Skip to main content
SpringerLink
Log in

Decentralized Adaptive Soft Computing Control of Distributed Parameter Bioprocess Plant

  • Chapter
Intelligent Systems: From Theory to Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 299))

  • 643 Accesses

Abstract

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are implemented by hierarchical fuzzy-neural direct and indirect multi-model controllers. The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking very closed to that of the optimal control.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Haykin, S.: Neural Networks, a Comprehensive Foundation, 2nd edn., Section 2.13, pp. 84–89, Section 4.13, pp. 208–213. Prentice-Hall, Upper Saddle River (1999)

    Google Scholar 

  2. Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)

    Article  Google Scholar 

  3. Chen, S., Billings, S.A.: Neural Networks for Nonlinear Dynamics System Modelling and Identification. International Journal of Control 56(2), 319–346 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  4. Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural Network for Control Systems (A survey). Automatica 28, 1083–1112 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  5. Miller III, W.T., Sutton, R.S., Werbos, P.J.: Neural Networks for Control. MIT Press, London (1992)

    Google Scholar 

  6. Pao, S.A., Phillips, S.M., Sobajic, D.J.: Neural Net Computing and Intelligent Control Systems. International Journal of Control, Special Issue on Intelligent Control 56(3), 263–289 (1992)

    MATH  MathSciNet  Google Scholar 

  7. Su, H.-T., McAvoy, T.J., Werbos, P.: Long-Term Predictions of Chemical Processes Using Recurrent Neural Networks: A Parallel Training Approach. Industrial Engineering Chemical Research 31(5), 1338–1352 (1992)

    Article  Google Scholar 

  8. Boskovic, J.D., Narendra, K.S.: Comparison of Linear, Nonlinear and Neural-Network-Based Adaptive Controllers for a Class of Fed-Batch Fermentation Processes. Automatica 31, 817–840 (1995)

    Article  MathSciNet  Google Scholar 

  9. Omatu, S., Khalil, M., Yusof, R.: Neuro-Control and its Applications. Springer, London (1995)

    Google Scholar 

  10. Bulsari, A., Palosaari, S.: Application of Neural Networks for System Identification of an Adsorption Column. Neural Computing and Applications 1, 160–165 (1993)

    Article  Google Scholar 

  11. Deng, H., Li, H.X.: Hybrid Intelligence Based Modelling for Nonlinear Distributed Parameter Process with Applications to the Curing Process. IEEE Transactions on Systems, Man and Cybernetics 4, 3506–3511 (2003)

    Google Scholar 

  12. Deng, H., Li, H.X.: Spectral-Approximation-Based Intelligent Modelling for Distributed Thermal Processes. IEEE Transactions on Control Systems Technology 13, 686–700 (2005)

    Article  Google Scholar 

  13. Gonzalez-Garcia, R., Rico-Martinez, R., Kevrekidis, I.: Identification of Distributed Parameter Systems: A Neural Net Based Approach. Computers and Chemical Engineering 22(4-supl. 1), 965–968 (1998)

    Article  Google Scholar 

  14. Padhi, R., Balakrishnan, S., Randolph, T.: Adaptive Critic Based Optimal Neuro- Control Synthesis for Distributed Parameter Systems. Automatica 37, 1223–1234 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  15. Padhi, R., Balakrishnan, S.: Proper Orthogonal Decomposition Based Optimal Neuro-Control Synthesis of a Chemical Reactor Process Using Approximate Dynamic Programming. Neural Networks 16, 719–728 (2003)

    Article  Google Scholar 

  16. Pietil, S., Koivo, H.N.: Centralized and Decentralized Neural Network Models for Distributed Parameter Systems. In: Proc. of the Symposium on Control, Optimization and Supervision, CESA 1996, IMACS Multiconference on Computational Engineering in Systems Applications, Lille, France, pp. 1043–1048 (1996)

    Google Scholar 

  17. Lin, C.T., Lee, C.S.G.: Neural Fuzzy Systems: A Neuro - Fuzzy Synergism to Intelligent Systems. Prentice Hall, Englewood Cliffs (1996)

    Google Scholar 

  18. Babuska, R.: Fuzzy Modeling for Control. Kluwer, Norwell (1998)

    Google Scholar 

  19. Baruch, I., Beltran-Lopez, R., Olivares-Guzman, J.L., Flores, J.M.: A Fuzzy-Neural Multi-Model for Nonlinear Systems Identification and Control. Fuzzy-Sets and Systems 159, 2650–2667 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  20. Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modelling and Control. IEEE Transactions on Systems, Man, and Cybernetics 15, 116–132 (1985)

