Applications & Pathways
Power-to-gas and Power-to-liquid Systems in Emerging Hydrogen Valleys: Techno-economic Assessment of Alternative Fuels
Feb 2025
Publication
This study presents a techno-economic assessment of power-to-gas and power-to-liquid pathways within the Hydrogen Valley concept to support the decarbonization of local energy systems. Using the EnergyPLAN software both business-as-usual and Hydrogen Valley scenarios were analyzed by varying renewable energy electrolyzer capacity and hydrogen storage. The levelized costs of green hydrogen electrofuels and synthetic natural gas were estimated for both scenarios. A sensitivity analysis was conducted to assess the impact of cost parameters on the levelized costs of hydrogen and alternative fuel production. The findings indicate that the Hydrogen Valley scenario results in a 5.9% increase in total annual costs but achieves a 29.5% reduction in CO2 emissions compared to the business-as-usual scenario. Additionally utilizing excess energy for power-to-gas and power-to-liquid conversion in the Hydrogen Valley scenario lowers the levelized cost of electrofuels from 0.28 €·kWh−1 to 0.21 €·kWh−1 . Similarly the levelized cost of synthetic natural gas decreases from 0.33 €·kWh−1 to 0.25 €·kWh−1 when transitioning from the businessas-usual scenario to the Hydrogen Valley scenario. The results highlight that Hydrogen Valleys enable low-emission energy systems with cost-effective alternative fuels underscoring the tradeoffs between deep decarbonization and cost optimization in the transition to clean energy systems.
Deep Low-Carbon Economic Optimization Using CCUS and Two-Stage P2G with Multiple Hydrogen Utilizations for an Integrated Energy System with a High Penetration Level of Renewables
Jul 2024
Publication
Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition integrating split-flow CCS of flue gas facilitates a short capture time giving priority to renewable energy. To address these limitations this paper develops a carbon capture utilization and storage (CCUS) method into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly a deep low-carbon economic optimization strategy for this IES which considers stepwise carbon trading coal consumption renewable curtailment penalties and gas purchasing costs is proposed. The effects of CCUS the twostage P2G system and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits cutting costs by 13.97% and emissions by 12.32%. In addition integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization and the renewable consumption proportion in IESs can reach 69.23%.
Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance
Nov 2024
Publication
The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships car carriers oil tankers bulk carriers and container ships under six different operational scenarios such as varying cargo loads and weather conditions. Traditional fuels like marine gas oil (MGO) and intermediate fuel oil (IFO) challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm which dynamically adjusts engine parameters demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL fuel efficiency increased by up to 10% while EEXI values decreased by 8% to 15% and CII ratings improved by 10% to 30% across different scenarios. Specifically under heavy cargo loads the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally the DRL approach consistently outperformed traditional optimization methods demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels integrating additional machine learning techniques to enhance optimization.
Energy Use and Greenhouse Gas Emissions of Traction Alternatives for Regional Railways
Feb 2024
Publication
This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric plug-in hybrid-electric fuel cell hybrid-electric or battery-electric counterparts combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels liquefied natural gas (LNG) hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel LNG or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis hydrogen negatively affected both aspects irrespective of the prime mover technology. However when produced via green electricity it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies.
Research of the Impact of Hydrogen Metallurgy Technology on the Reduction of the Chinese Steel Industry’s Carbon Dioxide Emissions
Feb 2024
Publication
The steel industry which relies heavily on primary energy is one of the industries with the highest CO2 emissions in China. It is urgent for the industry to identify ways to embark on the path to “green steel”. Hydrogen metallurgy technology uses hydrogen as a reducing agent and its use is an important way to reduce CO2 emissions from long-term steelmaking and ensure the green and sustainable development of the steel industry. Previous research has demonstrated the feasibility and emission reduction effects of hydrogen metallurgy technology; however further research is needed to dynamically analyze the overall impact of the large-scale development of hydrogen metallurgy technology on future CO2 emissions from the steel industry. This article selects the integrated MARKAL-EFOM system (TIMES) model as its analysis model constructs a China steel industry hydrogen metallurgy model (TIMES-CSHM) and analyzes the resulting impact of hydrogen metallurgy technology on CO2 emissions. The results indicate that in the business-as-usual scenario (BAU scenario) applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 203 million tons and make an average 39.85% contribution to reducing the steel industry’s CO2 emissions. In the carbon emission reduction scenario applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 353 million tons contributing an average of 41.32% to steel industry CO2 reduction. This study provides an assessment of how hydrogen metallurgy can reduce CO2 emissions in the steel industry and also provides a reference for the development of hydrogen metallurgy technology.
