Entwicklung verlässlicher und effizienter Verfahren zur Optimierung des Betriebspunkts verfahrenstechnischer Prozesse

• Antragsteller: Professor Dr.-Ing. Sebastian Engell
• Fachliche Zuordnung: Chemische und Thermische Verfahrenstechnik ; Automatisierungstechnik, Mechatronik, Regelungssysteme, Intelligente Technische Systeme, Robotik
• Förderung: Förderung von 2015 bis 2018
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 271280750

• Erstellungsjahr: 2020

Zusammenfassung der Projektergebnisse

The development of a strategy for efficient combination of modifier adaptation with model adaptation in iterative real-time optimization was presented. We demonstrated a parameter selection procedure based on a local sensitivity analysis as suggested in the initial proposal of the project. The method selects the most influential parameters from a given set in order to improve the performance of the proposed approach. Finally, the MAWQA method for robust iterative real-time optimization was combined with parameter estimation with EMA. The main objective of the project was to develop a reliable and efficient RTO scheme that is robust with respect to the structural deficiencies of the plant model and in the presence of measurement noise. It was illustrated using a fed-batch reactor as a case study that the proposed methods ensure the convergence to the true plant optimum and achieve fast convergence.

Projektbezogene Publikationen (Auswahl)

• (2017). Effective model adaptation in iterative RTO, In: Computer Aided Chemical Engineering 40, 1717-1722
  Ahmad, A., Gao, W., Engell, S.
  (Siehe online unter https://doi.org/10.1016/B978-0-444-63965-3.50288-9)
• (2018). Enforcing Model Adequacy in Real-Time Optimization via Dedicated Parameter Adaptation, IFAC-Papers On Line 51(18), 49-54
  Ahmad, A., Singhal, M., Gao, W., Bonvin, D., Engell, S.
  (Siehe online unter https://doi.org/10.1016/j.ifacol.2018.09.246)
• (2018). Modifier Adaptation with Model Adaptation in Iterative Real-Time Optimization. In: Computer Aided Chemical Engineering 44, 691-696
  Ahmad, A., Gao, W., Engell, S.
  (Siehe online unter https://doi.org/10.1016/B978-0-444-64241-7.50110-5)
• (2019) Model adaptation with quadratic approximation in iterative real-time optimization, in: 2019 22nd International Conference on Process Control (PC19), IEEE, 250–255
  Ahmad, A., Mukkula, A.R.G., Engell, S.
  (Siehe online unter https://doi.org/10.1109/PC.2019.8815377)
• (2019). A Study of Model Adaptation in Iterative Real-Time Optimization of Processes with Uncertainties, Computers & Chemical Engineering 122, 218-227
  Ahmad, A., Gao, W., Engell, S.
  (Siehe online unter https://doi.org/10.1016/j.compchemeng.2018.08.001)