The Security of Energy Supply from Internal Combustion Engines Using Coal Mine Methane—Forecasting of the Electrical Energy Generation

Marek Borowski, Piotr Życzkowski, Klaudia Zwolińska, Rafał Łuczak and Zbigniew Kuczera
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Marek Borowski: Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland
Piotr Życzkowski: Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland
Klaudia Zwolińska: Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland
Rafał Łuczak: Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland
Zbigniew Kuczera: Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland

Energies, 2021, vol. 14, issue 11, 1-18

Abstract: Increasing emissions from mining areas and a high global warming potential of methane have caused gas management to become a vital challenge. At the same time, it provides the opportunity to obtain economic benefits. In addition, the use of combined heat and power (CHP) in the case of coalbed methane combustion enables much more efficient use of this fuel. The article analyses the possibility of electricity production using gas engines fueled with methane captured from the Budryk coal mine in Poland. The basic issue concerning the energy production from coalbed methane is the continuity of supply, which is to ensure the required amount and concentration of the gas mixture for combustion. Hence, the reliability of supply for electricity production is of key importance. The analysis included the basic characterization of both the daily and annual methane capture by the mine’s methane drainage system, as well as the development of predictive models to determine electricity production based on hourly capture and time parameters. To forecast electricity production, predictive models that are based on five parameters have been adopted. Models were prepared based on three time variables, i.e., month, day, hour, and two values from the gas drainage system-capture and concentration of the methane. For this purpose, artificial neural networks with different properties were tested. The developed models have a high value of correlation coefficient. but showed deviations concerning the very low values persisting for a short time. The study shows that electricity production forecasting is possible, but it requires data on many variables that directly affect the production capacity of the system.

Keywords: internal combustion engine; energy efficiency; pollutant emission; coalbed methane; neural networks; electricity production forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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