Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm

• Published in: Neural computing & applications Vol. 31; no. 7; pp. 2537 - 2550
• Main Authors: Qiao, Jun-Fei, Hou, Ying, Han, Hong-Gui
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2019; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive control; Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer simulation; Crossovers; Data Mining and Knowledge Discovery; Evolutionary algorithms; Evolutionary computation; Image Processing and Computer Vision; Multiple objective analysis; Operating costs; Optimal control; Optimization; Original Article; Probability and Statistics in Computer Science; Process controls; Scaling factors; Wastewater treatment; Water treatment; Optimal strategy; Wastewater treatment processes; Intelligent multi-objective optimization control; Adaptive multi-objective differential evolution
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3212-4

In this paper, for the operations of wastewater treatment processes (WWTPs), an intelligent multi-objective optimization control (IMOOC), based on an adaptive multi-objective differential evolution (AMODE) algorithm, is proposed to search for the suitable set-points to balance the treatment performance and the operational costs. In this IMOOC, the combination of an AMODE algorithm and the multi-objective critical issues helps us to fulfill all the control objectives simultaneously. To improve the optimization efficiency and achieve fast convergence, the AMODE algorithm is designed to improve the local search and the global exploration abilities: The adaptive adjustment strategies are developed to select the suitable scaling factor and crossover rate in the process of searching. Meanwhile, the multi-objective critical issues, according to the state of the processes, are given as a nonlinear multi-objective optimization problem to evaluate the operational performance of WWTPs. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time, the most appropriate set of Pareto is selected as suitable set-points to achieve the process performance. To demonstrate the merits of our proposed method, the proposed IMOOC is applied to the Benchmark Simulation Model No. 1 of WWTPs. The results show that the proposed IMOOC effectively provides process control. The performance comparison with other algorithms also indicates that the proposed optimal strategy yields better effluent qualities and lower average operation consumption.