自适应粒子群算法在污水处理过程智能控制优化中的应用仿真研究

Application Simulation Research of Adaptive Particle Swarm Optimization in Intelligent Control Optimization of Wastewater Treatment Process

关键词:  污水处理过程  粒子群算法  智能控制策略  BSM1

Key Words:wastewater treatment process  particle swarm optimization  intelligent control strategy  BSM1

摘要:基于污水处理国际基准仿真平台，采用自适应粒子群算法进行污水处理过程智能控制优化仿真实验研究。首先，详细介绍了国际基准仿真平台BSM1的结构和原理。在此基础上，基于BSM1的仿真工况，以出水水质达标且能耗最低为目标，构造污水处理过程的优化目标函数。然后，基于时变参数策略，构造自适应粒子群算法对污水处理过程中的控制参数进行优化。最后，基于BSM1污水处理仿真流程，对自适应粒子群算法的控制优化效果进行了验证和分析。相关实验研究及结果表明，基于自适应粒子群算法的智能控制优化策略能够在保证水质达标的前提下有效降低能耗，对污水处理过程的实际控制优化具有一定工程指导意义。

Abstract:Based on the international benchmark simulation model of wastewater treatment simulation platform， the adaptive particle swarm optimization （APSO） was used to carry out the intelligent control optimization simulation experiment research of wastewater treatment process （WWTP） in this paper. Firstly， the structure and principle of the international benchmark simulation model BSM1 were introduced in detail. Based on the simulation conditions of BSM1， with the goal of effluent water quality reaching the standard and the minimum energy consumption， the optimization objective function of WWTP was constructed. Then， based on the time-varying parameter strategy， APSO was constructed to optimize the control parameters in WWTP. Finally， based on BSM1 wastewater treatment simulation process， the control optimization effect of APSO was verified and analyzed. Relevant experimental research and results showed that the intelligent control optimization strategy based on APSO could effectively reduce energy consumption under the premise of ensuring that the water quality met the standard， and had certain engineering guiding significance for the control optimization of the actual WWTP.