Publications

Presently there is no single clear route for the near-term production of the huge volumes of CO2-free hydrogen necessary for the global transition to any type of hydrogen economy. All conventional routes to produce hydrogen from hydrocarbon fossil fuels (notably natural gas) involve the production—and hence the emission—of CO2 most notably in the steam methane reforming (SMR) process. Our recent studies have highlighted another route; namely the critical role played by the microwave-initiated catalytic pyrolysis decomposition or deconstruction of fossil hydrocarbon fuels to produce hydrogen with low to near-zero CO2 emissions together with high-value solid nanoscale carbonaceous materials. These innovations have been applied firstly to wax then methane crude oil diesel then biomass and most recently Saudi Arabian light crude oil as well as plastics waste. Microwave catalysis has therefore now emerged as a highly effective route for the rapid and effective production of hydrogen and high-value carbon nanomaterials co-products in many cases accompanied by low to near-zero CO2 emissions. Underpinning all of these advances has been the important concept from solid state physics of the so-called Size-Induced-Metal-Insulator Transition (SIMIT) in mesoscale or mesoscopic particles of catalysts. The mesoscale refers to a range of physical scale in-between the micro- and the macro-scale of matter (Huang W Li J and Edwards PP 2018 Mesoscience: exploring the common principle at mesoscale Natl. Sci. Rev. 5 321-326 (doi:10.1093/nsr/nwx083)). We highlight here that the actual physical size of the mesoscopic catalyst particles located close to the SIMIT is the primary cause of their enhanced microwave absorption and rapid heating of particles to initiate the catalytic—and highly selective—breaking of carbon–hydrogen bonds in fossil hydrocarbons and plastics to produce clean hydrogen and nanoscale carbonaceous materials. Importantly also since the surrounding ‘bath’ of hydrocarbons is cooler than the microwave-heated catalytic particles themselves the produced neutral hydrogen molecule can quickly diffuse from the active sites. This important feature of microwave heating thereby minimizes undesirable side reactions a common feature of conventional thermal heating in heterogeneous catalysis. The low to near-zero CO2 production of hydrogen via microwave-initiated decomposition or cracking of abundant hydrocarbon fossil fuels may be an interim viable alternative to the conventional widely-used SMR that a highly efficient process but unfortunately associated with the emission of vast quantities of CO2. Microwave-initiated catalytic decomposition also opens up the intriguing possibility of using distributed methane in the current natural gas structure to produce hydrogen and high-value solid carbon at either central or distributed sites. That approach will lessen many of the safety and environmental concerns associated with transporting hydrogen using the existing natural gas infrastructure. When completely optimized microwave-initiated catalytic decomposition of methane (and indeed all hydrocarbon sources) will produce no aerial carbon (CO2) and only solid carbon as a co-product. Furthermore reaction conditions can surely be optimized to target the production of high-quality synthetic graphite as the major carbon-product; that material of considerable importance as the anode material for lithium-ion batteries. Even without aiming for such products derived from the solid carbon co-product it is of course far easier to capture solid carbon rather than capturing gaseous CO2 at either the central or distributed sites. Through microwave-initiated catalytic pyrolysis this decarbonization of fossil fuels can now become the potent source of sustainable hydrogen and high-value carbon nanomaterials.

Australia’s path to net-zero emissions by 2050 depends heavily on the development and commercialisation of hydrogen as a substitute for hydrocarbons across transport power generation and industrial heat sectors. With hydrocarbons currently supplying over 90% of national energy needs hydrogen must scale rapidly to fill the gap. Existing low-carbon hydrogen production methods blue hydrogen via steam methane reforming and green hydrogen via electrolysis are constrained by high water requirements posing a challenge in water-scarce regions targeted for hydrogen development. This paper investigates dry reforming of methane (DRM) as a water-independent alternative using CO₂ as a reactant. DRM offers dual benefits: reduced reliance on freshwater resources and the utilisation of CO₂ supporting broader emissions reduction goals. Recent improvements in nickel-copper catalyst performance enhance the viability of DRM for industrial-scale hydrogen production. The Middle Arm Precinct in the Northern Territory is highlighted as an ideal site for implementation given its access to offshore gas fields containing both methane and CO₂ presenting a unique opportunity for resource-integrated low-emission hydrogen production.

