Fast pyrolysis multiphase CFD-kinetics model in a drop tube reactor DOI
Yohannis Tobo,

Ashraf Lotfi,

Luis D. Virla

et al.

Fuel, Journal Year: 2023, Volume and Issue: 340, P. 127524 - 127524

Published: Jan. 25, 2023

Language: Английский

A review on role of process parameters on pyrolysis of biomass and plastics: Present scope and future opportunities in conventional and microwave-assisted pyrolysis technologies DOI

Dadi V. Suriapparao,

Ravi Tejasvi

Process Safety and Environmental Protection, Journal Year: 2022, Volume and Issue: 162, P. 435 - 462

Published: April 13, 2022

Language: Английский

Citations

96
100 years of scaling up fluidized bed and circulating fluidized bed reactors DOI
Jia Wei Chew, Casey Q. LaMarche,

Ray Cocco

et al.

Powder Technology, Journal Year: 2022, Volume and Issue: 409, P. 117813 - 117813

Published: Aug. 6, 2022

Language: Английский

Citations

51
Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques DOI
Mohammad Rahimi, Hossein Mashhadimoslem, Hung Vo Thanh

et al.

Energy, Journal Year: 2023, Volume and Issue: 283, P. 128546 - 128546

Published: July 25, 2023

Language: Английский

Citations

27
Machine learning for sustainable organic waste treatment: a critical review DOI Creative Commons
Rohit Gupta,

Zahra Hajabdollahi Ouderji,

Uzma Uzma

et al.

npj Materials Sustainability, Journal Year: 2024, Volume and Issue: 2(1)

Published: April 8, 2024

Abstract Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven techniques for treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, k -nearest neighbors. The application these explored terms their capacity complex processes. Additionally, the delves into physics-informed highlighting significance integrating domain knowledge improved model consistency. Comparative analyses are carried out to provide insights strengths weaknesses each technique, aiding practitioners selecting appropriate models diverse applications. Transfer learning specialized network variants also discussed, offering avenues enhancing predictive capabilities. work contributes valuable field modeling, emphasizing importance understanding nuances technique informed decision-making various treatment scenarios.

Language: Английский

Citations

12
Coupling coarse‐grained DEM‐CFD and intraparticle model for biomass fast pyrolysis simulation and experiment validation DOI

Bing Wang,

Jianjian Dai, Sijie Li

et al.

AIChE Journal, Journal Year: 2024, Volume and Issue: 70(5)

Published: March 12, 2024

Abstract The understanding of complex fluidization hydrodynamics and chemical reactions in biomass fast pyrolysis fluidized bed reactors is lacking requires further investigation. It urgent to develop accurate mathematical models capable describing the multiphase reaction system pyrolysis. A comprehensive multiscale model based on coarse‐grained discrete element method (DEM)‐computational fluid dynamics (CFD) was developed open‐source MFiX code. incorporates detailed kinetics an intraparticle model. To validate model, measurements were conducted a pyrolyzer, including quantifying segmental pressure drop along height determining yields compositions gas, liquid, solid products. particle mixing segregation, axial distribution residence time, Lacey index, products investigated. study provides experimental theoretical foundations for designing advancing thermochemical conversion.

Language: Английский

Citations

10
Advances in machine learning for high value-added applications of lignocellulosic biomass DOI
Hanwen Ge, Jun Zheng, Huanfei Xu

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128481 - 128481

Published: Dec. 10, 2022

Language: Английский

Citations

28
Application of deep learning neural networks for the analysis of fluid-particle dynamics in fibrous filters DOI
Mohammadreza Shirzadi, Tomonori Fukasawa, Kunihiro Fukui

et al.

Chemical Engineering Journal, Journal Year: 2022, Volume and Issue: 455, P. 140775 - 140775

Published: Dec. 5, 2022

Language: Английский

Citations

23
Multi-fluid modeling of heat transfer in bubbling fluidized bed with thermally-thick particles featuring intra-particle temperature inhomogeneity DOI
Hao Luo, Xiaobao Wang, Xiaoqin Wu

et al.

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 460, P. 141813 - 141813

Published: Feb. 10, 2023

Language: Английский

Citations

14
Can biomass structural composition be predicted from a small dataset using a hybrid deep learning approach? DOI
Jude A. Okolie

Industrial Crops and Products, Journal Year: 2023, Volume and Issue: 203, P. 117191 - 117191

Published: July 25, 2023

Language: Английский

Citations

13
Enhancing pyrolysis process monitoring and prediction for biomass: A machine learning approach DOI
Jingxin Liu,

Huafei Lyu,

Can Cheng

et al.

Fuel, Journal Year: 2024, Volume and Issue: 362, P. 130873 - 130873

Published: Jan. 6, 2024

Language: Английский

Citations

5