基于均匀分布NSGAII算法的污水处理多目标优化控制

doi:10.11949/j.issn.0438-1157.20181530

化工学报 ›› 2019, Vol. 70 ›› Issue (5): 1868-1878.DOI: 10.11949/j.issn.0438-1157.20181530

基于均匀分布NSGAII算法的污水处理多目标优化控制

李霏^1,²(),杨翠丽^1,²,李文静^1,²,乔俊飞^1,²()

1. 北京工业大学信息学部，北京 100124
2. 计算智能和智能系统北京市重点实验室，北京 100124

收稿日期:2019-01-02 修回日期:2019-02-19 出版日期:2019-05-05 发布日期:2019-05-05
通讯作者: 乔俊飞
作者简介:<named-content content-type="corresp-name">李霏</named-content>（1985—），女，博士研究生，<email>lifeiaedu@hotmail.com</email>|乔俊飞（1968—），男，博士，教授，<email>junfeiq@bjut.edu.cn</email>
基金资助:
国家自然科学基金项目(61533002, 61603012, 61603009);北京市教委项目(KM201710005025);北京市博士后工作经费资助项目(2017ZZ-028);北京市朝阳区博士后工作经费资助项目(2017ZZ-01-07);北京市自然科学基金项目(4182007)

Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution

Fei LI^1,²(),Cuili YANG^1,²,Wenjing LI^1,²,Junfei QIAO^1,²()

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China

Received:2019-01-02 Revised:2019-02-19 Online:2019-05-05 Published:2019-05-05
Contact: Junfei QIAO

摘要/Abstract

摘要：

针对污水处理过程控制中能耗过大、出水水质超标严重等问题，提出了一种基于均匀分布的NSGAII(non-dominated sorting genetic algorithm II based on uniform distribution, UDNSGAII)多目标优化智能控制系统。首先，该方法以污水处理能耗和出水水质作为优化目标，建立多目标优化模型。其次，为了获得溶解氧和硝态氮的优化设定值，提高Pareto解的性能，该算法将种群映射到目标函数对应的超平面, 并在该平面上进行聚类以增加解的多样性。此外，加入分布性判断模块和分布性加强模块提高解的分布性。最后，采用比例积分微分(proportional integral derivative, PID)控制器对溶解氧和硝态氮的优化设定值进行底层跟踪控制。为了验证该算法的有效性，采用国际基准的污水处理仿真平台(benchmark simulation model No.1, BSM1)来进行实验。结果显示，所提出的UDNSGAII多目标优化控制方法能够在满足出水水质达标的同时，有效地降低污水处理过程能耗。

关键词: 污水处理过程, 均匀分布, 遗传算法, 优化, 控制

Abstract:

Aiming at the problems of excessive energy consumption (EC) and exceeded seriously effluent quality (EQ) in wastewater treatment control process, an optimal control of wastewater treatment process using a multi-objective uniform distribution NSGAII algorithm (UDNSGAII) was proposed. Firstly, EC and EQ of wastewater treatment are regarded as optimization objectives, and the multi-objective optimal control model is established. Secondly, to obtain the optimal set values of dissolved oxygen (DO) and nitrate nitrogen (NO), and improve the performance of Pareto solution, the individuals which have been clustered are mapped to the hyperplane of the corresponding objective function, then, the diversity of population is increased. In addition, the distribution judgment module and distributed enhancement module are used to improve the distribution of solutions. Finally, PID controller as the bottom controller is used to track the optimal setting value of DO and NO. To test the effectiveness of the proposed algorithm, benchmark simulation model No.1 (BSM1) is used. The results show that the proposed UDNSGAII multi-objective optimization control method can effectively reduce EC of wastewater treatment process while meeting EQ standards.

Key words: wastewater treatment process, uniform distribution, genetic algorithm, optimal, control

中图分类号:

TQ 028.8

李霏, 杨翠丽, 李文静, 乔俊飞. 基于均匀分布NSGAII算法的污水处理多目标优化控制[J]. 化工学报, 2019, 70(5): 1868-1878.

