Multi-objective evolutionary algorithm for wastewater treatment process optimization control

被引：1
|
作者：
Yang Z. [1 ]
Yang C.-L. [1 ]
Gu K. [1 ]
Qiao J.-F. [1 ]
机构：
[1] Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Effluent quality; Energy consumption model; Intelligent optimal control; Multi-objective evolutionary algorithm; Wastewater treatment process;
D O I：
10.7641/CTA.2019.80408
中图分类号：
学科分类号：
摘要：
In the process of sewage treatment, energy consumption and effluent quality are a pair of contradictory indicators. In order to find the optimal solution of these two objectives, this paper improves multi-objective evolutionary algorithm based on decomposition (MOEA/D) that expects even distribution with fewer evolution times for an approximate Pareto front. This algorithm aims at the new solution by using the MOEA/D algorithm each time, finds the most suitable sub-problem of the new solution from all the sub-problems, and carries out replacement of the population within its neighborhood, based on the original sub-problem. Secondary search improves the utilization of the child generation and finds the approximate Pareto front in the optimization problem with fewer iterations. Experiments show that the algorithm significantly reduces the number of steps to find the Pareto front, which results in a significant increase in the performance of the MOEA/D algorithm and achieves the goal of optimization in the wastewater treatment process. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：169 / 175
页数：6
相关论文
共 22 条
  • [1] Lu Q., Wuh 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, Chinese Journal of Chemical Engineering, 23, 6, pp. 1027-1034, (2015)
  • [2] Judd S.J., The status of industrial and municipal effluent treatment with membrane bioreactor technology, Chemical Engineering Journal, 305, 8, pp. 37-45, (2016)
  • [3] Santin I., Pedret C., Vilanova R., Et al., Removing violations of the effluent pollution in a wastewater treatment process, Chemical Engineering Journal, 279, 3, pp. 207-219, (2015)
  • [4] Olsson G., ICA and me-A subjective review, Water Research, 46, 6, pp. 1585-1624, (2012)
  • [5] Mehrjouei M., Muller S., Moller D., A review on photocatalytic ozonation used for the treatment of water and wastewater, Chemical Engineering Journal, 263, 1, pp. 209-219, (2015)
  • [6] Hamilton R., Braun B., Dare R., Et al., Control issues and challenges in wastewater treatment plants, Control Systems IEEE, 26, 4, pp. 63-69, (2006)
  • [7] Han H., Zhang L., Qiao J., Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm, Chemical Engineering Journal, 68, 4, pp. 1474-1481, (2017)
  • [8] Zhang W., Qiao J., Multi-objective optimization control for wastewater treatment process based on neural network, CAAI Transactions on Intelligent Systems, 11, 5, pp. 594-599, (2016)
  • [9] Qi Y., Ma X., Liu F., Et al., MOEA/D with adaptive weight adjustment, Evolutionary Computation, 22, 2, pp. 231-264, (2014)
  • [10] Yu S., Kuang S., Convergence and convergence rate analysis of elitist genetic algorithm based on martingale approach, Control Theory & Applications, 27, 7, pp. 843-848, (2010)
123