TY  - JOUR
AU  - Milićević, Aleksandar
AU  - Belošević, Srđan
AU  - Žarković, Mileta
AU  - Tomanović, Ivan
AU  - Crnomarković, Nenad
AU  - Stojanović, Andrijana
AU  - Stupar, Goran
AU  - Deng, Lei
AU  - Che, Defu
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11021
AB  - When planning the development of the energy sector, significant attention is given to the energy from the renewable sources, amongst which the biomass has an important role. Computational fluid mechanics and machine learning models are the powerful and efficient tools which allow the analysis of various heat and mass transfer phenomena in energy facilities. In this study, the in-house developed CFD code and machine learning models (Random Forest, Gradient Boosting and Artificial Neural Network) for predicting the biomass trajectories, particle mass burnout and residence time in a swirl burner reactor are presented. Pulverized biomass combustion cases (fine straw, pinewood and switch grass) with various mean diameters (ranging between 60 and 650 μm) and different shape factors (within the range 0–1) are considered. The results of numerical simulations revealed a noticeably nonlinear dependence between the input values (particle types, sizes and shapes) and the output values (particle trajectories, mass burnout and residence time), mostly due to the complex swirling flow in the reactor. For particles with the mean diameters within the ranges considered, the mass burnout of particles generally decreases as the biomass particle shape factor increases. The residence time of pulverized biomass in the reactor shows in most cases a decreasing trend as the particle shape factor increases. Artificial Neural Network showed the best predictions for both particle mass burnout (RMSE = 0.083 and R2 = 0.937) and particle residence time (RMSE = 1.145 s and R2 = 0.900), providing the reliable assessment of these important indicators in the combustion process.
T2  - Biomass and Bioenergy
T1  - Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach
VL  - 174
SP  - 106817
DO  - 10.1016/j.biombioe.2023.106817
ER  - 

@article{
author = "Milićević, Aleksandar and Belošević, Srđan and Žarković, Mileta and Tomanović, Ivan and Crnomarković, Nenad and Stojanović, Andrijana and Stupar, Goran and Deng, Lei and Che, Defu",
year = "2023",
abstract = "When planning the development of the energy sector, significant attention is given to the energy from the renewable sources, amongst which the biomass has an important role. Computational fluid mechanics and machine learning models are the powerful and efficient tools which allow the analysis of various heat and mass transfer phenomena in energy facilities. In this study, the in-house developed CFD code and machine learning models (Random Forest, Gradient Boosting and Artificial Neural Network) for predicting the biomass trajectories, particle mass burnout and residence time in a swirl burner reactor are presented. Pulverized biomass combustion cases (fine straw, pinewood and switch grass) with various mean diameters (ranging between 60 and 650 μm) and different shape factors (within the range 0–1) are considered. The results of numerical simulations revealed a noticeably nonlinear dependence between the input values (particle types, sizes and shapes) and the output values (particle trajectories, mass burnout and residence time), mostly due to the complex swirling flow in the reactor. For particles with the mean diameters within the ranges considered, the mass burnout of particles generally decreases as the biomass particle shape factor increases. The residence time of pulverized biomass in the reactor shows in most cases a decreasing trend as the particle shape factor increases. Artificial Neural Network showed the best predictions for both particle mass burnout (RMSE = 0.083 and R2 = 0.937) and particle residence time (RMSE = 1.145 s and R2 = 0.900), providing the reliable assessment of these important indicators in the combustion process.",
journal = "Biomass and Bioenergy",
title = "Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach",
volume = "174",
pages = "106817",
doi = "10.1016/j.biombioe.2023.106817"
}

Milićević, A., Belošević, S., Žarković, M., Tomanović, I., Crnomarković, N., Stojanović, A., Stupar, G., Deng, L.,& Che, D.. (2023). Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach. in Biomass and Bioenergy, 174, 106817.
https://doi.org/10.1016/j.biombioe.2023.106817

Milićević A, Belošević S, Žarković M, Tomanović I, Crnomarković N, Stojanović A, Stupar G, Deng L, Che D. Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach. in Biomass and Bioenergy. 2023;174:106817.
doi:10.1016/j.biombioe.2023.106817 .

Milićević, Aleksandar, Belošević, Srđan, Žarković, Mileta, Tomanović, Ivan, Crnomarković, Nenad, Stojanović, Andrijana, Stupar, Goran, Deng, Lei, Che, Defu, "Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach" in Biomass and Bioenergy, 174 (2023):106817,
https://doi.org/10.1016/j.biombioe.2023.106817 . .

