Intelligent Control of Wastewater Treatment Processes Based on Adaptive Immune Optimization

doi:10.16182/j.issn1004731x.joss.21-FZ0821E

Abstract

Abstract: In order to solve the problems of excessive energy consumption and excessive effluent quality in wastewater treatment process control, an intelligent control system based on adaptive immune optimization (AIOIC) is proposed. A hierarchical control strategy is designed, and a fast online self-organizing fuzzy neural network based on singular value decomposition (SVDFNN) is used to construct the mathematical model of wastewater treatment energy consumption and effluent quality. In order to obtain the optimal set values of dissolved oxygen and nitrate nitrogen, an adaptive hybrid evolutionary immune optimization algorithm is designed. The self-organizing recursive fuzzy neural network controller is used to track this optimal set points at the bottom layer. The results show that the proposed immune optimization intelligent control strategy can not only meet the effluent quality standard, but also significantly reduce the energy consumption of wastewater treatment process.

Key words: wastewater treatment process, self-organization fuzzy neural network, immune multi-objective optimization, intelligent control system, energy consumption

CLC Number:

Li Fei, Su Zhong. Intelligent Control of Wastewater Treatment Processes Based on Adaptive Immune Optimization[J]. Journal of System Simulation, 2021, 33(12): 3012-3020.

References

[1] 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[J]. Water Research (S0043-1354), 2009, 43(20): 5129-5141.
[2] Zeng J, Liu J.Economic Model Predictive Control of Wastewater Treatment Processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2015, 54(21), 5710-5721.
[3] Olsson G.ICA and Me-a Subjective Review[J]. Water Res (S0043-1354), 2012, 46(6): 1585-1624.
[4] Castillo A, Cheali P, Gómez V, et al.An Integrated Knowledge-Based and Optimization Tool for the Sustainable Selection of Wastewater Treatment Process concepts[J]. Environmental Modelling & Software (S1364-8152), 2016, 84: 177-192.
[5] Ye J X, Xu H L, Feng E, et al.Optimization of A Fed-Batch Bioreactor for 1, 3-Propanediol Production Using Hybrid Nonlinear Optimal Control[J]. Journal of Process Control (S0959-1524), 2014, 24(10): 1556-1569.
[6] Zhou H B.Dissolved Oxygen Control of Wastewater Treatment Process Using Self-Organizing Fuzzy Neural Network[J]. CIESC Journal (S0438-1157), 2017, 68(4): 1516-1524.
[7] Dai H L, Chen W L, Lu X W.The Application of Multi-Objective Optimization Method for Activated Sludge Process: A Review[J]. Water Science & Technology (S0273-1223), 2016, 73(2): 223-235.
[8] Han H G, Qiao J F.Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2014, 61(4): 1970-1982.
[9] Sweetapple C, Fu G T, Butler D.Multi-Objective Optimization of Wastewater Treatment Plant Control to Reduce Greenhouse Gas Emissions[J]. Water Research (S0043-1354), 2014, 55(15): 52-62.
[10] Han H, Qiao J.Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2013, 61(4), 1970-1982.
[11] 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 & Applications (S0941-0643), 2018, 31(7): 2537-2550.
[12] Zhou H B, Qiao J F.Optimal Control of Wastewater Treatment Process Using Hybrid Multi-Objective Barebones Particle Swarm Optimization Algorithm[J]. CIESC Journal (S0438-1157), 2017, 68(9): 3511-3521.
[13] Qiao J, Han G, Han H, et al.Wastewater Treatment Control Method Based on a Rule Adaptive Recurrent Fuzzy Neural Network[J]. International Journal of Intelligent Computing and Cybernetics (S1756-378X), 2017, 10(2): 94-110.
[14] Petrie B, Barden R, Kasprzyk-Hordern B.A Review on Emerging Contaminants in Wastewaters and the Environment: Current Knowledge, Understudied Areas and Recommendations for Future Monitoring[J]. Water Research (S0043-1354), 2015, 72: 3-27.
[15] Li Fei, Yang Cuili, Li Wenjing, et al.Optimal Control of Wastewater Treatment Process Using NSGAII Algorithm Based on Multi-Objective Uniform Distribution[J]. CIESC Journal (S0438-1157), 2019, 70(5): 1868-1878.
[16] Qiao J F, Zhou H B.Prediction of Effluent Total Phosphorus Based on Self-Organizing Fuzzy Neural Network[J]. Control Theory Applic.(S1000-8152), 2017, 34(2): 224-232.
[17] Qiao J F, Li F, Yang S X.An Adaptive Hybrid Evolutionary Immune Multi-Objective Algorithm Based on Uniform Distribution Selection[J]. Information Sciences (S0020-0255), 2020, 512: 446-470.
[18] Qiao J, Zhang W.Dynamic Multi-Objective Optimization Control for Wastewater Treatment Process[J]. Neural Computing and Applications (S0941-0643), 2018, 29(11): 1261-1271.
[19] Han G, Zhang L, Liu H X, et al.Multi-objective Design of Fuzzy Neural Network Controller for Wastewater Treatment Process[J]. Applied Soft Computing (S1568-4946), 2018, 67: 467-478.
[20] Vega P, Revollar S, Francisco M, et al.Integration of Set Point Optimization Techniques into Nonlinear MPC for Improving the Operation of WWTPs[J]. Computers Chemical Engineer.(S0098-1354), 2014, 68(4): 78-95.
[21] Qiao J F, Hou Y, Zhang L, et al.Adaptive Fuzzy Neural Network Control of Wastewater Treatment Process with Multi-objective Operation[J]. Neurocomputing (S0925- 2312), 2018, 275(31): 383-393.