A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization

Kwon, Joseph; Nayhouse, Michael; Orkoulas, Gerassimos; Ni, Dong; Christofides, Panagiotis D.

doi:10.1016/j.ces.2015.01.033

Cited by 39 publications

(25 citation statements)

References 35 publications

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“…The first value, T j;1 , is then applied to the system until the next sampling time when a new set of optimal jacket temperatures is calculated. The interested readers may find more detailed analysis and handling of the effect of model parameter uncertainty on the optimal jacket temperature trajectories in RicardezSandoval (2014, 2015) and Kwon et al (2015).…”

Section: Model Predictive Controlmentioning

confidence: 99%

Modeling and control of ibuprofen crystal growth and size distribution

Tran

2015

Chemical Engineering Science

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Section: Model Predictive Controlmentioning

confidence: 99%

Modeling and control of ibuprofen crystal growth and size distribution

Tran

2015

Chemical Engineering Science

Self Cite

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“…One approach to improve the control performance of the batch processes in the presence of model-plant mismatch is run-torun control, where data from previous batches is used to update the parameters and reduce the uncertainty. 14,37 Alternatively, uncertainty analysis can be used to directly propagate the parametric uncertainty through the system model without the need of information from prior batch studies. One widelyused framework for uncertainty propagation is the samplingbased Monte Carlo (MC) method.…”

Section: Introductionmentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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in Wiley Online Library (wileyonlinelibrary.com) Multiscale models have been developed to simulate the behavior of spatially-heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially-dependent. While such a model provides an adequate representation of the catalytic reactor, model-plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE-based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters.

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“…25 State estimators of various internal structure and working principle have been developed, including the stochastic Kalman filter family, 26,27 Luenberger observers, 17,28 and moving horizon state estimators (MHE). 24,29 The MHE is an optimization based method involving the nonlinear process model, and contrary to the other estimators, it uses measurements gathered over a certain time interval for the observer correction. 25 In order to utilize the FBRM provided CLD, the CSD → CLD transformation needs to be carried out.…”

Section: ■ Introductionmentioning

confidence: 99%

Chord Length Distribution Based Modeling and Adaptive Model Predictive Control of Batch Crystallization Processes Using High Fidelity Full Population Balance Models

Szilágyi

Agachi

2018

Ind. Eng. Chem. Res.

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The control of batch crystallizers is an intensively investigated topic as suitable crystallizer operation can reduce considerably the downstream operation costs and produce crystals of desired properties (size, shape, purity etc.). Nevertheless, the control of crystallizers is still challenging. In this work the development of a fixed batch time full population balance model based adaptive predictive control system for cooling batch crystallizers is presented. The model equations are solved by the high resolution finite volume algorithm involving fine discretization, which provides a high fidelity, accurate solution. A physically relevant crystal size distribution (CSD) to chord length distribution (CLD) transformation is also developed making possible the direct, real-time application of the focused beam reflectance measurement (FBRM) probe in the control system.The measured CLD and concentration values are processed by the growing horizon estimator (GHE), whose roles are to estimate the un-measurable system states (CSD) and to re-adjust the kinetic parameters providing an adaptive feature for the control system. A repeated sequential optimization algorithm is developed for the nonlinear model predictive control (NMPC) optimization, enabling the reduction of sampling time to the order of minutes for the one-day long batch. According to the simulation results the strategy is highly robust to parametric plant-model mismatch and significant concentration measurement noise, providing very good control of the desired CLD.Nucleation activation energy 39760, J mol -1 K -1Growth rate constant 11266, µm s -1

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A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization

Cited by 39 publications

References 35 publications

Modeling and control of ibuprofen crystal growth and size distribution

Modeling and control of ibuprofen crystal growth and size distribution

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Chord Length Distribution Based Modeling and Adaptive Model Predictive Control of Batch Crystallization Processes Using High Fidelity Full Population Balance Models

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