Application of reduced order phenomenological model to pH nonlinear control

• Autorzy: Stebel K. , Metzger M.
• Języki publikacji: EN

Abstrakty

EN

Changes in process sensitivity with pH make the design of the conventional controllers difficult. pH processes are very hard to control because of strong nonlinearity and usually time varying characteristics. A lot of work has been done in the field of phenomenological modelling of the pH processes. However, there is a need to simplify the model to have it suitable for control purposes. This follows from the fact that the internal model for the controllers must be as simple as possible. Wiener models consisting of a linear dynamic element followed by a static nonlinear element are considered to be perfect to represent a wide range of nonlinear processes. The proposed reduced order phenomenological model of pH has a similar form as the Wiener models but includes bilinear reduced order phenomenological dynamic element. This model was validated using the real world pilot plant. The possibility of combining the phenomenological and arbitrary approaches occurred. The aim of this paper is to present reduced order model of pH process which can be used for simulation purposes and to design process model based controller.

Słowa kluczowe

EN

pH processes; nonlinear systems; input-output linearization; pH control; model-based control; reduced order models polynomial approximation

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2005
• Tom: Vol. 15, no. 2
• Strony: 199--216
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Stebel K.

• Institute of Automatic Control Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Metzger M.

• Institute of Automatic Control Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

• [1] A. Faanes and S. Skogestad: Controller design for serial processes. J. Process Control, 15 (2005), 259-271.
• [2] sc J. Gomez and E. Baeyens: Identification of block-oriented nonlinear systems using orthonormal bases. J. Process Control 14 (2004), 685-697.
• [3] S. F. Graebe, M. M. Seron and G. C. Goodwin: Nonlinear tracking and input disturbance rejection with application to pH control. J. Process Control, 6(2/3), (1996), 195-202.
• [4] T. K. Gustaffson, B. O. Skrifvars, K. V. Sandstrom and K. V. Waller: Modeling of pH for Control. Ind. Eng. Chem. Res., 34 (1995), 820-827.
• [5] M. A. Henson and D. E. Seborc: Adaptive input-output linearization of a pH neutralization process. Int. J. Adaptive Control and Signal Proc, 11 (1997), 171-200.
• [6] A. Isldori: Nonlinear control Systems. Springer Verlag, Berlin Heidelberg, NY. 1995.
• [7] P. L. Lee and G. R. Sullivan: Generic model control. Comput. Chem. Engng., 12 (1988), 573-580.
• [8] P. Jutila and A. Visala: Pilot plant testing of an adaptive pH-control algorithm based on physico-chemical modeling. Mathematics and Comp., in Sim. XXVI, (1984). 523-533.
• [9] sc A. Kalafatis, L. Wang and W. Cluett: A new approach to the identification of pH processes based on the Wiener model. Chemical Engineering Science, 50(23), (1995), 3693-3701.
• [10] sc A. Kalafatis, L. Wang and W. Cluett: Linearizing feedforward-feedback control of pH processes based on the Wiener model. J. Process Control, 15 (2005), 103-112.
• [11] A. Kalafatis, L. Wang and W. Cluett: Identification of time-varying pH processes using sinusoidal signals. Automatica, 41 (2005), 685-691.
• [12] K. Kavsek-Biasizzo, I. Skrjanc and D. Matko: Fuzzy predictive control of highly nonlinear pH process. Computers Chem. Engn., 21. Suppl.. (1997), 613-618.
• [13] T. J. McAvoy: Dynamic models for pH and other fast equilibrium systems. Ind. Eng. Chem. Process Des. Develop., 11 (1972), 630-631.
• [14] M. Metzger: Modeling, simulation and control of continuous processes. Edition of Jacek Skalmierski, Gliwice, 2000.
• [15] M. Metzger: Easy programmable MAPI controller based on simplified process model. Proc. IFAC Workshop on Programmable Devices and Systems. Gliwice. Poland, (2001), 166-170.
• [16] E. V. Musvoto, M. C. Wentzel, R. E. Loewenthal and G. A. Ekama: Integrated chemical-physical processes modeling - I. Development of a kinetic-based model for mixed weak acid/base systems. Wat. Res., 34(6), (2000), 1857-1867.
• [17] A. Niederliński, J. Moocinski and Z. Ogonowski: Adaptive control. Wydawnictwo Naukowe PWN, Warszawa, Poland. (1995), (in Polish).
• [18] M. Nihtila and P. Jutila: Dynamic models and state-linear filtering schema for pH processes. Proc Symp. Application of Multivariahle Systems Theory, Plymouth, England. (1982), 185-194.
• [19] S. Norquay, A. Palazglu and J. Romagnoli: Model predictive control based on Wiener models. Chemical Engineering Science, 53( 1), (1998), 75-84.
• [20] R. H. Nystrom, K. V. Sandstrom. T. K. Gustafsson and H. T. Toivonen: Multimodel robust control applied to pH neutralization process. Computers Chem. Engn., 22 Suppl.. (1998), 467-474.
• [21] K. Stebel: Analysis of physical appropriateness of semi-analytical solution of pll-process mathematical model for real-lime simulation. Proc. 16th IMACS World Congress, Lausanne, Belgium. (2000).
• [22] K. Stebel: Input-output linearization and PI control algorithms applied for pH process. Proc. 7th IEEE Int. Conf. on 'Methods and Models in Automation and Robotics', Międzyzdroje, Poland, 2 (2001), 885-890.
• [23] K Stebel: pH model approximation for purpose of industrial programmable controllers. Proc. IFAC Workshop on Programmable Devices and Systems, Gliwice, Poland, (2001b), 195-200.
• [24] K. Stebel: Comparative evaluation of PI and GMC algorithms on neutralization pilot plant installation. Proc. 8th IEEE Int. Conf. on 'Methods and Models in Automation and Robotics', Szczecin, Poland, 2 (2002), 1169-1174.
• [25] K. Stebel: Practical validation of polynomial pH model aproximation. Proc. IEEE Int. Conf. on 'Methods and Models in Automation and Robotics', Szczecin, Poland. 2 (2002). 1131-1136.
• [26] K. Stebel: Polynomial approximation approach to modelling and control of pH process. Proc. XVth IFAC Congers, Barcelona, Spain, (2002), 156-161.
• [27] K. Stebel: Comparative evaluation of PI, GMC and PFC algorithms via real-time simulation of pH-process. Systems Analysis Modelling Simulation, 43(8), (2003), 1095-1106.
• [28] R. A. Wright and C. Kravaris: Nonlinear control of pH processes using strong acid equivalent. Ind. Eng. Chem. Res., 30(7) (1991), 1561-1572.
• [29] R. A. Wright and C. Kravaris: On-line identification and nonlinear control of an industrial pH process. J. Process Control, 11 (2001), 361-374.