基于记忆分子动理论算法的污水处理过程优化

详细信息    查看全文 | 推荐本文 |

英文篇名：Study on Optimization Control of Wastewater Treatment Process Based on Memory Knetic-Molecular Theory Optimization Algorithm 作者：张明涛 ; 王瑞峰 英文作者：ZHANG Ming-tao;WANG Rui-feng;School of Automation & Electrical Engineering, Lanzhou Jiaotong University; 关键词：污水处理 ; 记忆分子动理论算法 ; 智能优化控制 ; 能耗模型 ; 出水水质模型 英文关键词：Sewage treatment;;Memory kinetic-molecular theory optimization algorithm;;Intelligent optimal control;;Energy consumption model;;Effluent quality model 中文刊名：计算机仿真 英文刊名：Computer Simulation 机构：兰州交通大学自动化与电气工程学院; 出版日期：2019-03-15 出版单位：计算机仿真 年：2019 期：03 语种：中文; 页：407-411 页数：5 CN：11-3724/TP ISSN：1006-9348 分类号：X703

摘要

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.

引文

[1] Wan Junfeng, Gu Jun, Zhao Qian, Liu Yu. COD capture: a feasible option towards energy selfsu-fficient domestic wastewater treatment[J]. Scientific reports,2016,6(4): 1-9.
    [2] M A Oturan, J J Aaron. Advanced oxidation processes in water/wastewater treatment: principles and applications. A review[J]. Critical Reviews in Environmental Science and Technology, 2014,44(23):2577-2641.
    [3] L J Alvarez, et al. Multiobjective paretooptimal control: an applic-ation to wastewater management[J]. Computational Optimiz-ation and Applications, 2010,46: 135-157.
    [4] R Zhang, et al. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method[J]. Bioresource Technology, 2014,157(4):161-165.
    [5] 林梅金,罗飞,许玉格. 基于蛙跳粒子群算法的污水生化处理优化控制[J]. 华南理工大学学报(自然科学版), 2013,41(9):51-57.
    [6] 刘伟. 基于改进粒子群算法的污水处理氧化沟工艺优化控制研究与实现[D]. 合肥工业大学, 2014.
    [7] 黄光球,李涛,陆秋琴. 人工记忆优化算法[J]. 系统工程理论与实践, 2014,34(11): 2900-2912.
    [8] 范朝冬,章兢,易灵芝. M-精英协同进化分子动理论优化算法[J]. 通信学报, 2015,36(7):144-152.
    [9] U Jeppsson, M N Pons. The COST benchmark simulation model-current state and future perspective[J] .Control Engineering Practice, 2004,12(3):299-304.
    [10] J F Qiao, G Han, H G Han. Neural network online modeling and controlling method for multivariable control of wastewater treatment processes[J]. Asian Journal of Control, 2014,16(4):1213-1223.
    [11] 韩红桂,张璐,乔俊飞. 基于多目标粒子群算法的污水处理智能优化控制[J]. 化工学报, 2017,68(04):1474-1481.
    [12] P Vega, F Ballesteros. Integration of setpoint optimization technologies into nonlinear MPC for improving the operation of WWTPs[J]. Computers and Chemical Engineering, 2014,68(10):78-95.