National Center of Technology Innovation for Digital Construction, Huazhong University of Science & Technology, Wuhan, Hubei, 430074, China; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
National Center of Technology Innovation for Digital Construction, Huazhong University of Science & Technology, Wuhan, Hubei, 430074, China; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
J Environ Manage. 2024 Jul;364:121430. doi: 10.1016/j.jenvman.2024.121430. Epub 2024 Jun 13.
Optimization and control of wastewater treatment process (WTP) can contribute to cost reduction and efficiency. A wastewater treatment process multi-objective optimization (WTPMO) framework is proposed in this paper to provide suggestions for decision-making in setting parameters of WTP. Firstly, the prediction models based on Extreme Gradient Boosting (XGB) with Bayesian optimization (BO) are developed for predicting effluent water quality (EQ) and energy consumption (EC) for different influent quality and process parameter settings. Then, the SHapley Additive exPlanations (SHAP) algorithm is used to complement the interpretability of machine learning to quantitatively evaluate the impact of different features on the predicted targets. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Ordering Preferences on Similarity of Ideal Solutions (TOPSIS) is introduced to solve and make decisions on the multi-objective optimization problem. The WTPMO applicability is validated on Benchmark Simulation Model 1 (BSM1). The results show that BOXGB achieves accurate prediction for EQ and EC with R values of 0.923 and 0.965, respectively, indicating that BO can effectively select the model hyperparameters in XGB. Based on SHAP supplemented the interpretability of the model to fully explain how the influent water quality and decision variables affect the EQ and EC of the WTP. In addition, the optimized process parameters are determined based on NSGA-II and TOPSIS, and the EC optimization rate is 1.552% while guaranteeing water quality compliance. Overall, this research can effectively achieve the optimization of WTP, ensure that the effluent water quality meets the standards while reducing energy consumption, assist Wastewater treatment plants (WWTPs) to achieve more intelligent and efficient operation and maintenance management, and provide strong support for environmental protection and sustainable development goals.
污水处理过程（WTP）的优化和控制可以有助于降低成本和提高效率。本文提出了一种污水处理过程多目标优化（WTPMO）框架，为设置 WTP 参数提供决策建议。首先，基于极端梯度提升（XGB）和贝叶斯优化（BO）的预测模型被开发出来，用于预测不同进水质量和工艺参数设置下的出水水质（EQ）和能源消耗（EC）。然后，使用 SHapley Additive exPlanations（SHAP）算法来补充机器学习的可解释性，定量评估不同特征对预测目标的影响。最后，引入非支配排序遗传算法 II（NSGA-II）和理想解逼近排序法（TOPSIS）来解决和决策多目标优化问题。WTPMO 的适用性在基准模拟模型 1（BSM1）上得到验证。结果表明，BOXGB 对 EQ 和 EC 的预测具有较高的准确性，R 值分别为 0.923 和 0.965，表明 BO 可以有效地选择 XGB 的模型超参数。基于 SHAP 补充了模型的可解释性，以充分解释进水水质和决策变量如何影响 WTP 的 EQ 和 EC。此外，基于 NSGA-II 和 TOPSIS 确定了优化的工艺参数，在保证水质达标合规的同时，EC 的优化率为 1.552%。总的来说，这项研究可以有效地实现 WTP 的优化，确保出水水质达标，同时降低能源消耗，帮助污水处理厂（WWTPs）实现更智能和高效的运行和维护管理，为环境保护和可持续发展目标提供有力支持。
