Natural Gas Emissions Research Articles

Impact of Mg on Pd-based methane oxidation catalysts for lean-burn natural gas emissions control

More efficient lean-burn, natural gas engines are limited by greenhouse gas emissions due to methane oxidation catalysts (MOC) that suffer from water inhibition and high temperature activation. Herein, we report that the addition of Mg to supported 1 wt% Pd MOCs improved hydrothermal stability even after severe hydrothermal aging. The superior methane oxidation activity compared to the corresponding Mg-free catalyst was attributed to (1) influence of Mg during surface roughening and restructuring at 700 °C on metal-support interaction, (2) reducibility of PdOx sites and (3) preferential stabilization of active Pd (1 0 0) facets in the sample as was evidenced by H2 TPR and CO TPD characterization experiments. Methane conversion under synthetic exhaust conditions relevant to natural gas, lean-burn engines were investigated. BET, TPR, CO pulse chemisorption followed by TPD provided valuable insights into the surface area, pore volume, reducibility, Pd dispersion and Pd particle size of the selected catalyst samples.

Decadal decrease in Los Angeles methane emissions is much smaller than bottom-up estimates

Methane, a powerful greenhouse gas, has a short atmospheric lifetime ( ~ 12 years), so that emissions reductions will have a rapid impact on climate forcing. In megacities such as Los Angeles (LA), natural gas (NG) leakage is the primary atmospheric methane source. The magnitudes and trends of fugitive NG emissions are largely unknown and need to be quantified to verify compliance with emission reduction targets. Here we use atmospheric remote sensing data to show that, in contrast to the observed global increase in methane emissions, LA area emissions decreased during 2011-2020 at a mean rate of (–1.57 ± 0.41) %/yr. However, the NG utility calculations indicate a much larger negative emissions trend of −5.8 %/yr. The large difference between top-down and bottom-up trends reflects the uncertainties in estimating the achieved emissions reductions. Actions taken in LA can be a blueprint for COP28 and future efforts to reduce methane emissions.

Techno-Economic Feasibility of Biomass Gasification for the Decarbonisation of Energy-Intensive Industries

The current climatic and geopolitical situation leads to strong decarbonisation policies in several industries worldwide. Moreover, the European Union is pushing intensive industries to achieve a 55% reduction in CO2 emissions towards 2030. Among them, the steel manufacturing sector is at the lead of alternative projects that can help achieve this ambitious target. Co-production of syngas and biochar is one potential solution for this sector. Herein, a techno-economic analysis is provided to evaluate the economic feasibility and the effect of the most influential parameters for a successful deployment. A bibliographic review has been carried out to establish a clear baseline for such an analysis in terms of investment costs at several scales for gasification projects. Additionally, the cost evolution for coke, natural gas, and CO2 emission credits on the profitability of these projects are given. The case scenario processing 20,000 tbiomass/y is the most feasible solution, with a payback of around three years and a net present value (NPV) of around 15 million EUR, showing that biomass gasification can be an up-and-coming alternative in the mid-term.

Techno-economic analysis of biomass gasification for hydrogen production in three US-based case studies

Production of low-emissions hydrogen can improve the sustainability of certain hard-to-decarbonize sectors, like industrial and residential heating. Gasification of biomass has been identified as a promising production method for high-volume clean hydrogen, but uncertainty around its economic conditions have prevented it from being used in practice. This work evaluates the economics of three potential gasification facilities in the United States to identify the optimal conditions for a successful hydrogen plant. The locations were selected based on local policies that create a need for high volume clean hydrogen by regulating natural gas emissions. Local features, like biomass type, availability, and distance between the biomass and hydrogen market, are taken into account. A novel simulation is used to build upon previous models to determine the effects of biomass composition on hydrogen yield and utility usage. The environmental impact of each location is then assessed using GREET, Argonne's greenhouse gas emissions quantification tool, to determine the plant's qualifications for tax credits under the Inflation Reduction Act. Ultimately, the cost of transported hydrogen in a 40 metric ton/day facility was calculated to be $3.47/kg H2 in Klamath County, Oregon (which serves both the Oregon and California markets), $4.11/kg H2 in Park County, Colorado, and $3.63/kg H2 in Middlesex County, Massachusetts. A sensitivity analysis found transportation distance and biomass availability to be major factors in these costs. Variability in biomass composition had minimal effects on plant performance.

