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Robust optimal control for anaerobic-anoxic-oxic reactors

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Abstract

Anaerobic-anoxic-oxic (A2O) reactors, as the core parts of wastewater treatment process (WWTP), have attracted considerable attention to achieve the reliability of denitrification and dephosphorization. However, it is difficult to realize the optimal operation of A2O reactors due to the existence of nonlinear dynamics and large uncertainties. To solve this problem, a robust optimal control (ROC) strategy is developed to improve the operation performance of A2O reactors. First, data-driven systematic evaluation criteria are developed to describe the operational indicators of changeable conditions. Second, a robust optimization algorithm is designed to select the optimal solution. Third, a fuzzy neural network (FNN) is used to track the optimal solution in the control process. Finally, this proposed ROC strategy is applied to the phosphorus removal benchmark simulation model (BSM1-P) and the real A2O reactors. The results demonstrate that the strategy developed in this paper has great potential for application in real A2O reactors.

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References

  1. Boiocchi R, Mauricio-Iglesias M, Vangsgaard A K, et al. Aeration control by monitoring the microbiological activity using fuzzy logic diagnosis and control. Application to a complete autotrophic nitrogen removal reactor. J Process Control, 2015, 30: 22–33

    Article  Google Scholar 

  2. Yin X, Li Z, Zhang L, et al. Distributed state estimation of sensor-network systems subject to Markovian Channel switching with application to a chemical process. IEEE Trans Syst Man Cybern Syst, 2018, 48: 864–874

    Article  Google Scholar 

  3. de Faria A B B, Spérandio M, Ahmadi A, et al. Evaluation of new alternatives in wastewater treatment plants based on dynamic modelling and life cycle assessment (DM-LCA). Water Res, 2015, 84: 99–111

    Article  Google Scholar 

  4. Huang Y S, Pan Y L, Zhou M C. Computationally improved optimal deadlock control policy for flexible manufacturing systems. IEEE Trans Syst Man Cybern A, 2012, 42: 404–415

    Article  Google Scholar 

  5. Jia Y H, Chen W N, Gu T, et al. A dynamic logistic dispatching system with set-based particle swarm optimization. IEEE Trans Syst Man Cybern Syst, 2018, 48: 1607–1621

    Article  Google Scholar 

  6. Li H, Bai L, Zhou Q, et al. Adaptive fuzzy control of stochastic nonstrict-feedback nonlinear systems with input saturation. IEEE Trans Syst Man Cybern Syst, 2017, 47: 2185–2197

    Article  Google Scholar 

  7. Zhao D, Ding S X, Karimi H R, et al. On robust Kalman filter for two-dimensional uncertain linear discrete time-varying systems: A least squares method. Automatica, 2019, 99: 203–212

    Article  MathSciNet  Google Scholar 

  8. van Daal-Rombouts P, Benedetti L, de Jonge J, et al. Performance evaluation of a smart buffer control at a wastewater treatment plant. Water Res, 2017, 125: 180–190

    Article  Google Scholar 

  9. Zhong X, Li J, Ertl S M, et al. A system-theoretic approach to modeling and analysis of mammography testing process. IEEE Trans Syst Man Cybern Syst, 2016, 46: 126–138

    Article  Google Scholar 

  10. Amand L, Carlsson B. Optimal aeration control in a nitrifying activated sludge process. Water Res, 2012, 46: 2101–2110

    Article  Google Scholar 

  11. Revollar S, Vega P, Vilanova R, et al. Optimal control of wastewater treatment plants using economic-oriented model predictive dynamic strategies. Appl Sci, 2017, 7: 813

    Article  Google Scholar 

  12. Guerrero J, Guisasola A, Comas J, et al. Multi-criteria selection of optimum WWTP control setpoints based on microbiology-related failures, effluent quality and operating costs. Chem Eng J, 2012, 188: 23–29

    Article  Google Scholar 

  13. Machado V C, Lafuente J, Baeza J A. Model-based control structure design of a full-scale WWTP under the retrofitting process. Water Sci Tech, 2015, 71: 1661–1671

    Article  Google Scholar 

  14. Santín I, Barbu M, Pedret C, et al. Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions. ISA Trans, 2018, 77: 146–166

    Article  Google Scholar 

  15. Qiao J F, Bo Y C, Chai W, et al. Adaptive optimal control for a wastewater treatment plant based on a data-driven method. Water Sci Tech, 2013, 67: 2314–2320

    Article  Google Scholar 

  16. Asadi A, Verma A, Yang K, et al. Wastewater treatment aeration process optimization: A data mining approach. J Environ Manage, 2017, 203: 630–639

    Article  Google Scholar 

  17. Han H G, Zhang L, Liu H X, et al. Multiobjective design of fuzzy neural network controller for wastewater treatment process. Appl Soft Computing, 2017, 67: 467–478

    Article  Google Scholar 

  18. Pang J W, Yang S S, He L, et al. An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system. Chemosphere, 2019, 234: 893–901

    Article  Google Scholar 

  19. Yin X, Liu J. Distributed moving horizon state estimation of two-time-scale nonlinear systems. Automatica, 2017, 79: 152–161

    Article  MathSciNet  Google Scholar 

  20. Kiss A M N, Marx B, Mourot G, et al. State estimation of two-time scale multiple models. Application to wastewater treatment plant. Control Eng Practice, 2011, 19: 1354–1362

