Monitoring the developmental trend and competitive landscape of natural gas hydrate through patent analysis.

Natural gas hydrate (NGH) is a significant alternative energy resource in achieving carbon neutrality. The developmental trend and competitive landscape of NGH exploitation and production are crucial for policymakers in government, managers of enterprises, and researchers. This study introduces a novel framework for conducting an in-depth analysis of NGH, integrating patentometrics, technology evolution, and correlation relationships to monitor developmental trends and competitive landscape through patent analysis. The results indicate that China, the USA, and Japan have distinct technology advantages. Current technological developments in the NGH field focus primarily on extraction technologies, equipment, and processing systems. The co-opetition analysis among countries reveals that the most extensive international cooperation network is primarily in Europe and the USA, with national partnerships in Asia concentrated in China and Japan. Institutional cooperation remains limited, primarily within universities in China, while both the USA and Japan foster collaboration between enterprises. The competitive landscapes of key NGH-related technologies among countries and institutions are also examined. This study contributes not only to monitoring the developmental trend and competitive landscape in NGH but also to providing policy recommendations for government and enterprises regarding strategic management and collaborative innovation.

Oil and gas development exposure and atrial fibrillation exacerbation: a retrospective study of atrial fibrillation exacerbation using Colorado's all payer claims dataset.

Introduction: Emerging risk factors for atrial fibrillation (AF) incidence and episodes (exacerbation), the most common and clinically significant cardiac arrhythmia, include air and noise pollution, both of which are emitted during oil and natural gas (O&G) well site development. Methods: We evaluated AF exacerbation risk and proximity to O&G well site development by employing a novel data source and interrupted time-series design. We retrospectively followed 1,197 AF patients living within 1-mile of an O&G well site (at-risk of exposure) and 9,764 patients living >2 miles from any O&G well site (unexposed) for AF claims in Colorado's All Payer Claims Dataset before, during, and after O&G well site development. We calculated AF exacerbation risk with multi-failure survival analysis. Results: The analysis of the total study population does not provide strong evidence of an association between AF exacerbation and proximity to O&G wells sites during (HR = 1.07, 95% CI: 0.94, 1.22) or after (HR = 1.01, 95% CI: 0.88, 1.16) development. However, AF exacerbation risk differed by patient age and sex. In patients >80 years living within 0.39 miles (2,059 feet) of O&G well site development, AF exacerbation risk increased by 83% (HR = 1.83, 95% CI: 1.25, 2.66) and emergency room visits for an AF event doubled (HR = 2.55, 95% CI: 1.50, 4.36) during development, with risk increasing with proximity. In female patients living within 0.39 miles of O&G well site development, AF exacerbation risk increased by 56% percent (95% CI: 1.13, 2.15) during development. AF exacerbation risk did not persist past the well development period. We did not observe increased AF exacerbation risk in younger or male patients. Discussion: The prospect that proximity to O&G well site development, a significant noise and air pollution source, may increase AF exacerbation risk in older and female AF patients requires attention. These findings support appropriate patient education to help mitigate risk and development of mitigation strategies and regulations to protect the health of populations in O&G development regions.

National Greenhouse Gas Emission Reduction Potential from Adopting Anaerobic Digestion on Large-Scale Dairy Farms in the United States.

Waste-to-energy systems can provide a functional demonstration of the economic and environmental benefits of circularity, innovation, and reimagining existing systems. This study offers a robust quantification of the greenhouse gas (GHG) emission reduction potential of the adoption of anaerobic digestion (AD) technology on applicable large-scale dairy farms in the contiguous United States. GHG reduction estimates were developed through a robust life cycle modeling framework paired with sensitivity and uncertainty analyses. Twenty dairy configurations were modeled to capture important differences in housing and manure management practices, applicable AD technologies, regional climates, storage cleanout schedules, and methods of land application. Monte Carlo results for the 90% confidence interval illustrate the potential for AD adoption to reduce GHG emissions from the large-scale dairy industry by 2.45-3.52 MMT of CO2-eq per year considering biogas use only in renewable natural gas programs and as much as 4.53-6.46 MMT of CO2-eq per year with combined heat and power as an additional biogas use case. At the farm level, AD technology may reduce GHG emissions from manure management systems by 58.1-79.8% depending on the region. Discussion focuses on regional differences in GHG emissions from manure management strategies and the challenges and opportunities surrounding AD adoption.

