2005
DOI: 10.2202/1542-6580.1219
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Optimal Operation of Semi-Batch Processes with a Single Reaction

Abstract: In a large number of industrial semi-batch reactor applications, a single reaction takes place in the reactor and the operational objective is to compute the optimal feed rate of reactants and the optimal batch temperature that optimizes an objective function at the end of a fixed batch time. In this work, it is shown that the optimal operating policy can be split into several time intervals. Analytical expressions for the optimal input are developed for each interval. Parametric uncertainty is handled by impl… Show more

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Cited by 11 publications
(8 citation statements)
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References 26 publications
(24 reference statements)
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“…In these approaches, as the input trajectory from the current time to the end of the batch has to be optimized, the size of the optimization problem can become intractable specifically during the early stages in the batch. Input parametrization techniques9, 10 constitute a fairly recently developed method for making nonlinear dynamic optimization problems computationally tractable for real‐time application. The impact of plant‐model mismatch and disturbances, however, remains significant in this approach (owing, in part, to use of approximate first‐principles models with parametric errors and un‐modeled disturbances).…”
Section: Introductionmentioning
confidence: 99%
“…In these approaches, as the input trajectory from the current time to the end of the batch has to be optimized, the size of the optimization problem can become intractable specifically during the early stages in the batch. Input parametrization techniques9, 10 constitute a fairly recently developed method for making nonlinear dynamic optimization problems computationally tractable for real‐time application. The impact of plant‐model mismatch and disturbances, however, remains significant in this approach (owing, in part, to use of approximate first‐principles models with parametric errors and un‐modeled disturbances).…”
Section: Introductionmentioning
confidence: 99%
“…This two-tier optimization problem is formulated in a similar fashion as the bottom two levels in the robust RTRR generation algorithm of Eqs. [17][18][19][20]. Following initialization at the current plant values (Eq.…”
Section: Robust Mpc Formulationmentioning
confidence: 99%
“…However, with significant improvements in realtime optimization algorithms and performance limitations associated with using linear MPC for highly nonlinear batch processes, MPC using the full nonlinear model is becoming increasingly common. [14][15][16][17] Input parametrization techniques, [18][19][20] on the other hand, strive to reduce the number of decision variables in the dynamic optimization problem. However, with modeling errors and process noise, plant process trajectories can deviate considerably from the nominal optimal trajectories on which the input parametrization is based, rendering a certain input parametrization suboptimal or infeasible.…”
Section: Introductionmentioning
confidence: 99%
“…In these approaches, as the input trajectory from the current time to the end of the batch has to be optimized, the size of the optimization problem can become intractable specifically during the early stages in the batch. Input parametrization techniques9, 10 constitute a fairly recently developed method for making nonlinear dynamic optimization problems computationally tractable for real‐time application. The impact of plant‐model mismatch and disturbances, however, remains significant in this approach (owing, in part, to use of approximate first‐principles models with parametric errors and un‐modeled disturbances).…”
Section: Introductionmentioning
confidence: 99%