王鼎, 赵慧玲, 李鑫. 基于多目标粒子群优化的污水处理系统自适应评判控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.04.15.001
引用本文: 王鼎, 赵慧玲, 李鑫. 基于多目标粒子群优化的污水处理系统自适应评判控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.04.15.001
Adaptive critic control for wastewater treatment systems based on multi-objective particle swarm optimization[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.04.15.001
Citation: Adaptive critic control for wastewater treatment systems based on multi-objective particle swarm optimization[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.04.15.001

基于多目标粒子群优化的污水处理系统自适应评判控制

Adaptive critic control for wastewater treatment systems based on multi-objective particle swarm optimization

  • 摘要: 考虑到污水处理系统保质降耗的需要, 将其运行过程视为一个多目标优化控制问题. 针对此问题, 提出一种基于多目标粒子群优化(Multi-objective particle swarm optimization, MOPSO)算法的污水处理系统自适应评判控制方案. 首先, 结合数据驱动思想对入水及出水组分数据进行分析, 构建关于出水水质和运行能耗的优化目标模型. 然后, 采用MOPSO算法对优化目标进行求解, 并设计一个决策方式选出偏好解, 作为溶解氧与硝态氮浓度的最优设定值. 接下来, 采用基于自适应动态规划的辅助控制器对比例-积分-微分算法的控制策略进行补充, 实现对最优设定值的底层跟踪控制. 将所提算法在污水处理仿真平台上进行验证, 结果表明所提算法能有效地提高污水处理过程的运行性能.

     

    Abstract: Considering the quality preservation and consumption reduction in wastewater treatment systems, the operation process is regarded as a multi-objective optimization control problem. An adaptive critic control scheme is developed based on multi-objective particle swarm optimization. First, the data information of inlet and outlet components is analyzed with the data-driven framework. The model of the optimization target is constructed, which reflects the effluent quality and energy consumption. Then, the MOPSO algorithm is used to solve the multi-objective optimization problem. A decision method is designed to select the preferred solution, which can be defined as the optimal set concentration of the dissolved oxygen and the nitrate nitrogen. Next, in order to realize the bottom tracking control of the optimal set-point, an adaptive dynamic programming based auxiliary controller is used to replenish the strategy of the proportional-integral-differential algorithm. Finally, the established algorithm is verified on the simulation platform of the wastewater treatment. The results show that the proposed algorithm can effectively improve the operational performance of the wastewater treatment process.

     

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