# Hydrogen revolution: Artificial intelligence and machine learning driven policies, feasibility, challenges and opportunities: Insights from Asian countries

Sankalp Raghuvanshi, Kalpana Singh, Malvika Chaudhary, Parul Verma, Shiwani Singhal, Sajid Ali, Himanshu Agarwal, Ritu Chauhan, Rupesh Kumar Basniwal, Naveen Chandra Joshi, et al.

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Sankalp Raghuvanshi, Kalpana Singh, Malvika Chaudhary, Parul Verma, Shiwani Singhal, et al. Hydrogen revolution: Artificial intelligence and machine learning driven policies, feasibility, challenges and opportunities: Insights from Asian countries. Energy Strategy Reviews, 2025, 61, pp.101838. 10.1016/j.esr.2025.101838. hal-05491033

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# Hydrogen revolution: Artificial intelligence and machine learning driven policies, feasibility, challenges and opportunities: Insights from Asian countries

Sankalp Raghuvanshi<sup>a,b</sup>, Kalpana Singh<sup>c,*,1</sup>, Malvika Chaudhary<sup>d</sup>, Parul Verma<sup>e</sup>, Shiwani Singhal<sup>e</sup>, Sajid Ali<sup>f</sup>, Himanshu Agarwal<sup>g,1</sup>, Ritu Chauhan<sup>h</sup>, Rupesh Kumar Basniwal<sup>i</sup>, Naveen Chandra Joshi<sup>j</sup>, Laurent Dufossé<sup>k,**</sup>, Abhishek Chauhan<sup>l</sup>

## ARTICLE INFO

Handling editor: Mark Howells

### Keywords:

- Hydrogen safety systems

- Monitoring

- Artificial intelligence algorithms

- Machine learning models

- Predictive modeling

- Energy policies

- Renewable energy source

- Artificial neural networks

- Green hydrogen production

## ABSTRACT

Green hydrogen, a zero-carbon emission fuel, has become a real competitor to transform the energy market, thanks to improvements in the electrolysis process, decreased costs, and the presence of renewable energy resources. Energy industries have shown considerable progress in hydrogen production due to the incorporation of artificial intelligence (AI) knowledge through algorithms, AI-based models and data programs. These techniques can greatly enhance the production, storage, and transportation of hydrogen fuel. The main goal of this article is to demonstrate the recent technological advancements and the influence of various AI techniques, algorithms, and models on the hydrogen energy sector along with this further examination of the energy policies of countries like China, Japan, India and South Korea. The key challenges related to these energy policies are addressed through standardized datasets, AI models and optimized environmental conditions. This paper serves as a valuable resource for researchers, engineers, and practitioners interested in applying cutting-edge technologies to enhance hydrogen safety systems. AI-based models contribute to the overall shift towards a sustainable energy future by enhancing efficiency, reducing costs, and facilitating hydrogen energy commerce for Asian countries. This study accelerates the global investigation and tremendous applications of sophisticated machine-learning methodologies for producing renewable green hydrogen.

## 1. Introduction

Interest in hydrogen gained momentum in 2019 after more than five decades of efforts to establish it as the main energy carrier, despite

facing numerous challenges [1]. While 324 g of hydrogen contain an energy equivalent to 1 Kg of petroleum, it occupies almost 4 L of space while 1 Kg gasoline occupies only about 1.35 L. The global community is currently focused on the development of efficient energy management

<footnote><sup>a</sup> Indian Institute of Management, Sambalpur, 768025, India</footnote>

<footnote><sup>b</sup> Precisely Software and Data India Private Limited, Noida, 201304, India</footnote>

<footnote><sup>c</sup> Lloyd Institute of Management and Technology, Plot No. 11, Knowledge Park-II, Greater Noida, 201306, India</footnote>

<footnote><sup>d</sup> Department of Applied Science, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, 110058, India</footnote>

<footnote><sup>e</sup> Department of Applied Sciences and Humanities, Ajay Kumar Garg Engineering College, Ghaziabad, 201009, India</footnote>

<footnote><sup>f</sup> Department of Chemistry, Jamia Millia Islamia, New Delhi, 110025, India</footnote>

<footnote><sup>g</sup> Independent Researcher, Asian Publication Corporation, Sahibabad, 201005, India</footnote>

<footnote><sup>h</sup> Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun, 248002, India</footnote>

<footnote><sup>i</sup> Amity Institute of Advanced Research and Studies (M&D), Amity University, Noida, 201313, India</footnote>

<footnote><sup>j</sup> Amity Institute of Microbial Technology, Amity University, Noida, 201313, India</footnote>

<footnote><sup>k</sup> Chemistry and Biotechnology of Natural Products, CHEMBIOPRO, Université de La Réunion, ESIROI Agroalimentaire, 15 Avenue René Cassin, CS, 92003, CEDEX 9, Saint-Denis, F-97744, France</footnote>

<footnote><sup>l</sup> Amity Institute of Environmental Toxicology Safety and Management, Amity University, Noida, 201313, India</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: kalpanasankalp@gmail.com (K. Singh), laurent.dufosse@univ-reunion.fr (L. Dufossé).</footnote>

<footnote n="1">¹ These authors have equal contribution.</footnote>
systems and hydrogen technologies for sustainable and renewable energy fuel [2]. To ensure the effective implementation of hydrogen-based renewable energy systems, it is essential to thoroughly understand how hybrid renewable energy systems can be optimized while simultaneously meeting commercial and societal objectives [3]. The importance of energy in promoting socio-economic progress and high living standard is widely acknowledged. On the other hand, the environmental consequences of this development are significant. The contemporary global energy landscape is expected to reshape the network, high-lighting renewable energy (RE) generation as a sustainable feasible option. Consequently, extensive investigations are currently underway regarding the various methods proposed for its production and application [4].

Hydrogen purity is a vital characteristic of the generation process. According to the current hydrogen classification, blue is the most expensive, while gray is the least expensive. And green is the most sustainable or cleanest [5]. Industrial growth is seen as a pivotal factor according to global energy forecasts. Additionally, rural regions notably impact energy demand within the international energy framework. A considerable decline in global energy consumption is anticipated, although an increase is projected by 2050 [6]. The existing global energy system is on the brink of a major shift. The swift exploitation of fossil fuel resources has triggered a technological and economic transformation within a relatively short span.

Hydrogen production (HP) can be achieved through biological, electrical, photonic, or thermal processes. Electrical techniques often employ water electrolysis (WEL) to dissociate H₂O into hydrogen (H₂) and oxygen (O₂) using electric power. WEL is vital for the sustainable generation of hydrogen. Various technologies depend on types of membranes, electrolytes, and operational parameters. Proton exchange membrane (PEM) technology is widely used in hydrogen production for high purity and efficiency (99.99 %) [7]. Other WEL technologies include alkaline water electrolysis (AEL) [8], anion exchange membrane electrolysis (AEM) [9], solid oxide electrolysis (SOEL) [10] and microbial electrolysis cells (MECs) [11]. However, the high costs and inconsistent electricity supply present a challenge to the large-scale production of green hydrogen [12].

Machine learning has been applied to predict future organic electrocatalyst materials. Researchers utilized models such as RNN, LASSO, XGBoost, LR, and Light GBM to determine the optimal combinations among carbon, carbon-turmeric, turmeric-carbon, carbon-tofu, and carbon-garlic [13]. RNN is an AI-based neural network that outperforms other evaluated algorithms while being a straightforward machine learning model. The design of a PEM system can be refined through logistic and polynomial regression methods [14]. The machine learning model can successfully forecast the design of small and medium-scale PEM electrolysis with an accuracy of 83.6 %. The decision-making process has been facilitated by the deep learning model. The deep learning model has facilitated the decarbonization of nuclear renewable power plants [15].

AI and deep learning (DL) can substantially optimize hydrogen production by analyzing large data streams and predicting optimal conditions for PEM fuel cells (PEMFCs) [16]. The Optimized Hydrogen Generation Regression (OHGR) model is an example of an AI model that can forecast hydrogen generation under specific conditions, thereby improving economic efficiency and performance. SHAP explainability further enhances the interpretability of AI models by enabling more efficient optimization [17].

Various sectors have already implemented AI and data-driven technologies as a result of the future smart grid's escalating complexity and data generation capabilities. These technologies have the potential to generate substantial value within the system. Nevertheless, the energy sector is a prime example of where uncertainties and a lack of common understanding impede investments in AI [8,14]. In contemporary industries, such as hydrogen purification and filling, AI is frequently employed to enhance production efficiency and performance, which can

be substantial. The primary functionality of artificial intelligence technologies is to forecast variables that are produced at various phases of the supply chain, including renewable resources, system power output, user activity, and power prices. The real-time processing of data-driven asset management decisions and the optimization of renewable energy integration into the power grid are achieved by integrating AI software with hydrogen hardware [15].

