Intelligent control of a pH process
-
Alois Mészáros
,
L’uboš Čirka
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.
[1] 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. http://dx.doi.org/10.1016/j.compchemeng.2006.10.00510.1016/j.compchemeng.2006.10.005Search in Google Scholar
[2] 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. http://dx.doi.org/10.1016/j.compchemeng.2003.12.00210.1016/j.compchemeng.2003.12.002Search in Google Scholar
[3] 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. http://dx.doi.org/10.1016/0098-1354(90)87028-N10.1016/0098-1354(90)87028-NSearch in Google Scholar
[4] Demuth, H., & Beale, M. (1994). Neural network toolbox for use with MATLAB. Natick, MA, USA: The MathWorks Inc. Search in Google Scholar
[5] 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. http://dx.doi.org/10.1016/j.bej.2006.02.00710.1016/j.bej.2006.02.007Search in Google Scholar
[6] 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. http://dx.doi.org/10.1016/j.engappai.2006.01.00810.1016/j.engappai.2006.01.008Search in Google Scholar
[7] 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. http://dx.doi.org/10.1016/0009-2509(83)80157-210.1016/0009-2509(83)80157-2Search in Google Scholar
[8] Gustafsson, T. K., & Waller, K. V. (1992). Nonlinear and adaptive control of pH. Industrial & Engineering Chemistry Research, 31, 2681–2693. DOI: 10.1021/ie00012a009. http://dx.doi.org/10.1021/ie00012a00910.1021/ie00012a009Search in Google Scholar
[9] 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. http://dx.doi.org/10.1016/0959-1524(95)00005-B10.1016/0959-1524(95)00005-BSearch in Google Scholar
[10] 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. http://dx.doi.org/10.1021/i260041a01310.1021/i260041a013Search in Google Scholar
[11] Mészáros, A., Rusnák, A., & Najim, K. (1997). Intelligent control of continuous bioprocess using neural network models. Chemical and Biochemical Engineering Quarterly, 11, 81–88. Search in Google Scholar
[12] 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. Search in Google Scholar
[13] 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. http://dx.doi.org/10.1007/s00449-004-0374-010.1007/s00449-004-0374-0Search in Google Scholar PubMed
[14] 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. http://dx.doi.org/10.1016/S0009-2509(97)00130-910.1016/S0009-2509(97)00130-9Search in Google Scholar
[15] 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. http://dx.doi.org/10.1098/rspa.1995.006410.1098/rspa.1995.0064Search in Google Scholar
[16] 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. http://dx.doi.org/10.1016/S0009-2509(98)00050-510.1016/S0009-2509(98)00050-5Search in Google Scholar
[17] 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. Search in Google Scholar
[18] 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. http://dx.doi.org/10.1021/ie00055a02210.1021/ie00055a022Search in Google Scholar
[19] 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. http://dx.doi.org/10.1021/ie980513310.1021/ie9805133Search in Google Scholar
© 2008 Institute of Chemistry, Slovak Academy of Sciences
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Articles in the same Issue
- Biosynthesis of methanol from methane by Methylosinus trichosporium OB3b
- Influence of reaction medium composition on enzymatic synthesis of galactooligosaccharides and lactulose from lactose concentrates prepared from whey permeate
- Immobilization of modified penicillin G acylase on Sepabeads carriers
- Granulation of activated sludge in a laboratory upflow sludge blanket reactor
- Investigation of the effect of fluid elasticity on a cake filtration process
- Lab-scale testing of a low-loaded activated sludge process with membrane filtration
- Calcium sulphate scaling in membrane distillation process
- Characterization and filtration performance of coating-modified polymeric membranes used in membrane bioreactors
- Informational analysis of the grinding process of granular material using a multi-ribbon blender
- Effects of vessel baffling on the drawdown of floating solids
- N2O catalytic decomposition — effect of pelleting pressure on activity of Co-Mn-Al mixed oxide catalysts
- Intelligent control of a pH process
- Influence of suspended solid particles on gas-liquid mass transfer coefficient in a system stirred by double impellers
- A three-phase nonequilibrium model for catalytic distillation
- Membrane processes used for separation of effluents from wire productions
- A simple and efficient synthesis of 3-substituted derivatives of pentane-2,4-dione
- Formation of hydrated titanium dioxide from seeded titanyl sulphate solution
- Pyrolytic and catalytic conversion of rape oil into aromatic and aliphatic fractions in a fixed bed reactor on Al2O3 and Al2O3/B2O3 catalysts
- Oxidation of thiophene over copper-manganese mixed oxides
- Study of partitioning and dynamics of metals in contaminated soil using modified four-step BCR sequential extraction procedure
- Preparation and properties of a new composite photocatalyst based on nanosized titanium dioxide
