The study was aimed to model and optimize the removal of cadmium from contaminated post-industrial soil via Sinapis alba 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 Sinapis alba 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 R-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.

该研究旨在通过比较两种建模方法：响应面方法（RSM）和人工神经网络（ANN），对通过Sinapis alba L.从污染的工业后土壤中去除镉进行建模和优化。实验设计使用Box–Behnken设计方法完成。在RSM模型中，与我们的实验数据相比，显示了二次模型可以预测最接近的结果。对于人工神经网络方法，设计了一个两层前馈反向传播神经网络模型。结果表明，污水污泥的补充提高了白芥的效率从土壤中去除镉的植物。暴露28天后，去除率从不添加任何添加剂的10.96％到添加了最大可能剂量（法律允许）的污水污泥后的65.9％。比较证明，ANN模型的预测能力远高于RSM模型（调整后的R平方：0.98，Cd预测去除的标准误：0.85±0.02）。因此，人工神经网络模型可以用于污水污泥辅助植物修复过程中的重金属去除预测。此外，这种方法还可以用于确定污水污泥的剂量，以确保最高的处理效率。