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An adaptive control method for the dissolved oxygen concentration in wastewater treatment plants

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Abstract

The activated sludge process plays a vital role in the treatment of the civil wastewater. In the operation of the activated sludge process, a key variable is dissolved oxygen (DO) concentration. In this paper, a neural nonlinear adaptive control design technique is presented to solve the DO concentration control problem for an uncertain wastewater treatment process. In the controller design, all uncertain dynamics of the wastewater treatment are approximated by using radial basis function neural networks. Then, it is rigorously proved that semiglobal uniform ultimate boundedness of all the closed-loop system signals is guaranteed by the Lyapunov method. Finally, simulation studies are performed to demonstrate the effectiveness of the proposed adaptive controller. Comparing with the existing controllers, simulation results show that a satisfactory performance is obtained using the proposed adaptive controllers.

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References

  1. Hamilton R, Braun B, Dare R, Koopman B, Svoronos SA (2006) Control issues and challenges in wastewater treatment plants. IEEE Trans Control Syst Mag 26(4):63–69

    Article  Google Scholar 

  2. Stare A, Vrečko D, Hvala N, Strmčnik S (2007) Comparison of control strategies for nitrogen removal in an activated sludge process in terms of operating costs: a simulation study. Water Res 41(9):2004–2014

    Article  Google Scholar 

  3. Ma Y, Peng Y-Z, Jeppsson U (2006) Dynamic evaluation of integrated control strategies for enhanced nitrogen removal in activated sludge processes. Control Eng Pract 14(11):1269–1278

    Article  Google Scholar 

  4. Tzoneva R (2007) Optimal pid control of the dissolved oxygen concentration in the wastewater treatment plant. In: IEEE AFRICON conference, pp 1–7

  5. Traoré A, Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J (2005) Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant. Chem Eng J 111(1):13–19

    Article  Google Scholar 

  6. Fiter M, Güell D, Comas J, Colprim J, Poch M, Rodriguez-Roda I (2005) Energy saving in a wastewater treatment process: an application of fuzzy logic control. Environ Technol 26(11):1263–1270

    Article  Google Scholar 

  7. Vrecko D, Hvala N, Carlsson B (2003) Feedforward–feedback control of an activated sludge process: a simulation study. Water Sci Technol 47(12):19–26

    Google Scholar 

  8. Ma Y, Peng Y-Z, Wang S-Y (2005) Feedforward–feedback control of dissolved oxygen concentration in a predenitrification system. Bioprocess Biosyst Eng 27(4):223–228

    Article  Google Scholar 

  9. Zhang P, Yuan M-Z, Wang H (2008) Improvement of nitrogen removal and reduction of operating costs in an activated sludge process with feedforward–cascade control strategy. Biochem Eng J 41(1):53–58

    Article  Google Scholar 

  10. Dochain D, Vanrolleghem PA (2001) Dynamical modelling and estimation in wastewater treatment processes. IWA Publishing, London

    Google Scholar 

  11. Vilanova R, Katebi R, Alfaro V (2009) Multi-loop pi-based control strategies for the activated sludge process. In: Proceedings of the IEEE conference on emerging technologies and factory automation, pp 1–8

  12. de Araujo ACB, Gallani S, Mulas M, Skogestad S (2013) Sensitivity analysis of optimal operation of an activated sludge process model for economic controlled variable selection. Ind Eng Chem Res 52(29):9908–9921

    Article  Google Scholar 

  13. Petre E, Marin C, Selisteanu D (2005) Adaptive control strategies for a class of recycled depollution bioprocesses. J Control Eng Appl Inform 7(2):25–33

    Google Scholar 

  14. Petre E, Selisteanu D (2013) A multivariable robust-adaptive control strategy for a recycled wastewater treatment bioprocess. Chem Eng Sci 90(7):40–50

    Article  Google Scholar 

  15. Brdys MA, Maiquez JD (2002) Application of fuzzy model predictive control to the dissolved oxygen concentration tracking in an activated sludge process. In: the 15th IFAC world congress, vol 15, pp 1394–1394

  16. Chotkowski W, Brdys MA, Konarczak K (2005) Dissolved oxygen control for activated sludge processes. Int J Syst Sci 36(12):727–736

    Article  MATH  Google Scholar 

  17. 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):1270–1278

    Article  Google Scholar 

  18. Logist F, Houska B, Diehl M, Van Impe JF (2011) Robust multi-objective optimal control of uncertain (bio) chemical processes. Chem Eng Sci 66(20):4670–4682

    Article  Google Scholar 

  19. Mohseni SS, Babaeipour V, Vali AR (2009) Design of sliding mode controller for the optimal control of fed-batch cultivation of recombinant \(e.coli\). Chem Eng Sci 64(21):4433–4441

