School of Automation, Beijing Information Science & Technology University, Beijing, 100192, People's Republic of China.
Beijing Jingxinke High-End Information Industry Technology Research Institute Co. Ltd, Beijing, 100192, People's Republic of China.
Environ Sci Pollut Res Int. 2022 Nov;29(53):79718-79733. doi: 10.1007/s11356-021-17505-3. Epub 2021 Nov 27.
To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.
为了解决复杂污水处理过程（WWTP）中多个性能指标之间的冲突，设计了一种基于动态多目标免疫系统（DMOIA-OC）的有效优化控制方案。首先开发了一种动态优化控制方案，其中控制过程分为动态层和跟踪控制层。然后，根据 WWTP 性能分析，通过优化层自适应地建立能量消耗和出水质量模型，以响应环境。接下来提出了一种自适应动态免疫优化算法，用于优化复杂且冲突的性能指标。此外，从众多帕累托解决方案中选择合适的首选解决方案，以获得溶解氧和硝酸盐氮的最佳值集。最后，在基准模拟平台（BSM1）上对该解决方案进行评估。结果表明，DMOIA-OC 方法可以解决 WWTP 中多个性能指标的复杂优化问题，并且在控制效果方面具有竞争优势。
