India

The current study evaluates the feasibility of a forward osmosis membrane bioreactor (FO-MBR) for dark fermentation aiming at simultaneous biohydrogen production and wastewater treatment. Optimal microbial inoculation was achieved via heat-treated activated sludge enriching Clostridium sensu stricto 1 and yielding up to 2.21 mol H2.(mol hexose)− 1 in batch mode. In continuous operation a substrate concentration of 4.4 g L− 1 and a hydraulic retention time (HRT) of 12 h delivered the best results producing 1.51 mol H2.(mol hexosesupplied) − 1 . The FO-MBR configured with a 1.1 m2 hollow fiber side-stream membrane module and operated under dynamic HRT (2.5–12 h) dependent on membrane flux was integrated with intermittent CSTR (Continuous stirred tank reactor) operation to counter metabolite accumulation. This system outperformed a conventional CSTR achieving a hydrogen yield of 1.78 mol H2.(mol hexosesupplied) − 1 . Remarkable treatment efficiencies were observed with BOD5 COD and TOC removal rates of 95.32 % 99.02 % and 99.10 % respectively and an 83.8 % reduction in total waste volume. Additionally the FO-MBR demonstrated strong antifouling performance with 96.14 % water flux recovery achieved after a brief 5 min hydraulic rinse following 47.5 h of continuous highstrength broth exposure. These results highlight the FO-MBR system’s ability as a sustainable and highperformance alternative for integrated hydrogen production and effluent treatment. Further studies are recommended to address long-term fouling control and metabolite management for industrial scalability.

Green hydrogen (GH2) a sustainable and clean energy carrier is increasingly regarded as a solution to energy challenges and environmental issues. Industrial wastewater possesses a significant potential for hydrogen generation using biological chemical and electrochemical methods. This review analysis evaluates progress in GH2 production from industrial wastewater highlighting its environmental and cost benefits. Process optimization technological improvements and enhancements in catalysts for chemical and electrochemical hydrogen generation are also provided. It also considers the integration of GH2 production methods with wastewater treatment procedures to achieve synergistic benefits including enhanced pollutant removal and energy recovery. Challenges associated with GH2 production include substrate variability economic viability reactor scalability and environmental sustainability are also discussed. Also this review provides a future outlook to promote sustainable energy solutions and tackle global environmental issues related to GH2 from industrial wastewater.

The reliance on hydrogen as part of the transition towards a low-carbon economy will require developing dedicated pipeline infrastructure. This deployment will be shaped by regulatory frameworks governing investment and access conditions ultimately structuring how the commodity is traded. The paper assesses the market design for hydrogen infrastructure assuming the application of unbundling requirements. For this purpose it develops a general economic framework for regulating pipeline infrastructure focusing on asset specificity market power and access rules. The paper assesses the scope of application of infrastructure regulation which can be set to individual pipelines or to entire networks. When treated as entire networks the infrastructure can provide flexibility to enhance market liquidity. However this requires establishing network monopolies which rely on central planning and reduce the overall dynamic efficiency of the sector. The paper further compares the regulation applied to US and EU natural gas pipeline infrastructure. Based on the different challenges faced by the EU hydrogen sector including absence of wholesale concentration and large infrastructure needs the paper draws lessons for a regulatory framework establishing the main building blocks of a hydrogen target model. The paper recommends a review of the current EU regulatory framework in the Hydrogen and Decarbonised Gas Package to enable i) the application of regulation to individual pipelines rather than entire networks; ii) the use of negotiated third-party access light-touch regulation and possibly marketbased coordination mechanisms for the access to the infrastructure and iii) a more significant role for long-term capacity contracts to underpin infrastructure investments.

Hydrogen energy has been proposed as a reliable and sustainable source of energy which could play an integral part in demand for foreseeable environmentally friendly energy. Biomass fossil fuels waste products and clean energy sources like solar and wind power can all be employed for producing hydrogen. This comprehensive review paper provides a thorough overview of various hydrogen storage technologies available today along with the benefits and drawbacks of each technology in context with storage capacity efficiency safety and cost. Since safety concerns are among the major barriers to the broad application of H2 as a fuel source special attention has been paid to the safety implications of various H2 storage techniques. In addition this paper highlights the key challenges and opportunities facing the development and commercialization of hydrogen storage technologies including the need for improved materials enhanced system integration increased awareness and acceptance. Finally recommendations for future research and development with a particular focus on advancing these technologies towards commercial viability.

