Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm

HAN Honggui; ZHANG Lu; QIAO Junfei

doi:10.11949/j.issn.0438-1157.20161390

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Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm

更新时间：2025-12-10

- Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm
- CIESC Journal Issue 4, Pages: 1474-1481(2017)
- 作者机构：
  
  北京工业大学信息学部,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161390
  CLC： Q028.8
- Online First：05 April 2017，
  
  Published：05 April 2017
- 稿件说明：
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HAN Honggui, ZHANG Lu, QIAO Junfei. Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm[J]. CIESC Journal, 2017, (4): 1474-1481.
DOI：

HAN Honggui, ZHANG Lu, QIAO Junfei. Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm[J]. CIESC Journal, 2017, (4): 1474-1481. DOI： 10.11949/j.issn.0438-1157.20161390.

摘要

为了满足污水处理过程出水水质排放达标的同时降低运行能耗，提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先，通过分析污水处理运行数据，建立了基于自适应回归核函数的污水处理能耗和出水水质模型；其次，设计出一种污水处理多目标优化方法，利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化，获得溶解氧和硝态氮浓度的优化设定值；最后，利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制，实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示，该多目标优化控制方法不但能够保证出水水质达标，而且能有效降低污水处理过程的能耗。

Abstract

To meet effluent quality (EQ) standard and reduce energy consumption (EC) of wastewater treatment process (WWTP)

a multi-objective optimal control method was developed based on multi-objective particle swarm optimization (MOPSO). First

EC and EQ models from adaptive regressive kernel functions were established by analysis of operation data. Then

MOPSO was designed to optimize EC and EQ models and to reach optimal set-points of dissolved oxygen (SO) and nitrate (SNO) simultaneously. Finally

PID controller was used to trace the optimal set-points of SO and SNO so as to achieve multi-objective optimal control of WWTP. The experimental results from Benchmark Simulation Model 1 (BSM1) for wastewater treatment demonstrated that the proposed multi-objective optimal control method could assuredly meet effluent quality standard as well as effectively reduce energy consumption.

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