Realization of Online Optimizing Control in an Industrial Polymerization Reactor

Finkler, Tiago Fiorenzano; Kawohl, Michael; Piechottka, Uwe; Engell, Sebastian

doi:10.3182/20120710-4-sg-2026.00025

Cited by 3 publications

(5 citation statements)

References 10 publications

(11 reference statements)

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“…where w δ is the state noise of the correction factors. Finkler et al 40 showed that this online estimation of multiplicative correction factors is well suited to correct a model with structural mismatch. Moreover this provides an implicit scaling of the correction factors.…”

Section: Cekf State Estimationmentioning

confidence: 99%

“…One possible implementation of this approach is to not estimate the parameters of some reaction kinetics as pseudostates but to multiply the reaction rates of important reactions by correction factors δ j which are then estimated as pseudostates by the CEKF: where w δ is the state noise of the correction factors. Finkler et al showed that this online estimation of multiplicative correction factors is well suited to correct a model with structural mismatch. Moreover this provides an implicit scaling of the correction factors.…”

Section: Cekf State Estimationmentioning

confidence: 99%

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Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models

Hebing

Tran²,

Brandt

et al. 2020

Ind. Eng. Chem. Res.

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The performance of most bioprocesses can be improved significantly by the application of model-based methods from advanced process control (APC). However, due to the complexity of the processes and the limited knowledge of them, plant–model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and deficiencies) can be applied, but still, the confidence in a single process model is often low; therefore, the application of classical APC is difficult. In order to operate under possible plant–model mismatch, a robust closed-loop optimizing control strategy was developed in which the mismatch is counteracted by an adaptive model correction and the parallel usage and evaluation of structurally different models. Robust multistage nonlinear model predictive control is used for the online optimization of the process trajectories in order to maximize the performance. The adapted, structurally different models are used herein as weighted scenarios for the prediction of the process, which account for structural uncertainties. It is shown in simulation studies of a CHO cultivation process that the usage of multiple, adapted models as scenarios improves (1) the accuracy of the state estimation and (2) the overall process performance.

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Section: Cekf State Estimationmentioning

confidence: 99%

Section: Cekf State Estimationmentioning

confidence: 99%

Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models

Hebing

Tran²,

Brandt

et al. 2020

Ind. Eng. Chem. Res.

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show abstract

“…In these simulations, the proposed OCS is compared with the ideal NMPC that always uses a perfect process model and with a standard NMPC scheme that always uses the 27 or by compensating the model errors online during batch. 19 In Figure 13, an additional set of simulations where the standard NMPC is formulated such that a set point for the heating/cooling valve of c set = 20% is tracked, what is realized by adding an additional penalty term in the cost function. The reductions of the reaction period provided by the OCS and by the NMPC schemes for all the considered operation scenarios are presented in Table 2 and the overall period of time in which the temperature constraints are violated are given in Table 3.…”

Section: Proposed Optimizing Control Structurementioning

confidence: 99%

“…In these simulations, the proposed OCS is compared with the ideal NMPC that always uses a perfect process model and with a standard NMPC scheme that always uses the nominal process model. The simulation of the standard NMPC scheme are introduced here to illustrate that the performance of the NMPC can deteriorate significantly in the presence of plant-model mismatch if the model uncertainties are not properly addressed, for example, by employing a robust NMPC approach or by compensating the model errors online during the batch . In Figure , an additional set of simulations where the standard NMPC is formulated such that a set point for the heating/cooling valve of c set = 20% is tracked, what is realized by adding an additional penalty term in the cost function.…”

Section: Proposed Optimizing Control Structurementioning

confidence: 99%

“…In Mauntz, a state estimator based control scheme for the time-optimal operation of semibatch emulsion polymerizations was proposed and tested in a laboratory reactor. In Finkler et al a NMPC (nonlinear model predictive control) scheme for the online optimization of an industrial semibatch polymerization was developed. Other online schemes where necessary conditions for optimality are tracked have also been reported. − Several surveys that summarize the techniques for the optimization of batch and semibatch processes can be found in the literature. , …”

Section: Introductionmentioning

confidence: 99%

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Simple Control Scheme for Batch Time Minimization of Exothermic Semibatch Polymerizations

Finkler

Lucia

Dogru

et al. 2013

Ind. Eng. Chem. Res.

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Polymerization processes are typically characterized by strongly exothermic reactions with complicated nonlinear kinetics, and they usually require a very precise temperature control to maintain product uniformity. Polymerization reactors are usually operated with a constant monomer feed, while a cascade of PI controllers manipulates the heating and cooling power to keep the reaction temperature within the tolerance limits. Because a robust operation has to be guaranteed for different products under various disturbing influences, for example, fouling in the reactor, oscillations in the supply of the utilities and feed impurities, the monomers are usually slowly dosed into the reactor, leading to large batch times and limiting the process productivity. In this work, a simple and efficient control scheme for the time optimal operation of exothermic semibatch reactors that can be easily implemented in industry is introduced and evaluated by means of simulations using a well-known industrial benchmark problem. The results show that the new control scheme can shorten the reaction period by more than fifty percent without jeopardizing the quality of the temperature control and performs more robustly than a fully model-based controller that employs a fixed nominal process model.

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Trends in Emulsion Polymerization Processes from an Industrial Perspective

Hungenberg¹,

Jahns

2017

Advances in Polymer Science

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Realization of Online Optimizing Control in an Industrial Polymerization Reactor

Cited by 3 publications

References 10 publications

Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models

Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models

Simple Control Scheme for Batch Time Minimization of Exothermic Semibatch Polymerizations

Trends in Emulsion Polymerization Processes from an Industrial Perspective

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