There are an inordinate number of AI optimization algorithms that cannot be classified. Optimization strategies are classified as physical or chemical heuristics, non-swarm biological, and swarm biological in order to effectively resolve the issue. Over the past few years, artificial intelligence has been implemented to forecast and regulate renewable energy sources. The most frequently employed technique in machine learning algorithms is artificial neural networks, which are noted for their generalization ability [16]. The global energy markets that have been incorporated with AI are expected to reach $7.8 billion by 2025. The market is expected to expand at a compound annual growth rate (CAGR) of 23.50 % from 2018 to 2025. This concept is currently prevalent in the majority of energy sector-based companies, and investors anticipate receiving their rewards soon. Companies such as Honeywell, Google Deep Mind, Siemens, Microsoft, General Electric, Schlumberger, Rockwell Automation, Equinor, Shell, Enel X, ABB, Schneider Electric, C3, AI and others are currently investigating AI in the energy sector. This article focuses on the current status, challenges, and Asian countries policies for commercialization and the implementation of AI and machine learning (ML) tools for revolutionizing hydrogen production.

The article is organized into seven sections: Section 1 discusses systematic literature review methodology; Section 2 discusses the latest technological Advancements driving green Hydrogen innovation especially artificial Intelligence and Machine Learning Models. Section 3 explains the potential for new economies in hydrogen-based commerce; Section 4 provides an overview of government policies and strategic road maps for hydrogen development in China, India, Japan and South Korea; Section 5 provides research gaps and opportunities for hydrogen energy research, Section 6 explains current challenges in green hydrogen storage and transportation. The summary and conclusion are mentioned in Section 7.

## 2. Methodology

PRISMA guidelines describe how to make machine learning (ML) and AI models to improve the electrolysis process for green hydrogen production. A systematic search of databases such as IEEE Xplore, PubMed, Scopus, and Google Scholar is conducted, focusing on studies from 2010 and later that employ AI & ML techniques to optimize hydrogen production through solar and wind power (Fig. 1). The inclusion criteria encompass technical reports, conference papers, and peer-reviewed papers on renewable energy sources and machine learning applications. Incomplete and irrelevant data for machine learning were excluded in the article. Bassey & Ibegbulam [18] mentioned AI, ML based models, electrolyzer types, energy sources and data reproducibility and validation processes. The effectiveness of AI and ML models and renewable energy sources is compared in a meta-analysis, which is illustrated through flow diagrams and graphs demonstrating publication trends, particularly from 2022 to 2024 (Fig. 2). ML models' environmental impact, optimization strategies, and efficacy were discussed in this review article. Integrating advanced ML techniques for hydrogen production was suggested by hydrogen council for future research [19–21]. The Annual Research Trends in Hydrogen Energy Systems (2016–2025) highlights the multi-domain distribution of peer-reviewed publications, high-impact journal output, and thematic focus on AI/ML applications, policy, and fundamental hydrogen technologies [4,18, 22–26]. Policy-related literature also grew, especially after 2021, spurred by government initiatives like South Korea's Hydrogen Economy Roadmap [27], Japan's Basic Hydrogen Strategy [28,29], and India's National Hydrogen Mission [30]. These studies assess governance
<figure><img src="image_4.png" /><figcaption>Fig. 1. Flow chart for the selection of research articles covering the years 2022–2024.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. PRISMA flow diagram for the selection of research articles for green hydrogen production and machine learning.</figcaption></figure>

processes, economic feasibility, and market readiness [31,32]. Research has consistently aimed at improving hydrogen production techniques, including electrolysis, photoelectrochemical water splitting, and catalyst development.

A carefully selected collection of academic references, provides a thorough, multi-layered overview of research trends in hydrogen energy systems from 2017 to 2024. It shows how scientific attention has changed over the last 10 years in several ways, such as journal impact, topic focus, and overall research output (Fig. 3). This graph incorporates five essential layers to offer a sophisticated viewpoint: Total publications (yellow line): indicates the total amount of research that is published annually. The increasing trend highlights the growing interest in hydrogen technology around the world, particularly after 2020. High-impact journal publications (orange line): Emphasizes research that has been published in prestigious journals, such as Advanced Energy Materials and Energy & Environmental Science. A significant uptick in 2023–2024 indicates that hydrogen research is becoming more high-quality and well-known AI/ML-oriented research (red bar) demonstrates the growing integration of AI and machine learning in hydrogen systems, specifically in modelling, optimization and control techniques.

Beginning in approximately 2020, this area will experience rapid expansion in line with the larger AI revolution in scientific research. Publications addressing regulatory frameworks, market viability, policy frameworks, and techno-economic studies are included in the policy & economic studies (blue bar) category. The consistent rise is a reflection of the concurrent requirement to evaluate hydrogen's contribution to the global clean energy transition from a cost-effectiveness and governance perspective. Technology-focused research (pink line) includes technical developments in hydrogen generation (such as electrolysis and thermochemical cycles), storage options (such as solid-state and cryogenic), and safety evaluations. This category's steady and robust growth has demonstrated the field's fundamental research direction.

## 2.1. Latest technological advancements driving green hydrogen innovation

### 2.1.1. Advancement in green hydrogen technology: photocatalyst

The development of photocatalysts for hydrogen production has made significant progress since 2000s. The initial research primarily concentrated on materials such as metal oxides, including $TiO_2$ and traditional semiconductors. Despite demonstrating potential, these
<figure><img src="image_6.png" /><figcaption>Fig. 3. Progress of annual Research trends in hydrogen energy systems (2017–2024).</figcaption></figure>

materials were impeded by their inefficiencies and instability [31,35]. Over the years, innovations in novel materials and optimization of existing ones have led to significant progress. Putting perovskite materials, metal chalcogenides and composite structures together has improved charge separation, light absorption, and catalytic activity [36, 37]. The development of tandem photo-electrochemical cells was a significant advancement in this field. These cells employ multiple photo electrodes connected in series to capture a broader solar spectrum, thereby improving the overall efficiency of energy conversion.

Besides improving the structure, adding co-catalysts such as platinum nanoparticles and ruthenium, as well as materials that are simple to find on Earth, has improved the surface of photo electrodes. These co-catalysts have improved the hydrogen evolution reaction (HER), making photo electrocatalytic systems much more effective overall [38]. Researchers appreciate perovskites for their excellent light absorption properties and their adjustable bandgaps, which makes them intriguing candidates for making photo electrocatalyst more stable and effective.

Recent research has prioritized customizing photo electrode band structures to correspond with the water-splitting reaction's redox potential [39]. This precise alignment facilitates the more efficient separation and transfer of charges, further improving the system's performance [40,41]. Individual photo catalysts and their integration into holistic systems that mimic natural photosynthesis have emerged. These comprehensive systems aim to achieve sustainable and efficient hydrogen production by synergistically combining various technologies and components green hydrogen innovation is being driven by photo-catalytic process.

Researchers are still looking into new materials that can be used as photocatalysts. Some of the materials they are looking into are metal-organic frameworks (MOFs), metal chalcogenides (MoSe₂, WSe₂ and BiTe₂), carbon-based materials (graphene and carbon nanotubes) and metal oxides (ZnO, WO₂ and TiO₂). Recently, a groundbreaking advancement in brand-new photo-electrocatalyst prepared from tungsten diselenide (WSe₂) was found more effective and stable at splitting water [42]. This was accomplished by introducing a small quantity of sulfur into the WSe₂ structure, which resulted in defects that enhanced its reactivity to sunlight. Furthermore, a protective titanium dioxide (TiO₂) layer was implemented to avert degradation [43,44]. This novel photo-electrocatalyst attains an extraordinary water-splitting efficiency

of 18 %, considerably higher than that of conventional systems, which typically operate at approximately 5–10 % efficiencies. Moreover, it maintained its performance for more than 100 h of continuous operation, indicating a significant advancement in renewable hydrogen production.

The other innovative materials have also demonstrated potential. For example, Chan et al. [45] demonstrated the ability to generate hydrogen gas from water at room temperature using a developed gallium-aluminum composite. The composite did this because gallium has two functions: it dissolves the aluminum oxide coating and breaks up aluminum into reactive nanoparticles [42]. In a different study, Qiao et al. [46] used a hydrothermal method to develop a new heterojunction photocatalyst of WS₂ and WSe₂. This photocatalyst effectively broke down organic pollutants and released hydrogen when exposed to visible light. It created a Z-scheme heterostructure, which let charges move around and separate more easily. Foundational works by An et al. [33] and Saraswat et al. [34] are acknowledged for their significant contributions in this field.

HER (Hydrogen Evolution Reaction) can also be rapidly accelerated with hybrid materials such as MoSe and WSe that have been modified with pyridine rings and cobalt-porphyrin. This is true in both acidic and basic conditions. These materials offer lower energy barriers, quicker reaction rates, and enhanced durability [47]. These new developments show that new materials might be able to get around common problems in photo electrocatalytic hydrogen production. This may result in the development of more effective and affordable solutions (Fig. 4). Despite these substantial advancements, some challenges persist. For example, minimum absorption of sunlight and the recombination of electron-holes remain significant challenges to achieving high efficiency and electron-hole recombination continues to pose obstacles to achieving high efficiency. Secondly, the exorbitant prices of materials and fabrication processes also hinder scalability. Thirdly, the stability of photo-electrocatalysts under the difficult conditions of water splitting is a matter of concern, as they are susceptible to degradation by oxygen and water. However, ongoing research is dedicated to addressing these challenges, offering reassurance about the continuous progress in enhancing the effectiveness, cost-efficiency and scalability of photo-electric hydrogen production technologies [48]. Table-1 lists the details of some new materials for the photocatalytic hydrogen production using
<figure><img src="image_7.png" /><figcaption>Fig. 4. Flow diagram of advancements in green hydrogen technology using photo electrocatalyst.</figcaption></figure>

visible light sources.