Technology Focus: Gas Production (August 2023)

The demand for natural gas remains unabated and with it the need to further develop and disseminate gas-production-related expertise. A few of the common themes that emerge from recent SPE publications are elaborated upon here. The recovery of condensate from gas condensate depletion drive reservoirs is much lower than the recovery of gas from the same reservoirs because of condensate dropping out below dewpoint pressure. Besides pressure maintenance, no field-proven methods are yet available to mobilize this trapped condensate. Most papers present model or laboratory results without mentioning field data. The same applies to inflow impairment because of condensate dropout; many modeling studies lack validation against field data. Case in point: Pressure/volume/temperature testing generally does not venture below 500–1,000 psi pressure while many reservoirs reach (near-wellbore) pressures well below that range. Reducing emissions of natural gas or CO2 has become a key deliverable of most projects. Some papers present dedicated technology, while many others emphasize emission reduction as an important side benefit. Artificial intelligence (AI) is permeating the exploration and production business, as it does society at large, and features in many recent papers. So far, most applications use advanced data analysis to achieve higher derived data accuracy. This appears to be very successful as long as the evaluated data is within the domain of the training data. It is not always clear how well the AI models perform outside of their training domain. A relevant question is whether complex AI techniques applicable within a restricted domain can or should be used to replace simple, well-established engineering relations. Time will tell how deep and wide AI will affect day-to-day gas production applications. Recommended additional reading at OnePetro: www.onepetro.org. SPE 210136 The Reversible Relationship Between Choke Management and Liquid Yield Trends in Shale Reservoirs by Yalda Barzin, Repsol, et al. SPE 210099 Innovative, Cost-Effective, and Zero-Flaring Solution to Clean Up and Start Up New Wells in a High-H2S Field in Kazakhstan— The Vital Step Forward Toward Net-Zero Carbon Footprint by Timur Tankiyev, Karachaganak Petroleum Operating, et al. SPE 209745 A Data-Driven Approach To Predict the Critical Gas Flow Rate in Gas Wells by Ehsan Rahmati, Ambyint, et al.

Measurements of Volatile Organic Compounds at a rural site in India: Variability and sources during the seasonal transition

Volatile Organic Compounds (VOCs) play a vital role in tropospheric ozone formation that controls the oxidative capacity of the troposphere. A total of 31 potential ozone precursor VOCs have been measured at a tropical rural site, Gadanki (13.5°N, 79.2°E) in Southern peninsular India. This study provides the primary information about different VOCs, their chemical classification and potential sources. There is a strong seasonal and diurnal variability among the VOC composition. n-Decane and n-dodecane dominate the other VOCs and contribute to a large fraction (>50 %) of the concentration of total VOCs (TVOCs) in winter and summer, monsoon is dominated by n-dodecane and post monsoon season has been dominated by ethane emissions. The source apportionment using interspecies correlation and Positive Matrix Factorisation (PMF) analysis resulted in four potential emission source factors namely biogenic, biomass burning/biofuel, fossil fuel and natural gas emissions. Winter and summer seasons have been dominated by VOCs originating from biomass burning/biofuel factors, monsoon has been dominated by biogenic emissions and post-monsoon season has been dominated by natural gas emissions. Even though it is a rural site, there are significant finger prints of anthropogenic emissions in the form of fossil fuel and natural gas most probably due to an adjacent national highway and long range transport. However, for the overall period, the VOCs emitted from biogenic and biomass burning together dominate the other two factors, indicating the expected source factor behaviour of a rural atmosphere.

