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

2022

  1. P. BakaráčM. HorváthováL. GalčíkováJ. Oravec – M. Bakošová: Approximated MPC for embedded hardware: Recursive random shooting approach. Computers & Chemical Engineering, vol. 165, 2022.
  2. J. Drgoňa – K. Kiš – A. Tuor – D. Vrabie – M. Klaučo: Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems. Journal of Process Control, vol. 116, pp. 80–92, 2022.
  3. R. FáberR. Valo – M. Roman – R. Paulen: Towards Temperature Monitoring in Long-Term Grain Storage. In 2022 Cybernetics & Informatics (K&I), 2022.
  4. K. FedorováM. Kvasnica: Predictive Thermal Management of an Industrial Battery Energy Storage System. In 2022 Cybernetics & Informatics (K&I), 2022.
  5. M. FikarM. KlaučoR. Paulen: Theory of Automatic Control I. Practice Examples, FCHPT STU v Bratislave, 2022.
  6. L. GalčíkováM. HorváthováJ. Oravec – M. Bakošová: Self-Tunable Approximated Explicit Model Predictive Control of a Heat Exchanger. Chemical Engineering Transactions, 2022, Vol. 94, no. 94, pp. 1015–1020, 2022.
  7. L. GalčíkováJ. Oravec: Fixed complexity solution of partial explicit MPC. Computers & Chemical Engineering, vol. 157, pp. 107606, 2022.
  8. A. R. Gottu Mukkula – R. Paulen: Robust Design of Optimal Experiments Considering Consecutive Re-Designs. Editor(s): Luis Ricardez-Sandoval, Jesus Pico, In 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, IFAC, pp. 14–19, 2022.
  9. F. Hejazi – H. Karim – H. Kazemi – S. Shahbazpanahi – A. Mosavi: Fracture mechanics modeling of reinforced concrete joints strengthened by CFRP sheets. Case Studies in Construction Materials, no. 11, vol. 6, 2022.
  10. M. HorváthováL. GalčíkováJ. Oravec: Control Design for a Nonlinear Reactors-Separator Plant. In 2022 Cybernetics & Informatics (K&I), pp. 1–6, 2022.
  11. C. Jugade – D. Ingole – D. Sonawane – M. Kvasnica – J. Gustafson: A Memory Efficient FPGA Implementation of Offset-Free Explicit Model Predictive Controller. IEEE Transactions on Control Systems Technology, pp. 1–12, 2022.
  12. R. KohútM. Kvasnica: Construction of Robust Load Forecasting Models for the Process Industry. In 2022 Cybernetics & Informatics (K&I), 2022.
  13. J. MiklešĽ. ČirkaJ. OravecM. Fikar: Design of H2 and Hinf control using Lyapunov functions (in Slovak), FCHPT STU v Bratislave, 2022.
  14. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-based Design of Inferential Sensors for an Industrial Depropanizer Column with Data Pre-treatment Analysis. Editor(s): Mário Mihaľ, In 48th International Conference of the Slovak Society of Chemical Engineering SSCHE 2022 and Membrane Conference PERMEA 2022, Slovak Society of Chemical Engineering, Bratislava, SK, pp. 200, 2022.
  15. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Support Vector Machine-based Design of Multi-model Inferential Sensors. Editor(s): Ludovic Montastruc, Stephane Negny, In 32nd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 32, pp. 1045–1050, 2022.
  16. J. OravecM. Klaučo: Real-time tunable approximated explicit MPC. Automatica, vol. 142, pp. 110315, 2022.
  17. A. Roshanianfard – N. Noguchi – S. Ardabili – C. Mako – A. Mosavi: Autonomous Robotic System for Pumpkin Harvesting. Agronomy, no. 7, vol. 12, 2022.
  18. S. S. Band – S. Ardabili – M. SOOKHAK – A. T. CHRONOPOULOS – S. ELNAFFAR – M. MOSLEHPOUR – M. CSABA – B. TOROK – H. PAI – A. Mosavi: When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review. IEEE ACCESS, 2022.
  19. C. E. ValeroR. Paulen: Zonotope Order Reduction in Robust Estimation. In 23rd International Carpathian Control Conference, IEEE, vol. 23, pp. 392–397, 2022.
  20. M. Yaseliani – A. Zeinal Hamadani – A. Ijadi Maghsoodi – A. Mosavi: Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers. IEEE ACCESS, no. 8, vol. 11, 2022.

2021

  1. M. Bakošová – A. Vasičkaninová: Neural Network-based Innovative Control of a Fermentation Process. 2021.
  2. 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.
  3. 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.
  4. M. HorváthováJ. Oravec – M. Bakošová – A. Mészáros: Carbon Footprint Analysis of a Laboratory Plate Heat Exchanger Control. Chemical Engineering Transactions, vol. 88, pp. 847–852, 2021.
  5. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, vol. 153, pp. 107437, 2021.
  6. C. E. ValeroR. Paulen: Set-membership State Estimation for a Continuous Stirred-Tank Reactor. In 9th International Conference on Systems and Control, 2021.

Investigators


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