### 2.1.2. Electrocatalytic driving green hydrogen innovation

One promising method for producing CO₂ or CO-free hydrogen and oxygen is electricity-driven water splitting. High costs and limited supply hamper the use of Pt-based and Pd-based materials as common electrocatalysts for hydrogen evolution reactions (HER) [67–69]. The domains of chemical engineering and renewable energy are progressively focusing on the exploration of highly effective catalysts utilizing non-precious metals. Transition metal chalcogenides (TMCs) are gaining popularity as electrocatalysts for hydrogen production because they have more unpaired d-orbital electrons, which help them connect with H₂ [70]. Nonetheless, single-transition metal sulfides such as MoS₂ and WS₂ present challenges for commercial applications due to their limited active sites, poor electrical conductivity and sub-optimal interaction with conductive surfaces. Joshi et al. [71] investigated the efficacy of electrodes composed of one, two, or three varieties of metal chalcogenides in the production of hydrogen without the use of solvents. Lots of advances are going on the development of superior and more effective electrocatalysts for generating green hydrogen through the enhancement of multi-metal-based electrocatalysts. When used with electrodes based on mono-, bi-, and tri-metallic chalcogenides, Cu₂CoSnS₄ has excellent HER activity, displaying greater stability, a larger current density, and a lower over potential [72]. Wei et al. [73] focused on the most recent developments of noble metal (Pt, Ru, and Ir)-based materials as promising catalysts toward HER. Pt with moderate price and relatively poor stability has been the most widely studied as HER electrocatalysts with superior activity in a wide pH range. Ru with the lowest price and high stability is explored as an excellent HER electrocatalyst to produce hydrogen in both acid and alkaline conditions, specifically when it is on the N-doped porous carbon matrix. However, Ir with the highest price and stability can be adopted as an ideal model to study the

chemisorption of hydrogen during the HER process. Several strategies have been exploited to address the high cost and instability issues of those materials, including downsizing the noble metals to single-atom catalysts, forming alloys or hybridizing with other inexpensive transition metals and interface engineering. By modifying the catalysts' surface hydrogen adsorption behaviour, those methods have greatly improved the performance of noble-metal-based catalysts. Still, challenges remain in the development of highly active noble-metal-based catalysts [69]. For single-atom catalysts (SACs), active sites remain elusive and may originate from the synergistic effect of complex structures on the carbon matrix and the electronic properties of the SACs. In addition, the relatively lower density, easily transferred nature, as well as the undesirable tendency to the aggregation, need to be further resolved in SACs. The preparation of noble-metal-based alloys is much more complex and challenging, and many of the proposed bimetallic structures tend to change during the reaction [74]. Thus, identifying the atomic arrangement of alloy catalysts is critical, using some powerful characterization methods, such as *in situ* spectroscopic or microscopic techniques and/or EXAFS. Interface engineering strategies can lead to metal segregation and diffusion under reaction conditions, especially during long-term stability tests [75]. Further experimental and theoretical efforts are necessary to minimize the gap between water splitting and hydrocarbon reforming, for highly efficient hydrogen production. Tables 2 and 3 summarized the comparative analysis and key limitations and commercialization barriers in electrocatalysis systems, respectively.

### 2.2. Advancement in green hydrogen technology using hydrogen fuel cells

The efficiency and environmental advantages of hydrogen fuel cells have made them a primary focus of energy research. These cell's anodes undergo electrochemical hydrogen oxidation, and result, the emission of protons and electrons. The protons migrate through a proton-exchange
<caption>**Table 1**</caption>

Photocatalytic hydrogen production from monomeric substrates using visible light sources.

<table><thead><tr><th>Photocatalyst</th><th>Substrate</th><th>Power intensity (mW cm<sup>-1</sup>)</th><th>Production rates (mmol h<sup>-1</sup> g<sub>cat</sub>)</th><th>Ref.</th></tr></thead><tbody><tr><td>Pt/TiO<sub>2</sub>/SiO<sub>2</sub></td><td>MeOH/H<sub>2</sub>O</td><td>100 (AM 1.5G)</td><td>497</td><td>[49]</td></tr><tr><td>Au/TiO<sub>2</sub></td><td>MeOH/H<sub>2</sub>O</td><td>(Solar simulator)</td><td>1.4–7.0</td><td>[50]</td></tr><tr><td>Au/TiO<sub>2</sub></td><td>EtOH</td><td>100 (Solar simulator)</td><td>5–6</td><td>[51]</td></tr><tr><td>CuO<sub>x</sub>/TiO<sub>2</sub></td><td>EtOH</td><td>100 (Solar simulator equipped with 150 W Xe lamp)</td><td>(4 mg h<sup>-1</sup> g<sub>cat</sub><sup>-1</sup>)</td><td>[52]</td></tr><tr><td>Fe<sub>2</sub>O<sub>3</sub></td><td>EtOH</td><td>(Solar simulator)</td><td>(20.0 mmol h<sup>-1</sup> m<sup>-2</sup>)</td><td>[53]</td></tr><tr><td>Ag/Fe<sub>2</sub>O<sub>3</sub></td><td>EtOH</td><td>(Solar simulator)</td><td>(24.0 mmol h<sup>-1</sup> m<sup>-2</sup>)</td><td>[53]</td></tr><tr><td>Au/Fe<sub>2</sub>O<sub>3</sub></td><td>EtOH</td><td>(Solar simulator)</td><td>(45.0 mmol h<sup>-1</sup> m<sup>-2</sup>)</td><td>[53]</td></tr><tr><td>Pt/TiO<sub>2</sub>-nanotubes</td><td>EtOH</td><td>(Low-power solar lamp, 60 W tungsten)</td><td>(37.1 µmol h<sup>-1</sup> m<sup>-2</sup>)</td><td>[54]</td></tr><tr><td>Cu/TiO<sub>2</sub>-nanorods</td><td>Glycerol</td><td>(Natural sunlight)</td><td>50.339</td><td>[55]</td></tr><tr><td>TiO<sub>2</sub>-nanorods</td><td>Glycerol</td><td>(Natural sunlight)</td><td>2.95</td><td>[55]</td></tr><tr><td>CuO/TiO<sub>2</sub>-nanotubes</td><td>Glycerol</td><td>(Natural sunlight)</td><td>99.823</td><td>[56]</td></tr><tr><td>ZnO/ZnS-nanorods</td><td>Glycerol</td><td>(500 W Xe)</td><td>0.3884</td><td>[57]</td></tr><tr><td>Cu<sub>2</sub>O-microcrystals</td><td>Formaldehyde</td><td>(50 (Xe > 420 nm))</td><td>(-82.2 µmol in 3 h)</td><td>[58]</td></tr><tr><td>Pt@ZnIn<sub>2</sub>S<sub>4</sub>/RGO/BiVO<sub>4</sub> (Z-scheme)</td><td>Formaldehyde</td><td>–</td><td>1.687</td><td>[59]</td></tr><tr><td>Ir-Bpy-ENT (iridium-based bipyridine- and ethenyl-incorporated bifunctional organosilica nanotubes)</td><td>Formaldehyde</td><td>–</td><td>(14.9 mL in 5 h)</td><td>[60]</td></tr><tr><td>Cu/TiO<sub>2</sub></td><td>Acetic acid</td><td>100 (AM 1.5 G)</td><td>0.036</td><td>[61]</td></tr><tr><td>NiS/CdS</td><td>Lactic acid (with lignin)</td><td>(300 W Xe ≥ 400 nm)</td><td>1.5124</td><td>[62]</td></tr><tr><td>Pt/Holey carbon nitride-N-acylethanolamine (HCN-NEA)</td><td>Triethanolamine</td><td>(300 W Xe)</td><td>22.043</td><td>[63]</td></tr><tr><td>Poly(3-hexylthiophene)/g-C<sub>3</sub>N<sub>4</sub></td><td>Ascorbic acid</td><td>334.8 (300 W Xe ≥ 500 nm)</td><td>(-3.045 µmol h<sup>-1</sup>)</td><td>[64]</td></tr><tr><td>Poly(3-hexylthiophene)/g-C<sub>3</sub>N<sub>4</sub></td><td>Triethanolamine</td><td>6.3 (300 W Xe ≥ 500 nm)</td><td>(-0.104 µmol h<sup>-1</sup>)</td><td>[64]</td></tr><tr><td>Pt/C<sub>3</sub>N<sub>4</sub>-TiO<sub>2</sub></td><td>Triethanolamine</td><td>(250 W visible light source)</td><td>1.042</td><td>[65]</td></tr><tr><td>Mn-MOF@Au</td><td>Triethylamine</td><td>(2.02 W white LED)</td><td>0.6</td><td>[66]</td></tr></tbody></table>

membrane (PEM) to the cathode, forming water and releasing substantial energy by combining with oxygen. This process is being extensively implemented in various industries, with hydrogen fuel cell electric vehicles (FCEVs) being the most notable application and offer a cleaner alternative to internal combustion engine vehicles. The growth in this sector is being driven by advancements in fuel cell technology. For instance, the platinum-cobalt alloy catalyst developed by Yang et al. [85] reduces the quantity of platinum required by 70 % without compromising performance, thereby reducing the overall cost. Equally significant is the new poly(styrenesulfonic acid)-based PEM created by

<caption>**Table 2**</caption>

Performance comparison of recent electrocatalyst systems for HER.

