Faculty of Infrastructure and Environment, Institute of Environmental Engineering, Czestochowa University of Technology, Czestochowa, Poland.
Laboratory of Civil Engineering and Environment (LGCgE), Environmental Axis, University of Lille, Lille, France.
Int J Phytoremediation. 2020;22(12):1321-1330. doi: 10.1080/15226514.2020.1768513. Epub 2020 May 29.
The study was aimed to model and optimize the removal of cadmium from contaminated post-industrial soil via L. by comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). The experimental design was done using the Box-Behnken Design method. In the RSM model, the quadratic model was shown to predict the closest results in comparison to our experimental data. For ANN approach, a two-layer Feed-Forward Back-Propagation Neural Network model was designed. The results showed that sewage sludge supplementation increased the efficiency of the plant in removing Cd from the soil. After 28 days of exposure, the removal rate varied from 10.96% without any supplementation to 65.9% after supplementation with the highest possible (law allowed) dose of sewage sludge. The comparison proved that the prediction capability of the ANN model was much higher than that of the RSM model (adjusted -square: 0.98, standard error of the Cd prediction removal: 0.85 ± 0.02). Thus, the ANN model could be used for the prediction of heavy metal removal during assisted phytoremediation with sewage sludge. Moreover, such approach could also be used to determinate the dose of sewage sludge that will ensure highest process efficiency.
本研究旨在通过比较两种建模方法（响应面法（RSM）和人工神经网络（ANN））来模拟和优化受污染的工业后土壤中镉的去除。实验设计采用 Box-Behnken 设计方法完成。在 RSM 模型中，二次模型被证明比我们的实验数据更能预测接近的结果。对于 ANN 方法，设计了一个两层前馈反向传播神经网络模型。结果表明，污水污泥的添加提高了植物从土壤中去除 Cd 的效率。在 28 天的暴露后，在没有任何补充的情况下，去除率从 10.96%变化到最高可能（法律允许）剂量的污水污泥补充后的 65.9%。比较证明，ANN 模型的预测能力远高于 RSM 模型（调整后的 -square：0.98，Cd 预测去除的标准误差：0.85 ± 0.02）。因此，ANN 模型可用于预测污水污泥辅助植物修复过程中重金属的去除。此外，这种方法还可以用来确定确保最高工艺效率的污水污泥剂量。