Impact of energy imports, renewable electricity production, alternative, and nuclear energy sources on natural gas resource rents.

The world faces several problems related to natural gas resource rents and energy production from renewable sources. One of the main problems is the influence of energy imports, manufacturing exports, and alternative energy sources on natural gas and electricity production from renewable sources. Energy imports, manufacturing exports, and alternative energy sources can impact natural gas and electricity production. This paper examines natural gas resource rents and electricity production from renewable sources nexus from 1971 to 2021, using energy imports, manufacturer's exports, and alternative energy sources in China. Electricity production from renewable sources and manufacturing exports are negatively associated with natural gas resource rents. Energy imports and alternative energy sources positively relate to natural gas resource rents in China. These results suggest that the energy sector in China is highly interconnected and that policies that seek to promote renewable energy sources and other alternatives can positively affect natural gas resource rents. China needs to develop an energy policy considering the policy implications of energy imports and natural gas resource rents. Such a policy should focus on increasing domestic production, reducing energy imports, and ensuring adequate revenue from natural gas resource rents. Additionally, regulations could be implemented that support the development of alternative energy sources, such as requiring utilities to purchase a certain percentage of their power from renewable sources.

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6-7100 g) CH4/h over durations (0.4-10.2 h) under a wind speed range of (0.7-9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9-13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1-1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Metagenome-assembled genomes provide insight into the metabolic potential during early production of Hydraulic Fracturing Test Site 2 in the Delaware Basin.

Demand for natural gas continues to climb in the United States, having reached a record monthly high of 104.9 billion cubic feet per day (Bcf/d) in November 2023. Hydraulic fracturing, a technique used to extract natural gas and oil from deep underground reservoirs, involves injecting large volumes of fluid, proppant, and chemical additives into shale units. This is followed by a "shut-in" period, during which the fracture fluid remains pressurized in the well for several weeks. The microbial processes that occur within the reservoir during this shut-in period are not well understood; yet, these reactions may significantly impact the structural integrity and overall recovery of oil and gas from the well. To shed light on this critical phase, we conducted an analysis of both pre-shut-in material alongside production fluid collected throughout the initial production phase at the Hydraulic Fracturing Test Site 2 (HFTS 2) located in the prolific Wolfcamp formation within the Permian Delaware Basin of west Texas, USA. Specifically, we aimed to assess the microbial ecology and functional potential of the microbial community during this crucial time frame. Prior analysis of 16S rRNA sequencing data through the first 35 days of production revealed a strong selection for a Clostridia species corresponding to a significant decrease in microbial diversity. Here, we performed a metagenomic analysis of produced water sampled on Day 33 of production. This analysis yielded three high-quality metagenome-assembled genomes (MAGs), one of which was a Clostridia draft genome closely related to the recently classified Petromonas tenebris. This draft genome likely represents the dominant Clostridia species observed in our 16S rRNA profile. Annotation of the MAGs revealed the presence of genes involved in critical metabolic processes, including thiosulfate reduction, mixed acid fermentation, and biofilm formation. These findings suggest that this microbial community has the potential to contribute to well souring, biocorrosion, and biofouling within the reservoir. Our research provides unique insights into the early stages of production in one of the most prolific unconventional plays in the United States, with important implications for well management and energy recovery.

Design of an innovative system for hydrogen production by electrolysis using waste heat recovery technology in natural gas engines.