Optimal economic and environmental design of multi-energy systems

Designing decentralized energy systems in an optimal way can substantially reduce costs and environmental burdens. However, most models for the optimal design of multi-energy systems (MESs) exclude a comprehensive environmental assessment and consider limited technology options for relevant energy-intensive sectors, such as the industrial and mobility sectors. This paper presents a multi-objective optimization framework for designing MESs, which includes life cycle environmental burdens and considers a wide portfolio of technology options for residential, mobility, and industrial sectors. The optimization problem is formulated as a mixed integer linear program that minimizes costs and greenhouse gas (GHG) emissions while meeting the energy demands of given end-users. Whereas our MESs optimization framework can be applied for a large range of boundary conditions, the geographical island Eigerøy (Norway) is used as a showcase as it includes substantial industrial activities. Results demonstrate that, when properly designed, MESs are already cost-competitive with incumbent energy systems, and significant reductions in the amount of natural gas (92%) and GHG emissions (73%) can be obtained with a marginal cost increase (18%). Stricter decarbonization targets incur larger costs. A broad portfolio of technologies is deployed when minimizing GHG emissions and integrating the industrial sector. Environmental trade-offs are identified when considering the construction phase of energy technologies. Therefore, we argue that (i) MES designs and assessments require a thorough life cycle assessment beyond GHG emissions, and (ii) the entire life cycle should be considered when designing MESs, with the construction phase contributing up to 80% of specific environmental impact categories.

Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model

To deal with global warming and fulfil China’s commitment to carbon neutrality by 2060, reducing carbon emissions has become a necessary requirement. As one of China’s three major economic circles, the Beijing–Tianjin–Hebei region (B–T–H) has a great responsibility. This paper measures energy-related carbon emissions of B–T–H from 2005 to 2019 and uses the extended Logarithmic Mean Division Index (LMDI) to decompose the carbon emission effect factors. Then, a Tapio index model was constructed to analyse the contribution of each effect factor. The results showed that: (1) the total carbon emissions of B–T–H increased by 1.5 times, with Hebei having the highest proportion, followed by Tianjin and Beijing. Coal was the biggest emitter in all three regions. Natural gas emissions in Tianjin and Beijing were growing rapidly. (2) Consistent with most studies, economic development promoted carbon emissions, while energy intensity and energy structure inhibited them. It was found that innovative factors also have significant impacts: research and development efficiency was the primary emission inhibition factor in Hebei and the secondary inhibition factor in Tianjin and Beijing. The effects of investment intensity and research and development intensity differed between regions. (3) Beijing took the lead in achieving strong decoupling, followed by Tianjin. Hebei maintained weak decoupling. Innovative factors also played an important role in decoupling, which cannot be ignored in achieving emission reduction targets.

A study on the carbon footprint contributions from a large wastewater treatment plant

The present work analyses aspects of the carbon footprint of a large wastewater treatment plant in central Italy. The plant mainly consists of a traditional activated sludge system along with an anaerobic digester providing a partial contribution of energy to the management. An integrated approach was adopted to evaluate the environmental sustainability of the treatment plant in terms of carbon footprint. For the assessment different sources of greenhouse gas emissions such as nitrous oxide and carbon dioxide were considered: effluent, production and transport of natural gas, energy consumption, boiler, co-generator, substrate and endogenous decays. According to the methodology adopted, energy consumption, production and transport of natural gas and N2O emissions from the effluent were found the most contributing sources of greenhouse gases. Based on this, these sources are suggested as the most relevant ones on which wastewater treatment plant managers should pay more attention when taking actions for carbon footprint mitigation. Considerations on the role of CO2 of biogenic origin (specifically the one in the biogas) in terms of sequestration options demonstrate that the analysis in this field should not be limited to the calculation and comment of non-fossil contributions to the overall balance.

