Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review DOI Open Access
S. Mohammad Shojaei, Reihaneh Aghamolaei, Mohammad Reza Ghaani

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9555 - 9555

Published: Nov. 2, 2024

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched density and flexibility. However, as world shifts toward renewable energy, overcoming limitations intermittent power sources requires a bold reimagining storage integration. Power-to-X (PtX) technologies, which convert excess electricity into storable carriers, offer promising solution for long-term sector coupling. Recent advancements in machine learning (ML) revolutionized PtX systems by enhancing efficiency, scalability, sustainability. This review provides detailed analysis how ML techniques, such deep reinforcement learning, data-driven optimization, predictive diagnostics, are driving innovation Power-to-Gas (PtG), Power-to-Liquid (PtL), Power-to-Heat (PtH) systems. example, has improved real-time decision-making PtG reducing operational costs improving grid stability. Additionally, diagnostics powered increased system reliability identifying early failures critical components proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges data quality, processing, scalability remain, presenting future research opportunities. These to decarbonizing hard-to-electrify sectors, heavy industry, transportation, aviation, aligning with global sustainability goals.

Language: Английский

Synergistic Enhancement of Oxygen Evolution by S-Ti3C2Tx MXene Folded Nanosheets Supported by Cobalt Hydroxide Nanoparticles DOI

Yuerong Pei,

Mingzheng Gu, Hao Wang

et al.

ACS Applied Nano Materials, Journal Year: 2024, Volume and Issue: 7(17), P. 20544 - 20552

Published: Aug. 29, 2024

The metal active site-induced adsorbate evolution mechanism (AEM) and the lattice oxygen-mediated (LOM) can significantly improve performance of electrocatalytic oxygen reaction, but LOM is not easily triggered on AEM. Herein, a unique mixture transition Ti3C2Tx MXene cobalt hydroxide introduced. A sulfur-doped substrate with clear composition layered structure was formed thin-layer nanosheets by sulfur template method for study alkaline electrochemical evolution. Sufficient sites, robust structures, good kinetics, increased catalytic activity are provided resulting nanohybrids. Significantly, has more abundant vacancies better hydrophilicity; this interaction favorable improving reaction (OER). results tests support hypothesis that interfacial electron coupling two different components doping vacancy may optimize adsorption energy H2O *OH, leading to small overpotential 207 mV at 10 mA cm–2, material stable OER. This great potential development electrocatalysts where both AEM work together their applications in energy-related fields.

Language: Английский

Citations

1
Trimetallic Phosphide Nanostructures Derived from In Situ Coated ZIF-L Film for Overall Water Splitting DOI

Wan Cui,

Shuangxing Cui,

Yifan Tang

et al.

ACS Applied Nano Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 3, 2024

Language: Английский

Citations

1
Nitrogen atoms doping strengthens the interaction between Fe3C and carbon support for boosted hydrogen production performance in wide pH range DOI
Yiming Liu, Chaojie Lyu, Jiarun Cheng

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 93, P. 1363 - 1376

Published: Nov. 11, 2024

Language: Английский

Citations

1
Asymmetric Charge Distribution in Atomically Precise Metal Nanoclusters for Boosted CO2 Reduction Catalysis DOI Open Access
Yuanxin Du,

Pei Wang,

Yi Fang

et al.

ChemSusChem, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Recently, atomically precise metal nanoclusters (NCs) have been widely applied in CO

Language: Английский

Citations

0
Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review DOI Open Access
S. Mohammad Shojaei, Reihaneh Aghamolaei, Mohammad Reza Ghaani

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9555 - 9555

Published: Nov. 2, 2024

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched density and flexibility. However, as world shifts toward renewable energy, overcoming limitations intermittent power sources requires a bold reimagining storage integration. Power-to-X (PtX) technologies, which convert excess electricity into storable carriers, offer promising solution for long-term sector coupling. Recent advancements in machine learning (ML) revolutionized PtX systems by enhancing efficiency, scalability, sustainability. This review provides detailed analysis how ML techniques, such deep reinforcement learning, data-driven optimization, predictive diagnostics, are driving innovation Power-to-Gas (PtG), Power-to-Liquid (PtL), Power-to-Heat (PtH) systems. example, has improved real-time decision-making PtG reducing operational costs improving grid stability. Additionally, diagnostics powered increased system reliability identifying early failures critical components proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges data quality, processing, scalability remain, presenting future research opportunities. These to decarbonizing hard-to-electrify sectors, heavy industry, transportation, aviation, aligning with global sustainability goals.

Language: Английский

Citations

0