Publications

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.

This paper provides a comprehensive review of novel aviation technologies analyzing the advancements and challenges associated with the transition to sustainable air transport. The study explores three key pillars: unconventional aerodynamic configurations novel propulsion systems and advanced materials. Unconventional airframe architectures such as box-wing blended-wing-body and truss-braced wings demonstrate potential for improved aerostructural efficiency and reduced fuel consumption compared to traditional tube-and-wing designs. Aeropropulsive innovations as distributed propulsion boundary layer ingestion and advanced turbofan configurations are also promising in this regard. Significant progress in propulsion technologies including hybrid-electric hydrogen and extensive use of sustainable aviation fuels (SAF) plays a pivotal role in reducing air transport greenhouse gas emissions. However energy storage limitations and infrastructure constraints remain critical challenges and hence in the near future SAF could represent the most feasible solution. The introduction of advanced lightweight materials could further enhance aircraft overall performance. The results presented and discussed in this paper show that there is no a unique solution to the problem of the sustainability of air transport but a combination of all the novel technologies is necessary to achieve the ambitious environmental goals for the air transport of the future.

Since the early days of space exploration the efficient production of oxygen and hydrogen via water electrolysis has been a central task for regenerative life-support systems. Water electrolysers are however challenged by the near-absence of buoyancy in microgravity resulting in hindered gas bubble detachment from electrodes and diminished electrolysis efficiencies. Here we show that a commercial neodymium magnet enhances water electrolysis with current density improvements of up to 240% in microgravity by exploiting the magnetic polarization of the electrolyte and the magnetohydrodynamic force. We demonstrate that these interactions enhance gas bubble detachment and displacement through magnetic convection and achieve passive gas–liquid phase separation. Two model magnetoelectrolytic cells a proton-exchange membrane electrolyser and a magnetohydrodynamic drive were designed to leverage these forces and produce oxygen and hydrogen at near-terrestrial efficiencies in microgravity. Overall this work highlights achievable lightweight low-maintenance and energy-efficient phase separation and electrolyser technologies to support future human spaceflight architectures.

The Allam power cycle is a groundbreaking elevated-pressure power generation unit that utilizes oxygen and fossil fuels to generate low-cost electricity while capturing carbon dioxide (CO2) inherently. In this project we utilize the CO2 generated from the Allam cycle as feedstock for a newly envisioned industrial complex dedicated to producing renewable hydrocarbons. The industrial complex (FAAR) comprises four subsystems: (i) a Fischer–Tropsch synthesis plant (FTSP) (ii) an alkaline water electrolysis plant (AWEP) (iii) an Allam power cycle plant (APCP) and (iv) a reverse water-gas shift plant (RWGSP). Through effective material heat and power integration the FAAR complex utilizing 57.1% renewable energy for its electricity needs can poly-generate sustainable hydrocarbons (C1–C30) pure hydrogen and oxygen with near-zero emissions from natural gas and water. Economic analysis indicates strong financial performance of the development with an internal rate of return (IRR) of 18% a discounted payback period of 8.7 years and a profitability index of 2.39. The complex has been validated through rigorous modeling and simulation using Aspen Plus version 14 including sensitivity analysis.

The decarbonization of remote energy systems presents both technical and economic challenges due to their dependance on fossil fuels and the variability of renewable energy sources. This study introduces a Two-Stage Stochastic Programming approach to optimize Hybrid Renewable Energy Systems under uncertainty in renewable energy production. The methodology is applied to the island of Pantelleria aiming to minimize Total Annualized Costs and CO2 emissions using an ε-constraint approach. Results show that within the set of optimized configurations stricter CO2 emissions constraints increase costs due to the need for oversized components to ensure supply reliability. Nevertheless even the zeroemissions scenario offers significant economic benefits compared to the current diesel-based system. Total Annualized Costs are reduced from 15.5 M€ to 8.10 M€ in the deterministic case and to 9.37 M€ in the stochastic one. The additional cost in the stochastic configuration is offset by improved reliability ensuring demand is met under all scenarios. A sensitivity analysis on electricity demand reveals the necessity of further larger components leading to a 27.0% cost increase in a fully renewable scenario with stochastic optimization for a 10% demand increase. These findings highlight the importance of stochastic optimization in designing cost-effective off-grid renewable energy systems.

Pilot projects to generate hydrogen using proton exchange membrane (PEM) electrolyzers coupled to nuclear power plants (NPPs) began in 2022 with further developments anticipated over the next decade. However the co-location of electrolyzers with NPPs requires an understanding and mitigation of potential risks. In this work we identify and rank failure contributors for a 1 MW PEM electrolysis system. We used fault trees to define the component failure logic parameterized them with generic data and calculated failure frequencies and minimal cut sets for four top events: hydrogen release oxygen release nitrogen release and hydrogen and oxygen mixing. We use risk reduction worth importance measures to determine the most risk-significant components. The results provide insight into primary risk drivers in PEM electrolyzer systems and provide the foundational steps towards quantitative risk assessment of large-scale PEM electrolyzers at NPPs. The results include recommended riskmitigation actions include recommendations about design maintenance and monitoring strategies.