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Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change

Author

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  • Krzysztof Gaska

    (Department of Water and Wastewater Engineering, Silesian University of Technology, ul. Konarskiego 18, 44-100 Gliwice, Poland)

  • Agnieszka Generowicz

    (Department of Environmental Technologies, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland)

  • Anna Gronba-Chyła

    (Faculty of Natural and Technical Sciences, The John Paul II Catholic University of Lublin, ul. Konstantynów 1H, 20-708 Lublin, Poland)

  • Józef Ciuła

    (Faculty of Engineering Sciences, State University of Applied Sciences in Nowy Sącz, ul. Zamenhofa 1A, 33-300 Nowy Sącz, Poland)

  • Iwona Wiewiórska

    (Sądeckie Wodociągi sp. z o.o., Wincentego Pola 22, 33-300 Nowy Sącz, Poland)

  • Paweł Kwaśnicki

    (Faculty of Natural and Technical Sciences, The John Paul II Catholic University of Lublin, ul. Konstantynów 1H, 20-708 Lublin, Poland)

  • Marcin Mala

    (MDConsulting, ul. Wielopolska 62, 39-200 Dębica, Poland)

  • Krzysztof Chyła

    (Department of Water and Wastewater Engineering, Silesian University of Technology, ul. Konarskiego 18, 44-100 Gliwice, Poland)

Abstract

Combined heat and power generation is the simultaneous conversion of primary energy (in the form of fuel) in a technical system into useful thermal and mechanical energy (as the basis for the generation of electricity). This method of energy conversion offers a high degree of efficiency (i.e., very efficient conversion of fuel to useful energy). In the context of energy system transformation, combined heat and power (CHP) is a fundamental pillar and link between the volatile electricity market and the heat market, which enables better planning. This article presents an advanced model for the production of fuel mixtures based on landfill biogas in the context of energy use in CHP units. The search for optimal technological solutions in energy management requires specialized domain-specific knowledge which, using advanced algorithmic models, has the potential to become an essential element in real-time intelligent ICT systems. Numerical modeling makes it possible to build systems based on the knowledge of complex systems, processes, and equipment in a relatively short time. The focus was on analyzing the applicability of algorithmic models based on artificial intelligence implemented in the supervisory control systems (SCADA-type systems including Virtual SCADA) of technological processes in waste management systems. The novelty of the presented solution is the application of predictive diagnostic tools based on multithreaded polymorphic models, supporting making decisions that control complex technological processes and objects and solving the problem of optimal control for intelligent dynamic objects with a logical representation of knowledge about the process, the control object, and the control, for which the learning process consists of successive validation and updating of knowledge and using the results of this updating to determine control decisions.

Suggested Citation

  • Krzysztof Gaska & Agnieszka Generowicz & Anna Gronba-Chyła & Józef Ciuła & Iwona Wiewiórska & Paweł Kwaśnicki & Marcin Mala & Krzysztof Chyła, 2023. "Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change," Energies, MDPI, vol. 16(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5732-:d:1207826
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    References listed on IDEAS

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    Cited by:

    1. Dawid Czajor & Łukasz Amanowicz, 2024. "Methodology for Modernizing Local Gas-Fired District Heating Systems into a Central District Heating System Using Gas-Fired Cogeneration Engines—A Case Study," Sustainability, MDPI, vol. 16(4), pages 1-30, February.

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