Publications

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.

Hydrogen energy generation faces challenges in efficiency and economic viability due to reliance on scarce noble metal catalysts. This study aimed to develop platinum-doped nickel-iron metal-organic framework (Pt-NiFe-MOF) catalysts with controlled metal ratios and pore architecture for enhanced water electrolysis. The NiFe-MOF framework was first synthesized via a solvothermal method which was then subjected to post-synthetic modification to introduce controlled platinum loadings (0.5- 2.0 wt%). The pore structure was tuned using a mixed-linker strategy (H₄DOBDC ratios 1:0 to 1:1). Catalysts were characterized using PXRD HRTEM BET XPS and ICP-OES techniques. Electrochemical performance was analyzed in 1.0 M KOH. A custom-designed integrated electrolysis system at 75 °C assessed practical performance. The Pt-NiFe-MOF-1.0 catalyst with H₄DOBDC ratio of 1:0.5 achieved remarkable effectiveness requiring overpotentials of only 253 mV for OER and 58 mV for HER when operating at 10 mA/cm². This catalyst featured an optimal pore diameter of 4.2 nm and surface area of 1325 m²/g. DFT calculations revealed platinum incorporation created synergistic effects by modifying hydrogen binding energies. Furthermore DFT calculations and XPS analysis revealed that the role of platinum in the OER is not direct catalysis but rather a powerful electronic modulation effect; Pt dopants withdraw electron density from adjacent Ni and Fe centers promoting the formation of higher-valent Ni³⁺/Fe³⁺ species that are intrinsically more active and lowering the energy barrier for the rate-determining O-O bond formation step. The integrated system achieved 1.62V at 100 mA/cm² with 75.8% energy efficiency maintaining stability for 200 h with 15–30 times lower precious metal loading than conventional systems. Strategic incorporation of low platinum concentrations within optimized NiFe-MOF structures significantly enhances water electrolysis performance while maintaining economic viability advancing development of industrial-scale hydrogen generation systems.

The EU targets 10 Mt of green hydrogen production by 2030 but has not committed to targets for 2040. Green hydrogen competes with carbon capture and storage biomass and imports as well as direct electrification in reaching emissions reductions; earlier studies have demonstrated the great uncertainty in future costoptimal development of green hydrogen. In spite of this we show that Europe risks little by setting green hydrogen production targets at around 25 Mt by 2040. Employing an extensive scenario analysis combined with novel near-optimal techniques we find that this target results in systems that are within 10% of cost-optimal in all considered scenarios with current-day biomass availability and baseline transportation electrification. Setting concrete targets is important in order to resolve significant uncertainty that hampers investments. Targeting green hydrogen reduces the dependence on carbon capture and storage and green fuel imports making for a more robust European climate strategy.

The escalating global demand for primary energy—still predominantly met by conventional carbon-based fuels—has led to increased atmospheric pollution. This underscores the urgent need for alternative energy strategies capable of reducing carbon emissions while meeting global energy requirements. Hydrogen as a clean combustible fuel offers a promising alternative to hydrocarbons producing neither soot CO2 nor unburned hydrocarbons. Although nitrogen oxides (NOx) are the primary combustion by-products their formation can be mitigated by controlling flame temperature. This study investigates the viability of hydrogen as a clean energy vector by simulating an unsteady turbulent non-premixed hydrogen jet flame interacting with an air co-flow. The numerical simulations employ the Unsteady Reynolds-Averaged Navier–Stokes (URANS) framework for efficient and accurate prediction of transient flow behavior. Turbulence is modeled using the Shear Stress Transport (SST k-ω) model which enhances accuracy in high Reynolds number reactive flows. The combustion process is described using a presumed Probability Density Function (PDF) model allowing for a statistical representation of turbulent mixing and chemical reaction. The simulation results are validated by comparison with experimental temperature and mixture fraction data demonstrating the reliability and predictive capability of the proposed numerical approach.