Metal-organic frameworks (MOFs) have emerged as promising candidates for solid-state hydrogen storage owing to their exceptional specific surface area high pore volume and chemically tunable structural properties. In this work a diverse set of experimentally synthesized MOFs were evaluated to model and predict hydrogen storage capacity (wt%) using 4 key descriptors which are Brunauer–Emmett–Teller (BET) surface area pore volume operating pressure and temperature. Correlation analysis revealed positive associations between BET surface area pressure and pore volume with storage capacity and a negative association with temperature consistent with physisorption mechanism. Six machine learning models were developed: support vector regression (SVR) artificial neural networks (ANN) random forest (RF) Gaussian process regression (GPR) gradient boosting (GB) and a Committee of Expert Systems (CES) integrating all base learners. While GB was the top-performing standalone model the CES delivered the highest predictive fidelity (R2 = 0.9958 MSE = 0.0094) as confirmed by parity plots and residual analysis. SHapley Additive exPlanations (SHAP) corroborated the statistical feature rankings consistently identifying BET surface area and pressure as the most influential positive contributors in alignment with adsorption thermodynamics. Paired t-tests on root-mean-square error (RMSE) values confirmed statistically significant CES improvements over all individual models. The CES framework thus offers a dataefficient accurate and interpretable approach for rapid MOF screening with straightforward adaptability to other porous materials and adsorption-based energy storage systems.

Rapid urbanization and globalization are causing a rise in the energy demand within the residential sector. Currently majority of the energy demand for the residential sector being supplied from fossil fuels these sources account for greenhouse gas emissions responsible for anthropogenic-driven climate change. About 85 % of the world’s energy demands are being met by non-renewable sources of energy. An immediate need to shift towards renewable energy sources to generate electricity is the need of the hour. These long-standing renewable energy sources including solar hydropower and wind energy have been crucial pillars of sustainable energy for years. However as their implementation has matured we are increasingly recognizing their limitations. Issues such as the scarcity of suitable locations and the significant carbon footprint associated with constructing renewable energy infrastructure are becoming more apparent. Hydrogen has been found to play a vital role as an energy carrier in framing the energy picture in the 21st century. Currently about 1 % of the global energy demands are being met by hydrogen energy harnessed through renewable methods. Its low carbon emissions when compared to other methods lower comparative production costs and high energy efficiency of 40–60 % make it a suitable choice. Integrating hydrogen production systems with other renewable source of energy such as solar and wind energy have been discussed in this review in detail. With the concepts of green buildings or net zero energy buildings gaining attraction integration of hydrogen-based systems within residential and office sectors through the use of devices such as micro–Combined Heat and Power devices (mCHP) have proven to be effective and efficient. These devices have been found to save the consumed energy by 22 % along with an effective reduction in carbon emissions of 18 % when used in residential sectors. Using the rejected energy from other processes these mCHP devices can prove to be vital in meeting the energy demands of the residential sector. Through the support of government schemes mCHP devices have been widely used in countries such as Japan and Finland and have benefitted from the same. Hydrogen storage is critical for efficient operation of the integrated renewable systems as improper storage of the hydrogen produced could lead to human and environmental disasters. Using boron hydrides or ammonia (121 kg H2/m3 ) or through organic carriers hydrogen can be stored safely and easily regenerated without loss of material. A thorough comparison of all the renewable sources of energy that are used extensively is required to evaluate the merits of using hydrogen as an energy carrier which has been addressed in this review paper. The need to address the research gap in application of mCHP devices in the residential sector and the benefits they provide has been addressed in this review. With about 2500 GW of energy ready to be harnessed through the mCHP devices globally the potential of mCHP systems globally are discussed in detail in this paper. This review discusses challenges and solutions to hydrogen production storage and ways to implement hydrogen technology in the residential sector. This review allows researchers to build a renewable alternative with hydrogen as a clean energy vector for generating electricity in residential systems.