Fei LI, Cuili YANG, Wenjing LI, Junfei QIAO. Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution[J]. CIESC Journal, 2019, 70(5): 1868-1878.

图/表 12

图1 BSM1分布图

Fig.1 Layout of BSM1

图2 多目标优化系统结构

Fig.2 Multi-objective optimization control architecture

图3 EC建模结果

Fig.3 Model results of EC

图4 EQ建模结果

Fig.4 Model results of EQ

图5 Pareto最优解集及偏好解确定

Fig.5 Pareto optimal solutions and identify of preferred solution

图6 溶解氧优化设定值及跟踪控制

Fig.6 Optimized set-point and on-line tracking control performance of S O,5

图8 溶解氧跟踪控制误差

Fig.8 Tracking control errors of S O,5

图7 硝态氮优化设定值及跟踪控制

Fig.7 Optimized set-point and on-line tracking control performance of S NO,2

图9 硝态氮跟踪控制误差

Fig.9 Tracking control errors of S NO,2

图10 KLa随时间的变化

Fig.10 Variations of KLa with different time

图11 Qa 随时间的变化

Fig.11 Variations of Qa with different time

表1 不同优化控制方法能耗和出水水质约束量对比

Table 1 Energy consumption and effluent constraints comparison of different optimal control methods

优化控制策略	S _O,5/(mg/L)	S _NO,2/(mg/L)	SS/(mg/L)	S _NH/(mg/L)	BOD₅/(mg/L)	COD₅/(mg/L)	EC/(kW?h)	EQ/(kg pollution unit)
influent^[23]	7.58	0.23	198.57	30.14	183.49	167.31	—	—
UDNSGAII-PID	1.6385	1.0123	11.8659	2.3896	2.7269	45.0298	3239.65	7231.72
MOPSO-PID^[31]	1.6947	1.1329	12.6108	2.8156	2.6816	47.5183	3256.16	7394.9
PID^[23]	2	1	11.2149	10.2328	2.5683	45.2938	3763.70	7115.98
ADPOC^[26]	1.5799	1.087	—	2.7230	3.1103	44.8962	3676.28	7409.84
NNOMC^[32]	1.8	1.4	12.72	3.41	2.71	47.68	3782.44	6334.90
RO-NMPC^[33]	1.01	0.94	—	2.23	—	—	3232.99	8102.09

