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Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

Author

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  • Krzysztof Lalik

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
    These authors contributed equally to this work.)

  • Mateusz Kozek

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
    These authors contributed equally to this work.)

  • Szymon Podlasek

    (Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
    These authors contributed equally to this work.)

  • Rafał Figaj

    (Faculty of Energy and Fuels, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Paweł Gut

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

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.

Suggested Citation

  • Krzysztof Lalik & Mateusz Kozek & Szymon Podlasek & Rafał Figaj & Paweł Gut, 2021. "Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems," Energies, MDPI, vol. 14(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5334-:d:623536
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    References listed on IDEAS

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    1. Bianco, Vincenzo & Szubel, Mateusz & Matras, Beata & Filipowicz, Mariusz & Papis, Karolina & Podlasek, Szymon, 2021. "CFD analysis and design optimization of an air manifold for a biomass boiler," Renewable Energy, Elsevier, vol. 163(C), pages 2018-2028.
    2. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    3. Sornek, Krzysztof & Filipowicz, Mariusz & Żołądek, Maciej & Kot, Radosław & Mikrut, Małgorzata, 2019. "Comparative analysis of selected thermoelectric generators operating with wood-fired stove," Energy, Elsevier, vol. 166(C), pages 1303-1313.
    4. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
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