An Overview of Application-orientated Multifunctional Large-scale Stationary Battery and Hydrogen Hybrid Energy Storage System
Dec 2023
Publication
The imperative to address traditional energy crises and environmental concerns has accelerated the need for energy structure transformation. However the variable nature of renewable energy poses challenges in meeting complex practical energy requirements. To address this issue the construction of a multifunctional large-scale stationary energy storage system is considered an effective solution. This paper critically examines the battery and hydrogen hybrid energy storage systems. Both technologies face limitations hindering them from fully meeting future energy storage needs such as large storage capacity in limited space frequent storage with rapid response and continuous storage without loss. Batteries with their rapid response (90%) excel in frequent short-duration energy storage. However limitations such as a selfdischarge rate (>1%) and capacity loss (~20%) restrict their use for long-duration energy storage. Hydrogen as a potential energy carrier is suitable for large-scale long-duration energy storage due to its high energy density steady state and low loss. Nevertheless it is less efficient for frequent energy storage due to its low storage efficiency (~50%). Ongoing research suggests that a battery and hydrogen hybrid energy storage system could combine the strengths of both technologies to meet the growing demand for large-scale long-duration energy storage. To assess their applied potentials this paper provides a detailed analysis of the research status of both energy storage technologies using proposed key performance indices. Additionally application-oriented future directions and challenges of the battery and hydrogen hybrid energy storage system are outlined from multiple perspectives offering guidance for the development of advanced energy storage systems.
Grid-neutral Hydrogen Mobility: Dynamic Modelling and Techno-economic Assessment of a Renewable-powered Hydrogen Plant
Jun 2024
Publication
The seasonally varying potential to produce electricity from renewable sources such as wind PV and hydropower is a challenge for the continuous supply of hydrogen for transport and mobility. Seasonal storage of energy allows to avoid the use of grid electricity when it is scarce; storage systems can thus increase the resilience of the energy system. For grid-neutral and renewable hydrogen production an electrolyser is considered together with a Power-to-Gas seasonal storage system which consists of a methanation the gas grid as intermediate storage and a steam reformer. As feed stream electricity from an own photovoltaic (PV) system is considered and for some cases additional electricity from the grid or from a wind turbine. The dynamic operation of the plant during a year is simulated. It is possible to safely supply fuel cell vehicles with hydrogen from the grid-neutral plant without using electricity when it is scarce and expensive. To supply 135 kgH2/day unit sizes of 1 MW–2.9 MW for the PV system and 0.9 MW–2.6 MW for the electrolysis are required depending on the amount of available grid-electricity. The usage of grid-electricity increases the capacity factor of the electrolysis which results in decreased unit sizes and in a better economic performance. Seasonal storage of energy is required which results in an increased hydrogen production in summer of approximately 50% more than directly needed by the fuel cell vehicles. The overall efficiency from electricity to hydrogen is decreased due to the storage path by 10%-points to 56% based on the higher heating value. Assuming a cost-equivalent hydrogen price per driven kilometre in comparison to the actual diesel price and electricity costs of 10 Ct/kWhel from the grid the revenues of the system are higher than the operating costs.
Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines
Jan 2025
Publication
The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogenpowered internal combustion engines represent a promising alternative offering the potential to reduce carbon-based emissions while improving efficiency. However the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure with a particular focus on improvements in computational efficiency. Additionally the artificial neural network is employed to predict the equivalent flame radius thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy achieving an RMSE consistently below 0.44% across various conditions. In comparison GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally the neural network effectively estimates the equivalent flame radius maintaining an RMSE of less than 3% ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis offering a practical advantage in real-time diagnostics.
Advances and Challenges in Thermoacoustic Network Modeling for Hydrogen and Ammonia Combustors
Jan 2025
Publication
The transition to low-carbon energy systems has heightened interest in hydrogen and ammonia as sustainable alternatives to traditional hydrocarbon fuels. However the development and operation of combustors utilizing these fuels like other combustion systems are challenged by thermoacoustic instabilities arising from the interaction between unsteady heat release and acoustic wave oscillations. Among many different methods for studying thermoacoustic instabilities thermoacoustic network models have played an important role in analyzing the essential dynamics of these instabilities in combustors operating with low-carbon fuels. This paper provides a comprehensive review of thermoacoustic network modeling techniques focusing specifically on their application to hydrogen- and ammonia-based combustion systems. We outline the key mathematical frameworks derived from fundamental equations of motion along with experimental validations and practical applications documented in existing studies. Furthermore current research gaps are identified and future directions are proposed to improve the reliability and effectiveness of thermoacoustic network models contributing to the advancement of efficient and stable low-carbon combustors.
Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities
Oct 2024
Publication
Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features such as high energy efficiency zero emissions and extended driving range. However FCEVs have some drawbacks including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters controllers and method execution. To address these challenges smart energy management involving appropriate converters controllers intelligent algorithms and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore this paper presents emerging energy management strategies for FCEVs to improve energy efficiency system reliability and overall performance. In this context a comprehensive analytical assessment is conducted to examine several factors including research trends types of publications citation analysis keyword occurrences collaborations influential authors and the countries conducting research in this area. Moreover emerging energy management schemes are investigated with a focus on intelligent algorithms optimization techniques and control strategies highlighting contributions key findings issues and research gaps. Furthermore the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains relevant outcomes and existing challenges. Additionally this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs emphasizing the importance of suitable algorithms controllers and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods control schemes and optimization strategies for FCEVs towards greener transportation.
Hydrogen Energy Systems: Technologies, Trends, and Future Prospects
May 2024
Publication
This review critically examines hydrogen energy systems highlighting their capacity to transform the global energy framework and mitigate climate change. Hydrogen showcases a high energy density of 120 MJ/kg providing a robust alternative to fossil fuels. Adoption at scale could decrease global CO2 emissions by up to 830 million tonnes annually. Despite its potential the expansion of hydrogen technology is curtailed by the inefficiency of current electrolysis methods and high production costs. Presently electrolysis efficiencies range between 60 % and 80 % with hydrogen production costs around $5 per kilogram. Strategic advancements are necessary to reduce these costs below $2 per kilogram and push efficiencies above 80 %. Additionally hydrogen storage poses its own challenges requiring conditions of up to 700 bar or temperatures below −253 °C. These storage conditions necessitate the development of advanced materials and infrastructure improvements. The findings of this study emphasize the need for comprehensive strategic planning and interdisciplinary efforts to maximize hydrogen's role as a sustainable energy source. Enhancing the economic viability and market integration of hydrogen will depend critically on overcoming these technological and infrastructural challenges supported by robust regulatory frameworks. This comprehensive approach will ensure that hydrogen energy can significantly contribute to a sustainable and low-carbon future.
Economic and Resilient Operation of Hydrogen-based Microgrids: An Improved MPC-based Optimal Scheduling Scheme Considering Security Constraints of Hydrogen Facilities
Feb 2023
Publication
Optimally scheduling alkaline electrolyzers (AELs) in a hydrogen-based microgrid (HBM) can greatly unleash the operational flexibility of the HBM. However existing scheduling strategies of AELs mostly utilize a simplified AEL model which ignores the nonlinear coupling of electric-hydrogen-thermal sectors violating the AEL’s security constraints thereby making the scheduling scheme infeasible. This paper proposes an improved model predictive control (MPC) based optimal scheduling framework which incorporates a scheduling correction algorithm into the basic MPC structure. This framework is utilized for implementing economic and resilient scheduling of an HBM under normal and emergency conditions respectively. With the scheduling correction algorithm this framework can be formulated into a computationally efficient mixedinteger linear programming meanwhile guaranteeing the solutions strictly satisfy the security constraints of hydrogen facilities (i.e. AEL and hydrogen tank). Case studies are conducted based on real operating data of a Danish energy island Bornholm. The results demonstrate that the proposed scheduling scheme under normal conditions can contribute to significant comprehensive benefits from the daily operation cost saving of 68% computational time saving of 98% and satisfying the security constraints of hydrogen facilities compared to previous scheduling strategies. Besides it sharply reduces load shedding under emergency conditions by proactively allocating distributed energy sources in the HBM.
Investigation of a New Holistic Energy System for a Sustainable Airport with Green Hydrogen Fuels
Jun 2024
Publication
The advancement of sustainable solutions through renewable energy sources is crucial to mitigate carbon emissions. This study reports a novel system for an airport utilizing geothermal biomass and PV solar energy sources. The proposed system is capable of producing five useful outputs including electrical power hot water hydrogen kerosene and space heating. In open literature there has been no system reported with these combination of energy sources and outputs. The system is considered for Vancouver Airport using the most recent statistics available. The geothermal sub-system introduced is also unique which utilizes carbon dioxide captured as the heat transfer medium for power generation and heating. The present system is considered using thermodynamic analysis through energetic and exergetic approaches to determine the variation in system performance based on different annual climate conditions. Biomass gasification and kerosene production are evaluated based on the Aspen Plus models. The efficiencies of the geothermal system with the carbon dioxide reservoir are found to have energetic and energetic efficiencies of 78 % and 37 % respectively. The total hydrogen production projection is obtained to be 452 tons on an annual basis. The kerosene production mass flow rate is reported as 0.112 kg/s. The overall energetic and exergetic efficiencies of the system are found to be 41.8 % and 32.9 % respectively. This study offers crucial information for the aviation sector to adopt sustainable solutions more effectively.