This research proposes designing and implementing a system to produce hydrogen, utilizing the thermal energy from the exhaust gases in a natural gas engine. For the construction of the system, a thermoelectric generator was used to convert the thermal energy from the exhaust gases into electrical power and an electrolyzer bank to produce hydrogen. The system was evaluated using a natural gas engine, which operated at a constant speed (2400 rpm) and six load conditions (20 %, 40 %, 60 %, 80 %, and 100 %). The effect of hydrogen on the engine was evaluated with fuel mixtures (NG + 10 % HEF and NG + 15 % HEF). The results demonstrate that the NG + 10 % HEF and NG + 15 % HEF mixtures allow for a decrease of 1.84 % and 2.33 % in BSFC and an increase of 1.88 % and 2.38 % in BTE. Through the NG + 15 % HEF mixture, the engine achieved an energy efficiency of 34.15 % and an exergetic efficiency of 32.84 %. Additionally, the NG + 15 % HEF mixture reduces annual CO, CO2, and HC emissions by 9.52 %, 15.48 %, and 13.39 %, respectively. The addition of hydrogen positively impacts the engine's economic cost, allowing for a decrease of 1.56 % in the cost of useful work and a reduction of 3.32 % in the cost of exergy loss. In general, the proposed system for hydrogen production represents an alternative for utilizing the residual energy from exhaust gases, resulting in better performance parameters, reduced annual pollutant emissions, and lower economic costs.

Analysis of numerical modeling of steady-state modes of methane-hydrogen mixture transportation through a compressor station to reduce CO2 emissions.

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane-hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

Improved Synthesis of Hollow Fiber SSZ-13 Zeolite Membranes for High-Pressure CO2/CH4 Separation.

High-silica CHA zeolite membranes are highly desired for natural gas upgrading because of their separation performance in combination with superior mechanical and chemical stability. However, the narrow synthesis condition range significantly constrains scale-up preparation. Herein, we propose a facile interzeolite conversion approach using the FAU zeolite to prepare SSZ-13 zeolite seeds, featuring a shorter induction and a longer crystallization period of the membrane synthesis on hollow fiber substrates. The membrane thickness was constant at ~3 µm over a wide span of synthesis time (24-96 h), while the selectivity (separation efficiency) was easily improved by extending the synthesis time without compromising permeance (throughput). At 0.2 MPa feed pressure and 303 K, the membranes showed an average CO2 permeance of (5.2±0.5)×10-7 mol m-2 s-1 Pa-1 (1530 GPU), with an average CO2/CH4 mixture selectivity of 143±7. Minimal defects ensure a high selectivity of 126 with a CO2 permeation flux of 0.4 mol m-2 s-1 at 6.1 MPa feed pressure, far surpassing requirements for industrial applications. The feasibility for successful scale-up of our approach was further demonstrated by the batch synthesis of 40 cm-long hollow fiber SSZ-13 zeolite membranes exhibiting CO2/CH4 mixture selectivity up to 400 (0.2 MPa feed pressure and 303 K) without using sweep gas.

Methane emissions from residential natural gas meter set assemblies.

Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ∼7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.

Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines.

The work carried out in this paper focused on "Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines". The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture, then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision, using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result, calculation time, and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally, based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380, a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 %, both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems, firefighting systems, simulation, and prototyping tools.

Perception towards reducing natural gas consumption and imports in Europe: A theoretical and empirical investigation.

The European energy system is undergoing a drastic change, focusing on reducing natural gas consumption and import. European households, which are responsible for 25 % of final energy consumption, mainly based on natural gas, could play a significant role in such a transition. Therefore, the study aims to understand the perception towards natural gas consumption reduction and imports. An online questionnaire was designed based on an extended version of the theory of planned behaviour, which 257 highly educated respondents filled out. The results delineated the respondents' positive perception of reducing natural gas consumption. Specific attributes (i.e., environmental concerns, national sustainable and efficient energy system, and national energy independence) and personal moral norms (i.e., moral responsibility) significantly impact the willingness and effort to reduce natural gas consumption. The lack of control is the largest perceived control behaviour in reducing the respondents' natural gas consumption compared to available knowledge and affordability. Finally, the respondents care significantly and want to know about natural gas import sources, and they are highly against natural gas imports from Russia, the United States, the Middle Eastern, and Arab countries. Based on the insights, the study provides detailed recommendations. The study provides concrete recommendations for policy-makers to include environmental, humanitarian and energy-independence concerns in their decision-making processes related to natural gas imports and consumption. It also emphasises informing and involving individual households in such decision-making processes.

