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Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/9C188837-B841-42FB-8790-E6A589D29770 | ||
| Έτος | 2023 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | I. Nașcu, N. A. Diangelakis, S. G. Muñoz, and E. N. Pistikopoulos “Advanced model predictive control strategies for evaporation processes in the pharmaceutical industries,” Comput. Chem. Eng., vol. 173, May 2023, doi: 10.1016/j.compchemeng.2023.108212. https://doi.org/10.1016/j.compchemeng.2023.108212 | ||
| Εμφανίζεται στις Συλλογές |
In this paper we present a framework to design control systems for an evaporation process in the pharmaceutical industry with the aim to deliver guaranteed operability for different molecules and under different thermodynamic scenarios. Based on a mathematical model developed within the gPROMS platform calibrated and validated with real data from experiments, three control methods are implemented and compared, (i) Proportional Integrative Derivative control (PID), (ii) Model Predictive Control (MPC) and (iii) explicit/multi-parametric Model Predictive Control (mp-MPC). The performance and limits of the derived control schemes are then established and tested for reference tracking as well as disturbances rejection.
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