Project number:
VEGA 1/0545/20
Title of the project:
Advanced Control of Energy Intensive Processes with Uncertainties in Chemical, Biochemical and Food Technologies
Grant scheme:
VEGA
Project type:
VEGA Research Projects
Project duration (start):
01.01.2020
Project duration (end):
31.12.2023
Principal investigator:
Monika Bakošová
Deputy investigator:
Martin Klaučo
Investigators:
Tereza Ábelová, Ľuboš Čirka, Kristína Fedorová, Matúš Furka, Lenka Galčíková, Ľubomíra Horanská, Michaela Horváthová, Martin Kalúz, Karol Kiš, Michal Kvasnica, Alajos Mészáros, Martin Mojto, Juraj Oravec, Zdenko Takáč, Carlos E. Valero, Richard Valo, Anna Vasičkaninová

Keywords: process control, modelling, uncertainty, robust model predictive control, neuro-fuzzy system, energy savings

Project summary

The research project deals with the development of advanced control methods and algorithms for systems with uncertainties whose implementation will provide significant energy savings in control of energy intensive processes in chemical, biochemical and food technologies. The core of the project is the development of methods and design of algorithms for predictive control, robust predictive control and fuzzy control of systems with uncertainty. Computational efficiency and feasibility in practice will be taken into account when designing control algorithms. Designed control algorithms, controllers, and control structures will be tested by simulations and experiments in laboratory conditions and will be compared according to energy consumption with conventional control approaches. The controlled processes will be chemical reactors, biochemical reactors, heat exchangers, distillation columns and other energy intensive processes typical for chemical, biochemical and food technologies.

Scientific goals

The main objective is to design effective algorithms and control systems using advanced control methods that will address not only the problems of processes with uncertainties in chemical, biochemical and food production, and the problems of the boundaries on manipulated and output variables, but also the high energy consumption in these processes, while algorithms and control systems will ensure not only the required control quality but also significant energy savings compared to conventional control.


Sub-goals:
- development of mathematical models, fuzzy and neuro-fuzzy models of chemical and biochemical energy intensive processes with various types of uncertainty
- extension of the toolbox of mathematical models of chemical and biochemical processes
- development of model-based predictive control (MPC) and robust predictive control (RMPC) methods, which take into account boundaries on manipulated and controlled variables already in the design of control algorithms
-development of MPC and RMPC algorithms reducing computational effort in order to increase efficiency of their real-time implementation and energy saving
- development of control algorithms for energy intensive processes with uncertainty using fuzzy and neuro-fuzzy systems
- development of software tools that will enable effective implementation of the proposed advanced control algorithms with consideration of their implementation within the Industrial Internet of Things concept
- implementation of advanced control algorithms dealing with boundaries on manipulated variables that directly affect energy consumption, as well as boundaries on controlled outputs that directly affect quality of control and efficiency in simulations and real-time laboratory experiments
- implementation of fuzzy and neuro-fuzzy control in simulations and real-time laboratory experiments
- analysis of the control quality and energy savings achieved with the designed control algorithms and comparison of the designed advanced control with conventional PID based control.

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. 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. L. GalčíkováJ. Oravec: Fixed complexity solution of partial explicit MPC. Computers & Chemical Engineering, vol. 157, pp. 107606, 2022.
  6. Ľ. Horanská: Extensions of Fuzzy Measures Based on Double Generalization of the Lovász Extension Formula, In Computational Intelligence and Mathematics for Tackling Complex Problems 3., Editor(s): Harmati I.Á., Kóczy L.T., Medina J., Ramírez-Poussa E., Springer Nature Switzerland AG, Cham, no. 959, pp. 81–88, 2022.
  7. M. HorváthováL. GalčíkováJ. Oravec: Control Design for a Nonlinear Reactors-Separator Plant. In 2022 Cybernetics & Informatics (K&I), pp. 1–6, 2022.
  8. K. KišP. BakaráčM. Klaučo: Nearly Optimal Tunable MPC Strategies on Embedded Platforms. In 18th IFAC Workshop on Control Applications of Optimization, IFAC-PapersOnline, pp. 326–331, 2022.
  9. J. MiklešĽ. ČirkaJ. OravecM. Fikar: Design of H2 and Hinf control using Lyapunov functions (in Slovak), FCHPT STU v Bratislave, 2022.
  10. A. Vasičkaninová – M. Bakošová – A. Mészáros: Cascade fuzzy control of a tubular chemical reactor. Editor(s): Ludovic Montastruc, Stephane Negny, In 32nd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 32, pp. 1021–1026, 2022.