Application of extreme learning machine (ELM) forecasting model on CO2 emission dataset of a natural gas-fired power plant in Dhaka, Bangladesh.

Understanding and predicting CO2 emissions from individual power plants is crucial for developing effective mitigation strategies. This study analyzes and forecasts CO2 emissions from an engine-based natural gas-fired power plant in Dhaka Export Processing Zone (DEPZ), Bangladesh. This study also presents a rich dataset and ELM-based prediction model for a natural gas-fired plant in Bangladesh. Utilizing a rich dataset of Electricity generation and Gas Consumption, CO2 emissions in tons are estimated based on the measured energy use, and the ELM models were trained on CO2 emissions data from January 2015 to December 2022 and used to forecast CO2 emissions until December 2026. This study aims to improve the understanding and prediction of CO2 emissions from natural gas-fired power plants. While the specific operational strategy of the studied plant is not available, the provided data can serve as a valuable baseline or benchmark for comparison with similar facilities and the development of future research on optimizing operations and CO2 mitigation strategies. The Extreme Learning Machine (ELM) modeling method was employed due to its efficiency and accuracy in prediction. The ELM models achieved performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE), values respectively 3494.46 (<5000), 2013.42 (<2500), and 0.93 close to 1, which falls within the acceptable range. Although natural gas is a cleaner alternative, emission reduction remains essential. This data-driven approach using a Bangladeshi case study provides a replicable framework for optimizing plant operations and measuring and forecasting CO2 emissions from similar facilities, contributing to global climate change.

Insights from First Nations, Government and Industry Leaders on Criteria for Successful Impact Benefit Agreements.

Over the past decade, British Columbia, Canada's westernmost province, has begun developing liquified natural gas (LNG) mega projects that can transport Canadian resources to foreign markets across the Pacific region. These projects have gained significant profile due to high-level debates over their environmental, social and economic impacts. While LNG projects are required to undergo environmental impact assessment procedures, there is growing recognition of the need to ensure that positive social, economic and environmental impacts are fairly distributed to local communities. Similar to other extractive industries, many corporations leading the development of these projects engage in negotiations over so-called "impact benefit agreements (IBAs)"-legal agreements between a corporation, a government and/or a community that details how projects can benefit the local community and on what timeline so as to build social license to operate and investor confidence. This contribution details the findings of a qualitative study highlighting the perspectives of First Nations, provincial and federal government, and industry leaders to examine what makes an IBA successful and from whose perspective. The paper provides an introduction to IBA structures and processes, a brief review of the legal context, a qualitative methodology co-developed between academic researchers and Indigenous community leaders, and describes key criteria to inform future successful IBA agreements that create equitable multi-party benefits in an era of Indigenous reconciliation.

Dual isotopic evidence of δ15N and δ18O for priority control of vehicle emissions in a megacity of East China: Insight from measurements in summer and winter.

