Skip to main content
SpringerLink
Log in

Intelligent control of a pH process

  • Original Paper
  • Published:
Chemical Papers Aims and scope Submit manuscript

Abstract

A new strategy, to augment the pH process control is offered in this paper. The intelligent controller proposed herein is based on an inverse neural plant model. An integration term is introduced to improve the pure inverse neural controller performance. This element, adjusted by a fuzzy system with respect to the control error, operates in parallel with the neural controller to ensure offset-free performance, in case of system uncertainties or modelling mismatch. Four fuzzy rules were applied to generate the integrator parameters. Experimental results, carried out under pH control on a laboratory scale set-up, demonstrate the feasibility of the proposed control system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Altinten, A. (2007). Generalized predictive control applied to a pH neutralization process. Computers & Chemical Engineering, 31, 1199–1204. DOI: 10.1016/j.compchemeng.2006.10.005.

    Article  CAS  Google Scholar 

  • Andrášik, A., Mészáros, A., & de Azevedo, S. F. (2004). On-line tuning of a neural PID controller based on hybrid modeling. Computers & Chemical Engineering, 28, 1499–1509. DOI: 10.1016/j.compchemeng.2003.12.002.

    Article  CAS  Google Scholar 

  • Bhat, M., & McAvoy, T. J. (1990). Use of neural nets for dynamic modelling and control of chemical process systems. Computers & Chemical Engineering, 14, 573–582. DOI: 10.1016/0098-1354(90)87028-N.

    Article  CAS  Google Scholar 

  • Demuth, H., & Beale, M. (1994). Neural network toolbox for use with MATLAB. Natick, MA, USA: The MathWorks Inc.

    Google Scholar 

  • Duan, S., Shi, Z., Feng, H., Duan, Z., & Mao, Z. (2006). An on-line adaptive control based on DO/pH measurements and ANN pattern recognition model for fed-batch cultivation. Biochemical Engineering Journal, 30, 88–96. DOI: 10.1016/j.bej.2006.02.007.

    Article  CAS  Google Scholar 

  • Fuente, M. J., Robles, C., Casado, O., Syafiie, S., & Tadeo, F. (2006). Fuzzy control of a neutralization process. Engineering Applications of Artificial Intelligence, 19, 905–914. DOI: 10.1016/j.engappai.2006.01.008.

    Article  Google Scholar 

  • Gustafsson, T. K., & Waller, K. V. (1983). Dynamic modelling and reaction invariant control of pH. Chemical Engineering Science, 38, 389–398. DOI: 10.1016/0009-2509(83)80157-2.

    Article  CAS  Google Scholar 

  • Gustafsson, T. K., & Waller, K. V. (1992). Nonlinear and adaptive control of pH. Industrial & Engineering Chemistry Research, 31, 2681–2693. DOI: 10.1021/ie00012a009.

    Article  CAS  Google Scholar 

  • Loh, A. P., Looi, K. O., & Fong, K. F. (1995). Neural network modeling and control strategies for a pH process. Journal of Process Control, 5, 355–362. DOI: 10.1016/0959-1524(95)00005-B.

    Article  CAS  Google Scholar 

  • McAvoy, T. J., Hsu, E., & Lowenthal, S. (1972). Dynamics of pH in a controlled stirred tank reactor. Industrial & Engineering Chemistry Process Design and Development, 11, 68–70. DOI: 10.1021/i260041a013.

    Article  CAS  Google Scholar 

  • Mészáros, A., Rusnák, A., & Najim, K. (1997). Intelligent control of continuous bioprocess using neural network models. Chemical and Biochemical Engineering Quarterly, 11, 81–88.

    Google Scholar 

  • Mészáros, A., Andrášik, A., & Šperka, L’. (2002). Influence of the adaptation gain on robust neural control. In Proceed ings of 5th International Scientific - Technical Conference on Process Control, 9–12 June 2002 (pp. R241a1–R241a6). Pardubice, Czech Republic: University of Pardubice.

    Google Scholar 

  • Mészáros, A., Andrášik, A., Mizsey, P., Fonyó, Z., & Illeová, V. (2004). Computer control of a laboratory fermenter using neural network technique. Bioprocess and Biosystems Engineering, 26, 331–340. DOI: 10.1007/s00449-004-0374-0.

    Article  CAS  Google Scholar 

  • Narayanan, N. R. L., Krishnaswamy, P. R., & Rangaiah, G. P. (1997). An adaptive internal model control strategy for pH neutralization. Chemical Engineering Science, 52, 3067–3074. DOI: 10.1016/S0009-2509(97)00130-9.

    Article  CAS  Google Scholar 

  • Ramasamy, S., Desphande, P. B., Tambe, S. S., & Kulnakarni, B. D. (1995). Robust nonlinear control with neural networks. Proceedings A of the Royal Society of London, 449, 655–667.

    Article  CAS  Google Scholar 

  • Sung, S. W., Lee, I.-B., Choi, J. Y., & Lee, J. (1998). Adaptive control for pH systems. Chemical Engineering Science, 53, 1941–1953. DOI: 10.1016/S0009-2509(98)00050-5.

    Article  CAS  Google Scholar 

  • Šperka, L., & Mészáros, A. (2004). Fuzzy tuning of PID-like inverse neural controller. In Proceedings of 6th International Scientific - Technical Conference on Process Control, 8–11 June 2004 (pp. R157-1–R157-9). Pardubice: University of Pardubice.

    Google Scholar 

  • Wright, R. A., & Kravaris, C. (1991). Nonlinear control of pH processes using the strong acid equivalent. Industrial & Engineering Chemistry Research, 30, 1561–1572. DOI: 10.1021/ie00055a022.

    Article  CAS  Google Scholar 

  • Yeo, Y.-K., & Kwon, T.-I. (1999). A neural PID controller for the pH neutralization process. Industrial & Engineering Chemistry Research, 38, 978–987. DOI: 10.1021/ie9805133.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Automation and Mathematics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, SK-812 37, Bratislava, Slovakia

    Alois Mészáros, L’uboš Čirka & L’ubomír Šperka

Authors
  1. Alois Mészáros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L’uboš Čirka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L’ubomír Šperka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alois Mészáros.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mészáros, A., Čirka, L. & Šperka, L. Intelligent control of a pH process. Chem. Pap. 63, 180–187 (2009). https://doi.org/10.2478/s11696-009-0005-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11696-009-0005-y

Keywords

Search

Navigation