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Chord length distribution based modeling and adaptive model predictive control of batch crystallization processes using high fidelity full population balance models
journal contribution
posted on 2018-11-20, 09:51 authored by Botond Szilagyi, Serban P. Agachi, Zoltan NagyZoltan NagyThe 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 unmeasurable system states (CSD) and to readjust 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.
Funding
The financial support of the International Fine Particle Research Institution is acknowledged gratefully. Funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC Grant Agreement 280106-CrySys is also acknowledged.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Chemical Engineering
Published in
Industrial & Engineering Chemistry ResearchVolume
57Issue
9Pages
3320 - 3332Citation
SZILAGYI, B., AGACHI, S.P. and NAGY, Z.K., 2018. Chord length distribution based modeling and adaptive model predictive control of batch crystallization processes using high fidelity full population balance models. Industrial & Engineering Chemistry Research, 57 (9), pp.3320-3332.Publisher
© American Chemical SocietyVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-11-01Publication date
2018-02-12Notes
Reprinted (Adapted or Reprinted in part) with permission from SZILAGYI, B., AGACHI, S.P. and NAGY, Z.K., 2018. Chord length distribution based modeling and adaptive model predictive control of batch crystallization processes using high fidelity full population balance models. Industrial & Engineering Chemistry Research, 57 (9), pp.3320-3332.. Copyright © 2018 American Chemical Society.ISSN
0888-5885eISSN
1520-5045Publisher version
Language
- en