Fuel-based Methods Research Articles

Educational Station for the Generation and Use of Green Hydrogen

Globally, electrical energy predominantly derives from fossil fuels such as coal, oil, and natural gas, which significantly emit greenhouse gases, thereby accelerating ecosystem degradation and climate change. In response, recent decades have witnessed a surge in the development of clean, sustainable electricity generation technologies such as wind and solar photovoltaic systems. These renewable sources are expected to replace fossil fuel-based methods but are limited by their dependency on variable weather conditions, which cannot align with electricity demand. Periods when energy production exceeds demand highlight the critical role of innovative storage solutions. Notably, surplus energy can be utilized for hydrogen production via water electrolysis, producing “Green Hydrogen”, which generates no polluting emissions. This shift towards sustainable technologies necessitates novel educational approaches. It is essential to revise curricula to include these technologies, ensuring students are well-prepared for the evolving energy sector. This article discusses the design and construction of a green hydrogen educational station built with commercial materials for laboratory use, embodying the integration of renewable energy technologies into academic programs and promoting hands-on learning experiences in energy management and sustainability.

Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis

During the past 20 years, due to climate change, the government and the private sector have significantly focused on relying on non-fossil fuel-based methods for their energy needs. Climate change-related events, such as unusual weather conditions, abnormal temperature spikes, etc., have an adverse influence on clean energy-based investments. In the given study, we intend to focus on how an incremental temperature rise could affect investors’ perceptions of clean energy assets. To understand the investor-based sentiment on climate change, we utilize prominent clean energy ETFs (exchange traded funds) and consider the temperature’s effect on them. The daily average temperatures of the three most dynamic international financial centers: New York, London and Tokyo, are taken as predictors. Deep learning-based neural networks are applied to understand both the linear and non-linear relationships between the desired variables and identify the causal effects. The results indicate that in almost all the cases with desired lags, there is some sort of non-linear causality, irrespective of linear causality effects. We hope this occurrence can help portfolio managers and environmental professionals in identifying novel climate change-related factors when considering the temperature-related risks.

Differential impacts of urbanization characteristics on city-level carbon emissions from passenger transport on road: Evidence from 360 cities in China

Although it's well known that the carbon intensity from passenger transport of cities varies widely, few studies assessed the disparities of that in city-level and its underlying factors due to the limited availability of data, and thus developed effective strategies for different types of cities. This study is the first to present a comprehensive inventory of emissions from passenger transport on road for 360 cities in mainland China for 2018, based on the data from 5 transport modes and evaluated by combining distance-based and top-down fuel-based methods. In 2018, passenger transport on road in China emitted 1076 MtC. A large portion of CO2 emissions was identified in the southern and eastern coastal areas and capital cities. GDP, population, and policy were the major factors determining the total CO2 emissions, but not carbon intensity. Clustering analysis of carbon intensity and 9 socio-economic predictors, using a tree-based regression model, clustered the 360 cities into 6 groups and showed that higher carbon intensities occurred in both affluent city groups with a high active population share and less affluent city groups with a low population density but high density of trip destinations. Forward-and-backward stepwise multiple regression analysis indicated that constructing a compact city is more effective for city groups with a high income and high active population share. Enhancing land-use mixed degree is more critical for city groups with a high income and low active population share, while shortening travel distance by intensifying infrastructure construction is more important for the less affluent city groups.

Techno-economic analysis and Monte Carlo simulation of green hydrogen production technology through various water electrolysis technologies

The green hydrogen produced by water electrolysis combined with renewable energy is inevitable for a sustainable and clean energy society. However, the green hydrogen production cost is still more expensive than fossil fuel-based methods such as steam methane reforming and it is the biggest challenge. In this study, to determine the most economical way to obtain green hydrogen, a technoeconomic analysis was performed on four different water electrolysis technologies, that is, alkaline water electrolysis (AWE), proton exchange membrane electrolysis (PEMEC), solid oxide electrolysis with electric heaters (SOEC (E.H)), and solid oxide electrolysis combined with a waste heat source (SOEC (W.H)). It is assumed that the SOEC (W.H) system is linked to the adjacent power plant and receives waste heat. The net present value calculation, sensitivity analysis, and Monte Carlo method were performed for the 4 cases presented above. Calculated unit hydrogen production cost was 7.60, 8.55, 10.16, and 7.16 $/kgH2 for AWE, PEMEC, SOEC (E.H), and SOEC (W.H), respectively. The analysis results show that the SOEC (W.H) has the most competitive case due to sensible heat energy saving and higher stack efficiency. Sensitivity analysis determined the most influential cost factor, and consequently, it seems necessary to reduce capital costs along with subsidizing electricity costs and tax rates to compete with gray hydrogen.

