基于多策略自适应差分进化算法的污水处理过程多目标优化控制

doi:10.11949/0438-1157.20201068

化工学报 ›› 2021, Vol. 72 ›› Issue (4): 2167-2177.DOI: 10.11949/0438-1157.20201068

基于多策略自适应差分进化算法的污水处理过程多目标优化控制

赵杨^1,²(),熊伟丽^1,²()

^1.江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122
^2.江南大学物联网工程学院，江苏无锡 214122

收稿日期:2020-07-30 修回日期:2020-11-08 出版日期:2021-04-05 发布日期:2021-04-05
通讯作者: 熊伟丽
作者简介:赵杨（1997—），男，硕士研究生，zhaoyang970730@163.com
基金资助:
国家自然科学基金项目(61773182);国家重点研发计划子课题(2018YFC1603705-03);江苏高校 “青蓝工程”资助项目

Multi-objective optimization control of wastewater treatment process based on multi-strategy adaptive differential evolution algorithm

ZHAO Yang^1,²(),XIONG Weili^1,²()

^1.Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
^2.School of the Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2020-07-30 Revised:2020-11-08 Online:2021-04-05 Published:2021-04-05
Contact: XIONG Weili

摘要/Abstract

摘要：

针对污水处理过程中的能耗过高和出水水质不达标等问题，提出一种基于多策略自适应差分进化算法的污水处理过程多目标优化控制方法。首先，在常规跟踪控制结构的基础上，增加对第3、4单元溶解氧浓度的跟踪控制，扩大了能耗和出水水质的优化调节范围。然后，设计一种多策略自适应差分进化算法（MSADE），该算法采用多策略融合变异和排序优选方法，选取合适的变异策略和较优的随机个体引导种群变异，并根据进化过程信息自适应地更新交叉率，以提升算法的收敛性和pareto解的多样性。最后，将MSADE算法与PID控制器相结合，并构建以能耗和出水水质为优化目标的多目标优化问题，实现对溶解氧和硝态氮浓度设定值的动态寻优和跟踪控制。基于国际基准仿真平台BSM1进行验证，结果表明所提的多目标优化控制方法能有效降低污水处理过程的能耗并提升出水水质。

关键词: 污水处理, 多目标优化控制, 差分进化算法, 自适应更新, 多策略

Abstract:

To address the problems of high energy consumption and substandard effluent quality in the wastewater treatment process, a multi-objective optimization control method for wastewater treatment process based on multi-strategy adaptive differential evolution algorithm is proposed. Firstly, the tracking control of the dissolved oxygen concentration of the 3rd and 4th units is added under the conventional differential tracking control framework, which aims to expand the optimal adjustment range of energy consumption and effluent water quality. Then, a multi-strategy adaptive differential evolution algorithm (MSADE) is proposed, which selects proper mutation strategy and random individuals to guide the population mutation by combining the multi-strategy fusion mutation technique and sorting optimization method. Further, the convergence of proposed algorithm and the diversity of the pareto solution can be greatly improved by updating the crossover rate adaptively according to the evolution process information. Finally, the MSADE algorithm and the PID controller are combined, and a novel multi-objective optimization method is built with a good balance of energy consumption and effluent quality, realizing the dynamic optimization process and the tracking control of setting values of dissolved oxygen and nitrate nitrogen concentration. The simulation results on the international benchmark simulation platform BSM1 show that the proposed method can effectively reduce energy consumption and improve effluent quality in the wastewater treatment process.

Key words: wastewater treatment, multi-objective optimization control, differential evolution algorithm, update adaptively, multi-strategy

中图分类号:

TP 273

赵杨, 熊伟丽. 基于多策略自适应差分进化算法的污水处理过程多目标优化控制[J]. 化工学报, 2021, 72(4): 2167-2177.

ZHAO Yang, XIONG Weili. Multi-objective optimization control of wastewater treatment process based on multi-strategy adaptive differential evolution algorithm[J]. CIESC Journal, 2021, 72(4): 2167-2177.