参考文献 33

1	Petrie B , Barden R , Kasprzyk-Hordern B . A review on emerging contaminants in wastewaters and the environment: current knowledge, understudied areas and recommendations for future monitoring[J]. Water Research, 2015, 72(1): 3-27.
2	Bayram A , Kankal M , Tayfur G , et al . Prediction of suspended sediment concentration from water quality variables[J]. Neural Computing and Appl., 2014, 24(5): 1079- 1087.
3	Prasse C , Stalter D , Schulte-Oehlmann U , et al . Spoilt for choice: a critical review on the chemical and biological assessment of current wastewater treatment technologies[J]. Water Research, 2015, 87(15): 237-270.
4	Guerrero J , Guisasola A , Comas J , et al . Multi-criteria selection of optimum WWTP control setpoints based on microbiology- related failures, effluent quality and operating costs[J]. Chemical Engineering Journal, 2012, 188(15): 23-29.
5	Han H G , Qiao J F . Nonlinear model-predictive control for industrial processes: an application to wastewater treatment process[J]. IEEE Transactions on Industrial Electronics, 2014, 61(4): 1970-1982.
6	Hameed M , Sharqi S S , Yaseen Z M , et al . Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia[J]. Neural Computing and Appl., 2017, 28(1): 893- 905.
7	Han G , Qiao J F , Han H G , et al . Optimal control for wastewater treatment process based on Hopfield neural network[J]. Control and Decision, 2014, 71(11): 1661-1671.
8	Hakanen J , Miettinen K , Sahlstedt K . Wastewater treatment: new insight provided by interactive multiobjective optimization[J]. Decision Support Systems, 2011, 51(2): 328- 337.
9	Han H G , Zhang L , Hou Y , et al . Nonlinear model predictive control based on a self-organizing recurrent neural network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 402-415.
10	Cadet C , Beteau J F , Hernandez S C . Multicriteria control strategy for cost/quality compromise in wastewater treatment plants[J]. Control Engineering Practice, 2004, 12(3): 335-347.
11	Shen W , Chen X , Corriou J P . Application of model predictive control to the BSM1 benchmark of wastewater treatment process[J]. Computers & Chemical Engineering, 2008, 32(12): 2849-2856.
12	Stare A , Vrečko D , Hvala N , et al . Comparison of control strategies for nitrogen removal in an activated sludge process in terms of operating costs: a simulation study[J]. Water Rresearch, 2007, 41(9): 2004-2014.
13	O’Brien M , Mack J , Lennox B , et al . Model predictive control of an activated sludge process: a case study[J]. Control Engineering Practice, 2011, 19(1): 54-61.
14	Sadeghassadi M , Macnab C J B , Westwick D . Design of a generalized predictive controller for a biological wastewater treatment plant[J]. Water Science and Technology, 2016, 73(8): 1986-2006.
15	Zhang K , Achari G , Sadiq R , et al . An integrated performance assessment framework for water treatment plants[J]. Water Research, 2012, 46(6): 1673-1683.
16	Han H G , Qian H H , Qiao J F . Nonlinear multiobjective model-predictive control scheme for wastewater treatment process[J]. Journal of Process Control, 2014, 24(3): 47-59.
17	Plakas K V , Georgiadis A A , Karabelas A J . Sustainability assessment of tertiary wastewater treatment technologies: a multi- criteria analysis[J]. Water Science and Technology, 2016, 73(7): 1532-1540.
18	Guerrero J , Guisasola A , Vilanova R , et al . Improving the performance of a WWTP control system by model-based setpoint optimisation[J]. Environmental Modelling & Software, 2011, 26(4): 492-497.
19	Dai H , Chen W , Lu X . The application of multi-objective optimization method for activated sludge process: a review[J]. Water Science and Technology, 2016, 73(2): 223-235.
20	Olsson G , Carlsson B , Comas J , et al . Instrumentation, control and automation in wastewater–from London 1973 to Narbonne 2013[J]. Water Science and Technology, 2014, 69(7): 1373-1385.
21	Francisco M , Skogestad S , Vega P . Model predictive control for the self-optimized operation in wastewater treatment plants: analysis of dynamic issues[J]. Computers & Chemical Engineering, 2015, 82(2): 259-272.
22	Sweetapple C , Fu G , Butler D . Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions[J]. Water Research, 2014, 55(15): 52-62.
23	周洪标 . 基于自组织模糊神经网络的污水处理过程溶解氧控制[J]. 化工学报, 2017, 68(4): 1516-1524.
	Zhou H B . Dissolved oxygen control of the wastewater treatment process using self-organizing fuzzy neural network[J]. CIESC Journal, 2017, 68(4): 1516-1524.
24	Jeppsson U , Pons M N . The cost benchmark simulation model-current state and future perspective[J]. Control Engineering Practice, 2004, 12(3): 299-304.
25	Yang S X , Jiang S Y , Jiang Y . Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes[J]. Soft Computing, 2016, 21(2): 1-15.
26	乔俊飞, 周红标 . 基于自组织模糊神经网络的出水总磷预测[J]. 控制理论与应用, 2017, 34(2): 224-232.
	Qiao J F , Zhou H B . Prediction of effluent total phosphorus based on self-organizing fuzzy neural network[J]. Control Theory and Appl., 2017, 34(2): 224-232.
27	Qiao J F , Bo Y C , Chai W , et al . Adaptive optimal control for a wastewater treatment plant based on a data-driven method[J]. Water Science and Technology, 2013, 67(10): 2314-2320.
28	Sindhya K , Miettinen K , Deb K . A hybrid framework for evolutionary multi-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(4): 495-511.
29	栗三一, 李文静, 乔俊飞 . 一种基于密度的局部搜索NSGA2算法[J]. 控制与决策, 2018, 33(1): 60-66.
	Li S Y , Li W J , Qiao J F . A local search NSGA2 algorithm based on density[J]. Control and Decision, 2018, 33(1): 60-66.
30	Qiao J F , Zhang W . Dynamic multi-objective optimization control for wastewater treatment process[J]. Neural Computing and Applications, 2018, 29(11): 1261-1271.
31	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, 2016, 68(4): 1474-1481.
	Han H G , Zhang L , Qiao J F . Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm[J]. CIESC Journal, 2016, 68(4): 1474-1481.
32	Qiao J F , Han G , Han H G . Neural network on line modeling and controlling method for multi variable control of wastewater treatment processes[J]. Asian Journal of Control, 2014, 16(4): 1213-1223.
33	Vega P , Revollar S , Francisco M , et al . Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs[J]. Computers & Chemical Engineering, 2014, 68(4): 78-95.