Hydrogen farm concept: A Perspective for Turkey

Hydrogen farm concept: A Perspective for Turkey

A fuel-based method for updating mobile source emissions during the COVID-19 pandemic

The COVID-19 pandemic and ensuing lockdown of many US States resulted in rapid changes to motor vehicle traffic and their associated emissions. This presents a challenge for air quality modelling and forecasting during this period, in that transportation emission inventories need to be updated in near real-time. Here, we update the previously developed fuel-based inventory of vehicle emissions (FIVE) to account for changes due to COVID-19 lockdowns. We first construct a 2020 business-as-usual (BAU) case inventory and adjust the emissions for a COVID-19 case using monthly fuel sales information. We evaluate cellular phone-based mobility data products (Google COVID-19 Community Mobility, Apple COVID-19 Mobility Trends) in comparison to embedded traffic monitoring sites in four US cities. We find that mobility datasets tend to overestimate traffic reductions in April 2020 (i.e. lockdown period), while fuel sales adjustments are more similar to changes observed by traffic monitors; for example, mobility-based methods for scaling emissions result in an approximately two-times greater estimate of on-road nitrogen oxide (NO x ) reductions in April 2020 than we find using a fuel-based method. Overall, FIVE estimates a 20%–25% reduction in mobile source NO x emissions in April 2020 versus BAU, and a smaller 6%–7% drop by July. Reductions in April showed considerable spatial heterogeneity, ranging from 6% to 39% at the state level. Similar decreases are found for carbon monoxide (CO) and volatile organic compounds. Decreases to mobile source NO x emissions are expected to lower total US anthropogenic emissions by 9%–12% and 3%–4% in April and July, respectively, with larger relative impacts in urban areas. Changes to diurnal and day-of-week patterns of light- and heavy-duty vehicular traffic are evaluated and found to be relatively minor. Beyond the applicability to modelling air quality in 2020, this work also represents a methodology for quickly updating US transportation inventories and for calibrating mobility-based estimates of emissions.

Particle Size Distribution Measurements of Neat and Water-Emulsified Oxymethylene Ethers in a Heavy-Duty Diesel Engine

Diesel-fueled compression ignition engines display a distinct trade-off in particulate matter (PM) and nitrogen oxide (NOX) emissions due to the nature of diffusive combustion. The modification of fuel properties has drawn much attention since these methods offer additional potential to reduce emissions. Oxygenated fuels are reported to greatly diminish particle emissions while water emulsification of regular diesel causes a significant decrease in NOX. However, recent studies indicate that these fuel-based approaches may lead to an increase in nanoparticle emissions, which are known to be more dangerous to human health than large particles. This has raised the question about whether current engine technology is prone to nanoparticle formation.In this work, the authors present a detailed study on combustion and emission performance of the oxygenate fuel Oxymethylene Ether (OMEn, the mixture contains neat OME with chain length n = 2 − 6). In a novel approach, a single-cylinder heavy-duty diesel engine was fueled with both neat and water-emulsified OME to combine the two fuel-based methods in order to simultaneously reduce both NOX and particle emissions to a great extent. Particular emphasis was put on the particle size distribution (PSD) of emitted PM to elaborate a potentially severe drawback of these fuel-based approaches. In the process, hydrogenated vegetable oil (HVO) was used as the diesel reference fuel. The findings are summarized as such: PSD measurements of OME2-6 reveal similar particle diameters in mid-load operation and a shift to smaller particles at unfavorable engine operations compared to HVO. Water-emulsified OME2-6 reduces NOX by roughly 2-3% with a one percent increase in water concentration while maintaining a nearly constant combustion efficiency. Adverse effects on nanoparticle formation by water emulsification were not observed.

