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Review

Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

by
Seyed Mohammad Shojaei
1,
Reihaneh Aghamolaei
2,* and
Mohammad Reza Ghaani
1,*
1
School of Engineering, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland
2
Department of Mechanical Engineering, Dublin City University, D09 E432 Dublin, Ireland
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(21), 9555; https://doi.org/10.3390/su16219555 (registering DOI)
Submission received: 23 September 2024 / Revised: 18 October 2024 / Accepted: 28 October 2024 / Published: 2 November 2024
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)

Abstract

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

Share and Cite

MDPI and ACS Style

Shojaei, S.M.; Aghamolaei, R.; Ghaani, M.R. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review. Sustainability 2024, 16, 9555. https://doi.org/10.3390/su16219555

AMA Style

Shojaei SM, Aghamolaei R, Ghaani MR. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review. Sustainability. 2024; 16(21):9555. https://doi.org/10.3390/su16219555

Chicago/Turabian Style

Shojaei, Seyed Mohammad, Reihaneh Aghamolaei, and Mohammad Reza Ghaani. 2024. "Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review" Sustainability 16, no. 21: 9555. https://doi.org/10.3390/su16219555

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