Sun, Feiran ; Liu, Tao ; Song, Bo ; Cui, Yan ; Nagy, Zoltan K. ; Findeisen, Rolf (2024)
Multi-objective optimization based nonlinear model predictive control of seeded cooling crystallization process with application to β form L-glutamic acid.
In: Chemical Engineering Science, 299
doi: 10.1016/j.ces.2024.120475
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
For batch performance optimization of seeded cooling crystallization processes with respect to multiple production objectives, a multi-objective optimization (MOO) based nonlinear model predictive control (NMPC) design is proposed in this paper. By taking into account three important production objectives related to the target crystal size, yield, and batch time, respectively, an MOO program is established with respect to the important operation conditions of seed loading ratio, initial solution supersaturation and cooling temperature profile. To find a good trade-off between these cross-coupled objectives, an enhanced goal attainment method (EGAM) is adopted to acquire the Pareto solution set for the above MOO program, by taking a piece-wise linear cooling profile for implementation. Then a hybrid decision making (HDM) strategy is developed to determine the optimal compromise solution. Based on the optimized objectives and operation conditions, a NMPC scheme is established for batch run of the seeded cooling crystallization process. Meanwhile, a receding-horizon nonlinear Kalman filter (RNKF) is designed to estimate the zero- and third-order moments of crystal population (related to the total number and volume of crystals) during crystallization for control implementation. Moreover, the kinetic model parameters with higher impact on the NMPC scheme are timely updated by moment estimation to improve system performance under time-varying uncertainties. Simulation results and experiments on the seeded cooling crystallization of β form L-glutamic acid (β-LGA) are shown to demonstrate the effectiveness and advantage of the proposed optimization and control scheme.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Sun, Feiran ; Liu, Tao ; Song, Bo ; Cui, Yan ; Nagy, Zoltan K. ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Multi-objective optimization based nonlinear model predictive control of seeded cooling crystallization process with application to β form L-glutamic acid |
| Sprache: | Englisch |
| Publikationsjahr: | 5 November 2024 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Chemical Engineering Science |
| Jahrgang/Volume einer Zeitschrift: | 299 |
| DOI: | 10.1016/j.ces.2024.120475 |
| Kurzbeschreibung (Abstract): | For batch performance optimization of seeded cooling crystallization processes with respect to multiple production objectives, a multi-objective optimization (MOO) based nonlinear model predictive control (NMPC) design is proposed in this paper. By taking into account three important production objectives related to the target crystal size, yield, and batch time, respectively, an MOO program is established with respect to the important operation conditions of seed loading ratio, initial solution supersaturation and cooling temperature profile. To find a good trade-off between these cross-coupled objectives, an enhanced goal attainment method (EGAM) is adopted to acquire the Pareto solution set for the above MOO program, by taking a piece-wise linear cooling profile for implementation. Then a hybrid decision making (HDM) strategy is developed to determine the optimal compromise solution. Based on the optimized objectives and operation conditions, a NMPC scheme is established for batch run of the seeded cooling crystallization process. Meanwhile, a receding-horizon nonlinear Kalman filter (RNKF) is designed to estimate the zero- and third-order moments of crystal population (related to the total number and volume of crystals) during crystallization for control implementation. Moreover, the kinetic model parameters with higher impact on the NMPC scheme are timely updated by moment estimation to improve system performance under time-varying uncertainties. Simulation results and experiments on the seeded cooling crystallization of β form L-glutamic acid (β-LGA) are shown to demonstrate the effectiveness and advantage of the proposed optimization and control scheme. |
| ID-Nummer: | Artikel-ID: 120475 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 28 Nov 2024 12:07 |
| Letzte Änderung: | 28 Nov 2024 12:07 |
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