Project number:
VEGA 1/0691/21
Title of the project:
Efficient control of industrial plants using data
Grant scheme:
Vega
Project type:
VEGA Research Projects
Project duration (start):
01.01.2021
Project duration (end):
31.12.2024
Principal investigator:
Radoslav Paulen
Deputy investigator:
Miroslav Fikar
Investigators:
Diana Dzurková, Rastislav Fáber, Martin Kalúz, Alajos Mészáros, Martin Mojto, Erika Pavlovičová, Carlos E. Valero, Richard Valo, Marek Wadinger

Project summary

The project is focused in driving the industrial chemical plants towards effective use of resources and energy. Effective plant management will be reached as a synergy of tools for production planning and for advanced automatic feedback control. The technology enabling the reaching of these goals is based on the use of data a) for creation of input-output data-based models or of first-principles models with corrective data-based terms and b) for reliable monitoring of unmeasured process variables. The improved mathematical models are subsequently used for optimization of steady-state operating regimes and for optimization-based control of industrial plants. The designed algorithms and control structures are tested in simulations as well as in laboratory conditions. The project also stimulates cooperation with industry.

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

Scientific goals

Scientific goals for whole period of this project Economic pressures require greater flexibility of (chemical or biochemical) production plants in processing of raw materials of different quality or taking into account various restrictions, e.g. to reduce the environmental impact of production. To achieve operational efficiency in such a context, production units must be managed in an integrated way, i.e. one of the main principles of Industry 4.0. Most plants use planning tools to determine the production targets of individual production units, taking into account the price of products and customer orders. However, due to the integrated nature of production facilities, it is possible to achieve the set goals through several production routes, which is not always used even in the modern facilities. The aim of the work is therefore the development of methods for selecting efficient production routes in terms of consumption of materials and energy in real time. This objective, contained in the RIS3 priorities, will be achieved through the development of accurate predictive mathematical models enabling plant-wide optimization and by improving the functionality and efficiency of control systems to ensure that the desired objectives are met for each unit. The main feature of the developed algorithms will be the use of data a) for off-line design of monitoring, optimization and control algorithms using (large amounts of) historical data (big data) and b) for real-time processing of on-line data to improve the response of automatic control. The obtained results will be published at renowned conferences and in top-tier journals. They will also be implemented in open source software packages and made available on the internet. The project will also make efforts to transfer the results into practice with an industrial partner, Slovnaft, a.s. The developed methods should achieve the acceptance of the proposed solution by the company's management and operators. Thus, the project not only proposes theoretical methods but also implements them in open software.

Publications

2024

  1. A. Ahmad – R. Paulen – R. Valo – M. Fikar – S. Engell: Iterative real-time optimization of a membrane pilot plant (in Anglo-Saxon). Control Engineering Practice, vol. 147, pp. 105907, 2024.   Zenodo

2023

  1. R. Fáber – K. Ľubušký – M. Mojto – R. Paulen: Enhancing Industrial Data Analysis through Machine Learning-based Classification of Petrochemical Datasets. In 49th International Conference of the Slovak Society of Chemical Engineering SSCHE 2023, Slovak Society of Chemical Engineering, Bratislava, SK, pp. 160–160, 2023.   Zenodo
  2. R. Fáber – K. Ľubušký – R. Paulen: Machine Learning-based Classification of Online Industrial Datasets. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 2023 24th International Conference on Process Control, IEEE, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia, pp. 132–137, 2023.   Zenodo
  3. M. KalúzR. KohútD. Dzurková: MPC-Mimicking Neural Network Based on Homomorphic Encryption. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 2023 24th International Conference on Process Control, IEEE, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia, pp. 126–131, 2023.   Zenodo
  4. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Comparing Linear and Nonlinear Soft Sensor Approaches for Industrial Distillation Columns (in Anglo-Saxon). In 49th International Conference of the Slovak Society of Chemical Engineering SSCHE 2023, Slovak Society of Chemical Engineering, Bratislava, SK, pp. 159–159, 2023.   Zenodo
  5. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Input Structure Selection for Soft-Sensor Design: Does It Pay Off?. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 2023 24th International Conference on Process Control, IEEE, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia, pp. 162–167, 2023.   Zenodo
  6. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-Driven Indication of Flooding in an Industrial Debutanizer Column. Editor(s): Antonis Kokossis, Michael C. Georgiadis, Efstratios N. Pistikopoulos, In 33rd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 33, pp. 1001–1006, 2023.   Zenodo
  7. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Design of Multi-Model Linear Inferential Sensors with SVM-based Switching Logic. In IFAC World Congress 2023, Yokohama, Japan, pp. 2545–2550, 2023.     Zenodo
  8. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-Based Design of Multi-Model Inferential Sensors. Computers & Chemical Engineering, vol. 178, 2023.   arXiv   Zenodo
  9. M. Ružička – I. Helgeland – R. Paulen: Modelling of a Forward Osmosis Process (in Slovak). In Membránové procesy pro udržitelný rozvoj - MEMPUR 2023, pp. 35–36, 2023.
  10. J. Vargan – J. Puk – K. Ľubušký – M. Fikar: Steady-State Analysis of Industrial MPC Controllers. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 24th International Conference on Process Control - Summaries Volume, Slovak Chemical Library, Slovak University of Technology in Bratislava, Radlinského 9, SK812-37, Bratislava, Slovakia, 2023.   Zenodo

