Development of photoelectrochemical water splitting photoanode: bibliometric analysis and artificial intelligence advancement

Hadiyawarman,; Wisely, Nick; Iqbal, Muhammad; Timuda, Gerald Ensang; Darsono, Nono; Yuliarto, Brian; Khaerudini, Deni Shidqi

doi:10.1007/s10098-023-02686-x

Clean Techn Environ Policy

2023

DOI: 10.1007/s10098-023-02686-x

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Development of photoelectrochemical water splitting photoanode: bibliometric analysis and artificial intelligence advancement

Hadiyawarman,

Nick Wisely,

Muhammad Iqbal

et al.

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2024

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Cited by 3 publications

References 98 publications

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Bibliometric review of carbonaceous-based sorbent for hydrogen storage

Hadiyawarman,

Aji,

Timuda

et al. 2024

Clean Techn Environ Policy

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Bibliometric review of carbonaceous-based sorbent for hydrogen storage

Hadiyawarman,

Aji,

Timuda

et al. 2024

Clean Techn Environ Policy

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Advances of nanostructured metal oxide as photoanode in photoelectrochemical (PEC) water splitting application

Mohd Raub,

Bahru,

Mohd Nashruddin

et al. 2024

Heliyon

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

Shojaei,

Aghamolaei,

Ghaani

2024

Sustainability

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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.

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Development of photoelectrochemical water splitting photoanode: bibliometric analysis and artificial intelligence advancement

Cited by 3 publications

References 98 publications

Bibliometric review of carbonaceous-based sorbent for hydrogen storage

Bibliometric review of carbonaceous-based sorbent for hydrogen storage

Advances of nanostructured metal oxide as photoanode in photoelectrochemical (PEC) water splitting application

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

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