Decarbonizing residential energy systems is essential for Canada to achieve its net-zero emissions target by 2050, particularly in regions where natural gas remains the dominant fuel for space heating. This study presents a techno-economic assessment of blending green hydrogen into residential natural gas distribution networks in Alberta, Ontario, Saskatchewan, and British Columbia using grid-connected hybrid energy systems integrating solar photovoltaics, wind turbines, battery storage, and electrolyzers. Hydrogen blending ratios of 10%, 15%, and 20% by volume are evaluated for a representative cluster of 500 households under province-specific demand and renewable resource conditions. The novelty of this study lies in developing a province-specific techno-economic framework that integrates hybrid renewable energy systems, residential hydrogen blending scenarios, and multi-parameter sensitivity analysis to evaluate hydrogen integration within existing Canadian gas infrastructure. Annual hydrogen demand increases from 9.5 t/year in British Columbia at 10% blending to 25.8 t/year in Alberta at 20% blending. At a 20% hydrogen blend, residential CO2 emissions decrease from 2,820 to 2,200 t/year in Alberta (∼22%), from 2,610 to 2,100 t/year in Ontario (∼20%), from 2,358 to 1,900 t/year in Saskatchewan (∼19%), and from 2,081 to 1,600 t/year in British Columbia (∼23%), corresponding to reductions of up to 620 t CO2/year. Net present costs range from $0.54 M to $1.33 M, while the levelized cost of hydrogen varies between $1.53 and $2.60/kg H2, primarily governed by renewable resource availability and electricity prices. Electricity demand for electrolysis increases with hydrogen blending, raising grid electricity contributions from approximately 34–45% at 10% blending to 58–66% at 20%, particularly during winter periods with limited solar generation. Sensitivity analysis shows that increasing solar irradiation from 2 to 5.2 kWh/m2/day can reduce hydrogen production costs by up to 24%, while higher grid electricity prices may increase total system costs by more than 150%.
This paper investigates a computational study on magnetohydrodynamic (MHD) flow, exploring solution structure and unsteady nature of thermal flows, in a rotating curved rectangular duct (CRD) incorporating the effects of Hall and ion-slip currents, motivated by the board range of engineering applications involving magnetic flow control, thermomagnetic devices, filtration systems and thermal transport in a rotating system. The spectral method serves as the main computational approach, supported by Chebyshev polynomials, Collocation techniques, and Fourier series expansions, to study non-isothermal flow behavior across varying magnetic field strengths (M). Steady curves are obtained and linear stability of the flows is examined. The findings indicate that the asymmetry in the flow structure becomes weaker, eventually transitioning to a symmetrical state as M increases. The flux decreases, the intensity of the axial velocity reduces and moves to the middle of the duct as M is increased, while the linearly stable zone gradually expands. Furthermore, the linearly stable flow regime expands significantly with the increase of the magnetic field strength. The results further identify a magnetic parameter Mc = 10.15 as a critical value beyond which the flow transitions to a fully stable state. In the unsteady regime, oscillatory and chaotic flow behaviors are progressively weakened, and the steady-state region becomes dominant for higher values of M. The Hall parameter (m) is found to enhance both flux and axial velocity intensity, whereas the ion-slip parameter (α) shows minimal impact on secondary and axial flows. Heat transfer effects remain relatively unchanged within the curved channel under MHD influence. Overall, the findings provide valuable findings on MHD flow dynamics, with potential relevance to various industrial and engineering applications.
The maritime industry is among the key players in global trade, transporting the largest portion of the world’s goods. It connects economies, supports millions of jobs, and enables the movement of raw materials, energy resources, and manufactured products efficiently across continents. However, this industry is also a major source of greenhouse gas emissions. Considering these emissions, this industry is undergoing a transition to maritime electrification with net-zero emissions. In order to achieve that goal, batteries are the main tool, including the development of fully electric and hybrid vessels. To this end, this paper provides a structured overview of battery systems in marine electrification, covering various aspects of this rapidly evolving field. It presents an analysis of different ship types and the power topologies of electric and hybrid vessels, supported by updated examples of real-world commercialized and produced vessels across different regions. Additionally, the evolution of battery technology is demonstrated by detailing both commercialized and early-stage batteries, with a focus on their power rating, energy density, specific energy, cycle life, and management systems. Furthermore, the applications of Artificial Intelligence (AI) and digital twin technologies in marine electrification are presented with a detailed review of recent developments. The Artificial Intelligence (AI) strategies are classified into several categories, and a case study from a representative strategy of each class in battery and marine electrification is presented. It also includes guidelines and standards issued by official organizations to ensure compliance and safety in the sector. Additionally, it highlights recently completed and ongoing funded projects in marine electrification in the U.S. and the EU, as well as the related software tools and industry white papers. Moreover, the correlation between battery systems and marine electrification is provided with the United Nation (UN) Sustainable Development Goals (SDGs) and which SDGs they are effective and aligned. Finally, the updated challenges and future research directions are presented for a forward-looking perspective on the marine electrification sector and its transition to a net-zero industry.
The complexes (Cy3P)Ni(η6-PhMe) and [(Cy3P)2Ni]2(μ-N2) were utilized in a study of nickel C(sp2)−O bond cleavage. Unexpectedly, a mixture of H2C=CHOEt with [(Cy3P)2Ni]2(μ-N2) underwent a stoichiometric reaction upon heating to provide the ester MeCO2Et and (Cy3P)2Ni(η2-C2H4). An extension of this reaction allowed for the cross-coupling of aldehydes and vinyl ethers to give esters. The vinyl adducts (Cy3P)2Ni(η2-H2C=CHOR), (R = tBu, Cy, SiMe3) were isolated and fully characterized. Other prospective intermediates, such as (Cy3P)Ni(η2-O=CHMe)(η2-tBuOCH=CH2), were tentatively assigned by NMR spectroscopy. Calculations using density functional theory were performed to evaluate the relevance of proposed mechanistic manifolds. This work sets a foundation for the development of a novel selective methodology for aliphatic ester synthesis, as well as giving insight into C(sp2)−O bond cleavage mechanisms accessible to Ni.
Air particulate matter is linked to several health risks globally. Its sources are known to include industrial and vehicular emissions, biomass burning, and even the long-range transport of pollutant gases. Source apportionment studies are essential for accurately tracking sources and enabling effective mitigation. Common techniques of receptor modeling, such as Unmix, Chemical Mass Balance (CMB), and Positive Matrix Factorisation (PMF), have evolved, with increased reliability and accuracy. Recent advancements in modeling techniques, along with high-resolution measurements, have also been instrumental in enhancing the understanding of secondary aerosol formation. Despite these developments, challenges remain due to variations in source emissions, secondary atmospheric processes, and measurement uncertainties. This review examines recent advances in receptor modeling over the past decade. It also discusses their applications under different atmospheric conditions. Also, it highlights key research gaps that need to be addressed in future studies. Ongoing improvements in these models have made them more effective tools for policymaking and in the design of measures to reduce health risks linked to PM.
