Project number:
VEGA 1/0297/22
Title of the project:
Controller design methods for low-level carbon footprint process automation
Grant scheme:
Project type:
VEGA Research Projects
Project duration (start):
Project duration (end):
Principal investigator:
Juraj Oravec
Deputy investigator:
Radoslav Paulen
Peter Bakaráč, Diana Dzurková, Lenka Galčíková, Michaela Horváthová, Martin Kalúz, Roman Kohút, Alajos Mészáros, Richard Valo, Anna Vasičkaninová

Period: 1.1.2022 - 31.12.2025
Project Code: VEGA 1/0297/22

Principal investigator: doc. Ing. Juraj Oravec, PhD.
Principal investigator deputy: doc. Ing. Radoslav Paulen, PhD.
Scientific co-workers: Ing. Peter Bakaráč, Ing. Michaela Horváthová, Ing. Martin Kalúz, Ing. Roman Kohút, Prof. Ing. Alajos Mészáros, PhD., Dr.h.c., Ing. Richard Valo, Ing. Anna Vasičkaninová, Ing. Diana Dzurková

Project summary: The project aims to develop advanced controller design methods for low-level carbon footprint process automation. Decreased energy consumption is achieved by implementing the advanced methods of model predictive control. These methods are based on the robust control approach, parallel computing, machine learning, and economic criteria. The model predictive control methods will be designed considering the requirements of the chemical, biochemical, pharmaceutical, and food industries. However, the implementation range will not be limited just to these fields of industry. The theoretical results of the project will be implemented and experimentally analyzed using laboratory devices. The practical aspects of implementation on standard industrial hardware will be considered to design the advanced control methods for low-level carbon footprint process automation.



  1. P. BakaráčM. HorváthováL. GalčíkováJ. Oravec – M. Bakošová: Approximated MPC for embedded hardware: Recursive random shooting approach. Computers & Chemical Engineering, vol. 165, 2022.
  2. R. FáberR. Valo – M. Roman – R. Paulen: Towards Temperature Monitoring in Long-Term Grain Storage. In 2022 Cybernetics & Informatics (K&I), 2022.
  3. M. FikarM. KlaučoR. Paulen: Theory of Automatic Control I. Practice Examples, FCHPT STU v Bratislave, 2022.
  4. 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.
  5. 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.
  6. M. HorváthováL. GalčíkováJ. Oravec: Control Design for a Nonlinear Reactors-Separator Plant. In 2022 Cybernetics & Informatics (K&I), pp. 1–6, 2022.
  7. J. MiklešĽ. ČirkaJ. OravecM. Fikar: Design of H2 and Hinf control using Lyapunov functions (in Slovak), FCHPT STU v Bratislave, 2022.
  8. 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.
  9. 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.
  10. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Multi-Model Soft-Sensor Design for a Depropanizer Distillation Column. In Advanced Process Modelling Forum 18-19 October 2022, 2022.
  11. J. OravecM. Klaučo: Real-time tunable approximated explicit MPC. Automatica, vol. 142, pp. 110315, 2022.
  12. J. Shi – Y. Jiang – J. Oravec – B. Houska: Parallel MPC for Linear Systems with State and Input Constraints. IEEE Control Systems Letters, vol. 7, pp. 229–234, 2022.


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