Malaysia

An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification ofbiomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition wascharacterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is usedto examine the effects of the significant input variables on response variables such as hydrogen mole fraction and hydrogen energycontent. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters wereconsidered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature while the gasification temperature has an optimum value. ANFISwas used to predict hydrogen of mole fraction 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure andtemperature of 1 bar and 848°C respectively. With the steam-gasification model operating at temperature (850°C) pressure (1 bar)and S/B ratio of 2.0 an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen while RSM gave an increaseof 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactorpressure and temperature of 1 bar and 850°C respectively. Varying the gasifier temperature and S/B ratio have on the other handa crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation prediction andoptimization to increase a significant impact on the products especially hydrogen.

As the commercialisation of two contrasting solar-powered water splitting devices with lower TRLs of proton exchange membrane (PEM) electrolyser systems and photoelectrochemical (PEC) systems gains momentum the path towards a sustainable H2 economy is taking shape. Ongoing pilot projects and demonstration plants are proving the feasibility and potential of these technologies in real-world applications. However to ensure their success we must confront the critical challenges of cost reduction and efficiency enhancement making green H2 economically competitive with traditional production methods. To achieve this a collaborative effort among academia industry and policymakers is paramount. This comprehensive review begins by examining traditional water electrolysis methods focusing on the production of green H2 through electrochemical splitting. It delves into crucial components and advancements in the PEM systems addressing challenges related to catalysts membranes gas diffusion layers and bipolar plates. The review also explores solar-driven PEC water splitting emphasizing the significance of efficient photoelectrodes and reactor design. Additionally it discusses the integration of photovoltaic cells with electrochemical or PEC systems for higher H2 yield. Commercialisation is underway and this endeavour necessitates a collaborative approach with active involvement from academia industry and policymakers. This collective effort not only propels us towards greener and more sustainable energy solutions but also represents a transformative step in the global journey towards a sustainable and environmentally conscious economy.

The concerns regarding the consumption of traditional fuels such as oil and coal have driven the proposals for several cleaner alternatives in recent years. Hydrogen energy is one of the most attractive alternatives for the currently used fossil fuels with several superiorities such as zero-emission and high energy content. Hydrogen has numerous advantages compared to conventional fuels and as such has been employed in gas turbines (GTs) in recent years. The main benefit of using hydrogen in power generation with the GT is the considerably lower emission of greenhouse gases. The performance of the GTs using hydrogen as a fuel is influenced by several factors including the performance of the components the operating condition ambient condition etc. These factors have been investigated by several scholars and scientists in this field. In this article studies on hydrogen-fired GTs are reviewed and their results are discussed. Furthermore some recommendations are proposed for the upcoming works in this field.

Hydrogen eliminates carbon emissions from compression ignition (CI) engines while noble gases eliminate nitrogen oxide (NOx) emissions by replacing nitrogen. Noble gases can increase the in-cylinder temperature during the compression stroke due to their high specific heat ratio. This paper aims to find the optimum parameters for hydrogen combustion in an argon–oxygen atmosphere and to study hydrogen combustion in all noble gases providing hydrogen combustion data with suitable engine parameters to predict hydrogen ignitability under different conditions. Simulations are performed with Converge CFD software based on the Yanmar NF19SK direct injection CI (DICI) engine parameters. The results are validated with the experimental results of hydrogen combustion in an argon–oxygen atmosphere with a rapid compression expansion machine (RCEM) and modifications of the hydrogen injection timing and initial temperature are proposed. Hydrogen ignition in an argon atmosphere is dependent on a minimum initial temperature of 340 K but the combustion is slightly unstable. Helium and neon are found to be suitable for hydrogen combustion in low compression ratio (CR) engines. However krypton and xenon require temperature modification and a high CR for stable ignition. Detailed parameter recommendations are needed to improve hydrogen ignitability in conventional diesel engines with the least engine modification.

As a party to the United Nation Framework Convention on Climate Change (UNFCCC) Malaysia is committed to reduce its greenhouse gases (GHG) emission intensity of gross domestic product (GDP) by 45% by 2030 relative to the emission intensity of GDP in 2005. One of the ways for Malaysia to reduce its GHG emission is to diversify its energy mix and to include hydrogen fuel cell (HFC) in its energy mix. Since Malaysia does not have any legal framework for HFCs it is best to see how other countries are doing and how can it be replicated in Malaysia. This paper reviews the HFC legal framework in the United States Germany and South Korea as these countries are among those that have advanced themselves in this technology. The researchers conducted a library-based research and obtained the related materials from online databases and public domain. Based on the reviews the researchers find that these countries have a proper legal framework in place for HFC. With these legal frameworks funds will be available to support research and development as well as demonstration of HFC. Thus it is recommended that Malaysia to have a proper HFC legal framework in place in order to support the development of the HFC industry.

Fuel cell (FC) technologies are often regarded as a sustainable alternative to conventional combustion-based energy systems due to their low environmental impact and high efficiency. Thorough environmental assessments using Life Cycle Assessment (LCA) methodologies are needed to understand and mitigate their impacts. However there has been a lack of comprehensive reviews on LCA studies across all major types of FCs. This study reviews and synthesizes results from 44 peer-reviewed LCA studies from 2015 to 2024 covering six major FC types: alkaline (AFC) direct methanol (DMFC) molten carbonate (MCFC) proton- exchange membrane (PEMFC) solid oxide (SOFC) and phosphoric acid (PAFC). The review provides an updated overview of LCA practices and results over the past decade while identifying methodological inconsistencies and gaps. PEMFCs are the most frequently assessed FC typology covering 49 % of the studies followed by SOFCs at 38 % with no studies on DMFCs. Only 11 % of comparative studies carry out inter-comparison between FC types. Discrepancies in system boundary definitions across studies are identified highlighting the need for standardization to enhance comparability between studies. Global Warming Potential (GWP) evaluated in 100 % of the studies is the most assessed impact category. Fuel supply in the use phase a major contributor to greenhouse gas (GHG) emissions is under-assessed as it is usually aggregated with Operation and Maintenance (O&M) phase instead of discussed separately. GWP of energy production by all FC typologies spans from 0.026 to 1.76 kg CO₂-equivalent per kWh. Insufficient quantitative data for a meta-analysis and limited inter-comparability across FC types are noted as critical gaps. The study highlights the need for future research and policies focusing on green hydrogen supply and circular economy practices to improve FC sustainability.

