Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics
Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting per...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Upadhyay, Mukesh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:337 ; year:2022 ; day:20 ; month:02 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jclepro.2022.130490 |
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| 245 | 1 | 0 | |a Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics |
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| 520 | |a Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. | ||
| 520 | |a Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. | ||
| 650 | 7 | |a Circulating fluidized bed |2 Elsevier | |
| 650 | 7 | |a CFD |2 Elsevier | |
| 650 | 7 | |a Fast pyrolysis |2 Elsevier | |
| 650 | 7 | |a Machine learning |2 Elsevier | |
| 650 | 7 | |a Artificial neural network |2 Elsevier | |
| 700 | 1 | |a Nagulapati, Vijay Mohan |4 oth | |
| 700 | 1 | |a Lim, Hankwon |4 oth | |
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10.1016/j.jclepro.2022.130490 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001913.pica (DE-627)ELV056728832 (ELSEVIER)S0959-6526(22)00133-0 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Upadhyay, Mukesh verfasserin aut Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed Elsevier CFD Elsevier Fast pyrolysis Elsevier Machine learning Elsevier Artificial neural network Elsevier Nagulapati, Vijay Mohan oth Lim, Hankwon oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:337 year:2022 day:20 month:02 pages:0 https://doi.org/10.1016/j.jclepro.2022.130490 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 337 2022 20 0220 0 |
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10.1016/j.jclepro.2022.130490 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001913.pica (DE-627)ELV056728832 (ELSEVIER)S0959-6526(22)00133-0 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Upadhyay, Mukesh verfasserin aut Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed Elsevier CFD Elsevier Fast pyrolysis Elsevier Machine learning Elsevier Artificial neural network Elsevier Nagulapati, Vijay Mohan oth Lim, Hankwon oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:337 year:2022 day:20 month:02 pages:0 https://doi.org/10.1016/j.jclepro.2022.130490 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 337 2022 20 0220 0 |
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10.1016/j.jclepro.2022.130490 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001913.pica (DE-627)ELV056728832 (ELSEVIER)S0959-6526(22)00133-0 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Upadhyay, Mukesh verfasserin aut Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed Elsevier CFD Elsevier Fast pyrolysis Elsevier Machine learning Elsevier Artificial neural network Elsevier Nagulapati, Vijay Mohan oth Lim, Hankwon oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:337 year:2022 day:20 month:02 pages:0 https://doi.org/10.1016/j.jclepro.2022.130490 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 337 2022 20 0220 0 |
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10.1016/j.jclepro.2022.130490 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001913.pica (DE-627)ELV056728832 (ELSEVIER)S0959-6526(22)00133-0 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Upadhyay, Mukesh verfasserin aut Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed Elsevier CFD Elsevier Fast pyrolysis Elsevier Machine learning Elsevier Artificial neural network Elsevier Nagulapati, Vijay Mohan oth Lim, Hankwon oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:337 year:2022 day:20 month:02 pages:0 https://doi.org/10.1016/j.jclepro.2022.130490 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 337 2022 20 0220 0 |
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10.1016/j.jclepro.2022.130490 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001913.pica (DE-627)ELV056728832 (ELSEVIER)S0959-6526(22)00133-0 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Upadhyay, Mukesh verfasserin aut Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. Circulating fluidized bed Elsevier CFD Elsevier Fast pyrolysis Elsevier Machine learning Elsevier Artificial neural network Elsevier Nagulapati, Vijay Mohan oth Lim, Hankwon oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:337 year:2022 day:20 month:02 pages:0 https://doi.org/10.1016/j.jclepro.2022.130490 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 337 2022 20 0220 0 |
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Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. 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Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics |
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Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. |
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Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. |
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Circulating fluidized bed (CFB)-based co-pyrolysis is a promising technology for producing synthetic fuel and chemical co-products from biomass and waste feedstocks. Computational fluid dynamics (CFD) tools provide valuable insights for understanding gas-solid flow hydrodynamics, troubleshooting performance issues, and optimizing reactor operations. CFD simulations are computationally expensive and time-consuming; therefore, in this study, we developed an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign. The CFB riser axial solid holdup profile was predicted using a two-fluid model and validated using the experimental data. Finally, a dataset connecting the input features of the model parameter-based simulations with their axial solid holdup was constructed for training the ANN. The performance of the developed model was later compared with the experimental data. The developed ANN model exhibited low mean square error values of order of 10−3 for both validation datasets to predict axial solid holdup. The ANN model performed well with an interpolated solid circulation rate of 45 kg/m2s and 62 kg/m2s and an extrapolated solid circulation rate of 30 kg/m2s and 80 kg/m2s. |
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