The escalating global energy demand has intensified research into sustainable hydrogen production particularly through water splitting. A highly promising avenue involves photocatalytic water splitting which leverages readily available earth-abundant materials to generate clean hydrogen from water using only renewable energy sources. Among the various catalytic materials investigated metal-organic frameworks (MOFs) have recently attracted considerable interest. Their tunable porosity high crystallinity as well as the customisable molecular structures position them as a transformative class of catalysts for efficient and sustainable photocatalytic hydrogen generation. This review examines MOFs detailing their structural characteristics unique properties and diverse synthetic routes. The discussion extends to the various composite materials that can be derived from MOFs with particular emphasis on their application in photocatalytic hydrogen production via water splitting. Furthermore the review identifies current challenges hindering MOF implementation and proposes modification strategies to overcome these limitations. The concluding section summarises the presented information and future perspectives on the continued development of MOF composites for enhanced photocatalytic hydrogen production from water.

This paper presents TwinP2G a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling including electricity green hydrogen natural gas and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles including data scientists and energy experts using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid.

The reliability and sustainability of multi-energy networks are increasingly critical in addressing modern energy demands and environmental concerns. Hydrogen-based hybrid energy systems can mitigate the challenges of renewable energy utilization such as intermittency grid stability and energy storage by integrating hydrogen generation and electricity storage from renewable sources such as solar and wind. Therefore this review offers a comprehensive evaluation of the environmental economic and technological aspects of green hydrogen-based hybrid energy systems particularly highlighting improvements in terms of the economics of fuel cell and electrolysis procedures. It also highlights new approaches such as hybrid energy management strategies and power-to-gas (PtG) conversion to enhance the system’s dependability and resilience. Analyzing the role of green hydrogen-based hybrid energy systems in supporting global climate goals and improving energy security underscores their high potential to make a significant contribution to carbon-neutral energy networks and provide policymakers with useful recommendations for developing guidelines. In addition the social aspect of hydrogen systems like energy equity and community engagement towards a hydrogen-based society provides reasons for the continued development of next-generation energy systems.

Hydrogen-powered aircraft constitute a transformative innovation in aviation motivated by the imperative for sustainable and environmentally friendly transportation solutions. This paper aims to concentrate on the design of hydrogen powertrains employing a system approach to propose representative design models for distribution and propulsion systems. Initially the requirements for powertrain design are formalized and a usecase-driven analysis is conducted to determine the functional and physical architectures. Subsequently for each component pertinent to preliminary design an analytical model is proposed for multidisciplinary analysis and optimization for powertrain sizing. A doublewall pipe model incorporating foam and vacuum multi-layer insulation was developed. The internal and outer pipes sizing were performed in accordance with standards for hydrogen piping design. Valves sizing is also considered in the present study following current standards and using data available in the literature. Furthermore models for booster pumps to compensate pressure drop and high-pressure pumps to elevate pressure at the combustion chamber entrance are proposed. Heat exchanger and evaporator models are also included and connected to a burning hydrogen engine in the sizing process. An optimal liner pipe diameter was identified which minimizes distribution systems weight. We also expect a reduction in engine length and weight while maintaining equivalent thrust.

This extensive study delves into analyzing carbon dioxide (CO2)-capturing green hydrogen plant exploring its operation using multiple electrolysis techniques and examining their efficiency and impact on environment. The solar energy is used for the electrolysis to make hydrogen. Emitted CO2 from thermal power plants integrate with green hydrogen and produces methanol. It is a process crucial for mitigating environmental damage and fostering sustainable energy practices. The findings demonstrated that solid oxide electrolysis is the most effective process by which hydrogen can be produced with significant rate of 90 % efficiency. Moreover proton exchange membrane (PEM) becomes a viable and common method with an 80 % efficiency whereas the alkaline electrolysis has a moderate level of 63 % efficiency. Additionally it was noted that the importance of seasonal fluctuations where the capturing of CO2 is maximum in summer months and less in the winter is an important factor to consider in order to maximize the working of the plant and the allocation of resources.

While hydrogen production through pure water splitting remains a key focus in solar hydrogen research photocatalytic seawater splitting presents a more sustainable alternative better aligned with global development goals amid increasing freshwater scarcity. Nevertheless the deactivation of the photocatalyst by the corrosion of various ions present in seawater as well as the chloride ions’ redox side reaction limits the practical use of the photocatalytic seawater splitting process. In this context sulfur has emerged as a crucial component in photocatalytic composites for seawater splitting owing to its unique chemical properties. It acts as a chlorine-repulsive agent effectively suppressing chloride ion oxidation which mitigates corrosion enhances structural stability and significantly improves overall photocatalytic performance in saline environments. This review offers a thorough explanation of the basic ideas of solar-driven seawater splitting delves into various synthesis strategies and explores recent advancements in sulfur-based composites for efficient hydrogen generation using seawater. Optimizing synthesis techniques and incorporating strategies like doping cocatalyst and heterojunctions significantly enhance the performance of sulfur-based photocatalysts for seawater splitting. Future advances include integrating AI-guided material discovery sustainable use of industrial sulfur waste and precise control of sacrificial agents to ensure long-term efficiency and stability.

