Applications of machine learning in thermochemical conversion of biomass-A review

SR Naqvi, Z Ullah, SAA Taqvi, MNA Khan, W Farooq… - Fuel, 2023 - Elsevier
Thermochemical conversion of biomass has been considered a promising technique to
produce alternative renewable fuel sources for future energy supply. However, these …

[HTML][HTML] Machine learning applications in biomass pyrolysis: from biorefinery to end-of-life product management

DA Akinpelu, OA Adekoya, PO Oladoye… - Digital Chemical …, 2023 - Elsevier
The thermochemical conversion of biomass is a promising technology due to its cost-
effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method …

Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model

C Rao, Y Zhang, J Wen, X **ao, M Goh - Energy, 2023 - Elsevier
Analyzing the drivers of energy demand and predicting energy consumption can help to
shape national policies on energy transformation and energy security. This paper estimates …

Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening

J Li, L Pan, M Suvarna, X Wang - Chemical Engineering Journal, 2021 - Elsevier
Hydrogen production from wet organic wastes through supercritical water gasification
(SCWG) promotes sustainable development. However, it is always time-consuming and …

Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

X Tang, Q Guo, M Li, C Wei, Z Pan, Y Wang - Journal of Power Sources, 2021 - Elsevier
In this paper, the coupling system of liquid-cooled battery thermal management system
(BTMS) and heat pump air conditioning system (HPACS) for battery electric vehicles (BEV) …

Machine learning regression-CFD models for the nanofluid heat transfer of a microchannel heat sink with double synthetic jets

J Mohammadpour, S Husain, F Salehi, A Lee - … Communications in Heat …, 2022 - Elsevier
A comprehensive analysis consisting of computational fluid dynamics (CFD) and machine
learning algorithms (MLAs) is conducted to study the effect of geometrical and operational …

Study of highly efficient control and dust removal system for double-tunnel boring processes in coal mines

W Nie, C Jiang, Q Liu, L Guo, Y Hua, H Zhang, B Jiang… - Energy, 2024 - Elsevier
To reduce coal dust pollution during double-tunnel boring processes in coal mines, an
efficient control and dust removal system has been established and studied by combining …

Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review

HSH Wang, Y Yao - Resources, Conservation and Recycling, 2023 - Elsevier
Biomass-derived materials (BDM) have broad applications in water and agricultural
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …

Assessing bioenergy prospects of algal biomass and yard waste using an integrated hydrothermal carbonization and pyrolysis (HTC–PY): A detailed emission–to–ash …

A Kumar, IA Jamro, H Rong, L Kumari… - Chemical Engineering …, 2024 - Elsevier
Hydrothermal carbonization (HTC) presents a promising method for converting
carbonaceous waste into renewable fuels, facilitating energy recovery. This study focuses …

Analysis of multi-factor ventilation parameters for reducing energy air pollution in coal mines

W Nie, C Jiang, N Sun, L Guo, Q Xue, Q Liu, C Liu… - Energy, 2023 - Elsevier
To control the diffusion of highly concentrated coal dust during coal energy mining and
determine the order of significance of each factor for dust control, theoretical analysis …