TY  - CONF
AU  - Milićević, Aleksandar
AU  - Belošević, Srđan
AU  - Erić, Milić
AU  - Marković, Zoran
AU  - Tomanović, Ivan
AU  - Crnomarković, Nenad
AU  - Stojanović, Andrijana
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12766
AB  - Heating value is an important indicator for assessment of the coal quality. Machine learning models are powerful computational tools that allow for the analysis of various heat and mass transfer phenomena in energy systems. In this paper, Random forest model for determining the lower heating values of coal from the thermal power plant “Kolubara A” is developed based on proximate and ultimate fuel analysis. A database of the proximate and ultimate fuel analysis values and lower heating value of coal was created by experimental measurements in the accredited test laboratory of the Department of Thermal Engineering and Energy (“VINČA” Institute of Nuclear Sciences). The developed Random forest models, applied to a relatively small database, showed acceptable predictions for the lower heating value based on both the proximate analysis (RMSE = 0.22 MJ/kg and MAPE = 2.26%) and the ultimate analysis (RMSE = 0.64 MJ/kg and MAPE = 6.12%), with better accuracy achieved by the model whose input data consisted of the values of technical fuel analysis.
PB  - Belgrade : Society of Thermal Engineers of Serbia
C3  - International Conference Power Plants 2023 : Proceedings
T1  - Random forest model for determination of the lower heating value of TPP “Kolubara A” coal
SP  - 748
EP  - 752
UR  - https://hdl.handle.net/21.15107/rcub_vinar_12766
ER  - 

@conference{
author = "Milićević, Aleksandar and Belošević, Srđan and Erić, Milić and Marković, Zoran and Tomanović, Ivan and Crnomarković, Nenad and Stojanović, Andrijana",
year = "2023",
abstract = "Heating value is an important indicator for assessment of the coal quality. Machine learning models are powerful computational tools that allow for the analysis of various heat and mass transfer phenomena in energy systems. In this paper, Random forest model for determining the lower heating values of coal from the thermal power plant “Kolubara A” is developed based on proximate and ultimate fuel analysis. A database of the proximate and ultimate fuel analysis values and lower heating value of coal was created by experimental measurements in the accredited test laboratory of the Department of Thermal Engineering and Energy (“VINČA” Institute of Nuclear Sciences). The developed Random forest models, applied to a relatively small database, showed acceptable predictions for the lower heating value based on both the proximate analysis (RMSE = 0.22 MJ/kg and MAPE = 2.26%) and the ultimate analysis (RMSE = 0.64 MJ/kg and MAPE = 6.12%), with better accuracy achieved by the model whose input data consisted of the values of technical fuel analysis.",
publisher = "Belgrade : Society of Thermal Engineers of Serbia",
journal = "International Conference Power Plants 2023 : Proceedings",
title = "Random forest model for determination of the lower heating value of TPP “Kolubara A” coal",
pages = "748-752",
url = "https://hdl.handle.net/21.15107/rcub_vinar_12766"
}

Milićević, A., Belošević, S., Erić, M., Marković, Z., Tomanović, I., Crnomarković, N.,& Stojanović, A.. (2023). Random forest model for determination of the lower heating value of TPP “Kolubara A” coal. in International Conference Power Plants 2023 : Proceedings
Belgrade : Society of Thermal Engineers of Serbia., 748-752.
https://hdl.handle.net/21.15107/rcub_vinar_12766

Milićević A, Belošević S, Erić M, Marković Z, Tomanović I, Crnomarković N, Stojanović A. Random forest model for determination of the lower heating value of TPP “Kolubara A” coal. in International Conference Power Plants 2023 : Proceedings. 2023;:748-752.
https://hdl.handle.net/21.15107/rcub_vinar_12766 .

Milićević, Aleksandar, Belošević, Srđan, Erić, Milić, Marković, Zoran, Tomanović, Ivan, Crnomarković, Nenad, Stojanović, Andrijana, "Random forest model for determination of the lower heating value of TPP “Kolubara A” coal" in International Conference Power Plants 2023 : Proceedings (2023):748-752,
https://hdl.handle.net/21.15107/rcub_vinar_12766 .