Decarbonizing ammonia production is critical to meeting global climate targets in agriculture. This study evaluates two hydrogen pathways plasmalysis and electrolysis at Ontario’s Courtright Complex using detailed techno-economic modeling. The natural gas–based plasma system achieves the lowest hydrogen cost ($1.35/kg) but incurs high annual fuel expenses ($297.7 M/y) and shows strong sensitivity to natural gas prices. Electrolysis powered by 110 MW PV 1700 MW wind 60 MW biomass 95 MWh battery storage and a 2.0 GW electrolyzer produces hydrogen at $2.07/kg with lower fuel costs ($29.7 M/y) and significant grid interaction (2.67 TWh/y imports and 1.89 TWh/y exports) enhancing operational flexibility. Over a 15-year horizon both pathways deliver substantial CO2 reductions (plasmalysis: 27000 kt; electrolysis: 26045 kt). Extending plant lifetimes from 10 to 30 y reduces the levelized cost of hydrogen from $2.25 to $1.91/kg in the plasmalysis case and from $1.52 to $1.18/kg in the electrolysis case while increasing overall net present cost. Although electrolysis requires higher capital investment ($5.53 B compared with $1.79 B) it demonstrates resilience to fuel price volatility and provides additional grid revenue. In contrast plasmalysis offers near-term cost advantages but remains dependent on fossil gas underscoring its role as a transitional rather than fully green option for ammonia decarbonization.

Green hydrogen is emerging as a pivotal energy carrier in the global transition toward decarbonization offering a sustainable alternative to fossil fuels in sectors such as heavy industry transportation power generation and long-duration energy storage. Despite its potential large-scale deployment remains hindered by significant economic technological and infrastructure challenges. Current production costs for green hydrogen range from USD 3.8 to 11.9/kg H2 significantly higher than gray hydrogen at USD 1.5–6.4/kg H2 due to high electricity prices and electrolyzer capital costs exceeding USD 2000 per kW. This review critically examines the key bottlenecks in green hydrogen production focusing on water electrolysis technologies electrocatalyst limitations and integration with renewable energy sources. The economic viability of green hydrogen is constrained by high electricity consumption capital-intensive electrolyzer costs and operational inefficiencies making it uncompetitive with fossil fuel-based hydrogen. Infrastructure and supply chain challenges including limited hydrogen storage transport complexities and critical material dependencies further restrict market scalability. Additionally policy and regulatory gaps disparities in financial incentives and the absence of a standardized certification framework hinder international trade and investment in green hydrogen projects. This review also highlights market trends and global initiatives assessing the role of government incentives and cross-border collaborations in accelerating hydrogen adoption. While technological advancements and cost reductions are progressing overcoming these challenges requires sustained innovation stronger policy interventions and coordinated efforts to develop a resilient scalable and cost-competitive green hydrogen sector.

Dual-fuel engines are a way of transitioning the marine sector to carbon-neutral fuels like hydrogen and methanol. For the development of these engines accurate simulation of the combustion process is needed for which calculating the pilot’s ignition delay is essential. The present work investigates novel methodologies for calculating this. This involves the use of chemical kinetic schemes to compute the ignition delay for various operating conditions. Machine learning techniques are used to train models on these data sets. A neural network model is then implemented in a dual-fuel combustion model to calculate the ignition delay time and is compared using a lookup table or a correlation. The numerical results are compared with experimental data from a dual-fuel medium-speed marine engine operating with hydrogen or methanol from which the method with best accuracy and fastest calculation is selected.