TY  - JOUR
AU  - Deng, Lei
AU  - Dong, Lingxiao
AU  - Bai, Yang
AU  - Wu, Yuhao
AU  - Liu, Hu
AU  - Belošević, Srđan
AU  - Tomanović, Ivan
AU  - Che, Defu
PY  - 2022
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10227
AB  - The ultra-supercritical double-reheat boiler has attracted more attention because of high parameters (steam with high temperature and pressure), low pollution and large capacity. However, there are some difficulties in the development of this technology, such as adjusting the reheat steam temperature. In this study, computational fluid dynamics simulation is used to analyze a 660 MW double-reheat tower-type pulverized coal-fired boiler. The influence of flue gas recirculation (FGR) on heat transfer characteristics and combustion process in the furnace (including heating surfaces in the upper parts of the furnace) are evaluated. The user-defined function approach predicates the CO reduction effect on NOx. The results show that the flow at the horizontal section through the centerline of recirculating flus gas nozzles is rotating. The velocity distribution changes into an elliptical rotating flow when FGR ratio is 20%. At higher FGR ratios, the high-temperature area (1565–1700 K) shrinks and both the NOx concentration at the low-temperature superheater outlet and the O2 concentration in the burner zone descend. The O2 concentration at the low-temperature superheater outlet first increases and then decreases. In the main combustion zone, the heat flux peak of water-cooled wall is about 330 kW m−2. As FGR ratio increases from 0% to 20%, the rate of heat absorption of water-cooled wall to that of total boiler decreases by 3.50%. These rates for reheater and superheater increase by 2.53% and 2.13%, respectively.
T2  - Fuel
T1  - Effects of flue gas recirculation on combustion and heat flux distribution in 660 MW double-reheat tower-type boiler
VL  - 321
SP  - 123988
DO  - 10.1016/j.fuel.2022.123988
ER  - 

@article{
author = "Deng, Lei and Dong, Lingxiao and Bai, Yang and Wu, Yuhao and Liu, Hu and Belošević, Srđan and Tomanović, Ivan and Che, Defu",
year = "2022",
abstract = "The ultra-supercritical double-reheat boiler has attracted more attention because of high parameters (steam with high temperature and pressure), low pollution and large capacity. However, there are some difficulties in the development of this technology, such as adjusting the reheat steam temperature. In this study, computational fluid dynamics simulation is used to analyze a 660 MW double-reheat tower-type pulverized coal-fired boiler. The influence of flue gas recirculation (FGR) on heat transfer characteristics and combustion process in the furnace (including heating surfaces in the upper parts of the furnace) are evaluated. The user-defined function approach predicates the CO reduction effect on NOx. The results show that the flow at the horizontal section through the centerline of recirculating flus gas nozzles is rotating. The velocity distribution changes into an elliptical rotating flow when FGR ratio is 20%. At higher FGR ratios, the high-temperature area (1565–1700 K) shrinks and both the NOx concentration at the low-temperature superheater outlet and the O2 concentration in the burner zone descend. The O2 concentration at the low-temperature superheater outlet first increases and then decreases. In the main combustion zone, the heat flux peak of water-cooled wall is about 330 kW m−2. As FGR ratio increases from 0% to 20%, the rate of heat absorption of water-cooled wall to that of total boiler decreases by 3.50%. These rates for reheater and superheater increase by 2.53% and 2.13%, respectively.",
journal = "Fuel",
title = "Effects of flue gas recirculation on combustion and heat flux distribution in 660 MW double-reheat tower-type boiler",
volume = "321",
pages = "123988",
doi = "10.1016/j.fuel.2022.123988"
}

Deng, L., Dong, L., Bai, Y., Wu, Y., Liu, H., Belošević, S., Tomanović, I.,& Che, D.. (2022). Effects of flue gas recirculation on combustion and heat flux distribution in 660 MW double-reheat tower-type boiler. in Fuel, 321, 123988.
https://doi.org/10.1016/j.fuel.2022.123988

Deng L, Dong L, Bai Y, Wu Y, Liu H, Belošević S, Tomanović I, Che D. Effects of flue gas recirculation on combustion and heat flux distribution in 660 MW double-reheat tower-type boiler. in Fuel. 2022;321:123988.
doi:10.1016/j.fuel.2022.123988 .

Deng, Lei, Dong, Lingxiao, Bai, Yang, Wu, Yuhao, Liu, Hu, Belošević, Srđan, Tomanović, Ivan, Che, Defu, "Effects of flue gas recirculation on combustion and heat flux distribution in 660 MW double-reheat tower-type boiler" in Fuel, 321 (2022):123988,
https://doi.org/10.1016/j.fuel.2022.123988 . .