    Article  Google Scholar 

  21. Aguilar-Garnica E, Dochain D, Alcaraz-González V, et al. A multivariable control scheme in a two-stage anaerobic digestion system described by partial differential equations. J Process Control, 2009, 19: 1324–1332

    Article  Google Scholar 

  22. Rincon A, Angulo F, Olivar G. Control of an anaerobic digester through normal form of fold bifurcation. J Process Control, 2009, 19: 1355–1367

    Article  Google Scholar 

  23. Wang Y, Karimi H R, Lam H K, et al. An improved result on exponential stabilization of sampled-data fuzzy systems. IEEE Trans Fuzzy Syst, 2018, 26: 3875–3883

    Article  Google Scholar 

  24. Santín I, Pedret C, Vilanova R. Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. J Process Control, 2015, 28: 40–55

    Article  Google Scholar 

  25. Sweetapple C, Fu G, Butler D. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Res, 2014, 55: 52–62

    Article  Google Scholar 

  26. Gao S, Bo C, Li J, et al. Multi-objective optimization and dynamic control of biogas pressurized water scrubbing process. Renew Energy, 2019, 147: 2335–2344

    Article  Google Scholar 

  27. Qiao J F, Hou Y, Zhang L, et al. Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation. Neurocomputing, 2018, 275: 383–393

    Article  Google Scholar 

  28. 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. Comput Chem Eng, 2018, 115: 150–160

    Article  Google Scholar 

  29. Marques J, Cunha M, Savić D A. Multi-objective optimization of water distribution systems based on a real options approach. Environ Model Software, 2015, 63: 1–13

    Article  Google Scholar 

  30. Yousefi M, Banihabib M E, Soltani J, et al. Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater. Agric Water Manage, 2018, 208: 224–231

    Article  Google Scholar 

  31. de Faria A B B, Ahmadi A, Tiruta-Barna L, et al. Feasibility of rigorous multi-objective optimization of wastewater management and treatment plants. Chem Eng Res Des, 2016, 115: 394–406

    Article  Google Scholar 

  32. Tudor R, Lavric V. Dual-objective optimization of integrated water/wastewater networks. Comput Chem Eng, 2011, 35: 2853–2866

    Article  Google Scholar 

  33. Vaghefi S A, Jafari M A, Zhu J, et al. A hybrid physics-based and data driven approach to optimal control of building cooling/heating systems. IEEE Trans Automat Sci Eng, 2016, 13: 600–610

    Article  Google Scholar 

  34. Nasir A N K, Tokhi M O. An improved spiral dynamic optimization algorithm with engineering application. IEEE Trans Syst Man Cybern Syst, 2015, 45: 943–954

    Article  Google Scholar 

  35. Halder U, Das S, Maity D. A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments. IEEE Trans Cybern, 2013, 43: 881–897

    Article  Google Scholar 

  36. Fang F, Qiao L L, Cao J S, et al. Quantitative evaluation of A2O and reversed A2O processes for biological municipal wastewater treatment using a projection pursuit method. Separation Purification Tech, 2016, 166: 164–170

    Article  Google Scholar 

  37. Jibouri A K H A, Upreti S R, Wu J N. Optimal control of continuous ozonation of non-biodegradable pollutants. J Process Contr, 2018, 66: 1–11

    Article  Google Scholar 

  38. Qiao J, Zhang W. Dynamic multi-objective optimization control for wastewater treatment process. Neural Comput Applic, 2018, 29: 1261–1271

    Article  Google Scholar 

  39. Xu H Y, Vilanova R. Application of fuzzy control on wastewater treatment plant for P-removal. In: The 23rd Mediterranean Conference on Control and Automation (MED). Torremolinos: IEEE, 2015. 545–550

    Google Scholar 

  40. Ayesa E, De la Sota A, Grau P, et al. Supervisory control strategies for the new WWTP of Galindo-Bilbao: The long run from the conceptual design to the full-scale experimental validation. Water Sci Tech, 2006, 53: 193–201

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    HongGui Han, JiaCheng Zhang, ShengLi Du, HaoYuan Sun & JunFei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    HongGui Han, JiaCheng Zhang, ShengLi Du, HaoYuan Sun & JunFei Qiao

  3. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    HongGui Han, JiaCheng Zhang, ShengLi Du, HaoYuan Sun & JunFei Qiao

Authors
  1. HongGui Han
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  2. JiaCheng Zhang
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  3. ShengLi Du
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  4. HaoYuan Sun
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  5. JunFei Qiao
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Corresponding author

Correspondence to HongGui Han.

Additional information

This work was supported by the National Key Research and Development Project (Grant No. 2018YFC1900800-5), the National Natural Science Foundation of China (Grant Nos. 61890930-5 and 61622301), Beijing Natural Science Foundation (Grant No. 4172005), and Beijing Outstanding Young Scientist Program (Grant No. BJJWZYJH01 201910005020).

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Han, H., Zhang, J., Du, S. et al. Robust optimal control for anaerobic-anoxic-oxic reactors. Sci. China Technol. Sci. 64, 1485–1499 (2021). https://doi.org/10.1007/s11431-020-1821-2

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  • DOI: https://doi.org/10.1007/s11431-020-1821-2

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