The present investigation aims to provide a comparative assessment of using hydrogen-enriched wood waste-derived producer gas (PG) for a dual-fuel diesel engine fueled with a 20% Jatropha biodiesel/80% diesel blend (BD20) with the traditional mode. The experiments were conducted at 23°bTDC of injection timing 240 bar of injection pressure 17.5:1 of compression ratio and 1500 rpm of engine speed under various engine loads. Gas carburetor induction (GCI) port injection (PI) and inlet manifold injection (IMI) methods were used to supply H2-enriched PG while B20 is directly injected into the combustion chamber. Among all the combinations the IMI method provided the highest brake thermal efficiency of 30.91% the lowest CO emission of 0.08% and smoke opacity discharge of 49.26 HSU while NOx emission reached 1744.32 ppm which was lower than that of the PI mode. Furthermore the IMI method recorded the highest heat release rate of 91.17 J/°CA and peak cylinder pressure of 83.29 bar reflecting superior combustion quality. Finally using the IMI method for H2-enriched PG in dual-fuel diesel engines could improve combustion efficiency reduce greenhouse gas emissions and improve fuel economy showing that the combination of BD20 with H2-enriched PG offers a cleaner more sustainable and economically viable technology.

Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore hybrid storage systems have been used to evaluate their viability and cost-benefits. Examined under a 100% renewable energy microgrid framework three setup configurations are as follows: (1) photovoltaic (PV) and Battery Storage System (BSS) (2) Hybrid PV/Wind Turbine (WT)/BSS and (3) Integrated PV/WT/BSS/Electrolyzer/ Hydrogen Tank/Fuel Cell (FC). Using its geographical solar irradiance and wind speed data this paper inspires on an industrial community in Neom Saudi Arabia. HOMER software evaluates technical and economic aspects net present cost (NPC) levelized cost of energy (COE) and operating costs. The results indicate that the PV/ BSS configuration offers the most sustainable solution with a net present cost (NPC) of $2.42M and a levelized cost of electricity (LCOE) of $0.112/kWh achieving zero emissions. However it has lower reliability as validated by the provided LPSP. In contrast the PV/WT/BSS/Elec/FC system with a higher NPC of $2.30M and LCOE of $0.106/kWh provides improved energy dependability. The PV/WT/BSS system with an NPC of $2.11M and LCOE of $0.0968/kWh offers a slightly lower cost but does not provide the same level of reliability. The surplus energy has been implemented for hydrogen production. A sensitivity analysis was performed to evaluate the impact of uncertainties in renewable resource availability and economic parameters. The results demonstrate significant variability in system performance across different scenarios

In recent years the utilization of aluminium (Al) Al alloys and their composite powder and anode encourages the generation of green hydrogen through hydrolysis and water splitting electrolysis with zero emissions. As such in this study the development and characterization of Al Al alloys and Al-based composite powder and compacted Al composites for clean hydrogen production using hydrolysis and water splitting processes were reviewed. Herein based on the available literature it is worth mentioning that the incorporation of active additives such as h-BN Bi@C g-C3N4 MoS2 Ni In Fe and BiOCl@CNTs in the Al-based composites using ball milling melting smelting casting and spark plasma sintering technique remarkably improved the rate of hydrogen evolution and hydrogen gas conversion yield particularly during hydrolysis of Al-water reaction. Again Al-based electrodes with improved electrical conductivity notably results in better water splitting electrolysis as well as fast chemical reaction in achieving hydrogen gas production at low energy consumption with efficiency. Though notwithstanding the significance of Al Al alloy and Al-based composite hydrogen generation performances there are still some challenges associated with the Al-based materials for hydrogen production via hydrolysis and water electrolysis. For example the low current density and poor electrochemical properties of Al which on the other hand results in long induction time high overpotential and cost remains a gap to bridge. Hence the authors concluded the review study with recommendations for future improvement of Al-based composite electrodes on hydrogen production and sustainability via hydrolysis and water electrolysis. Thus the study will pave the way for further research on clean hydrogen energy generation.

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however uncertainty remains about optimal grid architectures to minimize cost including how and when to incorporate long-duration energy storage. This study implements a novel multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue which has already successfully deployed solar photovoltaics wind turbines and battery storage systems. Using real community load resource and generation data we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability sustainability and security.

Smart energy systems can be used to generate additional financial value by providing flexibility to the electricity network. It is fundamental to the effective economic implementation of these systems that an assessment can be made in advance to determine available value in comparison with any additional costs. The basic premise is that there is a distinct advantage in using similar algorithms to an actual smart energy system implementation for value assessment and that this is practical in this context which is confirmed in comparison with simpler modelling methods. Analysis has been undertaken using a ‘green’ hydrogen system case study of the impact of various simplifications to the value assessment algorithms used to speed computation time without sacrificing the decisionmaking potential of the output. The results indicate that for localised energy systems with a small number of controllable assets an rolling horizon optimisation model with a significant degree of temporal and component complexity is viable for planning phase value assessment requirements and would be a similar level of complexity to a computationally suitable implementation algorithm for actual asset control decision making.

