Machine learning applications in forest and biomass supply chain management: a review DOI
Jinghan Zhao, Jingxin Wang,

Nathaniel Anderson

et al.

International Journal of Forest Engineering, Journal Year: 2024, Volume and Issue: 35(3), P. 371 - 380

Published: July 21, 2024

Language: Английский

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review DOI
Shiqi Wang, Peng Xia, Keyu Chen

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 80, P. 108065 - 108065

Published: Nov. 3, 2023

Language: Английский

Citations

62
Emerging applications of biochar: A review on techno-environmental-economic aspects DOI
Zhu Hui,

Qing Long An,

Amirah Syafika Mohd Nasir

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 388, P. 129745 - 129745

Published: Sept. 9, 2023

Language: Английский

Citations

42
Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy DOI
Van Giao Nguyen, Prabhakar Sharma, Ümit Ağbulut

et al.

Biofuels Bioproducts and Biorefining, Journal Year: 2024, Volume and Issue: 18(2), P. 567 - 593

Published: Feb. 5, 2024

Abstract Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand sustainable energy. Efficient management systems are needed in order exploit fully of biochar. Modern machine learning (ML) techniques, and particular ensemble approaches explainable AI methods, valuable forecasting properties efficiency biochar properly. Machine‐learning‐based forecasts, optimization, feature selection critical improving techniques. In this research, we explore influences these techniques on accurate yield range sources. We emphasize importance interpretability model, improves human comprehension trust ML predictions. Sensitivity analysis shown be an effective technique finding crucial characteristics that influence synthesis Precision prognostics have far‐reaching ramifications, influencing industries such logistics, technologies, successful use renewable These advances can make substantial contribution greener future encourage development circular biobased economy. This work emphasizes using sophisticated data‐driven methodologies synthesis, usher ecologically friendly energy solutions. breakthroughs hold key more environmentally future.

Language: Английский

Citations

23
Machine learning applications for biochar studies: A mini-review DOI
Wei Wang, Jo‐Shu Chang, Duu‐Jong Lee

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 394, P. 130291 - 130291

Published: Jan. 4, 2024

Language: Английский

Citations

17
Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management DOI Creative Commons
David Akorede Akinpelu, Adekoya Oluwaseun Abiodun, Peter Olusakin Oladoye

et al.

Digital Chemical Engineering, Journal Year: 2023, Volume and Issue: 8, P. 100103 - 100103

Published: May 16, 2023

The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being particularly noteworthy method for diverse product range. Despite the potential pyrolysis, commercialization remains elusive, there growing need fully understand dynamics facilitate process scaling up. However, waste complex, time-consuming, capital-intensive. Machine Learning (ML) has emerged as possible means supporting accelerating research despite these challenges. This study provides comprehensive overview use ML in from biorefinery end-of-life management. In addition, success optimization control, predicting yield, real-time monitoring, life-cycle assessment (LCA), techno-economic analysis (TEA) during highlighted. Several methods have been utilized bid pyrolysis; potentiality artificial neural networks (ANNs) learn extremely non-linear input-output correlations led widespread adoption networks. Furthermore, current knowledge gaps future recommendations application are identified. Finally, this demonstrates development well scalability biomass.

Language: Английский

Citations

37
Biochar production and its environmental applications: Recent developments and machine learning insights DOI

Kolli Venkata Supraja,

Himanshu Kachroo, Gayatri Viswanathan

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 387, P. 129634 - 129634

Published: Aug. 21, 2023

Language: Английский

Citations

26
Multi-performance optimization of low-carbon geopolymer considering mechanical, cost, and CO2 emission based on experiment and interpretable learning DOI
Shiqi Wang, Keyu Chen, Jinlong Liu

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 425, P. 136013 - 136013

Published: April 1, 2024

Language: Английский

Citations

9
Recent studies on the comprehensive application of biochar in multiple environmental fields DOI Open Access
Yunsong Liu,

Zonglin Weng,

Bin Han

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 421, P. 138495 - 138495

Published: Aug. 17, 2023

Language: Английский

Citations

22
Utilizing machine learning approaches within concrete technology offers an intelligent perspective towards sustainability in the construction industry: a comprehensive review DOI

Suhaib Rasool Wani,

Manju Suthar

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Oct. 26, 2024

Language: Английский

Citations

8
Chemocatalytic production of sorbitol from cellulose via sustainable chemistry – a tutorial review DOI
Yingqiao Zhou, Richard L. Smith, Xinhua Qi

et al.

Green Chemistry, Journal Year: 2023, Volume and Issue: 26(1), P. 202 - 243

Published: Nov. 29, 2023

This work aims to review the latest progress in chemocatalytic production of sorbitol from cellulose with emphasis on sustainable chemistry.

Language: Английский

Citations

15