Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D
Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D
In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle's dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.
Artificial neural network, Biomass pyrolysis, Detailed pyrolysis kinetics, Intra-particle heat transfer, Thermally-thick particle, Zero-dimensional model
Luo, Hao
d2a0d21b-8143-4ae1-812c-67eb42c4e65e
Wang, Xiaobao
41f0c58d-34eb-4d85-9706-4971342ee538
Liu, Xinyan
99aad7d6-2635-48e2-babb-9d21c7c532a8
Yi, Lan
fc8a17ec-e3ef-4d0b-87d0-8e74772cc14d
Wu, Xiaoqin
7ae2c382-79cf-46b8-baf0-1d40b1664e88
Yu, Xi
7e4f553f-cc11-4c6e-ad6d-9fb5c3c07a60
Ouyang, Yi
fb782fc3-faf5-4db8-8edc-e30f50cb08f7
Liu, Weifeng
352b9b48-0026-45a3-af9f-281614be9792
Xiong, Qingang
ee66c6e3-4c7f-482e-ab6a-b4751bd74399
5 July 2023
Luo, Hao
d2a0d21b-8143-4ae1-812c-67eb42c4e65e
Wang, Xiaobao
41f0c58d-34eb-4d85-9706-4971342ee538
Liu, Xinyan
99aad7d6-2635-48e2-babb-9d21c7c532a8
Yi, Lan
fc8a17ec-e3ef-4d0b-87d0-8e74772cc14d
Wu, Xiaoqin
7ae2c382-79cf-46b8-baf0-1d40b1664e88
Yu, Xi
7e4f553f-cc11-4c6e-ad6d-9fb5c3c07a60
Ouyang, Yi
fb782fc3-faf5-4db8-8edc-e30f50cb08f7
Liu, Weifeng
352b9b48-0026-45a3-af9f-281614be9792
Xiong, Qingang
ee66c6e3-4c7f-482e-ab6a-b4751bd74399
Luo, Hao, Wang, Xiaobao, Liu, Xinyan, Yi, Lan, Wu, Xiaoqin, Yu, Xi, Ouyang, Yi, Liu, Weifeng and Xiong, Qingang
(2023)
Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D.
Chemical Engineering Science, 280, [119060].
(doi:10.1016/j.ces.2023.119060).
Abstract
In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle's dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.
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Accepted/In Press date: 29 June 2023
e-pub ahead of print date: 30 June 2023
Published date: 5 July 2023
Additional Information:
Funding Information:
This study was financially supported by the National Natural Science Foundation of China (No. 22208254 and 22178123 ), the Scientific Research Foundation of Hubei Educational Committee (No. D20221102 ), the Key Laboratory of Hubei Province for Coal Conversion and New Carbon Materials from Wuhan University of Science and Technology (No. WKDM202202 and WKDM202205 ), and International Exchanges 2022 Cost Share (NSFC) by the Royal Society (No. IEC\NSFC\223425).
Keywords:
Artificial neural network, Biomass pyrolysis, Detailed pyrolysis kinetics, Intra-particle heat transfer, Thermally-thick particle, Zero-dimensional model
Identifiers
Local EPrints ID: 483062
URI: http://eprints.soton.ac.uk/id/eprint/483062
ISSN: 0009-2509
PURE UUID: b25600ba-707b-4a13-957b-23a18a4203ef
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Date deposited: 20 Oct 2023 17:33
Last modified: 06 Jun 2024 02:19
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Contributors
Author:
Hao Luo
Author:
Xiaobao Wang
Author:
Xinyan Liu
Author:
Lan Yi
Author:
Xiaoqin Wu
Author:
Xi Yu
Author:
Yi Ouyang
Author:
Weifeng Liu
Author:
Qingang Xiong
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