The environmentally vulnerable Arctic’s harsh climate and remote geography demand innovative green energy solutions. This study introduces a hybrid off-grid system that integrates wave and wind energy with hydrogenelectricity conversion technologies. Designed to power cruise ships at berth fuel-cell hybrid electric vehicles and residential heating the system tackles the challenge of energy variability through dual optimization schemes. External optimization identifies a cost-effective architecture achieving a net present cost of $1.1M and a levelized hydrogen cost of $20.1/kg without a fuel cell. Internal optimizations employing multi-objective game theory and HYBRID algorithms further improve performance reducing the net present cost to $666K with a levelized hydrogen cost of $13.74/kg (game theory) and $729K with a levelized hydrogen of $15.63/kg (HYBRID). A key innovation is hydrokinetic turbines which streamline the design by cutting cumulative cash flow requirements by $470K from $1.85M to $1.38M. This approach prioritizes intelligent energy management shifting reliance from variable wind and wave inputs to optimized electrolyzer and battery operations. These results underscore the feasibility of cost-effective and scalable renewable energy systems and provide a compelling blueprint for addressing energy challenges in remote and resource-constrained environments.

Hydrogen fuel cell vehicles (HFCVs) are vital for advancing the hydrogen economy and decarbonizing the transportation sector. However research on HFCV market dynamics in passenger vehicles is limited especially incorporating both market competition from other vehicle types and the comprehensive supply–demand market dynamics. To bridge this gap our study proposed a spatial agent-based model to simulate the HFCV market evolution with the aim of finding effective strategies and policy implications for breaking the diffusion dilemma of the HFCV market. We calibrated the model using survey data (N=1065) collected from Beijing and evaluated its performance across five “What-If” scenarios. Results indicate that HFCVs and hydrogen stations are difficult to penetrate under the current conditions despite HFCV applicants and market share growing by 37.5% and 15.63% respectively. Consumer perceptions on cost social and environment have greater impacts on HFCV proliferation than facility availability. The HFCV purchase subsidy has much greater impact than the technological learning rate greatly accelerating its market emergence timing. Finally HFCVs’ diffusion significantly influences the market of battery electric vehicles.

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.

This study introduces the Smart Grid Hybrid Electrolysis-and-Combustion System (SGHE-CS) designed to seamlessly integrate hydrogen production storage and utilization within smart grid operations to maximize renewable energy use and maintain grid stability. The system achieves a hydrogen production efficiency of 98.5% indicating the effective conversion rate of electrical energy to hydrogen via PEM electrolysis. Combustion efficiency reaches 98.1% reflecting the proportion of hydrogen energy successfully converted into usable power through advanced staged combustion. Storage and transportation efficiency is 96.3% accounting for energy losses during hydrogen compression storage and delivery. Renewable integration efficiency is 97.3% representing the system’s capacity to utilize variable renewable energy inputs without curtailment. Operational versatility is 99.3% denoting the system’s ability to maintain high performance across load demands and grid conditions. Real-time monitoring and adaptive control strategies ensure reliability and resilience positioning SGHE-CS as a promising solution for sustainable low-carbon energy infrastructure.

In response to the global need to reduce greenhouse gas emissions and advance the decarbonization of thermal energy systems this study evaluates the performance of a tankless water heater operating with hydrogen–natural gas blends. The objective is to improve thermal efficiency and reduce pollutant emissions without requiring major modifications to existing equipment. Experimental tests were conducted at three thermal power levels (35 40 and 45 kW) and four hydrogen volume fractions (0% 20% 40% and 60%) analyzing operational variables such as temperatures flow rates efficiency and NOx emissions. Results show that efficiency increases with hydrogen content particularly at lower power levels reaching a maximum of 56%. NOx emissions tend to rise with both power and hydrogen fraction although this effect can be mitigated by controlling the water flow rate. In addition machine learning models were trained to predict efficiency and emissions with the scaled Support Vector Regression (SVR) model achieving R² values above 90% for both outputs. This approach not only enables system optimization but also represents a step toward the implementation of digital twins and opens the door to monitoring indirect variables offering broad potential for predictive applications in thermal equipment.