Impact of fuel uses in the formation and prevention of global secondary organic aerosol

This study estimates global anthropogenic and biogenic Secondary Organic Aerosols (a-SOA and b-SOA) and the contributions of four fuel sectors (Solid Biofuel, Coal, Liquid Oil and Natural Gas, and Process and non-combustion emissions (in metal production, chemical industry, manure management)) to their formation. The global landscape was divided into eleven domains (North America; South America; Europe; North Africa and the Middle East; Equatorial Africa; South of Africa; Russia and Central Asia; Eastern Asia; South Asia; Southeast Asia, and Australia). WRF-Chem model coupled with emission inventories as model inputs, atmospheric reactions from the RACM gas-phase mechanism, and the secondary organic aerosol module (SORGAM) were employed for estimating a-SOA and b-SOA for the year 2018. At the global scale, SOA contribution was dominated by liquid oil and natural gas emissions (38%), process and non-combustion emissions (24%), and solid biofuels (22%). Over 54% of SOA formations can be effectively prevented by eliminating emissions from the combustion of fossil fuel (liquid oil + natural gas and coal). However, the relative equilibrium between these fuels differed at the domain levels.

A nexus between sustainability, energy utilisation and economic growth at aggregate and disaggregate level: a case of India

PurposeThe growth of the Indian economy is accompanied by the rising trend of energy utilisation and its devastating effect on the environment. It is vital to understand the nexus between energy utilisation, climate and environment degradation and growth to devise a constructive policy framework for achieving the goal of sustainable growth. This study aims to analyse the long- and short-run association and direction of association between energy utilisation, carbon emission and growth of the Indian economy in the presence of structural break.Design/methodology/approachThe study probes the association and direction of association between variables at both aggregate (total energy utilisation, total carbon emission and gross domestic product [GDP]) and disaggregates level (coal utilisation and coal emission, oil utilisation and oil emission, natural gas utilisation and natural gas emission along with GDP) over the time period of 50 years, i.e. 1971–2020. Autoregressive distributed lag model is used to examine the association between the variables and presence of structural break is confirmed with the help of Zivot–Andrews unit root test. To check the direction of association, vector error correction model Granger causality is performed.FindingsAggregate carbon emissions are affected positively by aggregate energy consumption and GDP in both short and long run. Bidirectional causality exists between total emissions and GDP, whereas a unidirectional causality runs from energy consumption towards carbon emission and GDP in the long run. At disaggregate level, consumption of coal energy impacts positively, whereas GDP influences coal emission negatively in the long run only. Furthermore, consumption of oil and GDP influences oil emissions positively in the long run. Lastly, natural gas is the energy source that has the fewest emissions in both short and long run.Originality/valueThere is a rapidly growing body of research on the connections and cause-and-effect relationships between energy use, economic growth and carbon emissions, but it has not conclusively proved how important the presence of structural breaks or changes within the economy is in shaping the outcomes of the aforementioned variables, especially when focusing on the Indian economy. By including the impact of structural break on the association between energy use, carbon emission and growth, where energy use and carbon emission are evaluated at both aggregate and disaggregate level, the current study aims to fill this gap in Indian literature.

RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km2) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km2). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control.

Air Quality Impact Estimation Due to Uncontrolled Emissions from Capuava Petrochemical Complex in the Metropolitan Area of São Paulo (MASP), Brazil