Comparative study of maximum power point tracking techniques for fuel cell powered electric vehicle

Proton Exchange Membrane Fuel Cell System (FCS) based power generation is considered for powering electric vehicle in this paper. PEMFCS delivers a fresh and best substitute to the conservative vestige fuel-based methods which is the green future. Maximum Power Point Tracking (MPPT) method is essential to elicit utmost accessible power from FCS. Perturb and Observe (P&O) and Incremental Conductance (INC) method is implemented as MPPT technique. In the this work, Brush Less DC (BLDC) motor is connected to load, fed by a Fuel Cell System (FCS) through Boost Converter (BC). The efficient MPPT technique is observed using MATLAB/Simulink.

Comparison of Three Methods for Constructing Real Driving Cycles

This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO2, CO and NOx from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.

Driving Cycles Based on Fuel Consumption

Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle (DC) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.

정속항해 시 함정 주 추진 디젤엔진의 배기가스 배출량 예측

Abstract: This study was focused on the estimations of air pollutants, such as PM(Particulate matters), SOx(Sulfur Oxides), CO 2 (Carbon diOxides) and NOx(Nitrogen Oxides), from a diesel propulsion engine installed on a naval vessel. Legislative and regulatory actions for exhaust emissions from ships are being strengthened in international communities and national governments to protect human health and the environment. In this context, various technologies have been developed from all of the nations of the worldto meet strict standards. These regulations are based on commercial ship applications and according to size,but are not suitable for military naval vessels, which have much different engine operating conditions andhull architectures. Additionally, there is no international emission control system for military ships. Emission factors have been updated for commercial ship types from work at various research institutes; however, it is difficult to develop emission factors for military vessels because of their characteristics. In this paper, exhaust emissions from diesel engines installed on naval vessels under steady navigation condition were estimated with emission inventory methodology applied to ocean going vessels using fuel-based methods and fuel sulfur content analysis.

Carbon dioxide emissions from passenger transport in China since 1949: Implications for developing sustainable transport

This paper traces the historical evolution and spatial disparity of CO2 emissions from passenger transport in China. The general trends of CO2 emissions from four passenger transport modes are estimated by both the distance-based and fuel-based methods. The results suggest that CO2 emissions from road transport represented the leading source of passenger transport CO2 emissions in China. Moreover, they have continued to grow rapidly. Air transport was the second largest contributor since 1998. Emissions from rail and water transport have remained relatively stable with lower emission intensity. At the provincial level, great regional disparity was noticeable, especially in road transport. Moreover, the decomposition analysis shows that income growth was the principal factor leading to the growth of passenger transport CO2 emissions in China for both the 1949–1979 and 1980–2009 periods. The second most important factor was increased transport intensity and modal shifts for the former and the latter period, respectively. The main factor contributed to emission reduction was the lower emission intensity supported by policies, although the effect was weak. In the future, more policies to encourage modal shifts toward sustainable transport modes and travel reduction should be encouraged.

Review of Regional Locomotive Emission Modeling and the Constraints Posed by Activity Data

Diesel–electric locomotives used by U.S. freight railroads are relatively low emitters of criteria air pollutants and greenhouse gases when compared with competing modes. However, the continuous growth in goods movement is cause for concern because locomotive emissions may grow. Railroads account for only a small fraction of all mobile source emissions, but the concentration of emissions along rail facilities raises questions about equity, in particular, environmental justice, and the relative benefits of competing modes of goods movement. This paper provides a synthesis and review of current data and methods used to account for regional locomotive activity. Understanding data limitations and methodological issues at the regional scale provides a starting point for development of more spatially detailed locomotive emission models. Methods developed by the U.S. Environmental Protection Agency and the California Air Resources Board are considered. It is found that each method produces different results and is inadequate for use at the regional (or smaller) spatial scale. Problems arise from activity measures that ignore differences in geography and freight rail services between regions or that depend on detailed operational data that are no longer available. Although detailed activity data do exist, they are not always available because they are owned by private railroads. New methods should minimize the use of detailed or confidential railroad data yet still be sensitive to local factors. Fuel-based methods provide the most hope, but greater cooperation between regulatory agencies and railroads is required.