    Article  Google Scholar 

  20. 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 37:152–162

    Article  Google Scholar 

  21. Li Z-J, Yang C-G, Tang Y (2013) Decentralised adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non-rigid environments. IET Control Theory Appl 7(3):397–410

    Article  MathSciNet  Google Scholar 

  22. Yang T, Qiu W, Ma Y, Chadli M, Zhang L (2014) Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes. Neurocomputing 136(20):88–95

    Article  Google Scholar 

  23. Ge SS, Wang C (2002) Direct adaptive nn control of a class of nonlinear systems. IEEE Trans Neural Netw 13(1):214–221

    Article  MathSciNet  Google Scholar 

  24. Petre E, Selişteanu D, Şendrescu D, Ionete C (2010) Neural networks-based adaptive control for a class of nonlinear bioprocesses. Neural Comput Appl 19(2):169–178

    Article  Google Scholar 

  25. Baruch IS, Georgieva P, Barrera-Cortes J, de Azevedo SF (2005) Adaptive recurrent neural network control of biological wastewater treatment. Int J Intell Syst 20(2):173–193

    Article  MATH  Google Scholar 

  26. Xu B, Yang C-G, Shi Z-K (2014) Reinforcement learning output feedback nn control using deterministic learning technique. IEEE Trans Neural Netw Learn Syst 25(3):635–641

    Article  Google Scholar 

  27. Yang C-G, Ge SS, Xiang C, Chai T, Lee TH (2008) Output feedback nn control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Trans Neural Netw 19(11):1873–1886

    Article  Google Scholar 

  28. Dai S-L, Wang C, Wang M (2014) Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems. IEEE Trans Neural Netw Learn Syst 25(1):111–123

    Article  Google Scholar 

  29. Ge SS, Huang L, Lee TH (2001) Model-based and neural-network-based adaptive control of two robotic arms manipulating an object with relative motion. Int J Syst Sci 32(1):9–23

    Article  MATH  MathSciNet  Google Scholar 

  30. Zhao Z, He W, Ge SS (2014) Adaptive neural network control of a fully actuated marine surface vessel with multiple output constraints. IEEE Trans Control Syst Technol 22(4):1536–1543

    Article  Google Scholar 

  31. Dai S-L, Wang M, Wang C, Li L (2014) Learning from adaptive neural network output feedback control of uncertain ocean surface ship dynamics. Int J Adapt Control Signal Process 28(3):341–365

    Article  MathSciNet  Google Scholar 

  32. Alex J, Benedetti L, Copp J, Gernaey KV, Jeppsson U, Nopens I, Pons MN, Rieger L, Rosen C, Steyer JP, Vanrolleghem P (2008) Benchmark simulation model no. 1 (bsm1). IWA taskgroup on benchmarking of control strategies for WWTP, pp 1–35

  33. Lin M-J, Luo F (2014) A nonlinear adaptive control approach for an activated sludge process using neural networks. In: The 26th Chinese control and decision conference, pp 2435–2440

  34. Henze M (2000) Activated sludge models ASM1, ASM2, ASM2d and ASM3, vol 9. IWA publishing, London

    Google Scholar 

  35. Chen M, Ge SS, How B (2010) Robust adaptive neural network control for a class of uncertain mimo nonlinear systems with input nonlinearities. IEEE Trans Neural Netw 21(5):796–812

    Article  Google Scholar 

  36. Dai S-L, Wang C, Luo F (2012) Identification and learning control of ocean surface ship using neural networks. IEEE Trans Ind Inform 8(4):801–810

    Article  Google Scholar 

  37. Ge SS, Wang C (2004) Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Trans Neural Netw 15(3):674–692

    Article  Google Scholar 

  38. Liu J-K (2012) Intelligent control. Publishing House of Electronics Industry, Beijing

    Google Scholar 

  39. Ge SS, Hang CC, Lee TH, Zhang T (2010) Stable adaptive neural network control. Kluwer, Amsterdam

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Associate Editor and the anonymous reviewers for their helpful and insightful comments for further improving the quality and presentation of this paper. This work was supported in part by the National Natural Science Foundation of China under Grants 61473121 and 61374119.

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Correspondence to Mei-Jin Lin.

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Lin, MJ., Luo, F. An adaptive control method for the dissolved oxygen concentration in wastewater treatment plants. Neural Comput & Applic 26, 2027–2037 (2015). https://doi.org/10.1007/s00521-015-1858-3

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  • DOI: https://doi.org/10.1007/s00521-015-1858-3

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