2021

  1. M. Bakošová – A. Vasičkaninová: Neural Network-based Innovative Control of a Fermentation Process. 2021.
  2. F. Bardozzo – B. de la Osa – Ľ. Horanská – J. Fumanal – M. delli Priscoli – L. Troiano – R. Tagliaferri – J. Fernandez – H. Bustince: Sugeno integral generalization applied to improve adaptive image binarization. Information Fusion, vol. 68, pp. 37–45, 2021.
  3. H. Bustince – R. Mesiar – J. Fernandez – M. Galar – D. Paternain – A. H. Altalhi – G. P. Dimuro – B. Bedregal – Z. Takáč: d-Choquet integrals: Choquet integrals based on dissimilarities. Fuzzy Sets and Systems, vol. 414, pp. 1–27, 2021.
  4. A. Castillo-López – Z. Takáč – C. Lopez-Molina – J. Fernandez – H. Bustince: Restricted Equivalence Functions on L^n : A new equivalence measure between n-multisets applied on color pixels for image comparison. In IFSA-EUSFLAT 2021, pp. 54–54, 2021.
  5. M. Ferrero-Jaurieta – Z. TakáčĽ. Horanská – I. Rodríguez-Martínez – G. P. Dimuro – H. Bustince: Sequential information fusion based in multidimensional Choquet-like discrete integral. In IFSA-EUSFLAT 2021, pp. 48–48, 2021.
  6. M. Horváthová – N. Ishihara – J. Oravec – Y. Chida: Robust Setpoint Tracking of a Linear System with Discrete Actuators. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, pp. 229–236, 2021.
  7. Y. Jiang – J. Oravec – B. Houska – M. Kvasnica: Parallel MPC for Linear Systems with Input Constraints. IEEE Transactions on Automatic Control, no. 7, vol. 66, pp. 3401–3408, 2021.
  8. R. KohútL. GalčíkováK. FedorováT. Ábelová – M. Bakošová – M. Kvasnica: Hidden Markov Model-based Warm-start of Active Set Method in Model Predictive Control. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, 2021.
  9. M. MojtoM. HorváthováK. KišM. Furka – M. Bakošová: Predictive control of a cascade of biochemical reactors. Acta Chimica Slovaca, no. 1, vol. 14, pp. 51–59, 2021.
  10. J. OravecM. Horváthová – M. Bakošová: Multivariable Robust MPC Design for Neutralization Plant: Experimental Analysis. European Journal of Control, vol. 58, pp. 289–300, 2021.
  11. A. Urio – H. Bustince – L. De Miguel – J. Fernandez – G. P. Dimuro – Z. Takáč: Modification of the ADALINE using Choquet integrals and their generalizations. In IFSA-EUSFLAT 2021, pp. 49–49, 2021.
  12. C. E. Valero – M. Bakošová: Classic Methodologies in Control of a Yeast Fermentation Bioreactor. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, 2021.
  13. A. Vasičkaninová – M. Bakošová – A. Mészáros: Control of Heat Exchangers in Series Using Neural Networks. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, pp. 237–242, 2021.
  14. A. Vasičkaninová – M. Bakošová – A. Mészáros: Fuzzy Control Design for Energy Efficient Heat Exchanger Network. Chemical Engineering Transactions, vol. 88, pp. 529–534, 2021.

2020

  1. H. Bustince – R. Mesiar – J. Fernandez – M. Galar – D. Paternain – A. H. Altalhi – G. P. Dimuro – B. Bedregal – Z. Takáč: Dissimilarity Based Choquet Integrals, In Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, pp. 565–573, 2020.
  2. M. FurkaK. KišM. HorváthováM. Mojto – M. Bakošová: Identification and Control of a Cascade of Biochemical Reactors. In 2020 Cybernetics & Informatics (K&I), 2020.
  3. J. HolazaJ. OravecM. Kvasnica – R. Dyrska – M. Mönnigmann – M. Fikar: Accelerating Explicit Model Predictive Control by Constraint Sorting. Editor(s): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, In Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, vol. 21, pp. 11520–11525, 2020.
  4. Ľ. Horanská: On Compatibility of Two Approaches to Generalization of the Lovász Extension Formula, In Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, pp. 426–434, 2020.
  5. Ľ. Horanská – H. Bustince – J. Fernandez – R. Mesiar: Generalized decomposition integral. Information Sciences, vol. 538, pp. 415–427, 2020.
  6. M. HorváthováJ. Oravec – M. Bakošová: Efficient Convex-Lifting-Based Robust Control of a Chemical Reactor. Chemical Engineering Transactions, vol. 81, pp. 865–870, 2020.
  7. M. HorváthováJ. Oravec – M. Bakošová: Real-Time Convex-lifting-based Robust Control Using Approximated Control Law. In 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, vol. 59, pp. 2160–2165, 2020.
  8. K. KišM. KlaučoA. Mészáros: Neural Network Controllers in Chemical Technologies. In 2020 IEEE 15th International Conference of System of Systems Engineering, IEEE, pp. 397–402, 2020.
  9. J. Oravec – M. Bakošová: PIDDESIGN (in Slovak). 2020.
  10. J. OravecM. Horváthová – M. Bakošová: Energy efficient convex-lifting-based robust control of a heat exchanger. Energy reports, no. 201, pp. 117566, 2020.
  11. A. Vasičkaninová – M. Bakošová – A. Mészáros: Advanced Control of Heat Exchangers in Series. In 2020 IEEE 15th International Conference of System of Systems Engineering, IEEE, pp. 385–390, 2020.
  12. A. Vasičkaninová – M. Bakošová – A. Mészáros: Control of heat exchangers in series using neural network predictive controllers. Acta Chimica Slovaca, pp. 41–48, 2020.
  13. A. Vasičkaninová – M. Bakošová – A. MészárosM. Kalúz: Innovative Control Design of Tubular Chemical Reactor Using Fuzzy Controllers.. In The 4th Sustainable Process Integration Laboratory Scientific Conference Energy, Water, Emission & Waste in Industry and Cities 18-20 November 2020 (Online), 2020.
  14. A. Vasičkaninová – M. Bakošová – J. OravecM. Horváthová: Efficient Fuzzy Control of a Biochemical Reactor. Chemical Engineering Transactions, vol. 81, pp. 85–90, 2020.

Investigators


Responsibility for content: doc. Ing. Juraj Oravec, PhD.
Last update: 26.04.2020 14:51
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