Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 26 September 2024 (05:49:13 CEST)
How to cite:
Shojaei, S. M.; Aghamolaei, R.; Ghaani, M. R. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature review. Preprints2024, 2024092014. https://doi.org/10.20944/preprints202409.2014.v1
Shojaei, S. M.; Aghamolaei, R.; Ghaani, M. R. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature review. Preprints 2024, 2024092014. https://doi.org/10.20944/preprints202409.2014.v1
Shojaei, S. M.; Aghamolaei, R.; Ghaani, M. R. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature review. Preprints2024, 2024092014. https://doi.org/10.20944/preprints202409.2014.v1
APA Style
Shojaei, S. M., Aghamolaei, R., & Ghaani, M. R. (2024). Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature review. Preprints. https://doi.org/10.20944/preprints202409.2014.v1
Chicago/Turabian Style
Shojaei, S. M., Reihaneh Aghamolaei and Mohammad Reza Ghaani. 2024 "Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature review" Preprints. https://doi.org/10.20944/preprints202409.2014.v1
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, Power-to-Liquid, and Power-to-Heat systems. While ML applications have shown great potential in optimizing operational decisions and managing uncertainties in renewable energy integration, challenges such as data quality, real-time processing, and scalability remain. Addressing these gaps presents important 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
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
