Multi-objective optimal control of urban wastewater treatment considering microbiological risk

被引：0
作者：
Min J.-F. [1 ]
Peng X. [1 ]
Li Z. [1 ]
Zhong W.-M. [1 ]
Pan H.-G. [2 ]
机构：
[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai
[2] College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an
来源：
Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities | 2020年 / 34卷 / 06期
关键词：
Microbiological risk; Multi-objective differential evolutionary; Optimal control; Support vector regression machine; Wastewater treatment;
D O I：
10.3969/j.issn.1003-9015.2020.06.020
中图分类号：
学科分类号：
摘要：
A multi-objective optimal control approach considering microbiological risk was proposed due to the deterioration of effluent quality by insufficient sludge settlement in urban wastewater treatment process. The steady-state models of energy consumption (EC), effluent quality (EQ) and microbiological risk (MR) were developed through support vector regression machine by selecting variables affecting sludge settlement as controlled variables. Multi-objective differential evolutionary algorithm was utilized to optimize EC, EQ and MR, and the optimal setpoints of dissolved oxygen (ρO,3) and sludge retention time (SRT) were determined based on microbiological risk optimization and traced using anti-windup PI controllers. The experimental results on benchmark simulation model no.1 (BSM1) for wastewater treatment demonstrated that the proposed method reduces high microbiological risk ratio by 25.35% compared with the EC and EQ optimization merely. © 2020, Editorial Board of Journal of Chemical Engineering of Chinese Universities". All right reserved."
引用
收藏
页码：1482 / 1491
页数：9
相关论文
共 23 条
  • [1] ZHANG J F., Activated sludge process control, (2011)
  • [2] NANCHARAIAH Y V, SARVAJITH M., Aerobic granular sludge process: a fast growing biological treatment for sustainable wastewater treatment, Current Opinion in Environmental Science & Health, 12, pp. 57-65, (2019)
  • [3] GUVEN H, DERELI R K, OZGUN H, Et al., Towards sustainable and energy efficient municipal wastewater treatment by up-concentration of organics, Progress in Energy and Combustion Science, 70, pp. 145-168, (2019)
  • [4] DAVOUDI M, MOTTAGHI H R, SAMIEIRAD S, Et al., Characteristics, treatments, and management for disposal of sanitary wastewater in South Pars Gas processing plants, Journal of Natural Gas Science and Engineering, 19, pp. 91-97, (2014)
  • [5] HAN H G, WU X L, ZHANG L, Et al., Research on identification and suppression of abnormal operating conditions in municipal wastewater treatment process, Acta Automatica Sinica, 44, 11, pp. 1971-1984, (2018)
  • [6] DEEPNARAIN N, NASR M, KUMARI S, Et al., Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant, Process Safety and Environmental Protection, 126, pp. 25-34, (2019)
  • [7] LI G, LIU X., Optimal control problems with incomplete and different integral time domains in the objective and constraints, Nonlinear Dynamics, 78, 4, pp. 2883-2895, (2014)
  • [8] JENKINS D, RICHARD M G, DAIGGER G T., Manual on the causes and control of activated sludge bulking, foaming, and other solids separation problems, (2003)
  • [9] HAN H G, ZHANG L, QIAO J F., Intelligent optimal control of wastewater treatment based on multi-objective particle swarm, CIESC Journal, 68, 4, pp. 1474-1481, (2017)
  • [10] SADEGHASSADI M, MACNAB C J B, GOPALUNI B, Et al., Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment, Computers & Chemical Engineering, 115, pp. 150-160, (2018)
123