Advertisement

Applied Intelligence

, Volume 49, Issue 3, pp 1098–1126 | Cite as

Multiobjective optimal control for wastewater treatment process using adaptive MOEA/D

  • Hongbiao Zhou
  • Junfei QiaoEmail author
Article
  • 80 Downloads

Abstract

Through the analysis of the biological wastewater treatment process (WWTP), a multiobjective optimal control strategy is developed with the usage of energy consumption (EC) and effluent quality (EQ) as objectives to be optimized. To effectively handle the multiobjective optimization problem (MOP) with complex Pareto-optimal front (POF), an adaptive multiobjective evolutionary algorithm based on decomposition (AMOEA/D) is proposed in this paper. Since the efficiency of the multiple reference points and two-phase optimization strategies in solving MOPs with complex POFs has been proved. In the proposed AMOEA/D, an auto-switching strategy based on the aggregation function enhancement is designed to automatically make the algorithm switch from the first phase to the second phase. Besides, an adaptive differential evolution strategy is introduced into AMOEA/D to balance exploration and exploitation during the evolutionary process. Finally, the dynamic optimization, intelligent decision and bottom tracking control of the set-points of the dissolved oxygen and nitrate nitrogen in the WWTP are achieved via the combination of AMOEA/D with the self-organizing fuzzy neural network approximator and the self-organizing fuzzy neural network controller. The international benchmark simulation model No. 1 (BSM1) is utilized for experimental verification. Simulation results demonstrate that the proposed AMOEA/D can effectively reduce the EC of the WWTP under the premise of ensuring effluent parameters to meet the effluent discharge standards.

Keywords

Wastewater treatment process Multiobjective optimal control MOEA/D Two-phase optimization Auto-switching Adaptive differential evolution strategy 

Notes

Acknowledgements

The authors would like to thank the Editor-in-Chief, the Associate Editor and anonymous reviewers for their invaluable suggestions which have been incorporated to improve the quality of the paper. This work was supported in part by the National Science Foundation for Distinguished Young Scholars of China under Grant 61225016 and the State Key Program of National Natural Science of China under Grant 61533002.

