Modeling pH Neutralization Process Via Support Vector Machines

  • Dongwon Kim
  • Gwi-Tae Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


This paper discusses the use of support vector machines for modeling and identification of pH neutralization process. Support vector machines (SVM) and kernel method have become very popular as methods for learning from examples. We apply SVM to model pH process which has strong nonlinearities. The experimental results show that the SVM based on the kernel substitution including linear and radial basis function kernel provides a promising alternative to model strong nonlinearities of the pH neutralization but also to control the system. Comparisons with other modeling methods show that the SVM method offers encouraging advantages and has better performance.


Support Vector Machine Mean Square Error Kernel Function Continuously Stir Tank Reactor Radial Basis Function Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, Y., Rong, G., Wang, S.: Hybrid fuzzy modeling of chemical processes. Fuzzy Sets and Systems 130, 265–275 (2002)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Vapnik, V.: The Nature of Statistical Learning Theory. John Wiley, New York (1995)CrossRefMATHGoogle Scholar
  3. 3.
    Wang, W., Xu, Z.: A heuristic training for support vector regression. Neurocomputing 61, 259–275 (2004)CrossRefGoogle Scholar
  4. 4.
    Barzilay, O., Brailovsky, V.: On domain knowledge and feature selection using a support vector machine. Pattern Recognition Lett. 20, 475–484 (1999)CrossRefGoogle Scholar
  5. 5.
    Drucker, H., Wu, D., Vapnik, V.: Support vector machines for span categorization. IEEE Trans. Neural Networ. 10, 1048–1054 (1999)CrossRefGoogle Scholar
  6. 6.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2 (1998)Google Scholar
  7. 7.
    Shinskey, F.G.: pH and pION Control in Process and Waste Streams. John Wiley, New York (1973)Google Scholar
  8. 8.
    Hall, R.C., Seberg, D.E.: Modeling and Self-Tuning Control of a Multivariable pH Neutralization Process. In: Proc. ACC, pp. 1822–1827 (1989)Google Scholar
  9. 9.
    Nie, J., Loh, A.P., Hang, C.C.: Modeling pH neutralization processes using fuzzy-neural approaches. Fuzzy Sets and Systems 78, 5–22 (1996)CrossRefGoogle Scholar
  10. 10.
    McAvoy, T.J.: Time optimal and Ziegler-Nichols control. Ind. Engrg. Chem. Process Des. Develop. 11, 71–78 (1972)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dongwon Kim
    • 1
  • Gwi-Tae Park
    • 1
  1. 1.Department of Electrical EngineeringKorea UniversitySeoulKorea

Personalised recommendations