Design and implementation of automated flexible multisource charging system for hybrid electric vehicles

The scarcity and cost of fossil fuels, combined with their greenhouse gas emissions, make the development of nonfossil fuel-based methods of transportation a high-priority task. This paper proposes the designing and sizing of a Multiple Input Hybrid Charging System (MIHCS) to be used in Hybrid Electric Vehicle (HEV) propulsion. It includes a fuel cell generator, solar cell charger, Internal Combustion Engine (ICE), flywheel storage system, on-board sources and a combined Energy Storage System (ESS) comprising a battery pack. Based on the speed range of the vehicle, multiple sources like solar energy, fuel cells and wind power are automatically selected for propulsion or charging purposes by using computer and interfacing circuits. To realise this logic, a program has been developed in 'C' language. This logic also helps in controlling the charging and discharging times of the batteries placed in the energy storage unit.

An on-road motor vehicle emissions inventory for Denver: an efficient alternative to modeling

Emission inventories from mobile sources have traditionally been obtained through computational modeling. This method, however, has intrinsic shortcomings in that the factors used incorporate only a limited amount of real-world observations. The agreement between model predictions and measurements has often been poor. Recently, a fuel-based method of obtaining on-road emissions inventories has been developed. This technique calculates emission factors in grams of pollutant per unit of fuel used (kg, gallons or l) from remote sensing measurements. Combining these factors with fuel use data, available from tax records, results in a fuel-based emission inventory. This method for obtaining emission inventories is very economical and an ideal alternative for locations lacking the resources to develop an emissions model. We have used this routine to calculate CO, HC and NO on-road running exhaust emissions inventories for the Denver Metropolitan area during several years when the enhanced I/M program has been in place. These calculations indicate a continually decreasing inventory over the 6 yr study period. The calculations are also compared with results from the recent MOBILE6 model. The modeled inventories are 30–70% higher, 40% lower, and 40–80% higher for CO, HC and NO, respectively.

A Fuel-Based Inventory for Heavy-Duty Diesel Truck Emissions

ABSTRACT A fuel-based method for estimating heavy-duty diesel truck emissions is described. In this method, emission factors are normalized to fuel consumption; vehicle activity is measured by the amount of diesel fuel consumed. For the San Francisco Bay Area during summer 1996, on-road heavy-duty diesel trucks were estimated at the upper bound to emit 110 x 103 kg/day of nitrogen oxides (NOx) and 3.7 x 103 kg/day of fine black carbon (BC) particles. These upper bound values were 2.3 and 4.5 times, respectively, the corresponding predictions of California's motor vehicle emission inventory model, MVEI 7G. Significant decreases in diesel truck activity and emissions, 7080% below typical weekday levels, were observed in the Bay Area on weekends. Reductions in diesel NOx and BC particle emissions on weekends may contribute to higher ambient ozone concentrations and higher organic carbon (OC) to BC ratios observed on weekends. Heavy-duty truck traffic peaks on weekdays during the middle of the day and falls off before the afternoon rush hour. Therefore, the diurnal pattern of heavy-duty truck travel may contribute to increases in ambient OC/BC ratios observed during late afternoon hours.

A Fuel-Based Motor Vehicle Emission Inventory.

A fuel-based methodology for calculating motor vehicle emission inventories is presented. In the fuel-based method, emission factors are normalized to fuel consumption and expressed as grams of pollutant emitted per gallon of gasoline burned. Fleet-average emission factors are calculated from the measured on-road emissions of a large, random sample of vehicles. Gasoline use is known at the state level from sales tax data, and may be disaggregated to individual air basins. A fuel-based motor vehicle CO inventory was calculated for the South Coast Air Basin in California for summer 1991. Emission factors were calculated from remote sensing measurements of more than 70,000 in-use vehicles. Stabilized exhaust emissions of CO were estimated to be 4400 tons/day for cars and 1500 tons/day for light-duty and medium- duty trucks, with an estimated uncertainty of ±20% for cars and ±30% for trucks. Total motor vehicle CO emissions, including incremental start emissions and emissions from heavy-duty vehicles were estimated to be 7900 tons/day. Fuelbased inventory estimates were greater than those of California's MVEI 7F model by factors of 2.2 for cars and 2.6 for trucks. A draft version of California's MVEI 7G model, which includes increased contributions from high-emitting vehicles and off-cycle emissions, predicted CO emissions which closely matched the fuel-based inventory. An analysis of CO mass emissions as a function of vehicle age revealed that cars and trucks which were ten or more years old were responsible for 58% of stabilized exhaust CO emissions from all cars and trucks.