基于记忆分子动理论算法的污水处理过程优化

Study on Optimization Control of Wastewater Treatment Process Based on Memory Knetic-Molecular Theory Optimization Algorithm

为了在满足出水水质要求下降低污水处理的能耗,提出了一种基于记忆分子动理论算法的污水处理智能优化控制方法。首先利用MATLAB搭建BSM1模型,分析运行数据,利用多核LS-SVM方法建立污水处理的出水水质罚款和能耗模型并以此建立优化目标;其次为了提高算法的收敛速度和精度,防止陷入局部最小值,在记忆分子动理论算法中对变异率进行设计,并且增加了算法引导阶段;最后利用记忆分子动理论算法对优化目标进行优化,实现了对溶解氧和硝态氮浓度设定值的动态寻优,实验结果表明该方法能够在满足出水水质要求下有效地降低能耗。

In order to reduce the energy consumption of sewage treatment in meeting the requirements of water quality,an intelligent optimization control method for wastewater treatment based on memory molecular dynamic theory algorithm was proposed.Firstly,we built BSM1 simulation model with MATLAB,analyzed operation data,established effluent water quality penalty and energy consumption model of wastewater treatment using multi-core LS-SVM method,and set up optimization target.Secondly,in order to improve the convergence speed and accuracy of the algorithm,the mutation rate was designed,and the algorithm leading stage was added in the memory kinetic-molecules theory optimization algorithm(MKMTOA).Finally,the optimization goal was optimized using the MKMTOA,and the dynamic optimization of dissolved oxygen and nitrate nitrogen concentration values was achieved.The experimental results show that the method can effectively reduce the energy consumption in meeting the water quality requirements.