<table><thead><tr><th>Catalyst System</th><th>Over-potential (η<sub>10</sub>)</th><th>Tafel Slope (mV dec<sup>-1</sup>)</th><th>Operating environment</th><th>Reference</th></tr></thead><tbody><tr><td>NiMoP/CuF</td><td>159 mV</td><td>126.66</td><td>1.0 M KOH</td><td>[76]</td></tr><tr><td>NiCoP</td><td>130 mV</td><td>93</td><td>Alkaline</td><td>[77]</td></tr><tr><td>NiCoP@NC/NF</td><td>77 mV</td><td>62.1</td><td>1.0 M KOH as electrolyte</td><td>[78]</td></tr><tr><td>MoS<sub>2</sub>/MXene/CNTs</td><td>169 mV</td><td>51</td><td>DI water/DMF bisolvent</td><td>[79]</td></tr><tr><td>MoS<sub>2</sub>/NiO/RGO</td><td>–</td><td>55</td><td>0.5 M KOH</td><td>[80]</td></tr><tr><td>NiCoP Nanowires</td><td>55.1 mV</td><td>50.1</td><td>Alkaline</td><td>[81]</td></tr></tbody></table>

Sato et al. [86], which has an unmatched high proton conductivity—six times that of common materials like Nafion. These developments have the potential to significantly enhance the performance of high-performance fuel cells in challenging environments, such as low humidity and high temperatures.

Furthermore, stationary power systems incorporate hydrogen fuel cells to ensure sustainable and dependable energy, which also serves as reserve power for data centers and contributes to grid stability. Additionally, research is investigating the potential of hydrogen fuel cells in maritime and aerospace transportation, demonstrating their adaptability in addressing contemporary energy challenges [24,47]. Recent innovations have enhanced fuel cell durability by addressing issues such as corrosion during shutdowns in automotive systems. These advancements are not just scientific breakthroughs, but they are also expediting the commercialization of hydrogen fuel cells across multiple industries (Fig. 5). For instance, Jung et al. [87] developed a platinum and hydrogen tungsten bronze (Pt/H<sub>x</sub>WO<sub>3</sub>) catalyst that substantially extends the lifespan of fuel cells by mitigating oxygen reduction reactions. The development of novel catalysts such as sulfur-anchored platinum intermetallic nanoparticles (i-NPs) is enhancing the performance and durability of these fuel cells [34,88–94]. This technology has the potential to be instrumental in the realization of a carbon-neutral future by offering pure, efficient energy solutions for power generation, transportation and other applications.

## 2.3. Latest trends in hydrogen energy storage and transportation

Various methods are employed for storing and transporting hydrogen energy, each of which presents its own set of advantages and challenges. However, the most prevalent method of hydrogen storage is high-pressure gaseous hydrogen, which is subject to increasing costs due to safety concerns and pressure increases. The low boiling point of liquid hydrogen necessitates extensive insulation despite providing a higher energy density. Organic liquid hydrogen storage guarantees safer transportation; however, it encounters substantial energy consumption during the dehydrogenation process. While solid-state storage methods like metal hydrides offer high density and safety, there remains a need to enhance mass efficiency to broaden their applications [36,95–98]. For transportation purposes, liquid hydrogen is often moved in highly insulated tanker vehicles, but issues regarding energy usage and evaporation losses continue to be a concern (Fig. 6). To boost efficiency and reduce expenses, researchers are exploring novel liquefaction techniques, such as active magnetic regenerative liquefiers. Additionally, organic liquid hydrogen carriers (LOHCs) and solid substances including ammonia and methanol show promise as potential hydrogen carriers, offering safer and more efficient alternatives for hydrogen storage and transport [99–103].

## 2.4. Role of AI and machine learning models in hydrogen production

The significance of hydrogen fuel is growing continuously due to
<table><caption>**Table 3**
Summary of key limitations and commercialization barriers in HER/OER electrocatalysis.</caption><thead><tr><td>Challenge</td><td>Description</td><td>Impact on Commercialization</td><td>Mitigation Strategy</td><td>Ref.</td></tr></thead><tbody><tr><td>Corrosion in Harsh Electrolytes</td><td>Catalyst degradation in acidic/alkaline media</td><td>Reduced durability and performance</td><td>Protective coatings, robust support materials</td><td>[82]</td></tr><tr><td>High Material Cost & Scarcity</td><td>Dependence on PGMs like Pt, Ru, Ir</td><td>Unsustainable for large-scale deployment</td><td>Explore earth-abundant alternatives (e.g., Ni, Mo, Fe)</td><td>[17]</td></tr><tr><td>System Integration of HER/OER</td><td>Difficulties in kinetic matching and minimizing energy losses</td><td>Inefficient water splitting, higher power consumption</td><td>Co-engineered catalyst-electrolyzer systems</td><td>[83]</td></tr><tr><td>Electrode Substrate Limitations</td><td>Fragility, oxidation, or cost of traditional supports (e.g., Ti, Ni, GC)</td><td>Reduced electrode longevity or increased manufacturing cost</td><td>Use of more efficient and durable non-noble metal catalysts other stable, cost-effective substrates</td><td>[44]</td></tr><tr><td>Scalability and Fabrication Control</td><td>Reproducibility and large-scale deployment of multi-metallic inks</td><td>Hinders industrial adaptation</td><td>Scalable deposition and sintering techniques</td><td>[84]</td></tr></tbody></table>

tremendous applications in different sectors [104]. Recent studies indicate transition from fossil fuel to renewable energy sources (Table-4). This shift could mark the end of fossil fuels' dominance and usher in a new era in the production of this crucial element [120]. The emergence of data-driven models in fuel cell technology has attracted considerable attention from researchers exploring AI and machine learning applications. Several Researchers [121–124] discuss the most recent developments in AI and machine learning, including support vector machines (SVM), recurrent neural networks (RNN), artificial neural networks (ANN), and hybrid optimization methods such as the Group Method of Data Handling with Genetic Algorithm (GMDH-GA). Other important computer techniques contributing to the advancement of green hydrogen energy and fuel cells are LPV models, long short-term memory RNNs (LSTM RNNs) and gated LSTM RNNs (G-LSTM RNNs). As shown in Fig. 7, these techniques are essential for increasing efficiency, maximizing energy generation and lowering operating expenses. Based on these techniques, material selection processes and durability projections can be elaborated. In their various models, researchers use humidity, pressure, temperature, flow rate and current as input feature vectors to predict the fuel cell's voltage output.

### 2.4.1. AI-driven soft sensors for real-time monitoring and safety in hydrogen systems

To improve real-time monitoring and defect detection in hydrogen production, ML-based soft sensors have been created. Nkulikiyinka et al. [125] presented a soft sensor based on an artificial neural network (ANN) and random forest (RF) to monitor sorption-enhanced SMR. Both models demonstrated great accuracy ($R^2$ > 98 %); however, RF's predicted reliability was superior to ANN's. These soft sensors ensure continuous monitoring by acting as fallback options if hardware sensors malfunction or need maintenance. They also make real-time quality control easier, which lessens the need for sporadic laboratory analysis, which is frequently laborious and unresponsive in real-time. Predictive maintenance technologies utilize robotics and artificial intelligence (AI) to anticipate equipment issues and execute safe repairs. By enabling preventive maintenance rather than reactive repairs, this connection greatly improves operational safety and decreases downtime [126]. Integrating modern deep learning techniques for process control with AI-driven methods for materials discovery can significantly enhance the efficiency and advancement of hydrogen production systems Notwithstanding these developments, there are still significant obstacles that must be overcome for AI in hydrogen production to be widely used and commercially viable.

### 2.4.2. Hydrogen safety, leak detection, and the role of AI in predictive maintenance

In industrial settings, force maintenance can significantly drive up operational costs, comprising 15 %–60 % of total manufacturing expenses [68,127] underscore the benefits of predictive maintenance (PdM) in reducing costs, downtime and improvements in product quality. Franco & de Figueiredo [128] and Scaife et al. [129] focus on real-time equipment monitoring and historical data analysis to predict

potential failures before they occur. PdM algorithms can decrease costs and downtime, improve product quality and mitigate the risk of unscheduled production halts. By extending equipment lifespan, reducing maintenance costs, and enhancing overall manufacturing efficiency, PdM can also elevate customer satisfaction. Companies can reduce maintenance planning time by 20 %–50 %, increase equipment uptime by 10 %–20 %, and lower overall maintenance costs by 5 %–10 % [130, 131]. PdM algorithms are crucial across various sectors, including manufacturing, telecommunications, healthcare, aerospace, energy, and the automotive industry [132,133]. However, PdM algorithms, digital twin technologies, and the Internet of Robotic Things (IoRT) in big data-driven Industry 4.0 manufacturing systems can pose challenges. Interpreting large volumes of sensor-generated data is extremely complex, and the "catastrophic forgetting" phenomenon may affect the accuracy of equipment status predictions. Economic issues, such as high costs and organizational hurdles, are also associated with PdM [134]. Many companies may lack the technological infrastructure, including cloud systems, IoRT sensors and digital twin technologies for artificial intelligence. Although estimating all costs and benefits is challenging, researchers advise that organizations create a model to evaluate economic returns before to implementation to mitigate these issues [135, 136]. However, a recent case study shows that the models studied are powerful in predicting failures based on data from five different sensors. However, the model based on RF appears to be more effective both in predicting failure or no failure and the type of failure. Table-5 shows the accuracy rate for the two studied models [137].