In the second quarter of 2021, the companies at the Capuava Petrochemical Complex (CPC, Santo André, Brazil) carried out a 50-day scheduled shutdown for the maintenance and installation of new industrial equipment. This process resulted in severe uncontrolled emissions of particulate matter (PM) and volatile organic compounds (VOCs) in a densely populated residential area (~3400 inhabitants/km2). VOCs can be emitted directly into the atmosphere in urban areas by vehicle exhausts, fuel evaporation, solvent use, emissions of natural gas, and industrial processes. PM is emitted by vehicle exhausts, mainly those powered by diesel, industrial processes, and re-suspended soil dust, in addition to that produced in the atmosphere by photochemical reactions. Our statistical analyses compared the previous (2017–2020) and subsequent (2021–2022) periods from this episode (April–May 2021) from the official air quality monitoring network of the PM10, benzene, and toluene hourly data to improve the proportion of this period of uncontrolled emissions. Near-field simulations were also performed to evaluate the dispersion of pollutants of industrial origin, applying the Gaussian plume model AERMOD (steady-state plume model), estimating the concentrations of VOC and particulate matter (PM10) in which the population was exposed in the region surrounding the CPC. The results comparing the four previous years showed an increase in the mean concentrations by a factor of 2 for PM10, benzene, and toluene, reaching maximum values during the episode of 174 µg m−3 (PM10), 79.1 µg m−3 (benzene), and 58.7 µg m−3 (toluene). Meanwhile, these higher concentrations continued to be observed after the episode, but their variation cannot be fully explained yet. However, it is worth highlighting that this corresponds to the post-pandemic period and the 2022 data also correspond to the period from January to June, that is, they do not represent the annual variation. A linear correlation indicated that CPC could have been responsible for more than 60% of benzene measured at the Capuava Air Quality Station (AQS). However, the PM10 behavior was not fully explained by the model. AERMOD showed that the VOC plume had the potential to reach a large part of Mauá and Santo André municipalities, with the potential to affect the health of more than 1 million inhabitants.

A low carbon methanol process using natural gas pyrolysis in a catalytic molten metal bubble reactor

Due to its abundance and relatively low cost, natural gas will continue to be a common feedstock for methanol production, at least in the near to medium term. The present work focuses on natural gas pyrolysis in a catalytic liquid metal bubble reactor, combined with CO2utilization, as an alternative to traditional steam reforming processes. The pyrolysis reactor consists of multiple vertical tubes filled with molten Cu0.45Bi0.55alloy and heated in the radiant zone of a fired heater. The reactor geometry and operating conditions are optimized by rigorously accounting for the coupling of bubble flow hydrodynamics with the catalytic and non-catalytic kinetics of methane pyrolysis in molten metals. Pyrolysis generates hydrogen and solid carbon, which rise first through the liquid metal and then through a layer of molten salt (e.g., NaBr) whose purpose is to minimize the loss of liquid metal entrained with the carbon. The carbon forms a recoverable layer above the salt, and the hydrogen reacts with captured CO2in aseparate methanol synthesis reactor. The cradle-to-gate and cradle-to-grave CO2emissions are 0.074 and 1.45 t CO2-eq/t methanol, respectively, for median supply chain emissions of natural gas and captured CO2. This is a lower carbon footprint than other practical pyrolysis and steam methane reforming processes. Negative cradle-to-gate CO2emissions are achievable if natural gas and captured CO2are sourced from supply chains with low emissions. The estimated carbon production cost of $0.27/kg iswell below the recent market price of carbon black in the United States. The production cost of carbon is most sensitive to the fixed capital investment of the plant, the captured CO2cost, the natural gas and methanol costs, and the discount rate.

Experience of using solar energy in water lifting systems: A case study of Uzbekistan

This article discusses modern schemes of using solar energy in the systems of the Ministry of Water Management. The analysis of electricity generation by photovoltaic water-lifting stations by months has been carried out and the effectiveness of their use as a reliable source of power supply to pumps has been proved. The payback period of solar water lifting systems is calculated. Calculations show that the payback period for solar water-lifting systems, considering only the cost of saved natural gas and carbon dioxide emissions is no more than 6 years. It should be noted that the life of the PVP is more than 20 years. Tax breaks on property and land use, provided for by the legislation of the Republic of Uzbekistan, further shorten the payback period.

