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Modelling and Self-Tuning Control of a Multivariable pH Neutralization Process Part I: Modelling and Multiloop Control

Modelling and Self-Tuning Control of a Multivariable pH Neutralization Process Part I: Modelling and Multiloop Control,Raymond C. Hall,Dale E. Seborg

Modelling and Self-Tuning Control of a Multivariable pH Neutralization Process Part I: Modelling and Multiloop Control   (Citations: 9)
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This paper describes the design, modelling and control of a multivariable pH neutralization process. The experimental facility consists of two stirred tank reactors with an acid-base neutralization taking place in each tank. The process has been designed as a demonstration unit for the evaluation of advanced Control strategies such as adaptive and multivariable control. The neutralization process has four controlled variables, pH and liquid level in each of the two tanks. This strong acid, strong base neutralization poses a difficult multivariable problem because each manipulated variable has a significant effect on each controlled output. This process is also highly nonlinear and time varying due to the inherent nonlinearity associated with pH control and the shifts in the titration curve that occur when the amount of buffering agent changes in an unpredictable fashion. A physical model of the neutralization process has been developed which is in good agreement with experimental step response data over a wide range of experimental conditions. A multiloop control system consisting of four PID controllers was tedious to tune and had difficult coping with changes in the amount of buffering. In a companion paper (Hall and Seborg, 1989), self-tuning control resulted in improved control especially when only the process gain was estimated on-line, assuming that the dynamics were not changing significantly.
Published in 1989.
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    • ...The pH neutralization benchmark [10] was used to illustrate the proposed fuzzy modeling approach and to compare its effectiveness with that of other method...
    • ...The data set was obtained by simulating the model of [10] for random changes ofQ...

    Dongyeop Kanget al. Multivariable TS fuzzy model identification based on mixture of Gaussi...

    • ...Nominal inlet stream compositions were: 0.004 M nitric acid (HNO 3 ) as the acid stream (q 1 ), 0.003 M sodium bicarbonate (NaHCO 3 ) as the bu!er stream (q 2 ), and the base stream (q 3 ) consisted of a mixture of 0.003 M sodium hydroxide (NaOH) and 0.0005 M NaHCO 3 . The dynamic model of the pH neutralisation system (CSTR) is derived using conservation equations and equilibrium relations (Hall & Seborg, 1989)...

    Q. Huet al. Experimental evaluation of an augmented IMC for nonlinear systems

    • ...The process is simulated and output is being monitored and controlled [8, 9]. In this study, the pH is controlled by manipulating the base flow rate, and the acid and buffer flow rates are considered to be unmeasured disturbances...
    • ...where Cv4 is a constant valve coefficient, n is a constant valve exponent, and z is the vertical distance between the bottom of tank 1 and the outlet for exit flow rate q4. By combing mass balances on each of the ionic species in the system, the following differential equations for the effluent reaction invariants (Wa4, Wb4) can be derived (Hall, [8]):...

    S. Shobanaet al. Control of pH process using double-control scheme

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