### 2.4.3. Role of predictive maintenance in manufacturing excellence: a case study on developing reliable failure prediction methods

The industrial equipment studied here consists of several sensors, including air temperature, process temperature, and rotational speed, torque and tool wear duration. Such sensors offer six parameters useful for predicting failures, and it is important to highlight that the prediction model was created using a dataset composed entirely of synthetic data with no missing values (Table-6). The number of data used in this case study of maintenance prediction consists of 10,000 data, divided between equipment failure data and no-failure state data [137].

### 2.4.4. Machine learning methods and their roles in electrolyzer and fuel cell modeling

Table 7 summarized various machine learning methods, applied in the modeling of electrolyzers and fuel cells, highlighting specific techniques used for tasks such as optimization, prediction, and maintenance. In response to reviewer feedback, a comprehensive table is presented below to encapsulate the use of machine learning in this field, focusing on clarity and conciseness due to the paper's length.

## 3. Potential for new economies in hydrogen-based commerce

To reduce costs associated with transitioning to a net zero economy and accelerating the implementation of innovative technologies, cooperation among governments, businesses, and scholars is essential for
<figure><img src="image_8.png" /><figcaption>Fig. 5. Flow chart depicts latest advancements in green hydrogen technology using hydrogen fuel cells.</figcaption></figure>
Supporting Processes

<figure><img src="image_9.png" /><figcaption>Fig. 5a</figcaption></figure>

Fig. 5. (continued).
<figure><img src="image_10.png" /><figcaption>Fig. 6. Flow chart illustrates the latest trends in hydrogen energy storage and transportation.</figcaption></figure>
Liquid Hydrogen Transportation

<figure><img src="image_11.png" /><figcaption>Fig. 6a</figcaption></figure>

Innovative Liquefaction Methods

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 6b</figcaption></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 6c</figcaption></figure>

Fig. 6. (continued).

technology research, development, demonstration, and deployment (RDD&D). Nations across the globe are seeking partnerships to establish international hydrogen trade networks. For instance, Germany, which projects a hydrogen demand of 90–110 TWh/year by 2030, is investigating import opportunities from countries such as Morocco, Namibia, and Turkey due to their constrained renewable energy capacity [27], as shown in Fig. 8. Additionally, the European Union's REPower EU strategy emphasizes the importance of diversifying clean energy sources by importing and producing 10 Mt of green hydrogen domestically by 2030, in response to the energy crisis triggered by Russia's invasion of Ukraine. Emerging economies have a significant opportunity to become

hydrogen exporters, especially as developed countries seek alternative sources to meet their energy transition goals. This trade could establish local value chains, improve trade balances, and boost sustainable development in exporting countries. Scaling up hydrogen production for export may stimulate local demand, but countries must balance domestic needs with export opportunities. These partnerships present opportunities for technology transfer, particularly for countries with limited resources to develop their hydrogen value chains [145]. Table-8 illustrates the partnerships between various countries and emerging economies in hydrogen-based commerce, highlighting collaborative efforts to foster innovation and build sustainable hydrogen
<caption>**Table-4**</caption>

AI and Machine learning Models for Enhancing PEMFC Efficiency in Green Hydrogen energy.

<table><caption>**Table-4 (continued)**</caption><thead><tr><th>Method</th><th>Dataset</th><th>Accuracy</th><th>Limitation</th><th>Reference</th></tr></thead><tbody><tr><td>ANN and other ML models for predicting performance</td><td>Proton exchange membrane fuel cells (PEMFCs) with various conditions</td><td>Not explicitly mentioned</td><td>None</td><td>[105]</td></tr><tr><td>Support Vector Regression (SVR) for modeling PEMFCs</td><td>PEMFCs</td><td>Predicted voltage, power, and efficiency</td><td>Demonstrated correlation between ANN and SVR</td><td>[106]</td></tr><tr><td>Hybrid neural network (GMDH with genetic algorithm)</td><td>Micro PEMFCs</td><td>Not explicitly stated</td><td>None</td><td>[107]</td></tr><tr><td>Comparison between SVR and ANN</td><td>PEMFCs</td><td>Not explicitly stated</td><td>None</td><td>[108]</td></tr><tr><td>State-space framework with Recurrent Neural Network (RNN) for control</td><td>PEMFCs</td><td>Predicted stack temperature, voltage, power</td><td>None</td><td>[109]</td></tr><tr><td>Hybrid model combining catalyst layer with neural networks</td><td>PEMFCs</td><td>Predicted activation over potential</td><td>None</td><td>[110]</td></tr><tr><td>Long short-term memory (LSTM) RNN for continuous fuel cell operation prediction</td><td>PEMFCs</td><td>High accuracy</td><td>None</td><td>[111]</td></tr><tr><td>G-LSTM (Gated Long Short Term Memory) RNN for online prediction</td><td>PEMFCs</td><td>Not explicitly mentioned</td><td>Focused on degradation data</td><td>[112]</td></tr><tr><td>250 W PEMFC system</td><td>250 W PEMFC system</td><td>97 % for MLP, 99 % for SVM</td><td>Requires verification through alternative methods</td><td>[113]</td></tr><tr><td>Single-cell micro PEMFCs</td><td>Single-cell micro PEMFCs</td><td>98 %</td><td>Lacks interpretability</td><td>[114]</td></tr><tr><td>PEMFC (99 % for ANN, 97 % for SVM)</td><td>PEMFC</td><td>99 % for ANN, 97 % for SVM</td><td>May not apply to mixed oxygen-air situations</td><td>[115]</td></tr><tr><td>PEMFCs 50 kW</td><td>PEMFCs 50 kW</td><td>95 %</td><td>Complex and complicated</td><td>[116]</td></tr><tr><td>PEMFCs (R2 = 0.8 %)</td><td>PEMFCs</td><td>R2 = 0.8 %</td><td>Statistical methods rely on assumptions</td><td>[117]</td></tr><tr><td>PEMFCs (99 %)</td><td>PEMFCs</td><td>99 %</td><td>Didn't address over- or under-fitting issues</td><td>[118]</td></tr></tbody></table>

<table><thead><tr><th>Method</th><th>Dataset</th><th>Accuracy</th><th>Limitation</th><th>Reference</th></tr></thead><tbody><tr><td>PEMFCs 25 kW</td><td>PEMFCs 25 kW</td><td>RMSE 0.0040</td><td>Focused primarily on internal degradation aspects</td><td>[119]</td></tr></tbody></table>

infrastructures. To support effective hydrogen trade, developing international collaboration and infrastructure include transportation and storage technologies, is crucial to ensure hydrogen quality and delivery safety standardization [146]. Achieving this will require clean, affordable, secure, and reliable hydrogen supply chains, essential for transitioning to a global, hydrogen-based energy system.

## 4. Government policies and strategic roadmaps for hydrogen development for Asian countries

Government policies that provide the necessary financial incentives, subsidies, and grants can accelerate the deployment of renewable energy sources, particularly wind and solar, which are crucial components of green hydrogen production [148]. Furthermore, regulatory frameworks can establish a stable and predictable business climate, encouraging private sector investment in green hydrogen projects [149]. Government policies play a crucial role in stimulating market demand for renewable hydrogen [150]. By setting ambitious carbon reduction targets and incorporating green hydrogen into national energy and climate strategies, governments can cultivate a strong market for clean hydrogen applications, including fuel cell vehicles and industrial processes, using AI and ML-driven modern techniques [150]. These policies can promote innovation and economies of scale, potentially reducing the cost of green hydrogen production and making it a viable alternative to fossil fuels [32]. Moreover, government-backed international cooperation and standardization efforts can lead to the harmonization of green hydrogen technologies as illustrated in Fig. 9. This analysis offers a state-of-the-art exploration of the complex landscape of C-Suite issues pertinent to green hydrogen, encompassing both technological progress and policy considerations. The development of green hydrogen energy depends on a holistic approach supported by the integration of technological advancements and effective policy implementation [151]. The potential of green hydrogen has been extensively studied in various in Asian regions like China, Japan, South Korea and India.

### 4.1. Cost comparison of hydrogen types (2023 vs. 2050)

By 2050, it is anticipated that the cost of green hydrogen will decrease significantly as production increases and technology advances, thereby rendering it more competitive with traditional gray and blue hydrogen. The environmental impact, projected production costs, and maturation levels of various hydrogen types are compared in Table-9. In addition, Fig. 10 highlights the production cost comparison of different kinds of hydrogen for the years 2023 and 2050, offering a clear visual representation of the expected trends and economic viability across hydrogen types over time.