Temporally Detailed Modeling and Analysis of Global Net Zero Energy Systems Focussing on Variable Renewable Energy
Apr 2023
Publication
This study newly develops a recursive-dynamic global energy model with an hourly temporal resolution for electricity and hydrogen balances aiming to assess the role of variable renewable energy (VRE) in a carbonneutral world. This model formulated as a large-scale linear programming model (with 500 million each of variables and constraints) calculates the energy supply for 100 regions by 2050. The detailed temporal reso lution enables the model to incorporate the variable output of VRE and system integration options such as batteries water electrolysis curtailment and the flexible charging of battery electric vehicles. Optimization results suggest that combing various technical options suitable for local energy situations is critical to reducing global CO2 emissions cost-effectively. Not only VRE but also CCS-equipped gas-fired and biomass-fired power plants largely contribute to decarbonizing power supply. The share of VRE in global power generation in 2050 is estimated to be 57% in a cost-effective case. The results also imply economic challenges for an energy system based on 100% renewable energy. For example the average mitigation cost in 2050 is 69USD/tCO2 in the costeffective case while it increases to 139USD/tCO2 in the 100% renewable case. The robustness of this argument is tested by sensitivity analyses.
Clean Hydrogen and Ammonia Synthesis in Paraguay from the Itaipu 14 GW Hydroelectric Plant
Nov 2019
Publication
This paper aims at investigating clean hydrogen production from the large size (14 GW) hydroelectric power plant of Itaipu located on the border between Paraguay and Brazil the two countries that own and manage the plant. The hydrogen produced by a water electrolysis process is converted into ammonia through the well-known Haber-Bosch process. Hydraulic energy is employed to produce H2 and N2 respectively from a large-scale electrolysis system and an air separation unit. An economic feasibility analysis is performed considering the low electrical energy price in this specific scenario and that Paraguay has strong excess of renewable electrical energy but presents a low penetration of electricity. The proposal is an alternative to increase the use of electricity in the country. Different plant sizes were investigated and for each of them ammonia production costs were determined and considered as a term of comparison with traditional ammonia synthesis plants where H2 is produced from methane steam reforming and then purified. The study was performed employing a software developed by the authors’ research group at the University of Genoa. Finally an energetic environmental and economic comparison with the standard production method from methane is presented.
Optimal Scheduling of Electricity and Hydrogen Integrated Energy System Considering Multiple Uncertainties
Apr 2024
Publication
The spread of renewable energy (RE) generation not only promotes economy and the environmental protection but also brings uncertainty to power system. As the integration of hydrogen and electricity can effectively mitigate the fluctuation of RE generation an electricity-hydrogen integrated energy system is constructed. Then this paper studies the source-load uncertainties and corresponding correlation as well as the electricity-hydrogen price uncertainties and corresponding correlation. Finally an optimal scheduling model considering economy environmental protection and demand response (DR) is proposed. The simulation results indicate that the introduction of the DR strategy and the correlation of electricity-hydrogen price can effectively improve the economy of the system. After introducing the DR the operating cost of the system is reduced by 5.59% 10.5% 21.06% in each season respectively. When considering the correlation of EP and HP the operating cost of the system is reduced by 4.71% 6.47% 1.4% in each season respectively.
Assessing the Viability of Renewable Hydrogen, Ammonia, and Methanol in Decarbonizing Heavy-duty Trucks
Jan 2025
Publication
Decarbonizing heavy-duty trucks (HDTs) is both challenging and crucial for achieving carbon neutrality in the transport sector. Renewable hydrogen (H2) methanol (MeOH) and ammonia (NH3) offer potential solutions yet their economic viability and emission benefits remain largely unexplored. This study presents for the first time a comprehensive techno-economic analysis of using these three renewable fuels to decarbonize HDTs through detailed fuel and vehicle modeling. Six pathways are compared: hydrogen fuel cell electric trucks (FCET-H2) internal combustion engine trucks using MeOH (ICET-MeOH) and NH3 (ICET-NH3) as well as three indirect pathways that utilize these fuels for power generation to charge battery electric trucks (BETs). A novel “target powertrain cost” metric is introduced to assess the economic viability of FCET-H2 ICET-NH3 and ICET-MeOH relative to BETs. The results reveal that while BET pathways demonstrate higher well-to-wheel efficiencies significant opportunities exist for ICET-MeOH and ICET-NH3 in medium- and long-haul applications. Further more FCET-H2 achieves the lowest life cycle carbon emissions while ICET-MeOH and ICET-NH3 become more cost-effective as electricity costs decline. This study offers valuable insights and benchmarks for powertrain developers and policymakers addressing a critical gap in the comparative analysis of these three fuels for decarbonizing HDTs.