Accurate and efficient prediction of thermal radiant of hydrogen jet fire is important to schedule safety design and emergency rescue program for hydrogen pipelines. In response this paper proposes a novel Optuna-improved back propagation neural network (Optuna-BPNN) to estimate hydrogen jet flame radiation. A linear integral approach incorporating leakage rate and jet flame length is theoretically derived to establish dataset for machine learning. Then the Optuna tool is employed to optimize the initial weights and thresholds of the BP neural network. Input matrix of the Optuna-BPNN model includes pipeline diameter leakage aperture size and hydrogen pressure. 8 sets of experimental data are employed to verify its correctness. When the abnormal data is excluded the predicted thermal radiation of hydrogen jet fire agrees quite well with experimental results with average and maximum deviations being 12.4% and 24.4% respectively. Using the linear integral approach 32670 thermal radiation data points are generated to train and test the Optuna-BPNN model. The maximum deviation between predicted and theoretical radiant heat flux for training and testing sets are only 4.5% and 6.2% respectively. Parallel comparison trials using 6 different machine learning algorithms show that the Optuna-BPNN model gives the best mean absolute error root mean square error and determination coefficient which proves the effectiveness and feasibility of the developed OptunaBPNN model in predicting thermal radiation of hydrogen pipeline jet fires.

As a continuation of part 1 which examined the development status and system foundations of sustainable energy systems (SES) in the context of German energy transition this paper provides a comprehensive review of the core technologies enabling the development of SES. It covers recent advances in photovoltaic (PV) wind energy geo‑ thermal energy hydrogen and energy storage. Key trends include the evolution of high-efficiency solar and wind technologies intelligent control systems sector coupling through hydrogen integration and the diversification of electrochemical and mechanical storage solutions. Together these innovations are fostering a more flexible resil‑ ient and low-carbon energy infrastructure. The review further highlights the importance of system-level integration by linking generation conversion and storage to address the intermittency of renewable energy and support longterm decarbonization goals.

The transportation industry significantly contributes to greenhouse gas (GHG) emissions. Federal and provincial governments have implemented strategies to decrease dependence on gasoline and diesel fuels. This encompasses promoting the adoption of electric cars (EVs) and biofuel alternatives investing in renewable energy sources and enhancing public transit systems. There is a growing focus on enhancing infrastructure to facilitate active transportation modes like cycling and walking which provide the combined advantages of decreasing emissions and advancing public health. In this paper we propose a System Dynamics simulation model for evaluating factors for the adoption of alternative-fuel vehicles such as EVs biofuel vehicles bus bikes and hydrogen vehicles. Five factors— namely customer awareness government initiatives cost of vehicles cost of fuels and infrastructure developments—to increase the adoption of alternative-fuel vehicles are studied. Two scenarios are modeled: A baseline scenario that follows the existing trends in transportation (namely the use of gasoline vehicles) Scenario 1 which prioritizes greater adoption of electric vehicles (EVs) and biofuel-powered vehicles and Scenario 2 which prioritizes hydrogen fuel-based vehicles and improves biking culture. The simulation findings show that all scenarios achieve reductions in GHG emissions compared to the baseline with Scenario 2 showing the lowest emissions. The proposed work is useful for transport decision makers and municipal administrators in devising policies for reducing overall GHG emissions and this also aligns with Canada’s net zero goals.

The solid oxide electrolysis cell (SOEC) presents significant potential for transforming renewable energy into green hydrogen. Traditional modeling approaches however are constrained by their applicability to specific SOEC systems. This study aims to develop robust data-driven models that accurately capture the complex relationships between input and output parameters within the hydrogen production process. To achieve this advanced machine learning techniques were utilized including Random Forests (RFs) Convolutional Neural Networks (CNNs) Linear Regression Artificial Neural Networks (ANNs) Elastic Net Ridge and Lasso Regressions Decision Trees (DTs) Support Vector Machines (SVMs) k-Nearest Neighbors (KNN) Gradient Boosting Machines (GBMs) Extreme Gradient Boosting (XGBoost) Light Gradient Boosting Machines (LightGBM) CatBoost and Gaussian Process. These models were trained and validated using a dataset consisting of 351 data points with performance evaluated through various metrics and visual methods. The dataset’s suitability for model training was confirmed using the Monte Carlo outlier detection method. Results indicate that within the dataset and evaluation framework of this study ANNs CNNs Gradient Boosting and XGBoost models have demonstrated high accuracy and reliability achieving the largest R-squared scores and the smallest error metrics. Sensitivity analysis reveals that all input parameters significantly influence hydrogen production magnitude. Game-theoretic SHAP values underline current and cathode electrode conditions as critical factors. This research determines the performance of machine learning models particularly ANNs CNNs Gradient Boosting and XGBoost in predicting hydrogen production through the SOEC process. The outcomes of this paper can provide a certain reference for related research and applications in the hydrogen production field.