Green hydrogen is emerging as a key driver in global decarbonization efforts particularly in hard-to-abate sectors such as steel manufacturing ammonia production and long-distance transportation. This study evaluates the techno-economic and environmental aspects of green hydrogen production storage and integration with renewable energy systems. Electrolysis remains the dominant production method with efficiency rates ranging from 70–80% for Alkaline Electrolyzers (AEL) 75–85% for Proton Exchange Membrane Electrolyzers (PEMEL) and up to 90% for Solid Oxide Electrolyzers (SOEL). Capital costs are steadily decreasing with AEL costs falling from $1200/kW in 2018 to $800/kW in 2024 while PEMEL costs are projected to decline to $600/kW by 2030. Green hydrogen significantly reduces carbon emissions with a footprint of 0.5–1 kg CO₂ per kg of H₂ compared to 10–12 kg for gray hydrogen and 1–3 kg for blue hydrogen. Its potential to cut global CO₂ emissions by 6 gigatons annually by 2050 underscores its role in climate action. However its high water demand—approximately 9 liters per kilogram of hydrogen—necessitates efficient management strategies such as desalination and recycling. Economically green hydrogen is becoming more competitive with its levelized cost decreasing from $6/kg in 2018 to $3–4/kg in 2024 and projections indicating a further drop to $1.50/kg by 2030. Global investments exceeding $500 billion in 2024 along with major projects like Saudi Arabia's NEOM Green Hydrogen Project and Australia's Asian Renewable Energy Hub are accelerating adoption. Policy frameworks such as the EU Hydrogen Strategy and the U.S. Inflation Reduction Act further support deployment. Despite progress challenges remain in infrastructure storage and regulatory frameworks necessitating continued innovation and international collaboration. Green hydrogen aligns with key Sustainable Development Goals (SDGs) including SDG 7 (Affordable and Clean Energy) SDG 9 (Industry Innovation and Infrastructure) and SDG 13 (Climate Action). As the world transitions to a low-carbon economy green hydrogen presents a transformative opportunity contingent on sustained technological advancements investment and policy support.

More than 80% of the energy from fossil fuels is utilized in homes and industries. Increased use of fossil fuels not only depletes them but also contributes to global warming. By 2050 the usage of fossil fuels will be approximately lower than 80% than it is today. There is no yearly variation in the amount of CO2 in the atmosphere due to soil and land plants. Therefore an alternative source of energy is required to overcome these problems. Biohydrogen is considered to be a renewable source of energy which is useful for electricity generation rather than relying on harmful fossil fuels. Hydrogen can be produced from a variety of sources and technologies and has numerous applications including electricity generation being a clean energy carrier and as an alternative fuel. In this review a detailed elaboration about different kinds of sources involved in biohydrogen production various biohydrogen production routes and their applications in electricity generation is provided.

The global energy landscape is rapidly shifting toward cleaner lower-carbon electricity generation necessitating a transition to alternate energy sources. Hydrogen particularly green hydrogen looks to be a significant solution for facilitating this transformation as it is produced by water electrolysis with renewable energy sources such as solar irradiations wind speed and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models including Autoregressive Integrated Moving Average(ARIMA) Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTMRNN) are assessed for develop the statistical metrics values such as RMSE MAE and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization particularly when handling multiple objective functions. This integrated method shows enormous promise in technological economic and environmental terms emphasizing its ability to promote energy sustainability targets.

Pressure vessels are used for large commercial and industrial applications such as softening filtration and storage. It is expected that high-pressure hydrogen storage vessels will be widely used in hydrogen-fuelled vehicles. Progressive failure properties the burst pressure and fatigue life should be taken into account in the design of composite pressure vessels. In this work the model and analysis of hydrogen storage vessels along with complete structural and thermal analysis. Liquid hydrogen is seen as an outstanding candidate for the fuel of high altitude long-endurance unmanned aircraft. The design of lightweight and super-insulated storage tanks for cryogenic liquid hydrogen is since long identified as crucial to enable the adoption of the liquid hydrogen. The basic structural design of the airborne cryogenic liquid hydrogen tank was completed in this paper. The problem of excessive heat leakage of the traditional support structure was solved by designing and using a new insulating support structure. The thermal performance of the designed tank was evaluated. The structure of the tank was analyzed by the combination of the film container theory and finite element numerical simulation method. The structure of the adiabatic support was analyzed by using the Hertz contact theory and numerical simulation method. A simple and effective structure analysis method for a similar container structure and point-contact support structure was provided. Bases for further structural optimization design of hydrogen tank will be provided also. The analysis will be carried out with different materials like titanium nickel alloy and some coated powders like alumina Titania and zirconium oxide. The results will be compared with that.