The source apportionment and main formation pathway of nitrate aerosols in China are not yet fully understood. In this study, PM2.5 samples were collected in Shanghai in the summer and winter of 2019. Water-soluble inorganic ions and isotopic signatures of stable nitrogen (δ15N-NO3-) and stable oxygen (δ18O-NO3-) in PM2.5 were determined. The results showed that NO3- was less important in summer (NO3-/SO42- = 0.4 ± 0.8), while it became the dominant species in winter (52.1 %). The average values of δ15N-NO3- and δ18O-NO3- in summer were + 2.0 ± 6.1 ‰ and 63.3 ± 9.4 ‰ respectively, which were significantly lower than those in winter (+7.2 ± 3.4 ‰ and 88.3 ± 12.1 ‰), indicating discrepancies between NOx sources and nitrate formation pathways. Both δ15N-NO3- and δ18O-NO3- were elevated at night, demonstrating that N2O5 hydrolysis contributed to the nocturnal nitrate increase even in summer. The contribution of the OH oxidation pathway to nitrate aerosols averaged at 70.5 ± 17.0 % in summer and N2O5 hydrolysis dominated the nitrate production in winter (approximately 80 %). On average, vehicle exhaust, coal combustion, natural gas burning, and soil emission contributed 50.7 %, 21.5 %, 15.9 %, and 11.9 %, respectively, to nitrate aerosols in summer, and contributed 56.8 %, 23.9 %, 13.6 %, and 5.7 %, respectively, to nitrate production in winter. Notably, natural gas burning is a non-negligible source of nitrate aerosols in Shanghai. In contrast to an inverse correlation between δ15N-NO3- and PM2.5, the value of δ18O-NO3- was positively correlated with nitrate concentration and aerosol liquid water content (ALWC) in winter, suggesting that explosive growth of nitrate was driven by continuous accumulation of N-depleted NOx and rapid N2O5 hydrolysis under calm and humid conditions. To continuously improve air quality, priority control should be given to vehicle emissions as the dominant source of NOx and volatile organic compounds (VOCs) in Shanghai.

New insights into the coal-associated methane architect: the ancient archaebacteria.

Exploration and marketable exploitation of coalbed methane (CBM) as cleaner fuel has been started globally. In addition, incidence of methane in coal basins is an imperative fraction of global carbon cycle. Significantly, subsurface coal ecosystem contains methane forming archaea. There is a rising attention in optimizing microbial coal gasification to exploit the abundant or inexpensive coal reserves worldwide. Therefore, it is essential to understand the coalbeds in geo-microbial perspective. Current review provides an in-depth analysis of recent advances in our understanding of how methanoarchaea are distributed in coal deposits globally. Specially, we highlight the findings on coal-associated methanoarchaeal existence, abundance, diversity, metabolic activity, and biogeography in diverse coal basins worldwide. Growing evidences indicates that we have arrived an exciting era of archaeal research. Moreover, gasification of coal into methane by utilizing microbial methanogenesis is a considerable way to mitigate the energy crisis for the rising world population.

Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis.

The emergence of new suppliers and energy resources has reshaped the energy market in terms of contractual structures and pricing systems. The market shifts were accelerated in response to the latest Russian-Ukraine crisis, impacting natural gas supply chains from financing projects to contracting volumes. The increased demand for liquified natural gas volumes intensified the need to switch from long-term oil-indexed contracts to short-term gas-indexed contracts. Those shifts were anticipated to influence the selling strategies for the expected added 49 MTPA of Qatari LNG, wherein increasing the share of spot selling would be reflected in higher economic performance. This study used forecasted prices to investigate potential Qatari LNG selling strategies. Initially, projections of the most dominant pricing systems used for pricing Qatari LNG (i.e., brent, Henry Hub, Title Transfer Facility, and Japan Korea Marker) were estimated between 2023 and 2040. While Qatar has been relying on long-term oil-indexed contracts, the second step estimated annual LNG revenues under different combinations of selling strategies (i.e., long-term and spot sales). Finally, the influence of varying brent slopes on the estimated revenues was measured. Due to data limitations and non-stationarity, the double exponential smoothing model was selected among the different tested models. Considering current market dynamics, forecasts of the double exponential smoothing model showed an upward price trend until 2040. An annual average increase of 1.24% for the studied pricing systems was reported. Reducing the share of long-term brent-indexed contracts to 70% and dedicating the remaining 30% of volumes to spot sales yielded the highest premiums for revenue estimates. An average annual revenue of $62 bn was reported for the 70/30 strategy, around 6% higher than the 100% brent-indexed contracts strategy. The findings revealed that diversifying the selling approach and introducing spot sales can enhance revenues. From the buyers' perspective, the outcomes support policymakers in understanding the implications of escalated prices driven by a lack of liquidity investments.

