Integration of supervised machine learning for predictive evaluation of chemical looping hydrogen production and storage system DOI

Renge Li,

Jimin Zeng,

Yintao Wei

et al.

Sustainable Energy & Fuels, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Machine learning integrates with the chemical looping hydrogen production system to accelerate development process and reduce experimental trial-and-error costs.

Language: Английский

CO2 conversion to CO by reverse water gas shift and dry reforming using chemical looping DOI Creative Commons
Keke Kang, Hiroshi Sampei, Yasushi Sekine

et al.

RSC Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Chemical looping technology provides an efficient means of sustainable CO 2 conversion to the important chemical intermediate or syngas by changing conventional co-feeding reactant into alternating feeding. It...

Language: Английский

Citations

0

Advancements in non-renewable and hybrid hydrogen production: Technological innovations for efficiency and carbon reduction DOI Creative Commons
Vahid Madadi Avargani, Sohrab Zendehboudi, Xili Duan

et al.

Fuel, Journal Year: 2025, Volume and Issue: 395, P. 135065 - 135065

Published: March 30, 2025

Language: Английский

Citations

0

Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction DOI Open Access
Xuejia Du, Muhammad Noman Khan, Ganesh Thakur

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1160 - 1160

Published: April 11, 2025

Carbon Capture, Utilization, and Storage (CCUS) technologies have emerged as indispensable tools in reducing greenhouse gas (GHG) emissions combating climate change. However, the optimization scalability of CCUS processes face significant technical economic challenges that hinder their widespread implementation. Machine Learning (ML) offers innovative solutions by providing faster, more accurate alternatives to traditional methods across value chain. Despite growing body research this field, applications ML remain fragmented, lacking a cohesive synthesis bridges these advancements practical This review addresses gap systematically evaluating all major components—CO2 capture, transport, storage, utilization. We provide structured representative examples for each category critically examine various techniques, objectives, methodological frameworks employed recent studies. Additionally, we identify key parameters, limitations, future opportunities applying enhance systems. Our thus comprehensive insights guidance stakeholders, supporting informed decision-making accelerating ML-driven commercialization.

Language: Английский

Citations

0

Integrating triboelectric nanogenerators with precision acupuncture for technological advancement in traditional healing DOI
Dan Li, Wei Wei, Dan Zheng

et al.

Materials Today Chemistry, Journal Year: 2025, Volume and Issue: 46, P. 102744 - 102744

Published: May 12, 2025

Language: Английский

Citations

0

Material Engineering Solutions toward Selective Redox Catalysts for Chemical-Looping-Based Olefin Production Schemes: A Review DOI Creative Commons

Alexander Oing,

Elena von Müller,

Felix Donat

et al.

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(18), P. 17326 - 17342

Published: Sept. 10, 2024

Chemical looping (CL) has emerged as a promising approach in the oxidative dehydrogenation (ODH) of light alkanes, offering an opportunity for significant reductions emissions and energy consumption ethylene propylene production industry. While high olefin yields are achievable via CL, material requirements (e.g., electronic geometric structures) that prevent total conversion alkanes to CO

Language: Английский

Citations

3

2024 Pioneers in Energy Research: Juan Adánez DOI Creative Commons
Haibo Zhao

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(22), P. 21666 - 21671

Published: Nov. 6, 2024

Language: Английский

Citations

2

Integration of supervised machine learning for predictive evaluation of chemical looping hydrogen production and storage system DOI

Renge Li,

Jimin Zeng,

Yintao Wei

et al.

Sustainable Energy & Fuels, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Machine learning integrates with the chemical looping hydrogen production system to accelerate development process and reduce experimental trial-and-error costs.

Language: Английский

Citations

2