Hydrogen energy characterized by its abundant resources green and lowcarbon attributes and wide-ranging applications is a critical energy source for achieving carbon peaking and carbon neutrality goals. The operational efficiency of the hydrogen energy industrial chain is pivotal in determining the security of its supply chain and its contribution to China’s energy transition. This study investigates the efficiency of China’s hydrogen energy industrial chain by selecting 30 listed companies primarily engaged in hydrogen energy as the research sample. A three-stage data envelopment analysis (DEA) model is applied to assess the industry’s comprehensive technical efficiency pure technical efficiency and scale efficiency. Additionally kernel density estimation is utilized to analyze efficiency trends over time. Key factors influencing efficiency are identified and targeted recommendations are provided to enhance the performance and sustainability of the hydrogen energy industrial chain. These findings offer valuable insights to support the development and resilience of China’s hydrogen energy industry

Metal organic frameworks (MOFs) exhibit exceptional efficacy in hydrogen storage owing to their distinctive characteristics including elevated gravimetric densities rapid kinetics and reversibility. An in-depth look at existing literature indicates that while there are many studies using machine learning (ML) algorithms to develop predictive models for estimating hydrogen uptake by MOFs a great number of these models are not explainable. The novelty of this work lies in the integration of explainability approaches and ML models providing both accuracy and interpretability which is rarely addressed in existing studies. To fill this gap this paper attempts to develop explainable ML models for forecasting the hydrogen storage capacity of MOFs using three ML techniques including Bayesian regularized neural networks (BRANN) least squares support vector machines (LSSVM) and the extra tree algorithm (ET). An MOF databank comprising 1729 data points was assembled from literature. Surface area temperature pore volume and pressure were employed as input variables in this database. The findings demonstrate that of the three algorithms the ET intelligent model attained exceptional performance yielding precise estimates with a root mean square error (RMSE) of 0.1445 mean absolute error (MAE) of 0.0762 and a correlation coefficient (R2 ) of 0.995. In addition a novel contribution of this study is the generation of an explicit formula derived from BRANN enabling straightforward implementation of hydrogen storage predictions without requiring retraining of complex models. The sensitivity analysis employing Shapley Additive Explanation technique revealed that pressure and surface area were the most significant features influencing hydrogen storage with relevance values of 0.84 and 0.59 respectively. Furthermore the outlier detection evaluation using the leverage method showed that approximately 98 % of the utilized MOFs data are trustworthy and fell within the acceptable range. Altogether this work establishes a distinctive framework that combines accuracy interpretability and practical usability advancing the state of predictive modelling for hydrogen storage in MOFs.

The shift to renewable energy sources requires systems that are not only environmentally sustainable but also cost-effective and reliable. Mitigating the inherent intermittency of renewable energy optimally managing the hybrid energy storage efficiently integrating the microgrid with the power grid and maximizing the lifespan of system components are the significant challenges that need to be addressed. With this aim the paper proposes an economic viability assessment framework with an optimized dynamic operation approach to determine the most stable cost-effective and environmentally sound system for a specific location and demand. The green integrated hybrid microgrid combines photovoltaic (PV) generation battery storage an electrolyzer a hydrogen tank and a fuel cell tailored for deployment in remote areas with limited access to conventional infrastructure. The study’s control strategy focuses on managing energy flows between the renewable energy resources battery and hydrogen storage systems to maximize autonomy considering real-time changes in weather conditions load variations and the state of charge of both the battery and hydrogen storage units. The core system’s components include the interlinking converter which transfers power between AC and DC grids and the decentralized droop control approach which adjusts the converter’s output to ensure balanced and efficient power sharing particularly during overload conditions. A cloud-based Internet of Things (IoT) platform has been employed allowing continuous monitoring and data analysis of the green integrated microgrid to provide insights into the system's health and performance during the dynamic operation. The results presented in this paper confirmed that the proposed framework enabled the strategic use of energy storage particularly hydrogen systems. The optimal operational control of green hydrogen-integrated microgrid can indeed mitigate voltage and frequency fluctuations caused by variable solar input ensuring stable power delivery without reliance on the main grid or fossil fuel backups.

Fuel cell electric vehicles (FCEVs) provide a viable answer to transportation issues caused by fossil fuel limitations and environmental concerns. This review presents a thorough evaluation of the most recent advances in FCEV durability research. It addresses 4 major topics: component upgrades technical control techniques test optimization and durability prediction. Upgrades to components include improved catalysts bipolar plates gas diffusion layers proton exchange membranes and plant balancing. Technical control solutions include power energy temperature ventilation and control management. Stress acceleration and cold start tests are examples of test optimization whereas durability prediction requires parameter selection real-time monitoring dynamic modeling and lifespan prediction. This review also makes some novel recommendations targeted at improving the endurance of FCEVs. These include measures for raising public awareness lowering prices while increasing performance improving subsystems for greater durability updating health diagnostics to prevent performance deterioration and implementing supporting regulations to encourage industry upgrading. These findings are expected to accelerate the adoption of FCEVs and the transition to a more sustainable transportation system.