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La0.87Sr0.13CrO3, Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH4, two types of natural gas simulants, and CH4+NH3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

Discussion on the Life Cycle of Greenhouse Gas Emissions from the 2021 UNECE LCA Report and Korean LCI DB for Electricity Generation

Objectives : The greenhouse gas (GHG) emissions of coal, natural gas, wind, solar, hydropower, and nuclear electricity generation mentioned in the United Nations Economic Commission for Europe (UNECE) LCA report were discussed. Korean GHG emissions were calculated from the electricity database (DB) of the Korean national life cycle inventory (LCI), compared with the international average, and then discussed. The aim of this study was to discuss the necessity of updating and internationalizing the national LCI DB.Methods : The GHG emissions from the generation of coal power, natural gas power, wind power, onshore wind power, solar power, and hydropower in the UNECE LCA study were analyzed, and climate change, that is, GHG emissions, among the environmental impact assessment categories was discussed. The GHG emissions per 1 kWh functional unit were calculated from the electricity DB of the Korean national LCI by applying the global warming potential to GHGs.Results and Discussion : The GHG emissions from natural gas (452 g CO<sub>2</sub>-eq./kWh), nuclear (6.3 g CO<sub>2</sub>-eq./kWh), and solar power generation (53 g CO<sub>2</sub>-eq./kWh) presented in the national LCI DB were within the range of results presented in the UNECE LCA report. However, GHG emissions of Korean coal power generation exceeded the UNECE average and highest ranges. The GHG emissions of Korean hard coal and bituminous power generation were 1,351 g CO<sub>2</sub>-eq./kWh and 1,160 g CO<sub>2</sub>-eq./kWh, respectively, which were higher than the highest UNECE value, 1,095 g CO<sub>2</sub> eq./kWh (China). Analysis of the Korean electricity LCI DB for coal power plants showed that bituminous coal-fired power plants that use more brown coal, which is known to be relatively low-quality coal, produce less CO<sub>2</sub> than anthracite coal power plants. In Korean coal-fired power plants, it is necessary to check whether CO<sub>2</sub> generated from processes other than the combustion process is significant.Conclusion : Electricity accounts for the largest portion when calculating national greenhouse gas emissions, therefore, accurate data is necessary. Korea, which is highly dependent on coal-fired power generation for electricity production, should update its electricity LCI DB as soon as possible to increase national competitiveness and use it as a basis for realizing carbon neutrality by 2050.

Real-time management of distributed multi-energy resources in multi-energy networks

The replacement of fossil fuel power plants by variable renewable energy sources is reducing the flexibility of the energy system, which puts at risk its security. Exploiting the flexibility of distributed multi-energy resources through aggregators presents a solution for this problem. In this context, this paper presents a new hierarchical model predictive control framework to assist multi-energy aggregators in the network-secure delivery of multi-energy services traded in electricity, natural gas, green hydrogen, and carbon markets. This work builds upon and complements a previous work from the same authors related to bidding strategies for day-ahead markets — it closes the cycle of aggregators’ participation in multi-energy markets, i.e., day-ahead bidding and real-time activation of flexibility services. This new model predictive control framework uses the alternating direction method of multipliers on a rolling horizon to negotiate the network-secure delivery of multi-energy services between aggregators and distribution system operators of electricity, gas, and heat networks. We used the new model predictive control framework to conduct two studies. In the first study, we found that considering multi-energy network constraints at both day-ahead and real-time optimization stages produces the most cost-effective and reliable solution to aggregators, outperforming state-of-the-art approaches in terms of cost and network security. In the second study, we found that the adoption of a green hydrogen policy by multi-energy aggregators can reduce their consumption of natural gas and respective CO2 emissions significantly if carbon and green hydrogen prices are competitive.