References

  1. 1.
    Wan JF, Gu J, Zhao Q, Liu Y (2016) COD capture: a feasible option towards energy self-sufficient domestic wastewater treatment. Sci Rep 6(4):1–9Google Scholar
  2. 2.
    Oturan MA, Aaron JJ (2014) Advanced oxidation processes in water/wastewater treatment: principles and applications. A review. Crit Rev Environ Sci Technol 44(23):2577–2641Google Scholar
  3. 3.
    Santín I, Pedret C, Vilanova R, Meneses M (2015) Removing violations of the effluent pollution in a wastewater treatment process. Chem Eng J 279(11):207–219Google Scholar
  4. 4.
    Judd SJ (2016) The status of industrial and municipal effluent treatment with membrane bioreactor technology. Chem Eng J 305(12):37–45Google Scholar
  5. 5.
    Åmand L, Carlsson B (2012) Optimal aeration control in a nitrifying activated sludge process. Water Res 46(7):2101–2110Google Scholar
  6. 6.
    Wahab NA, Katebi R, Balderud J (2009) Multivariable PID control design for activated sludge process with nitrification and denitrification. Biochem Eng J 45(3):239–248Google Scholar
  7. 7.
    Song X, Zhao Y, Song Z (2012) Dissolved oxygen control in wastewater treatment based on robust PID controller. Int J Modell Identif Control 15(4):297–303Google Scholar
  8. 8.
    Holenda B, Domokos E, Redey A, Fazakas J (2008) Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Comput Chem Eng 32(6):1270–1278Google Scholar
  9. 9.
    Belchior CAC, Araújo RAM, Landeck JAC (2012) Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Comput Chem Eng 37:152–162Google Scholar
  10. 10.
    Qiao JF, Zhang W, Han HG (2016) Self-organizing fuzzy control for dissolved oxygen concentration using fuzzy neural network. J Intell Fuzzy Syst 30(6):3411–3422Google Scholar
  11. 11.
    Hreiz R, Latifi MA, Roche N (2015) Optimal design and operation of activated sludge processes: state-of-the-art. Chem Eng J 281(12):900–920Google Scholar
  12. 12.
    Ostace GS, Baeza JA, Guerrero J, Guisasola A (2013) Development and economic assessment of different WWTP control strategies for optimal simultaneous removal of carbon, nitrogen and phosphorus. Comput Chem Eng 53(6):164–177Google Scholar
  13. 13.
    Santin I, Pedret C, Vilanova R (2015) Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. J Process Control 28(4):40–55Google Scholar
  14. 14.
    Guerrero J, Guisasola A, Vilanova R, Baeza JA (2011) Improving the performance of a WWTP control system by model-based setpoint optimisation. Environ Modell Softw 26(4):492–497Google Scholar
  15. 15.
    Machado VC, Gabriel D, Lafuente J, Baeza JA (2009) Cost and effluent quality controllers design based on the relative gain array for a nutrient removal WWTP. Water Res 43(20):5129–5141Google Scholar
  16. 16.
    Qiao JF, Bo YC, Chai W, Han HG (2013) Adaptive optimal control for a wastewater treatment plant based on a data-driven method. Water Sci Technol 67(10):2314–2320Google Scholar
  17. 17.
    Han G, Qiao JF, Han HG, Chai W (2014) Optimal control for wastewater treatment process based on Hopfield neural network. Control Decis 29(11):2085–2088Google Scholar
  18. 18.
    Vega P, Revollar S, Francisco M, Martín JM (2014) Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Comput Chem Eng 68:78–95Google Scholar
  19. 19.
    Dai HL, Chen WL, Lu XW (2016) The application of multi-objective optimization method for activated sludge process: a review. Water Sci Technol 73(2):223–235Google Scholar
  20. 20.
    Hakanen J, Sahlstedt K, Miettinen K (2013) Wastewater treatment plant design and operation under multiple conflicting objective functions. Environ Model Softw 46(4):240–249Google Scholar
  21. 21.
    Sweetapple C, Fu G, Butler D (2014) Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Res 55(2):52–62Google Scholar
  22. 22.
    Hreiz R, Roche N, Benyahia B, Latifi MA (2015) Multi-objective optimal control of small-size wastewater treatment plants. Chem Eng Res Des 102(7):345–353Google Scholar
  23. 23.
    Chen WL, Lu XW, Yao CH (2015) Optimal strategies evaluated by multi-objective optimization method for improving the performance of a novel cycle operating activated sludge process. Chem Eng J 260(9):492–502Google Scholar
  24. 24.
    Zhang R, Xie WM, Yu HQ, Li WW (2014) Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method. Bioresour Technol 157(2):161–165Google Scholar
  25. 25.
    Qiao JF, Zhang W (2016) Dynamic multi-objective optimization control for wastewater treatment process. Neural Comput Applic 28(10):1–11Google Scholar
  26. 26.
    Qiao JF, Hou Y, Zhang L, Han HG (2018) Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation. Neurocomputing 275:383–393Google Scholar
  27. 27.
    Han HG, Zhang L, Liu HX, Qiao JF (2018) Multiobjective design of fuzzy neural network controller for wastewater treatment process. Appl Soft Comput 67:467–478Google Scholar
  28. 28.
    Zhang QF, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731Google Scholar
  29. 29.
    Li K, Zhang QF, Kwong S, Li MQ, Wang R (2014) Stable matching-based selection in evolutionary multiobjective optimization. IEEE Trans Evol Comput 18(6):909–923Google Scholar
  30. 30.
    Wu MY, Li K, Kwong S, Zhou Y, Zhang QF (2017) Matching-based selection with incomplete lists for decomposition multi-objective optimization. IEEE Trans Evol Comput 21(4):554–568Google Scholar
  31. 31.
    Zhao SZ, Suganthan PN, Zhang QF (2012) Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans Evol Comput 16(3):442–446Google Scholar
  32. 32.
    Wang ZK, Zhang QF, Zhou AM, Gong MG, Jiao LC (2016) Adaptive replacement strategies for MOEA/D. IEEE Trans Cybern 46(2):474–486Google Scholar
  33. 33.
    Qi YT, Ma XL, Liu F, Jiao LC, Sun JY, Wu JS (2014) MOEA/D with adaptive weight adjustment. Evol Comput 22(2):231–264Google Scholar
  34. 34.
    Yang SX, Jiang SY, Jiang Y (2017) Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes. Soft Comput 21(16):4677–4691Google Scholar
  35. 35.
    Jiang SY, Yang SX (2016) An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts. IEEE Trans Cybern 46(2):421–437Google Scholar
  36. 36.
    Wang ZK, Zhang QF, Li H, Ishibuchie H, Jiao LC (2017) On the use of two reference points in decomposition based multiobjective evolutionary algorithms. Swarm Evol Comput 34:89–102Google Scholar
  37. 37.
    Ho-Huu V, Hartjes S, Visser HG, Curran R (2018) An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization. Expert Syst Appl 92:430–446Google Scholar
  38. 38.
    Li H, Zhang QF (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans Evol Comput 13(2):284–302Google Scholar
  39. 39.
    Li K, Fialho A, Kwong S, Zhang QF (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 18(1):114–130Google Scholar
  40. 40.
    Sallam KM, Elsayed SM, Sarker RA (2017) Landscape-based adaptive operator selection mechanism for differential evolution. Inform Sci 418:383–404Google Scholar
  41. 41.
    Lin QZ, Liu ZW, Yan Q, Du ZH, Coello CAC, Liang ZP, Wang WJ, Chen JY (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inform Sci 339:332–352Google Scholar
  42. 42.
    Lin QZ, Ma YP, Chen JY, Zhu QL, Coello CAC, Wong KC, Chen F (2018) An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies. Inform Sci 430:46–64MathSciNetGoogle Scholar
  43. 43.
    Qiao JF, Zhou HB (2017) Prediction of effluent total phosphorus based on self-organizing fuzzy neural network. Control Theory Applic 34(2):224–232Google Scholar
  44. 44.
    Qiao JF, Zhou HB (2018) Modeling of energy consumption and effluent quality using density peaks-based adaptive fuzzy neural network. IEEE/CAA J Automatica Sinica 5(5):968–976MathSciNetGoogle Scholar
  45. 45.
    Zhou HB (2017) Dissolved oxygen control of the wastewater treatment process using self-organizing fuzzy neural network. CIESC J 68(4):1516–1524Google Scholar
  46. 46.
    Jeppsson U, Pons MN (2004) The COST benchmark simulation model-current state and future perspective. Control Eng Pract 12(3):299–304Google Scholar
  47. 47.
    Santín I, Pedret C, Vilanova R, Meneses M (2016) Advanced decision control system for effluent violations removal in wastewater treatment plants. Control Eng Pract 49:60–75Google Scholar
  48. 48.
    Jan MA, Khanum RA (2013) A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D. Appl Soft Comput 13(1):128–148Google Scholar
  49. 49.
    Zhu QL, Lin QZ, Chen WN, Wong KC, Coello CAC, Li JQ, Zhang J (2017) An external archive-guided multiobjective particle swarm optimization algorithm. IEEE Trans Cybern 47(9):2794–2808Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijingChina
  3. 3.Faculty of AutomationHuaiyin Institute of TechnologyHuai’anChina

Personalised recommendations