In 21st century the energy demand has grown incredibly due to globalization human population explosion and growing megacities. This energy demand is being mostly fulfilled by fossil-based sources which are non-renewable and a major cause of global warming. Energy from these fossil-based sources is cheaper however challenges exist in terms of climate change. This makes renewable energy sources more promising and viable for the future. Hydrogen is a promising renewable energy carrier for fulfilling the increasing energy demand due to its high energy density non-toxic and environment friendly characteristics. It is a non-toxic energy carrier as combustion of hydrogen produces water as the byproduct whereas other conventional fuels produce harmful gases and carcinogens. Because of its lighter weight hydrogen leaks are also easily dispersed in the atmosphere. Hydrogen is one of the most abundant elements on Earth yet it is not readily available in nature like other fossil fuels. Hence it is a secondary energy source and hydrogen needs to be produced from water or biomass-based feedstock for it to be considered renewable and sustainable. This paper reviews the renewable hydrogen generation pathways such as water splitting thermochemical conversion of biomass and biological conversion technologies. Purification and storage technologies of hydrogen is also discussed. The paper also discusses the hydrogen economy and future prospects from an Indian context. Hydrogen purification is necessary because of high purity requirements in particular applications like space fuel cells etc. Various applications of hydrogen are also addressed and a cost comparison of various hydrogen generation technologies is also analyzed. In conclusion this study can assist researchers in getting a better grasp of various renewable hydrogen generation pathways it's purification and storage technologies along with applications of hydrogen in understanding the hydrogen economy and its future prospect.

Hydrogen (H2 ) is considered a suitable substitute for conventional energy sources because it is abundant and environmentally friendly. However the widespread adoption of H2 as an energy source poses several challenges in H2 production storage safety and transportation. Recent efforts to address these challenges have focused on improving the efficiency and cost-effectiveness of H2 production methods developing advanced storage technologies to ensure safe handling and transportation of H2 and implementing comprehensive safety protocols. Furthermore efforts are being made to integrate H2 into the existing energy infrastructure and explore new opportunities for its application in various sectors such as transportation industry and residential applications. Overall recent developments in H2 production storage safety and transportation have opened new avenues for the widespread adoption of H2 as a clean and sustainable energy source. This review highlights potential solutions to overcome the challenges associated with H2 production storage safety and transportation. Additionally it discusses opportunities to achieve a carbon-neutral society and reduce the dependence on fossil fuels.

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.

This study explores the design and feasibility of a novel fuel cell-powered wind farm for residential electricity hydrogen/oxygen production and cooling/heating via a compression chiller. Wind turbine energy powers Proton Exchange Membrane (PEM) electrolyzers and a compression chiller unit. The proposed system was modeled using EES thermodynamic software and its economic viability was assessed. A case study across seven Italian regions with varying wind potentials evaluated the system’s feasibility in diverse weather conditions. Multi-objective optimization using Response Surface Methodology (RSM) determined the number of wind turbines as optimum number of electrolyzers & fuel cell units. Optimization results indicated that 37 wind turbines 1 fuel cell unit and 2 electrolyzer units yielded an exergy efficiency of 27.98 % and a cost rate of 619.9 $/h. TOPSIS analysis suggested 32 wind turbines 2 electrolyzers and 2 reverse osmosis units as an alternative configuration. Further twelve different scenarios were examined to enhance the distribution of wind farmgenerated electricity among the grid electrolyzers and reverse osmosis systems. revealing that directing 25 % to reverse osmosis 20 % to electrolyzers and 55 % to grid sales was optimal. Performance analysis across seven Italian cities (Turin Bologna Florence Palermo Genoa Milan and Rome) identified Genoa Palermo and Bologna as the most suitable locations due to favorable wind conditions. Implementing the system in Genoa the optimal site could produce 28435 MWh of electricity annually prevent 5801 tons of CO2 emissions (equivalent to 139218 $). Moreover selling this clean electricity to the grid could meet the annual clean electricity needs of approximately 5770 people in Italy