### 4.2. Regional investments in green hydrogen on a global scale

This section examines the global investment in green hydrogen, demonstrating the distribution of investments across different regions according to 2024 projections. The anticipated uses of green hydrogen by 2050 demonstrate its increasing significance across industries. Table-10 lists the various applications of green hydrogen in transportation, power generation, and industrial operations. Simultaneously, Fig. 11 presents a pie chart that illustrates the allocation of investments, emphasizing the increasing focus on industrial applications, expected to
<figure><img src="image_16.png" /><figcaption>Fig. 7. Flow chart illustrates the optimization techniques for AI and ML models in green hydrogen energy generation.</figcaption></figure>

<table><caption>**Table 5**
Accuracy ratio data of the two studied models.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Failure/no failure</th><th colspan="2">Type of failure</th></tr><tr><th>RF</th><th>ANN</th><th>RF</th><th>ANN</th></tr></thead><tbody><tr><th>Correct data predicted</th><td>70</td><td>67</td><td>67</td><td>66</td></tr><tr><th>Accuracy ratio</th><td>88 %</td><td>84 %</td><td>84 %</td><td>83 %</td></tr></tbody></table>

<table><caption>**Table 6**
The five sensor parameters of the studied industrial equipment.</caption><thead><tr><th></th><th>Air temperature (K)</th><th>Process temperature (K)</th><th>Rotational speed (RPM)</th><th>Torque (Nm)</th><th>Tool wear (min)</th></tr></thead><tbody><tr><th>Min</th><td>296</td><td>306</td><td>1168</td><td>8</td><td>0</td></tr><tr><th>Max</th><td>304</td><td>313</td><td>2886</td><td>72</td><td>253</td></tr></tbody></table>

lead the way, followed by transportation and power generation. When taken as a whole, these images offer a thorough grasp of the anticipated goals and trends in the use of green hydrogen by 2050.

### 4.3. Business model for green hydrogen production

This section examines the cost reductions in green hydrogen production across a variety of business models in European countries with a particular emphasis on the cost advantages of onsite renewable energy models, particularly in countries such as Norway and Spain. Table-11 displays the projected green hydrogen production based on the business model for the year 2030. The potential for cost reductions can be illustrated by a stacked bar graph that compares the costs of green hydrogen production across various business models in European countries, depending on the approach. This chart underscores the substantial cost savings that on-site renewable energy models provide, particularly in

countries such as Norway and Spain, where solar and wind resources are abundant.

Fig. 12 demonstrates that the European Union is the leader in green hydrogen investments, a trend that is fueled by ambitious policies such as the European Green Deal and the RePower EU strategy. The future success of green hydrogen initiatives is contingent upon global collaboration, with North America and the Asia-Pacific region following suit. The versatile function of hydrogen in the energy transition can be emphasized through the use of a pie chart to illustrate the expanding applications of hydrogen in a variety of sectors.

### 4.4. Prospects of green hydrogen in space exploration

The potential of green hydrogen for space exploration is consistent with the ongoing sustainability objectives, as it serves as a pure energy carrier on earth and beyond (Table-12).

Fig. 13 illustrates the potential of green hydrogen for space exploration in conjunction with the ongoing sustainability objectives, demonstrating its function as an energy carrier on Earth and the road to a sustainable hydrogen future. Green hydrogen has the potential to decarbonize challenging sectors, support sustainability in industries such as space exploration, and provide a zero-emission energy source. It anticipates that green hydrogen will become a significant participant in the energy sector by 2050, despite its initial high costs, as a result of technological advancements, policy support and global investments.

### 4.5. Strategies for cost reduction in hydrogen development in Asia: driven by artificial intelligence

As part of their endeavours to attain net zero emission targets by 2050, Asian nations, including Japan, South Korea, China, and India, are placing an increasing emphasis on the production of renewable hydrogen. These nations are prioritizing green hydrogen development, which is bolstered by aggressive ecological policies in order to achieve
<table><caption>**Table 7**

Overview of machine learning methods and their roles in electrolyzer and fuel cell modeling.</caption><thead><tr><td>Machine Learning Technique</td><td>Application Area</td><td>Description</td><td>References</td></tr></thead><tbody><tr><td>Support Vector Machines (SVM)</td><td>Prognostics &amp; Health Management</td><td>Used to predict the load voltage and assess the electrolyzer's state of health, enhancing techno-economic analysis.</td><td>[138]</td></tr><tr><td>Gaussian Process Regression (GPR)</td><td>Prognostics &amp; Health Management</td><td>Used alongside SVM for predictive modeling to evaluate electrolyzer performance under varying conditions.</td><td>[139]</td></tr><tr><td>Neural Networks</td><td>PEM Fuel Cell Design &amp; Optimization</td><td>Optimizes the gas diffusion layer (GDL) for improved current density and oxygen distribution in PEM fuel cells.</td><td>[140]</td></tr><tr><td>Random Forests</td><td>Production Forecasting &amp; Load Prediction</td><td>Applied for predicting future energy production, estimating system load variations under dynamic conditions.</td><td>[141]</td></tr><tr><td>Reinforcement Learning</td><td>Adaptive Control Systems</td><td>Applied to optimize operational parameters of electrolyzers and fuel cells for efficiency under variable renewable energy inputs.</td><td>[142]</td></tr><tr><td>XGBoost</td><td>Wear &amp; Tear Prediction in Compressors</td><td>Used to predict component wear and tear in compressors based on operational data, optimizing maintenance schedules and extending system lifespan.</td><td>[143]</td></tr><tr><td>Convolutional Neural Networks (CNN)</td><td>Image-based Detection</td><td>Used for analyzing components and detecting anomalies (e.g., wear or damage) based on visual data for fuel cell systems.</td><td>[144]</td></tr><tr><td>Polynomial Fitting</td><td>Electrolyzer Model Optimization</td><td>Used for optimizing parameters based on system data, improving the dynamic response and performance under different operational conditions.</td><td>[142]</td></tr><tr><td>Partial Reinforcement Optimizer</td><td>Electrolyzer Performance Optimization</td><td>Used for dynamic optimization of electrolyzer systems, enhancing efficiency under fluctuating renewable inputs.</td><td>[144]</td></tr></tbody></table>

ambitious hydrogen production targets for 2030. The Asia-Pacific Hydrogen Association held at Sydney, Australia un year 2023 anticipates that hydrogen could account for 10%–12% of the region's total energy requirement by 2050 (Table-13). The International Energy Agency (2023) [21] also highlights the rapid advancement of AI and digital technologies for improving hydrogen production efficiency throughout Asia. Artificial intelligence research has made significant strides in countries such as China, with more than 20,000 research papers published in 2020, surpassing European countries.

AI technologies are being incorporated in the hydrogen value chain at various stages, including electrolysis, storage, and distribution, to optimize processes, detect anomalies, and facilitate predictive maintenance. For example, Hyundai and SK, two South Korean companies, are employing big data analytics to enhance the performance and longevity of electrolyzers. The Asia Pacific Hydrogen Association (2023) also

<figure><img src="image_17.png" /><figcaption>**Fig. 8.** Circular flowchart of hydrogen exporter (blue) and importer (red) countries.</figcaption></figure>

observes that the Levelized Cost of Energy (LCoE) for renewable hydrogen could be reduced by 15–20% in the next decade by AI-based solutions. In addition, the advancement of AI tools that are designed to increase the production of renewable hydrogen is being facilitated by strategic partnerships among research institutions, industries and universities in Asia. The objective of these endeavours is to establish significant hydrogen capacity by 2030, with the projected cost of hydrogen derived from fossil fuels decreasing to approximately $2 per Kilogram. This capacity will range from 2 million metric tonnes in Japan to over 50 million metric tonnes in China.

The timeline and interconnections of major policies, strategic initiatives, and collaborative efforts among Asian countries like India, China, South Korea, and Japan to advance hydrogen technology, infrastructure, and commercialization are illustrated in the flow chart [Fig. 14]. The radar chart [Fig. 15] highlights the trends, balance and overlaps among nations while visualizing comparative performance across all factors showing circular axes with polygons and offering a clear, multifaceted picture of their development strategies. The heat map in Fig. 16 provides comprehensive factor-wise insights by highlighting and focusing on particular green hydrogen factors for each nation using a colour-coded matrix. It draws attention to advantages (like Japan's roadmap) and disadvantages (like India's infrastructure). The objective is to achieve significant milestones in hydrogen production and integration in various industrial sectors by 2050.

Asian countries like China, Japan, India, and South Korea are implementing hydrogen initiatives to reduce carbon emissions and contribute to the global shift towards sustainable energy. China uses AI in forecasting and logistics, Japan uses AI for fuel cell innovation, India focuses on green hydrogen, and South Korea uses AI-driven fleet management and infrastructure development. These policies highlight the importance of digital innovation in achieving sustainability goals and a regional trend towards AI integration in hydrogen transitions. The national hydrogen policies of China, Japan, India, and South Korea are compiled in Table-14 along with their hydrogen goals, investments, and AI integration. Every nation takes different paths depending on its technological and economic interests, and AI infrastructure is becoming more and more important in maximizing the production, distribution, and safety systems of hydrogen.
<caption>**Table 8**</caption>

Details of partnership between various countries and emerging economies in hydrogen-based commerce.