Evaluation of CNG Engine Conversion to Hydrogen Fuel for Stationary and Transient Operations
Dec 2024
Publication
This study investigates the use of hydrogen (H2 ) as a substitute for compressed natural gas (CNG) in a heavyduty (HD) six-cylinder engine focusing on both port fuel injection (PFI) and direct injection (DI) systems. Numerical modeling in a 0D/1D environment was employed simulating engine operation under stationary conditions and during the worldwide harmonized transient cycle (WHTC) and worldwide harmonized vehicle cycle (WHVC) homologation cycles. Results indicated a reduction in torque (7% for direct injection and 21.5% for port fuel injection) and power (32% for direct injection and 35.5% for port fuel injection) when switching from CNG to H2 . Efficiency slightly decreased primarily due to knocking at high engine loads and speeds during H2 operation. The reduced torque and power were mainly attributed to the turbocharger being undersized for H2 given its low density and the lean mixture combustion strategy used. Upgrading the turbocharger or implementing a two-stage compressor could restore or even improve torque and power levels compared to CNG. Heat transfer losses in the H2 engine were lower than with CNG due to the lower incylinder temperature resulting from the lean mixture strategy which also contributed to a significant reduction in nitrogen oxides (NOx ) emissions approximately 2.5 times lower than those with CNG. Despite a notable exhaust energy loss during H2 operation caused by delayed combustion due to knocking the lower NOx emissions and absence of carbon emissions are crucial for reducing pollution. During vehicle cycles selecting an optimal gear-shift strategy is critical to mitigating the performance gap resulting from reduced torque and power with H2 fueling.
Integrated Renewable Energy Systems for Buildings: An Assessment of the Environmental and Socio-Economic Sustainability
Jan 2025
Publication
Developing a green energy strategy for municipalities requires creating a framework to support the local production storage and use of renewable energy and green hydrogen. This framework should cover essential components for small-scale applications including energy sources infrastructure potential uses policy backing and collaborative partnerships. It is deployed as a small-scale renewable and green hydrogen unit in a municipality or building demands meticulous planning and considering multiple elements. Municipality can promote renewable energy and green hydrogen by adopting policies such as providing financial incentives like property tax reductions grants and subsidies for solar wind and hydrogen initiatives. They can also streamline approval processes for renewable energy installations invest in hydrogen refueling stations and community energy projects and collaborate with provinces and neighboring municipalities to develop hydrogen corridors and large-scale renewable projects. Renewable energy and clean hydrogen have significant potential to enhance sustainability in the transportation building and mining sectors by replacing fossil fuels. In Canada where heating accounts for 80% of building energy use blending hydrogen with LPG can reduce emissions. This study proposes a comprehensive approach integrating renewable energy and green hydrogen to support small-scale applications. The study examines many scenarios in a building as a case study focusing on economic and greenhouse gas (GHG) emission impacts. The optimum scenario uses a hybrid renewable energy system to meet two distinct electrical needs with 53% designated for lighting and 10% for equipment with annual saving CAD$ 87026.33. The second scenario explores utilizing a hydrogen-LPG blend as fuel for thermal loads covering 40% and 60% of the total demand respectively. This approach reduces greenhouse gas emissions from 540 to 324 tCO2/year resulting in an annual savings of CAD$ 251406. This innovative approach demonstrates the transformative potential of renewable energy and green hydrogen in enhancing energy efficiency and sustainability across sectors including transportation buildings and mining.
Exploring Decarbonization Priorities for Sustainable Shipping: A Natural Language Processing-based Experiment
Oct 2024
Publication
The shipping industry is currently the sixth largest contributor to global emissions responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper we use natural language processing techniques to analyze policies announcements and position papers from national and international organizations related to the decarbonization of shipping. In particular we perform the analysis using a novel matrix-based corpus and a fine-tuned machine learning model BERTopic. Our research suggests that the top four priorities for decarbonizing shipping are preventing emissions from methane leaks promoting non-carbon-based hydrogen implementing reusable modular containers to reduce packaging waste in container shipping and protecting Arctic biodiversity while promoting the Arctic shipping route to reduce costs. Our study highlights the validity of NLP techniques in quantitatively extracting critical information related to the decarbonization of the shipping industry.
No more items...