Random forest model for determination of the lower heating value of TPP “Kolubara A” coal
Само за регистроване кориснике
2023
Аутори
Milićević, AleksandarBelošević, Srđan
Erić, Milić
Marković, Zoran
Tomanović, Ivan
Crnomarković, Nenad
Stojanović, Andrijana
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 d...ata consisted of the values of technical fuel analysis.
Кључне речи:
machine learning / Random forest / coal / heating value / thermal power plantИзвор:
International Conference Power Plants 2023 : Proceedings, 2023, 748-752Издавач:
- Belgrade : Society of Thermal Engineers of Serbia
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200017 (Универзитет у Београду, Институт за нуклеарне науке Винча, Београд-Винча) (RS-MESTD-inst-2020-200017)
- United Nations Development Programme [Ref.: 00123168/01-04]
Напомена:
- Power Plants 2023 : Elektrane 2023; November 8-10, 2023, Zlatibor, Serbia
Колекције
Институција/група
VinčaTY - 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 .