Advanced control of chemical processes (ADCHEM '94) : IFAC - download pdf or read online

By D Bonvin; International Federation of Automatic Control

This booklet brings jointly the newest examine findings within the key region of chemical technique keep watch over; together with dynamic modelling and simulation - modelling and version validation for program in linear and nonlinear model-based keep watch over: nonlinear model-based predictive regulate and optimization - to facilitate restricted real-time optimization of chemical techniques; statistical keep watch over techniques - significant advancements within the statistical interpretation of measured facts to steer destiny study; knowledge-based v model-based control - the mixing of theoretical points of regulate and optimization concept with newer advancements in man made intelligence and machine technological know-how.

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Additional info for Advanced control of chemical processes (ADCHEM '94) : IFAC symposium, Kyoto, Japan, 25-27 May 1994

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5, with half minute sampling in r and five minute sampling in (J. The results for the time con­ stant, the deadtime and the sum-squared errors are summarized in Table I . Since t h e P W T contains redundancy, only por­ tions of the transform domain are needed to fit the parameters of the FOPDT. One can take ad­ vantage of this by selecting values of r close to the expected value of t d . 5 and ( 18) This is similar to taking the logarithm of the tran­ sient response in the time domain ( cf. Equation ( 1 1) ) .

Lastly, a discussion is presented on how the PWT can be used to differentiate among various model forms and the true process. (2) where J(t) = 0 for t < 0, n = {O, 1, ·} , and /] > 0 (Bolgiano & Piovoso, 196 8 ; Piovoso, 1 969) . For a fixed choice of /] , the sequence of Poisson transform coefficients uniquely defines a given signal J(t ) . For a fixed value of n , variations of fn (/J) for 0 < /] < oo also define J (t ) . The Pois­ son transform maps continuous time signals into discrete time signals in an intuitively appealing way.

9'3 � : Spccifcations for the distillation column. 49 request. 27 5. 0 Figure 5 a : Simulated optimal control trajectory for distillate flow rate (kmol/h). 0 Figure Sb : Simulated optimal control trajectory for distillate composition for 1-butene (mole fraction). ; Problem specification for the optimal control (start-up) of the MTBE process Unit Mixer CSTR Mixer CSTR Distillation column Variable Component hold-up, kmol n, n, n, n. n, n, n, n. 1 2 Constraints < 100 kmol/b < 100 kmol/b > 100 kmol/b > 50 kmol/h It has been shown that an analysis of the model equations and their method of solution leads to the identification of similarities and differences among various forms of models and methods of solution.

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