It is the fact that several process parameters are either unknown or uncertain. Therefore, an optimal control, profile calculated with developed process models with respect to such process parameters may not give an o...
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It is the fact that several process parameters are either unknown or uncertain. Therefore, an optimal control, profile calculated with developed process models with respect to such process parameters may not give an optimal performance when implemented to real processes. This study proposes a batch-to-batch optimization strategy for the estimation of uncertain *** in a batch crystallization process of potassium sulfate production. The knowledge of a crystal size distribution of the product at the end of batch operation is used in the proposed methodology. The updated kinetic parameters are applied for determining an optimal operating temperature policy for the next batch run.
batch crystallization is one of the widely used processes for separation and purification in many chemical industries. Dynamic optimization of such a process has recently shown the improvement of final product quality...
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batch crystallization is one of the widely used processes for separation and purification in many chemical industries. Dynamic optimization of such a process has recently shown the improvement of final product quality in term of a crystal size distribution (CSD) by determining an optimal operating policy. However, under the presenceof unknown or uncertain model parameters, the desired product quality may not be achieved when the calculated optimal control profile is implemented. In this study, a batch-to-batch optimization strategy is proposed for the estimation of uncertain kinetic parameters in the batch crystallization process, choosing the seeded batch crystallizer of potassium sulfate as a case study. The information of the CSD obtained at the end of batch run is employed in such an optimization-based estimation. The updated kinetic parameters are used to modify an optimal operating temperature policy of a crystallizer for a subsequent operation. This optimal temperature policy is then employed as new reference for a temperature controller which is based on a generic model control algorithm to control the crystallizer in a new batch run. (C) 2007 Elsevier B.V. All rights reserved.
Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compen...
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Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. Three approaches can be distinguished according to the quantities that are adapted: model-parameter adaptation updates the parameters of the process model and repeats the optimization, modifier adaptation modifies the constraints and gradients of the optimization problem and repeats the optimization, while direct input adaptation turns the optimization problem into a feedback control problem and implements optimality via tracking of appropriate controlled variables. This paper argues in favor of modifier adaptation, since it uses a model parameterization and an update criterion that are well tailored to meeting the KKT conditions of optimality. These considerations are illustrated with the real-time optimization of a semi-batch reactor system. (C) 2009 Elsevier Ltd. All rights reserved.
This paper presents a new measurement-based optimization framework, for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to...
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This paper presents a new measurement-based optimization framework, for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus the optimal input trajectories can be, adjusted using measurements without the use of a model of the process. When only batch-end measurements are available, the,proposed method leads itself to an efficient batch-to-batch optimization scheme. The approach is illustrated via the simulation of a semibatch reactor under uncertainty. (C) 2003 ISA-The Instrumentation, Systems, and Automation Society.
The standard approach to deal with uncertainty in dynamic optimization is to take a conservative stand. Measurement-based optimization schemes allow reducing this conservatism by using measurements to compensate for t...
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The standard approach to deal with uncertainty in dynamic optimization is to take a conservative stand. Measurement-based optimization schemes allow reducing this conservatism by using measurements to compensate for the uncertainty. On the example of productivity optimization of a batch distillation column with a terminal quality constraint, various measurement-based optimization schemes are compared. They all use measurements to update the input either from batch-to-batch or within the batch. A novel mid-course correction scheme for satisfying the terminal constraint is proposed.
Run-to-run optimization exploits the repetitive nature of batch processes to adapt the operating policy in the presence of uncertainty. For problems where terminal constraints play a dominant role in the optimization,...
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Run-to-run optimization exploits the repetitive nature of batch processes to adapt the operating policy in the presence of uncertainty. For problems where terminal constraints play a dominant role in the optimization, the system can be operated close to the optimum simply by satisfying terminal constraints. The input is parameterized by using the knowledge of the shape of the optimal solution and, in the presence of uncertainty, the input parameters are adapted to meet the terminal constraints. When the number of input parameters is greater than the number of terminal constraints, an adaptation methodology based on a projection matrix derived from the gain matrix between the input parameters and the terminal constraints is proposed. The run-to-run optimization scheme is illustrated in simulation for the minimization of the batch time of an emulsion polymerization process with terminal constraints on conversion and number average molecular weight.
Run-to-run optimization methodologies exploit the repetitive nature of batch processes to determine the optimal operating policy in the presence of uncertainty. In this paper, a parsimonious parameterization of the in...
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Run-to-run optimization methodologies exploit the repetitive nature of batch processes to determine the optimal operating policy in the presence of uncertainty. In this paper, a parsimonious parameterization of the inputs is used and the decision variables of the parameterization are updated on a run-to-run basis using a feedback control scheme which tracks signals that are invariant under uncertainty. In this run-to-run framework, terminal constraints of the optimization problem and cost sensitivities constitute the invariant signals. The methodology is conceived to improve the cost function from batch-to-batch without constraint violation. (C) 2001 Elsevier Science Ltd. All rights reserved.
Importance of batch processes has grown more in recent years as the increasing economic competition has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Acc...
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Importance of batch processes has grown more in recent years as the increasing economic competition has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, system engineering study on advanced control and dynamic optimization of batch operation has drawn much attention recently. The purpose of this paper is to introduce recent advances in batch process control discuss possible extensions focusing on the contributions by the authors. For this, the so-called run-to-run approaches, that may play a breakthrough in batch operation problems and also the authors' work have been based on, are briefly reviewed first from control and optimization points of view. Then a novel MPC(Model Predictive Control)-like run-to-run batch control framework, called QBMPC, that can simultaneously perform prediction-based end-product quality and profile control is proposed aiming at a generic industrial batch control technique. Some possible extensions and modifications are discussed subsequently. Performance of QBMPC is demonstrated with a semi-batch reactor model.
Run-to-run optimization methodologies exploit the repetitive nature of batch processes to find the optimal operating policy in the presence of uncertainty. For the class of batchoptimization problems where the soluti...
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Run-to-run optimization methodologies exploit the repetitive nature of batch processes to find the optimal operating policy in the presence of uncertainty. For the class of batchoptimization problems where the solution is determined by terminal constraints, the update of the decision variables towards their optimal values is realized using a constraint control scheme. The methodology is adapted to improve the cost function from batch to batch without constraint violation.
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