2022

  1. D. Dzurková – M. Bakošová – A. Vasičkaninová: Implementation of Fuzzy Logic Controllers on Laboratory System of Heat Exchangers. In 2022 Cybernetics & Informatics (K&I), 2022.
  2. R. Fáber – R. Valo – M. Roman – R. Paulen: Towards Temperature Monitoring in Long-Term Grain Storage. In 2022 Cybernetics & Informatics (K&I), pp. 1–6, 2022.   Zenodo
  3. M. FikarM. KlaučoK. Kiš: A General Controller Tuning using Governors. 2022.
  4. M. FikarM. KlaučoR. Paulen: Theory of Automatic Control I. Practice Examples, FCHPT STU v Bratislave, 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. J. MiklešĽ. ČirkaJ. OravecM. Fikar: Design of H2 and Hinf control using Lyapunov functions (in Slovak), FCHPT STU v Bratislave, 2022.
  7. 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.
  8. 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.
  9. 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.   Zenodo
  10. C. E. Valero – R. Paulen: Zonotope Order Reduction in Robust Estimation. In 23rd International Carpathian Control Conference, IEEE, vol. 23, pp. 392–397, 2022.
  11. C. E. Valero – R. Paulen: Fault Detection in Networked Control Systems. A Robust Approach. In Summer Workshop on Interval Methods 2022, https://www.swim2022.de/fileadmin/swim2022/musterbilder/13th_SWIM_Book_of_Abstracts.pdf, vol. 13, pp. 16–17, 2022.
  12. 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. A. Ahmad – R. Paulen – R. Valo – M. Fikar – S. Engell: Experimental validation of iterative real-time optimization for a continuously operated membrane separation pilot plant. In ECCE 13 & ECAB 6 - 13th European Congress of Chemical Engineering & 6th European Congress of Applied Biotechnology - Book of Abstracts, DECHEMA e.V., pp. 499–500, 2021.
  2. M. Fikar – M. Furka – M. HorváthováK. Kiš – M. Mojto: Dynamic Optimisation Toolbox dynopt 5.0. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, pp. 296–301, 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.   arXiv
  4. M. Mateáš – R. Paulen: Optimal Experiments via Sequential and Two-stage Designs. Editor(s): R. Paulen, M. Fikar and J. Oravec, In Proceedings of the 23rd International Conference on Process Control - Summaries Volume, Slovak Chemical Library, Slovak University of Technology in Bratislava, Radlinského 9, SK812-37, Bratislava, Slovakia, pp. 47–47, 2021.
  5. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data Treatment of Industrial Measurements: From Online to Inferential Sensors. Editor(s): R. Paulen, M. Fikar and J. Oravec, In Proceedings of the 23rd International Conference on Process Control - Summaries Volume, Slovak Chemical Library, Slovak University of Technology in Bratislava, Radlinského 9, SK812-37, Bratislava, Slovakia, pp. 52–53, 2021.
  6. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-based Industrial Soft-sensor Design via Optimal Subset Selection. Editor(s): Metin Türkay, Rafiqul Gani, In 31st European Symposium on Computer Aided Process Engineering, Elsevier, vol. 31, pp. 1247–1252, 2021.
  7. M. Mojto – K. Ľubušký – M. FikarR. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, vol. 153, pp. 107437, 2021.   arXiv
  8. 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.
  9. C. E. Valero – R. Paulen: Set-membership State Estimation for a Continuous Stirred-Tank Reactor. In 9th International Conference on Systems and Control, 2021.
  10. P. Valiauga – X. Feng – M. Villanueva – R. Paulen – B. Houska: Set-membership Estimation using Ellipsoidal Ensembles. Editor(s): Jong Min Lee, In 16th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2021, Elsevier, pp. 596–601, 2021.     arXiv
  11. 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.
  12. A. Vasičkaninová – M. Bakošová – A. Mészáros: Fuzzy Control Design for Energy Efficient Heat Exchanger Network. Chemical Engineering Transactions, vol. 88, pp. 529–534, 2021.

Investigators


Responsibility for content: doc. Ing. Radoslav Paulen, PhD.
Last update: 23.11.2020 9:25
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