参考文献 30

1	Bayram A, Kankal M, Tayfur G, et al. Prediction of suspended sediment concentration from water quality variables[J]. Neural Computing and Applications, 2014, 24(5): 1079-1087.
2	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: 237-270.
3	Oturan M A, Aaron J J. 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.
4	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.
5	Lu Q, Wu H Y, Li H Y, et al. Enhanced biological nutrient removal in modified carbon source division anaerobic anoxic oxic process with return activated sludge pre-concentration[J]. Chinese Journal of Chemical Engineering, 2015, 23(6): 1027-1034.
6	Chachuat B, Roche N, Latifi M A. Long-term optimal aeration strategies for small-size alternating activated sludge treatment plants[J]. Chemical Engineering and Processing: Process Intensification, 2005, 44(5): 591-604.
7	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.
8	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 Applications, 2017, 28(1): 893-905.
9	Syafiie S, Tadeo F, Martinez E, et al. Model-free control based on reinforcement learning for a wastewater treatment problem[J]. Applied Soft Computing, 2011, 11(1): 73-82.
10	Sweetapple C, Fu G T, Butler D. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions[J]. Water Research, 2014, 55: 52-62.
11	Ostace G S, Baeza J A, Guerrero J, et al. Development and economic assessment of different WWTP control strategies for optimal simultaneous removal of carbon, nitrogen and phosphorus[J]. Computers & Chemical Engineering, 2013, 53: 164-177.
12	Béraud B, Steyer J P, Lemoine C, et al. Towards a global multi objective optimization of wastewater treatment plant based on modeling and genetic algorithms[J]. Water Science and Technology, 2007, 56(9): 109-116.
13	张平, 苑明哲, 王宏. 前置反硝化污水生化处理过程优化控制[J]. 信息与控制, 2008, 37(1): 113-118, 128.
	Zhang P, Yuan M Z, Wang H. Optimization control for pre-denitrification type of biological treatment process for wastewater[J]. Information and Control, 2008, 37(1): 113-118, 128.
14	韩广, 乔俊飞, 韩红桂, 等. 基于Hopfield神经网络的污水处理过程优化控制[J]. 控制与决策, 2014, 29(11): 2085-2088.
	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, 29(11): 2085-2088.
15	Hreiz R, Roche N, Benyahia B, et al. Multi-objective optimal control of small-size wastewater treatment plants[J]. Chemical Engineering Research and Design, 2015, 102: 345-353.
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	Qiao J F, Zhang W. Dynamic multi-objective optimization control for wastewater treatment process[J]. Neural Computing and Applications, 2018, 29(11): 1261-1271.
18	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(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, 2017, 68(4): 1474-1481.
19	李永明, 史旭东, 熊伟丽. 基于工况识别的污水处理过程多目标优化控制[J]. 化工学报, 2019, 70(11): 4325-4336.
	Li Y M, Shi X D, Xiong W L. Condition recognition based intelligent multi-objective optimal control for wastewater treatment[J]. CIESC Journal, 2019, 70(11): 4325-4336.
20	Cristea V M, Pop C, Agachi P S. Model predictive control of the waste water treatment plant based on the benchmark simulation model No.1-BSM1[J]. Computer Aided Chemical Engineering, 2008, 25: 441-446.
21	Jeppsson U, Pons M N. The COST benchmark simulation model—current state and future perspective[J]. Control Engineering Practice, 2004, 12(3): 299-304.
22	Storn R, Price K. Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
23	艾兵, 董明刚, 敬超. 基于多策略排序变异的多目标差分进化算法[J]. 计算机应用研究, 2018, 35(7): 1950-1954.
	Ai B, Dong M G, Jing C. Multi-objective differential evolution algorithm with multi-strategy and ranking-based mutation[J]. Application Research of Computers, 2018, 35(7): 1950-1954.
24	Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
25	Qiao J F, Hou Y, Han H G. Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm[J]. Neural Computing and Applications, 2019, 31(7): 2537-2550.
26	Lara A, Sanchez G, Coello C A C, et al. HCS: a new local search strategy for memetic multiobjective evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(1): 112-132.
27	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.
28	Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.
29	Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279.
30	周红标, 乔俊飞. 混合多目标骨干粒子群优化算法在污水处理过程优化控制中的应用[J]. 化工学报, 2017, 68(9): 3511-3521.
	Zhou H B, Qiao J F. Optimal control of wastewater treatment process using hybrid multi-objective barebones particle swarm optimization algorithm[J]. CIESC Journal, 2017, 68(9): 3511-3521.

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基于多策略自适应差分进化算法的污水处理过程多目标优化控制

Multi-objective optimization control of wastewater treatment process based on multi-strategy adaptive differential evolution algorithm

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