Zhu Ziang, Dong Shaokang, Zhang Han, Parker Wayne, Yin Ran, Bai Xuanye, Yu Zhengxin, Wang Jinfeng, Gao Yang, Ren Hongqiang
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023 Jiangsu, PR China.
State Key Laboratory for Novel Software Technology, Nanjing University, 210023 Jiangsu, PR China.
Bioresour Technol. 2025 Apr;421:132210. doi: 10.1016/j.biortech.2025.132210. Epub 2025 Feb 9.
Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need to balance multiple operational objectives. In this study, Reinforcement Learning (RL) algorithms across different activated sludge process configurations was tested, and a novel approach that integrates RL with Bayesian Optimization (BO) to enhance the control of critical operational parameters in activated sludge processes was developed. This study extended the application of advanced machine learning techniques to complex WWTP control problems, moving beyond simplified benchmarks. The integration of BO with RL avoided sub-optimal performance and accelerated convergence to optimal control policies in controlling the A2O process, resulting in a significant 46% reduction in operational costs and a 12% decrease in energy consumption while maintaining compliance with effluent discharge standards. This approach offers a practical pathway for WWTPs to enhance treatment efficiency, reduce operational costs, and contribute to sustainable wastewater management practices.
污水处理厂（WWTPs）的控制器常常因进水特性动态变化以及需要平衡多个运行目标而难以维持最佳性能。在本研究中，测试了不同活性污泥工艺配置下的强化学习（RL）算法，并开发了一种将RL与贝叶斯优化（BO）相结合的新方法，以加强对活性污泥工艺关键运行参数的控制。本研究将先进机器学习技术的应用扩展到复杂的污水处理厂控制问题，超越了简化的基准测试。在控制A2O工艺时，BO与RL的结合避免了次优性能，并加速收敛到最优控制策略，在保持符合废水排放标准的同时，运营成本显著降低了46%，能源消耗减少了12%。这种方法为污水处理厂提高处理效率、降低运营成本以及推动可持续废水管理实践提供了一条切实可行的途径。
