Advertisement

Optimization Control for Wastewater Treatment Process Based on Data and Knowledge Decision

  • Wei Zhang
  • Ruifei Bai
  • JiaoLong Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

In this paper, a whole process optimization control (WPOC) method is proposed for the wastewater treatment process (WWTP). The WPOC method is studied under the scheme of hierarchical control. First, the intelligent decision part is designed based on the data and knowledge information of the system. The optimal direction is adjusted according to the preference of decision makers and the current system performance. Then, the weight coefficients of the performance indexes are provided to the optimization layer. The NSGA-II algorithm is adopted for solving the multi-objective optimization problem. The tracking control task is finished using the neural network control method. Simulation results, based on the international benchmark simulation model no. 1 (BSM1), show that WPOC method can achieve the energy saving with meeting effluent discharge, and the comprehensive evaluation of energy consumption and effluent quality is also improved.

Keywords

Whole process optimization Hierarchical control Intelligent decision Knowledge and data Wastewater treatment 

Notes

Acknowledgements

This work is supported by National Science Foundation of China under Grant 61703145, Doctor Fund Project of Henan Polytechnic University of China under Grant B2017-21.

References

  1. 1.
    R. Hamilton, B. Braun, R. Dare et al., Control issues and challenges in wastewater treatment plants. IEEE Control Syst. Mag. 26(4), 63–69 (2006)CrossRefGoogle Scholar
  2. 2.
    H.G. Han, H.H. Qian, J.F. Qiao, Nonlinear multi-objective model-predictive control scheme for wastewater treatment process. J. Process Control 24(3), 47–59 (2014)CrossRefGoogle Scholar
  3. 3.
    D. Vrecko, N. Hvala, J. Kocijan et al., System analysis for optimal control of a wastewater treatment benchmark. Water Sci. Technol. 43(7), 199–206 (2001)CrossRefGoogle Scholar
  4. 4.
    R. Piotrowski, M.A. Brdys, K. Konarczak et al., Hierarchical dissolved oxygen control for activated sludge processes. Control Eng. Pract. 16(1), 114–131 (2008)CrossRefGoogle Scholar
  5. 5.
    B. Chachuat, N. Roche, M.A. Latifi, Dynamic optimisation of small size wastewater treatment plants including nitrification and denitrification processes. Comput. Chem. Eng. 25(4–6), 585–593 (2001)CrossRefGoogle Scholar
  6. 6.
    V.C. Machado, D. Gabriel, J. Lafuente et al., Cost and effluent quality controllers design based on the relative gain array for a nutrient removal WWTP. Water Res. 43(20), 5129–5141 (2009)CrossRefGoogle Scholar
  7. 7.
    W.L. Chen, C.H. Yao, X.W. Lu, Optimal design activated sludge process by means of multi-objective optimization: case study in Benchmark Simulation Model 1 (BSM1). Water Sci. Technol. 69(10), 2052–2058 (2014)CrossRefGoogle Scholar
  8. 8.
    I. Santin, C. Pedret, R. Vilanova, Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. J. Process Control 28(1), 40–55 (2015)CrossRefGoogle Scholar
  9. 9.
    P. Vega, S. Revollar, M. Francisco et al., Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Comput. Chem. Eng. 68, 78–95 (2014)CrossRefGoogle Scholar
  10. 10.
    J. Guerrero, A. Guisasola, J. Comas et al., Multi-criteria selection of optimum WWTP control setpoints based on microbiology-related failures, effluent quality and operating costs. Chem. Eng. J. 188, 23–29 (2012)CrossRefGoogle Scholar
  11. 11.
    B. Beraud, J.P. Steyer, C. Lemoine et al., Towards a global multi objective optimization of wastewater treatment plant based on modeling and genetic algorithms. Water Sci. Technol. 56(9), 109–116 (2007)CrossRefGoogle Scholar
  12. 12.
    W. Zhang, Multi-objective intelligent optimization control study of wastewater treatment process (Beijing University of Technology, 2016)Google Scholar
  13. 13.
    W. Zhang, J.F. Qiao, Direct adaptive neural network control for wastewater treatment process, in Proceedings of the 11th World Congress on Intelligent Control and Automation, WCICA, Shenyang, China, 2014Google Scholar
  14. 14.
    J.F. Qiao, G. Han, H.G. Han, Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment processes. Asian J. Control 16(4), 1213–1223 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    N.A. Wahab, R. Katebi, J. Balderud, Multivariable PID control design for activated sludge process with nitrification and denitrification. Biochem. Eng. J. 45(3), 239–248 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and AutomationHenan Polytechnic UniversityHenanChina

Personalised recommendations