<table><thead><tr><th>Country</th><th>Hydrogen Role</th><th>Key Opportunities</th><th>Relevant Initiative</th><th>Reference</th></tr></thead><tbody><tr><td>Germany</td><td>Prospective importer</td><td>Hydrogen imports to meet demand of 90–110 TWh/year by 2030</td><td>Bilateral talks with countries like Angola, Morocco, Namibia and Turkey</td><td>[145]</td></tr><tr><td>European Union</td><td>Prospective importer</td><td>Diversification of clean gas supply; goal of 10 Mt domestic production and 10 Mt imports by 2030</td><td>REPower EU plan to accelerate hydrogen transition</td><td>[146]</td></tr><tr><td>Angola, Morocco, Namibia, Turkey</td><td>Prospective exporters</td><td>Opportunity to develop green hydrogen value chains and trade; improve trade balance</td><td>Bilateral discussions with Germany to export hydrogen</td><td>[147]</td></tr><tr><td>India</td><td>Emerging exporter</td><td>Potential for green hydrogen production with strong solar and wind resources, aiming to reduce dependence on fossil fuels</td><td>India's National Hydrogen Mission targeting 5 Mt of green hydrogen production by 2030</td><td>[30]</td></tr><tr><td>South Korea</td><td>Prospective importer</td><td>Diversifying energy sources; aiming to become a hydrogen economy by reducing carbon emissions</td><td>Hydrogen Economy Roadmap with a target of 6.2 Mt of hydrogen demand by 2040</td><td>[27]</td></tr><tr><td>China</td><td>Emerging self-sufficient producer</td><td>Rapidly scaling domestic hydrogen production with vast renewable resources; balancing imports and local demand</td><td>China's Hydrogen Strategy, targeting 100,000 hydrogen-powered vehicles by 2025</td><td>[148]</td></tr><tr><td>Japan</td><td>Prospective importer</td><td>Aiming to establish a hydrogen supply chain to meet its ambitious energy transition goals</td><td>Basic Hydrogen Strategy focusing on hydrogen imports from Australia and other nations</td><td>[28]</td></tr></tbody></table>

**5. Challenges, research gaps, and opportunities**

The primary challenges hindering renewable hydrogen energy development are the substantial expenses associated with hydrogen production and storage, along with the difficulties in integrating it into current energy systems [33,163]. For instance, Japan faces hurdles in modifying its fossil-fuel-based infrastructure to accommodate hydrogen [28], while China, being the world's largest energy consumer, needs to establish an extensive hydrogen infrastructure to support its vast economy [160]. Despite these obstacles, hydrogen energy development is advancing, with strong governmental backing from the United States, Europe, Japan, and China [164]. To fully harness green hydrogen's potential, improvements in electrolysis, photocatalysts and photo-electrocatalytic panel technologies are necessary. Industrial growth and early adoption require subsidies, carbon pricing, and policies that encourage investment in research and innovation [145]. Clear regulatory frameworks and significant investment in hydrogen

<figure><img src="image_18.png" /><figcaption>**Fig. 9.** Flow chart illustrates about government policies to green hydrogen development path.</figcaption></figure>

infrastructure, such as pipelines and refueling stations, are crucial. Japan's energy policy, for example, emphasizes integrating hydrogen with existing systems and sets ambitious carbon reduction targets [28]. The development of a global hydrogen market can be facilitated through trade agreements, technology collaboration, and harmonized standards [160]. Governments must set ambitious carbon reduction goals, incorporate them into climate plans, and increase public awareness to promote green hydrogen adoption [165]. India's National Hydrogen Mission aims to boost renewable energy utilization and foster a regulatory environment conducive to hydrogen development [30]. Simultaneously, South Korea's hydrogen economy roadmap focuses on reducing production costs and overcoming technological barriers for transition to a hydrogen-based economy [27]. To maximize green hydrogen's potential as a sustainable and clean energy solution, interested nations must work together to address economic, technological, and policy challenges. Green hydrogen, produced through water electrolysis using renewable energy, offers a promising solution for large-scale renewable energy storage and distribution [166,167]. However, it remains more expensive than traditional energy sources like natural gas due to various obstacles [23]. Table-15 presents a comprehensive review of several aspects of hydrogen study concerning multiple concerns like research emphasis, methodologies, challenges, deficiencies, and prospects for subsequent investigation and opportunities in each domain.

To make a link between the problems, research gaps, and chances of green hydrogen and the different types of visualizations like radar charts, flow charts, and research gap matrix, each one focuses on a different part of the problem and a possible solution. Fig. 17 illustrates the relative significance of a variety of factors, including safety, scalability, and data quality, in relation to distinct methods, thereby identifying opportunities and constraints. Fig. 18 sequentially delineates the process by which challenges (e.g. production costs) result in specific voids (e.g., lack of data) and proposes solutions, such as AI-driven enhancements. The heat map (research gap matrix) [Fig. 19] visually connects related aspects, demonstrating how overcoming research gaps can uncover opportunities for the advancement of green hydrogen technologies. Collectively, they provide a thorough understanding of the
<table><caption>**Table 9**

Environmental impact, cost, and maturity of various hydrogen production (2023 vs 2050).</caption><thead><tr><td>Type of Hydrogen</td><td>Environmental Impact</td><td>Current</td><td>Projected Production Cost (per kg)(2050)</td><td>Maturity Level</td><td>References</td></tr></thead><tbody><tr><td>Gray Hydrogen</td><td>High carbon emissions</td><td>$1.5 - $2.0</td><td>$3.64 (estimated increase)</td><td>Commercially available</td><td>[152]</td></tr><tr><td>Blue Hydrogen</td><td>Low carbon (with CCS)</td><td>$1.69 - $2.55</td><td>$4.6 (due to natural gas cost)</td><td>Commercially available</td><td>[153]</td></tr><tr><td>Purple Hydrogen</td><td>Carbon-free (nuclear energy)</td><td>$4.77 - $1.36</td><td>$3-$4 (depending on region)</td><td>Under development</td><td>[26]</td></tr><tr><td>Turquoise Hydrogen</td><td>Carbon-free (methane pyrolysis)</td><td>$2.0 - $2.5</td><td>Not projected</td><td>Under development</td><td>[154]</td></tr><tr><td>Green Hydrogen</td><td>Zero emissions (renewable energy)</td><td>$4.5 - $12</td><td>$1.50</td><td>Under development</td><td>[155]</td></tr></tbody></table>

<figure><img src="image_19.png" /><figcaption>**Fig. 10.** Production cost comparison of different types of hydrogen for year 2023 and 2050.</figcaption></figure>

<table><caption>**Table 10**

Green hydrogen applications in different sectors (Projection for the year 2050) (2050 Projections).</caption><thead><tr><td>Sector</td><td>Percentage (%)</td><td>References</td></tr></thead><tbody><tr><td>Power Generation</td><td>25</td><td>[156]</td></tr><tr><td>Space Exploration & Other</td><td>5</td><td>[157]</td></tr><tr><td>Industrial Applications</td><td>40</td><td>[158]</td></tr><tr><td>Transportation</td><td>20</td><td>[159]</td></tr><tr><td>Chemical Production</td><td>10</td><td>[155]</td></tr></tbody></table>

<table><caption>**Table 11**

The projected green hydrogen production based on the business model for year 2030.</caption><thead><tr><td>Country</td><td>Grid-Connected (€/kg)</td><td>On-Site Solar/Wind (€/kg)</td><td>References</td></tr></thead><tbody><tr><td>Germany</td><td>3.5</td><td>2.8</td><td>[159]</td></tr><tr><td>Norway</td><td>3.2</td><td>2.0</td><td>[155]</td></tr><tr><td>Spain</td><td>3.0</td><td>2.2</td><td>[157]</td></tr><tr><td>Great Britain</td><td>3.1</td><td>2.4</td><td>[157]</td></tr></tbody></table>

<figure><img src="image_20.png" /><figcaption>**Fig. 12.** Projected costs of green hydrogen production in Europe by business model.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Fig. 11.** Green hydrogen applications in various sectors for the year 2050.</figcaption></figure>
<caption>**Table 12**</caption>

Significant developments in the utilization of green hydrogen for space exploration.

<table><thead><tr><th>Year</th><th>Milestone</th></tr></thead><tbody><tr><td>1981</td><td>NASA uses hydrogen fuel cells in Space Shuttle missions for power and water generation.</td></tr><tr><td>2023</td><td>SpaceX explores methane-based propulsion but considers hydrogen for future rockets.</td></tr><tr><td>2030</td><td>Green hydrogen emerges as a potential sustainable fuel source for international space missions</td></tr></tbody></table>

<figure><img src="image_22.png" /><figcaption>Fig. 13. Prediction of the green hydrogen for space exploration.</figcaption></figure>

solutions.

## 6. Current challenges in green hydrogen storage and transportation

The process of evaluating the financial feasibility of large-scale green hydrogen storage ensures the sustainability of all supply chain stages; including production, storage, and transportation, while also providing environmental advantages. Fig. 20 shows the anticipated cost savings in green hydrogen and renewable energy production, suggesting significant economic efficiency in the upcoming years. Meanwhile, Table 15 also underscores the essential components for achieving these cost savings and ensuring widespread acceptance. It highlights the main obstacles and influencing factors in the adoption of green hydrogen. As a whole, it provides a comprehensive view of the logistical and economic factors influencing the development of green hydrogen. Numerous governments, organizations, and research institutions have offered financial support and research to evaluate the financial effects of these storage systems [168].