    MATH  Google Scholar 

  21. Teixeira, M., Zak, S.: Stabilizing Controller Design for Uncertain Nonlinear Systems, Using Fuzzy Models. IEEE Transactions on Systems, Man, and Cybernetics 7, 133–142 (1999)

    Google Scholar 

  22. Mastorocostas, P.A., Theocharis, J.B.: A Recurrent Fuzzy-Neural Model for Dynamic System Identification. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 32, 176–190 (2002)

    Article  Google Scholar 

  23. Mastorocostas, P.A., Theocharis, J.B.: An Orthogonal Least-Squares Method for Recurrent Fuzzy-Neural Modeling. Fuzzy Sets and Systems 140(2), 285–300 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  24. Galvan-Guerra, R., Baruch, I.S.: Anaerobic Digestion Process Identification Using Recurrent Neural Multi-Model. In: Gelbukh, A., Kuri-Morales, A.F. (eds.) Sixth Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, Special Session, Revised Papers, CPS, November 4-10, 2007, pp. 319–329. IEEE Computer Society, Los Alamos (2008) ISBN 978-0-7695-3124-3

    Google Scholar 

  25. Baruch, I., Olivares-Guzman, J.L., Mariaca-Gaspar, C.R., Galvan-Guerra, R.: A Sliding Mode Control Using Fuzzy-Neural Hierarchical Multi-Model Identifier. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. ASC, vol. 42, pp. 762–771. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  26. Baruch, I., Olivares-Guzman, J.L., Mariaca-Gaspar, C.R., Galvan-Guerra, R.: A Fuzzy-Neural Hierarchical Multi-Model for Systems Identification and Direct Adaptive Control. In: Melin, P., Castillo, O., Ramirez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) Analysis and Design of Intelligent Systems Using Soft Computing Techniques. ASC, vol. 41, pp. 163–172. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  27. Baruch, I.S., Flores, J.M., Nava, F., Ramirez, I.R., Nenkova, B.: An Advanced Neural Network Topology and Learning, Applied for Identification and Control of a D.C. Motor. In: Proc. 1st Int. IEEE Symp. Intelligent Systems, IS 2002, Varna, Bulgaria, vol. 1, pp. 289–295 (2002)

    Google Scholar 

  28. Nava, F., Baruch, I.S., Poznyak, A., Nenkova, B.: Stability Proofs of Advanced Recurrent Neural Networks Topology and Learning. Comptes Rendus (Proceedings of the Bulgarian Academy of Sciences) 57(1), 27–32 (2004)

    Google Scholar 

  29. Baruch, I.S., Mariaca-Gaspar, C.R., Barrera-Cortes, J.: Recurrent Neural Network Identification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes. In: Hu, X., Balasubramaniam, P. (eds.) Recurrent Neural Networks, ch. 4, pp. 61–88. I-Tech Education and Publishing KG, Vienna (2008)

    Google Scholar 

  30. Aguilar-Garnica, F., Alcaraz-Gonzalez, V., Gonzalez-Alvarez, V.: Interval Observer Design for an Anaerobic Digestion Process Described by a Distributed Parameter Model. In: Proc. of the 2-nd International Meeting on Environmental Biotechnology and Engineering (2IMEBE), CINVESTAV-IPN, Mexico City, paper 117, pp. 1–16 (2006)

    Google Scholar 

  31. Bialecki, B., Fairweather, G.: Orthogonal Spline Collocation Methods for Partial Differential Equations. Journal of Computational and Applied Mathematics 128, 55–82 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  32. Wan, E., Beaufays, F.: Diagrammatic Method for Deriving and Relating Temporal Neural Network Algorithms. Neural Computations 8, 182–201 (1996)

    Article  Google Scholar 

  33. Guerra, T.M., Kruszewski, A., Vermeiren, L., Tirmant, H.: Conditions of Output Stabilization for Nonlinear Models in the Takagi-Sugeno’s Form. Fuzzy Sets and Systems 157, 1248–1259 (2006)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automatic Control, CINVESTAV-IPN, Ave. IPN No 2508, A.P. 14-470, Mexico D.F., C.P. 07360, Mexico

    Ieroham S. Baruch & Rosalba Galvan-Guerra

Authors
  1. Ieroham S. Baruch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rosalba Galvan-Guerra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Information Techologies of the Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev  & Mincho Hadjiski  & 

  2. Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Baruch, I.S., Galvan-Guerra, R. (2010). Decentralized Adaptive Soft Computing Control of Distributed Parameter Bioprocess Plant. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13428-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13427-2

  • Online ISBN: 978-3-642-13428-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation