LAPSE:2023.19760
Published Article

LAPSE:2023.19760
Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems
March 9, 2023
Abstract
This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.
This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.
Record ID
Keywords
mCHP, Modelling, neural algorithms, PID controller, Renewable and Sustainable Energy
Suggested Citation
Lalik K, Kozek M, Podlasek S, Figaj R, Gut P. Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems. (2023). LAPSE:2023.19760
Author Affiliations
Lalik K: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Kozek M: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Podlasek S: Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Figaj R: Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Gut P: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Kozek M: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Podlasek S: Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Figaj R: Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Gut P: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [ORCID]
Journal Name
Energies
Volume
14
Issue
17
First Page
5334
Year
2021
Publication Date
2021-08-27
ISSN
1996-1073
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Original Submission
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PII: en14175334, Publication Type: Journal Article
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LAPSE:2023.19760
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https://doi.org/10.3390/en14175334
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Mar 9, 2023
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