Economy of scale for green hydrogen-derived fuel production in Nepal.

Opportunity for future green hydrogen development in Nepal comes with end-use infrastructural challenges. The heavy reliance of industries on fossil fuels (63.4%) despite the abundance of hydroelectricity poses an additional challenge to the green transition of Nepal. The presented work aims to study the possibility of storing and utilizing spilled hydroelectricity due to runoff rivers as a compatible alternative to imported petroleum fuels. This is achieved by converting green hydrogen from water electrolysis and carbon dioxide from carbon capture of hard-to-abate industries into synthetic methane for heating applications via the Sabatier process. An economy-of-scale study was conducted to identify the optimal scale for the reference case (Industries in Makwanpur District Nepal) for establishing the Synthetic Natural Gas (SNG) production industry. The techno-economic assessment was carried out for pilot scale and reference scale production unit individually. Uncertainty and sensitivity analyses were performed to study the project profitability and the sensitivity of the parameters influencing the feasibility of the production plant. The reference scale for the production of Synthetic Natural Gas was determined to be 40 Tons Per Day (TPD), with a total capital investment of around 72.15 Million USD. Electricity was identified as the most sensitive parameter affecting the levelized cost of production (LCOP). The 40 TPD plant was found to be price competitive to LPG when electricity price is subsidized below 3.55 NPR/unit (2.7 c/unit) from 12 NPR/unit (9.2 c/unit). In the case of the 2 TPD plant, for it to be profitable, the price of electricity must be subsidized to well below 2 NPR/kWh. The study concludes that the possibility of SNG production in Nepal is profitable and price-competitive at large scales and at the same time limited by the low round efficiency due to conversion losses. Additionally, it was observed that highly favorable conditions driven by government policies would be required for the pilot-scale SNG project to be feasible.

Development and Recent Progress of Hoses for Cryogenic Liquid Transportation.

Recently, the application of cryogenic hoses in the field of cryogenic media has become a hot topic, especially in the industry of offshore liquefied natural gas and aerospace field. However, the structure of cryogenic hoses is complex, and reasonable structural properties are required due to the harsh working conditions. There is still plenty of scope for further development to improve the performance in all aspects. In this paper, the current development status of cryogenic hoses for liquefied natural gas (LNG) transportation is reviewed first, including the types, manufacturers, structural forms, performance, and key technical challenges. And then, the recent progress and prospect of cryogenic hoses for cryogenic liquid transportation (such as LNG and liquid oxygen) are summarized, including structure design, low-temperature resistant polymers, liquid oxygen compatible polymers, and leakage monitoring technologies. This paper provides a comprehensive overview of the research development and application of cryogenic hoses. Moreover, future research directions have been proposed to facilitate its practical applications in aerospace.

Can the digital economy empower urban energy resource transition? A natural gas perspective.

The diffusion effect of the digital economy (DEN) has become increasingly prominent, but few scholars have investigated the energy transition effect of China's DEN. To this end, this study takes 207 cities in China as the research object to explore the potential role of DEN development in boosting energy resource transition (ERT). The endogeneity and asymmetry between variables are also analyzed. We find that (i) China's urban DEN and ERT show a synchronized fluctuating upward trend from 2006 to 2019; (ii) both DEN and ERT show significant positive nexus; put differently, the rapid evolution of DEN can significantly help enhance the strength of ERT; and (iii) substantial heterogeneity exists at different quantiles. In 10th and 90th quantiles, the impacts of DEN on ERT are insignificant, and both DEN and ERT exhibit significant positive linkage in 25th, 50th, and 75th quantiles. To this end, we put forward corresponding policy recommendations to boost ERT from the perspective of DEN.