the GA determined the optimum performance conditions. The results showed that using indole acetic acid and pseudomonas bacteria increased the efficiency of the sorghum plant in removing pyrene from the soil. The comparison obviously indicated that the prediction capability of the ANN model was much better than that of the RSM model.
本研究旨在采用响应面法（RSM）和人工神经网络（ANN）与遗传算法（GA）方法，对受高粱污染土壤中的芘进行建模和优化去除。在此，研究了吲哚乙酸（IAA）和铜绿假单胞菌对植物修复过程中芘去除效率的影响。实验设计采用 Box-Behnken 设计（BBD）技术。在 RSM 模型中，非线性二阶模型与实验室结果吻合较好。设计了一个两层前馈反向传播神经网络（FFBPNN）模型。评估了各种训练算法，选择了莱文贝格-马夸特（LM）算法作为最佳算法。在隐藏层中存在八个神经元可获得最高的 R 和最低的均方误差（MSE）和平均绝对误差（MAE）。GA 的结果确定了最佳性能条件。结果表明，使用吲哚乙酸和铜绿假单胞菌可提高高粱植物从土壤中去除芘的效率。比较明显表明，ANN 模型的预测能力远优于 RSM 模型。