This study develops and optimizes a solar-powered system for hydrogen generation with oxygen and power coproducts addressing the need for efficient scalable carbon-free energy solutions. The system combines a linear parabolic collector a Steam Rankine cycle and a Proton Exchange Membrane Electrolyzer (PEME) to produce electricity for electrolysis. Thermodynamic modeling was accomplished in Engineering Equation Solver while a hybrid Artificial Intelligence (AI) framework combining Artificial Neural Networks and Genetic Algorithms in Statistica coupled with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision support optimized technical and economic performance. Optimization considered seven key decision variables covering collector design thermodynamic inputs and component efficiencies. The optimization achieved energy and exergy efficiencies of 30.83 % and 26.32 % costing 47.02 USD/h and avoiding CO2 emissions equivalent to 190 USD/ton. Economic and exergy analyses showed the solar and hydrogen units had the highest costs (38.17 USD/h and 9.61 USD/h) with 4503 kWh of exergy destruction to generate 575 kWh of electricity. A case study across six cities suggested that Perth Bunbury and Adelaide with higher solar irradiance delivered the highest annual power and hydrogen outputs consistent with irradiance–electrolyzer correlation. Unlike conventional single-site studies this work delivers a climate-responsive multi-city analysis integrating solar thermal and PEME within an AI-driven framework. By linking techno-economic performance with quantified environmental value and co-production synergies of hydrogen oxygen and electricity the study highlights a novel pathway for scalable clean hydrogen measurable CO2 reductions and global decarbonization with future work focused on digital twins and dynamic uncertainty-aware optimization.

The widespread adoption of hydrogen-fueled vehicles (HFVs) and the deployment of Hydrogen Refueling Stations (HRS) hinge on the ability to accurately predict system performance and ensure operational reliability. This study proposes a novel predictive framework integrating mathematical modeling state-space analysis and advanced data mining techniques supported by reliability analysis to evaluate the performance of HFVs and their associated refueling infrastructure. Utilizing a public dataset of 500 real-time operational data points key performance indicators are statistically analyzed. A significant negative correlation (r = −0.56) between hydrogen consumption and maximum vehicle range is identified highlighting that improved hydrogen efficiency directly extends travel range. The average maximum range is 555.21 km with a standard deviation of 87.09 km and a median of 563.65 km indicating strong consistency across vehicles. These findings underscore the importance of optimizing fuel efficiency to enhance system sustainability and inform the design and operation of next-generation hydrogen mobility solutions. The proposed approach offers a robust foundation for performance forecasting infrastructure planning and policy development in hydrogen-based transportation systems.

Research on and the demand for hydrogen-powered vehicles have grown significantly over the past two decades as a solution for sustainable transportation. Bibliometric analysis helps to assess research trends key contributions and the impact of studies focused on the safety and reliability of hydrogen-powered vehicles. This study provides a novel methodology for bibliometric analysis that systematically evaluates the global research landscape on hydrogen-powered vehicle reliability using Scopus-indexed publication data (1965 to 2024). Eighteen key parameters were identified for this study that are often used by researchers for the bibliometric analysis of hydrogen-related studies. Data analytics VOSviewer-based visualization and research impact indicators were integrated to comprehensively assess publication trends key contributors and citation networks. The analysis revealed that hydrogen-powered vehicle reliability research has experienced significant growth over the past two decades with leading contributions from high-impact journals renowned institutions and influential authors. The present study emphasizes the significance of greater funding as well as open-access distribution. Furthermore while major worldwide institutions have significant institutional relationships there are gaps in real-world hydrogen infrastructure evaluations large-scale experimental validation and policy-driven research.

The urgency for sustainable and efficient hydrogen production has increased interest in heterostructured nanomaterials known for their excellent photocatalytic properties. Traditional synthesis methods often rely on trial-and-error resulting in inefficiencies in material discovery and optimization. This work presents a new AI-driven framework that overcomes these challenges by integrating advanced machine-learning techniques specific to heterostructured nanomaterials. Graph Neural Networks (GNNs) enable accurate representations of atomic structures predicting material properties like bandgap energy and photocatalytic efficiency within ±0.05 eV. Reinforcement Learning optimises synthesis parameters reducing experimental iterations by 40% and boosting hydrogen yield by 15–20%. Physics-Informed Neural Networks (PINNs) successfully predict reaction pathways and intermediate states minimizing synthesis errors by 25%. Variational Autoencoders (VAEs) generate novel material configurations improving photocatalytic efficiency by up to 15%. Additionally Bayesian Optimisation enhances predictive accuracy by 30% through efficient hyperparameter tuning. This holistic framework integrates material design synthesis optimization and experimental validation fostering a synergistic data flow. Ultimately it accelerates the discovery of novel heterostructured nanomaterials enhancing efficiency scalability and yield thus moving closer to sustainable hydrogen production with improvements in photolytic efficiency setting a benchmark for AI-assisted research.