Project number:
APVV-20-0261
Title of the project:
Energy-efficient Safe and Secure Process Control
Grant scheme:
APVV VV
Project type:
APVV Research Projects
Project duration (start):
01.07.2021
Project duration (end):
30.06.2024
Principal investigator:
Michal Kvasnica
Deputy investigator:
Radoslav Paulen
Investigators:
Tereza Ábelová, Peter Bakaráč, Monika Bakošová, Ľuboš Čirka, Kristína Fedorová, Miroslav Fikar, Matúš Furka, Lenka Galčíková, Michaela Horváthová, Martin Kalúz, Karol Kiš, Martin Klaučo, Roman Kohút, Alajos Mészáros, Martin Mojto, Juraj Oravec, Carlos E. Valero, Richard Valo, Anna Vasičkaninová

The proposed project will develop novel approaches to the design of industrial process control systems with four unique features:

  1. Energy efficiency of the operated plants via advanced control;
  2. Guaranteed safety of the control loop with a certifiable satisfaction of safety requirements;
  3. Security of the closed loop against attacks from outside and from inside; and
  4. Applicability on existing process control hardware without the need of costly upgrades.


The main aim of the project is to develop a systematic and universal design procedure that will yield safe and secure control systems in new applications (so-called greenfield setups), as well as for existing setups (so-called retrofits). This will open the door to industrial applications that will benefit from most progressive techniques for improving the safety, security, and economic performance in process industries.

Publications

2021

  1. K. FedorováR. KohútM. Kvasnica: Streamlining Active Set Method in MPC using Cache Memory. In Preprints of the 7th IFAC Conference on Nonlinear Model Predictive Control, IFAC-PapersOnLine, Bratislava, Slovakia, no. 6, vol. 54, 2021.
  2. A. R. Gottu Mukkula – M. Mateáš – M. FikarR. Paulen: Robust multi-stage model-based design of optimal experiments for nonlinear estimation. Computers & Chemical Engineering, vol. 155, pp. 107499, 2021.
  3. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, vol. 153, pp. 107437, 2021.

Investigators


Responsibility for content: doc. Ing. Michal Kvasnica, PhD.
Last update: 07.01.2021 13:44
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