## 7. Conclusion

This review article examines a wide range of recent developments in green hydrogen energy and the trade of hydrogen among different nations, and their practical applications. Research landscape is vast and constantly changing, encompassing various techniques from traditional experimental methods to state-of-the-art AI and machine learning. Each approach provides distinct solutions and insights to address the intricate challenges of hydrogen safety. This article emphasizes the role of AI and machine learning models in reducing the cost of hydrogen production. China, Japan, India, and South Korea have implemented green hydrogen energy policies and strategies, established safety protocols, and assessed

<caption>**Table 13**</caption>

Comparative analysis of Green Hydrogen Development (2010–2050).

<table><thead><tr><th>Countries</th><th>Government Policies</th><th>Key Industry Establishments</th><th>Important Events</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="3">Japan</td><td>Hydrogen Fuel Cell Vehicle Promotion Program (2014)</td><td>Toyota Mirai (2014)</td><td>Launch of the Hydrogen Highway Initiative (2002)</td><td>[28]</td></tr><tr><td>Basic Hydrogen Strategy (2018)</td><td>ENE-FARM (2015)</td><td>Hydrogen Energy Ministerial Meeting (2017)</td><td>[160]</td></tr><tr><td>C12Green Growth Strategy (2020)</td><td>Suiso Frontier (2021)</td><td>Hydrogen production target of 20 million tons (2050)</td><td>[160]</td></tr><tr><td rowspan="2">China</td><td>Hydrogen Energy and Fuel Cell Vehicle Industry Development Plan (2016)</td><td>Sinopec Green Hydrogen Project</td><td>Launch of Xinjiang Kuqa Green Hydrogen Demonstration Project (2021)</td><td>[161]</td></tr><tr><td>National Hydrogen Energy Development Plan (2021)</td><td>Partnerships with Australian and UAE companies</td><td>Major R&D funding initiatives (2017) &amp; Production target of 20,000 tons annually (2025)</td><td>[161]</td></tr><tr><td rowspan="2">India</td><td>National Hydrogen Mission (2021)</td><td>NTPC pilot projects</td><td>Launch of National Hydrogen Energy Mission (2021)</td><td>[157]</td></tr><tr><td>Green Hydrogen Policy (2021)</td><td>Adani Group initiatives</td><td>Target of 1 million tons production (2025)</td><td>[157]</td></tr><tr><td rowspan="2">South Korea</td><td>Hydrogen Economy Roadmap (2019)</td><td>Hyundai Hydrogen Fuel Cell Vehicles</td><td>Introduction of hydrogen buses (2017)</td><td>[27,162]</td></tr><tr><td>Green Hydrogen Initiative (2021)</td><td>First commercial hydrogen transport ship (2021)</td><td>Hydrogen production target of 6.2 million tons (2040)</td><td></td></tr></tbody></table>

<figure><img src="image_23.png" /><figcaption>Fig. 14. Comparison of Green Hydrogen production capacities for India, China, Japan and South Korea (year 2010–2050)].</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**Fig. 15.** A comparison of green hydrogen development road map for countries India, China, Japan, and South Korea.</figcaption></figure>

risk in various transportation and storage situations. Although substantial strides have been made in the utilization of these technologies, obstacles continue to exist, such as the optimization of models, the

scarcity of data, and the seamless integration of existing infrastructure. Nevertheless, these obstacles also offer prospects for additional research and development, including the establishment of standardized datasets, advanced AI algorithms, and interdisciplinary collaborations. Additionally, the sensitivity, dependability, and response time limitations of current hydrogen leak detection technologies may make it more difficult to identify and mitigate breaches promptly. This review provides a roadmap for researchers, engineers, and practitioners who are endeavoring to advance green hydrogen energy and safety technologies by synthesizing insights from a variety of research domains. It emphasizes the necessity of ongoing investment, collaboration, and innovation to implement the vision of a hydrogen economy that is more sustainable, safer, and cleaner. Ultimately, we can ensure the safety and well-being of communities worldwide while paving the way for a greener energy future by harnessing the force of technology and innovation.

### CRediT authorship contribution statement

**Sankalp Raghuvanshi:** Writing – original draft, Resources, Project administration, Investigation, Formal analysis, Conceptualization, Visualization, Validation, Software, Resources, Methodology, Data curation. **Kalpana Singh:** Writing – original draft, Resources, Project administration, Investigation, Formal analysis, Conceptualization. **Malvika Chaudhary:** Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Data curation, Conceptualization. **Parul Verma:** Writing – original draft, Visualization,

<figure><img src="image_25.png" /><figcaption>**Fig. 16.** Factor-wise assessment of green hydrogen development across selected nations using a colour-coded matrix.</figcaption></figure>

<caption>**Table 14**</caption>

Comparative study of Hydrogen Strategies and AI Integration in Asia-Pacific Countries (China, Japan, India, and South Korea).

<table><thead><tr><td>Country</td><td>Hydrogen Goals</td><td>Investment Commitments</td><td>AI Integration in Hydrogen Strategy</td><td>Policy Support for AI Infrastructure</td><td>Reference</td></tr></thead><tbody><tr><td>China</td><td>100,000 hydrogen vehicles and 1000 refueling stations by 2030</td><td>$16+ billion in hydrogen infrastructure including refueling networks</td><td>AI supports leak detection, fleet optimization, and risk analytics</td><td>AI applied through smart manufacturing and logistics systems under national AI plan</td><td>[26] [160]</td></tr><tr><td>Japan</td><td>3 million tons of hydrogen by 2030, 20 million tons by 2050.</td><td>¥15 trillion (~$110 billion) toward hydrogen and ammonia value chains</td><td>Integrated via Society 5.0 to optimize energy systems and digital hydrogen plants</td><td>Society 5.0 promotes AI in clean energy sectors and automation.</td><td>[28]</td></tr><tr><td>India</td><td>5 million metric tons/year green hydrogen production by 2030.</td><td>₹19,744 crore (~$2.3 billion) for green hydrogen under the National Mission</td><td>AI deployed in leak detection, plant control, and forecasting</td><td>India Mission supports a national GPU cloud for energy AI applications.</td><td>[18] [30]</td></tr><tr><td>South Korea</td><td>Hydrogen to power 30% of industrial and transport sectors by 2040</td><td>$19 billion in the hydrogen economy roadmap including R&D and transport</td><td>AI enhances refueling infrastructure, safety, and vehicle routing</td><td>AI chip innovation supported by $6.9B Semiconductor Vision 2030 initiative</td><td>[17] [162]</td></tr></tbody></table>
<table><caption>**Table 15**
Comprehensive analysis framework for hydrogen energy research gap.</caption><thead><tr><th>Aspect</th><th>Approach</th><th>Challenges</th><th>Research Gaps</th><th>Opportunities</th></tr></thead><tbody><tr><td>Conventional Methods</td><td>Experimental methods for sensor and leak detection</td><td>Limited scalability, real-world replication</td><td>Lack of standardization</td><td>Standardized protocols, advanced setups</td></tr><tr><td>Artificial Neural Networks</td><td>ANNs for H<sub>2</sub> detection, leak localization, and IoT</td><td>Data quality, complexity, model interpretability</td><td>Lack of standardized datasets, overfitting</td><td>Lightweight models, edge computing</td></tr><tr><td>Machine Learning</td><td>ML for dispersion prediction, leak detection</td><td>Interpretability, bias, transferability</td><td>Limited transferability, fairness</td><td>Explainable AI, multitasking models</td></tr><tr><td>Vision-Based Detection</td><td>CNNs for leak detection using image analysis</td><td>Dataset availability, environmental adaptation</td><td>Lack of annotated datasets, real-time processing</td><td>Sensor fusion, edge-based inference</td></tr><tr><td>Real-World Implementations</td><td>Hydrogen safety systems, IoT integration</td><td>Integration, scalability, cost</td><td>Interoperability, standardization</td><td>Safety standards, AI-driven solutions</td></tr></tbody></table>

<figure><img src="image_26.png" /><figcaption>**Fig. 17.** Comparison of hydrogen safety technology approaches.</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Fig. 18.** Research gaps matrix: approaches vs challenges (for the advancement of green hydrogen technologies).</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>**Fig. 19.** Radar Chart depicts the relative significance of a variety of factors and distinct methods.</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>**Fig. 20.** Projected cost reduction in renewable energy and green hydrogen production.</figcaption></figure>

Validation, Software, Resources, Methodology, Investigation, Data curation, Conceptualization. **Shiwani Singhal:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Investigation, Formal analysis. **Sajid Ali:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Investigation, Formal analysis. **Himanshu Agarwal:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Investigation, Formal analysis. **Ritu Chauhan:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Investigation, Formal analysis. **Rupesh Kumar Basniwal:** reviewing, revising the paper. **Naveen Chandra Joshi:** reviewing, revising the paper. **Laurent Dufossé:** reviewing, revising the paper. **Abhishek Chauhan:** reviewing, revising the paper.

## Consent for publication

All authors agreed with the final content of this manuscript.

## Funding

No funding

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

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