Condition recognition based intelligent multi-objective optimal control for wastewater treatment

被引：0
|
作者：
Li Y. [1 ]
Shi X. [1 ]
Xiong W. [1 ,2 ]
机构：
[1] School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, Jiangsu
[2] Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu
来源：
Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 11期
关键词：
Condition recognition; Dynamic simulation; Historical knowledge; Optimization; Process control; Wastewater treatment;
D O I：
10.11949/0438-1157.20190453
中图分类号：
学科分类号：
摘要：
Aiming at the problems in wastewater treatment process, such as high energy consumption and penalty, a condition recognition based intelligent optimal control system for wastewater treatment is proposed. In order to ensure the accuracy and real-time performance of condition identification, the adaptive genetic algorithm is used to select reference variables from a variety of influent parameters, then based on the established historical knowledge base, identifies the real-time influent condition. Multi-objective optimization for energy consumption and penalty is guided by historical knowledge, and through the method of intelligent decision-making, the optimal preference solution is selected from pareto solution set, then update the knowledge base. The international benchmark simulation platform BSM1 is used to verify the results. The results show that the proposed method effectively utilizes the optimal solution information of historical conditions, improves the convergence of the algorithm, reduces the computational cost, and can control the energy consumption and fines at a lower level. © All Right Reserved.
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页码：4325 / 4336
页数：11
相关论文
共 32 条
  • [1] Zhao J., Gui L., Wang Q., Et al., Aged refuse enhances anaerobic digestion of waste activated sludge, Water Research, 123, pp. 724-733, (2017)
  • [2] Santin I., Pedret C., Vilanova R., Et al., Advanced decision control system for effluent violations removal in wastewater treatment plants, Control Engineering Practice, 49, pp. 60-75, (2016)
  • [3] Wang L., Qiang Z., Li Y., Et al., An insight into the removal of fluoroquinolones in activated sludge process: sorption and biodegradation characteristics, Journal of Environmental Sciences, 56, 6, pp. 263-271, (2017)
  • [4] O'Brien M., Mack J., Lennox B., Et al., Model predictive control of an activated sludge process: a case study, Control Engineering Practice, 19, 1, pp. 54-61, (2015)
  • [5] Bulletin on the State of Ecological Environment of China, 2017
  • [6] Hreiz R., Latifi M.A., Roche N., Optimal design and operation of activated sludge processes: state-of-the-art, Chemical Engineering Journal, 281, pp. 900-920, (2015)
  • [7] Wei W., Wang X.Y., Wang P., Et al., Nitrogen removal in wastewater treatment processes based on linear active disturbance rejection control and its dynamic simulation, CIESC Journal, 67, 3, pp. 1032-1039, (2016)
  • [8] Oturan M.A., Aaron J.J., Advanced oxidation processes in water/wastewater treatment: principles and applications. A review, Critical Reviews in Environmental Science & Technology, 44, 23, pp. 2577-2641, (2014)
  • [9] Machado V.C., Gabriel D., Lafuente J., Et al., Cost and effluent quality controllers design based on the relative gain array for a nutrient removal WWTP, Water Research, 43, 20, pp. 5129-5141, (2009)
  • [10] 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, Computers & Chemical Engineering, 53, pp. 164-177, (2013)
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