Experimental Study of Effect of Injection Timing on Port Fuel Injection Gasoline, Port Fuel Injection Compressed Natural Gas, and Direct Injection Compressed Natural Gas Engine Performance, Combustion, and Emissions Characteristics

Abstract Compressed natural gas (CNG) has gained popularity due to its wide availability, higher efficiency, and lower emissions compared to gasoline. However, the lower flame speed characteristics of CNG with conventional port injection reduce the CNG engine volumetric efficiency and power output. CNG's lower gas jet momentum during a low load operation creates a non-uniform air-fuel mixture that affects ignition and combustion quality. Direct injection of CNG with optimum injection timing is expected to improve volumetric efficiency, ignition quality, and combustion process. In this study, a comparative study on the effect of end-of-injection (EOI) timing on volumetric efficiency, thermal efficiency, combustion duration, and emissions was carried out in a single-cylinder port fuel injection (PFI) spark-ignition engine using gasoline and CNG, and direct injection (DI) spark ignition engine using CNG. The experiments were performed at two-part load operations of 20% and 40% throttle at 900 and 1500 rpm. Experimental results indicate that the PFI CNG engine is more influential in EOI timing than gasoline engines. The performance of the PFI CNG engine is improved when the injection occurs during the intake valve open period compared to the closed valve period with higher thermal efficiency, volumetric efficiency, and indicated mean effective power (IMEP). A shorter flame development angle and combustion duration were observed when EOI timing was in the open intake valve condition. DI CNG improved volumetric efficiency at advanced EOI timing compared to the PFI CNG engine. However, the combustion process is critically dependent on injection timing and air-fuel mixing duration. A three-dimensional computational fluid dynamics simulation was conducted to evaluate the effect of advanced and retarded EOI timing on DI CNG engine's in-cylinder turbulent kinetic energy development and in-cylinder equivalence ratio near the ignition point. An excess advanced EOI timing resulted in stratified rich and retarded EOI timing results in loss of turbulence energy, leading to a slightly rich and lean mixture for advanced and retarded EOI timing, respectively. Hence, an optimum EOI timing provides a conducive environment to initiate the combustion and flame front propagation. Further, advanced EOI timing was required at higher throttle opening and engine speed. The emissions in DI CNG were also greatly affected by EOI timing.

Techno-economic analysis of advanced small modular nuclear reactors

Small modular nuclear reactors (SMRs) represent a robust opportunity to develop low-carbon and reliable power with the potential to meet cost parity with conventional power systems. This study presents a detailed, bottom-up economic evaluation of a 12 × 77 MWe (924 MWe total) light-water SMR (LW-SMR) plant, a 4 × 262 MWe (1,048 MWe) gas-cooled SMR (GC-SMR) plant, and a 5 × 200 MWe (1,000 MWe total) molten salt SMR (MS-SMR) plant. Cost estimates are derived from equipment costs, labor hours, material inputs, and process-engineering models. The advanced SMRs are compared to natural gas combined cycle plants with and without post-combustion carbon capture and a conventional large nuclear reactor. Overnight capital cost (OCC) and levelized cost of energy (LCOE) estimates are developed. The OCC of the LW-SMR, GC-SMR, and MS-SMR are found to be $4,844/kW, $4,355/kW, and $3,985/kW respectively. The LCOE of the LW-SMR, GC-SMR, and MS-SMR are found to be $89.6/MWh, $81.5/MWh, and $80.6/MWh respectively. A Monte Carlo analysis is performed, for which the OCC and construction time of the LW-SMR is found to have a lower mean and standard deviation than a conventional large reactor. The LW-SMR OCC is found to have a mean of $5,233/kW with a standard deviation of $658/kW and a 90 % probability of remaining between $4,254/kW and $6,399/kW, while the construction duration is found to have a mean of 4.5 years with a standard deviation of 0.8 years and a 90 % probability of remaining between 3.4 and 6.0 years. The economic impact of economies of scale, simplification, modularization, and construction time for SMRs are discussed. Additionally, policy implications for direct SMR capital subsidies and the impact of a carbon tax on natural gas emissions are explored.