[1]	杨欣, 王文, 徐凯, 马凡华. 高压氢气加注过程中温度特征仿真分析[J]. 化工学报, 2023, 74(S1): 280-286.
[2]	李艺彤, 郭航, 陈浩, 叶芳. 催化剂非均匀分布的质子交换膜燃料电池操作条件研究[J]. 化工学报, 2023, 74(9): 3831-3840.
[3]	康飞, 吕伟光, 巨锋, 孙峙. 废锂离子电池放电路径与评价研究[J]. 化工学报, 2023, 74(9): 3903-3911.
[4]	何松, 刘乔迈, 谢广烁, 王斯民, 肖娟. 高浓度水煤浆管道气膜减阻两相流模拟及代理辅助优化[J]. 化工学报, 2023, 74(9): 3766-3774.
[5]	陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902.
[6]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[7]	齐聪, 丁子, 余杰, 汤茂清, 梁林. 基于选择吸收纳米薄膜的太阳能温差发电特性研究[J]. 化工学报, 2023, 74(9): 3921-3930.
[8]	陈国泽, 卫东, 郭倩, 向志平. 负载跟踪状态下的铝空气电池堆最优功率点优化方法[J]. 化工学报, 2023, 74(8): 3533-3542.
[9]	刘文竹, 云和明, 王宝雪, 胡明哲, 仲崇龙. 基于场协同和耗散的微通道拓扑优化研究[J]. 化工学报, 2023, 74(8): 3329-3341.
[10]	张曼铮, 肖猛, 闫沛伟, 苗政, 徐进良, 纪献兵. 危废焚烧处理耦合有机朗肯循环系统工质筛选与热力学优化[J]. 化工学报, 2023, 74(8): 3502-3512.
[11]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[12]	邢雷, 苗春雨, 蒋明虎, 赵立新, 李新亚. 井下微型气液旋流分离器优化设计与性能分析[J]. 化工学报, 2023, 74(8): 3394-3406.
[13]	吴文涛, 褚良永, 张玲洁, 谭伟民, 沈丽明, 暴宁钟. 腰果酚生物基自愈合微胶囊的高效制备工艺研究[J]. 化工学报, 2023, 74(7): 3103-3115.
[14]	汤晓玲, 王嘉瑞, 朱玄烨, 郑仁朝. 基于Pickering乳液的卤醇脱卤酶催化合成手性环氧氯丙烷[J]. 化工学报, 2023, 74(7): 2926-2934.
[15]	文兆伦, 李沛睿, 张忠林, 杜晓, 侯起旺, 刘叶刚, 郝晓刚, 官国清. 基于自热再生的隔壁塔深冷空分工艺设计及优化[J]. 化工学报, 2023, 74(7): 2988-2998.

基于均匀分布NSGAII算法的污水处理多目标优化控制

Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution

RichHTML

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 33

相关文章 15

编辑推荐

Metrics

本文评价