Price Of Fuels Research Articles

Design and development of model for proton exchange membrane fuel cell

Fuel prices are rising, and fossil fuel resources are depleting. This fuel is consumed by conventional fuel vehicles, which contributes to increased greenhouse gas emissions and air pollution. Various vehicle technologies have been introduced in modern times. Among these innovations, fuel cell vehicle technology stands out due to its ease of use, robustness, and simplicity. The primary energy source for this system is hydrogen. Fuel cells use hydrogen to generate electricity through a chemical process that does not involve combustion. The purpose of this paper is to describe the design and development of a simulation model for a fuel cell (proton exchange membrane type) vehicle using mathematical equations and the MATLAB/Simulink R2020a software, taking into account all voltage losses and temperature variations of the proton exchange membrane (PEM) type fuel cell. The designed model is validated in two ways: first by generating V-I characteristics for the designed PEM-type fuel cell, and then by performing a performance test in Simulink using the driving cycle on one of the fuel cell vehicles. The obtained simulated results are nearly identical to the expected results, and the designed PEM-type fuel cell performed well in all real-time driving cycle test conditions.

Artificial intelligence applications in solar energy

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

Impact of Changes in Fuel Prices on Inflation and Economic Growth in Mozambique

The fuel oil is a basic input to production, so an increase in oil price leads to a rise in production costs that induces firms to lower output. In parallel, oil prices change also effects, indirectly, the consumption, through its positive relation with disposable income. However, despite the relevance of the variation in fuel prices in economy, there are no recent study conducted to analyse the impact of changes in fuel prices on GDP and CPI in Mozambique. This study aims to contribute to filling the information gap by providing the quantification of the impact of fuel prices on GDP and CPI. So, the study aim to determine the impact of changes in FUEL prices on Inflation (Consumer Price Index – CPI) and Economic Growth (Gross Domestic Product – GDP), using quarter data from 2007 to 2020. Descriptive statistics, Unit Root Test, Johansen Cointegration Test and Error Correction Model, Granger Causality Econometric Models, Impulse and Response Function and Variance Decomposition Analysis are used. The results indicate that in Mozambique there are a cointegrating long-run relationship between FUEL prices and CPI and GDP, which suggest that the impact of FUEL prices shocks to CPI and GDP in short-run and long-run path. Also, the results show that changes in GDP and FUEL prices lead to an increase in CPI by 3.0% and 1.3%, respectively, <i>coeteris paribus</i>. The previous quarter’s errors (or deviation from long-run equilibrium) are corrected for with the current quarter at a convergence speed of 3.5% to GDP, and 4.8% to CPI. Granger causality tests also indicate causality between CPI and GDP, and the changes in GDP causes changes in other variables.

Investigation of hybrid power-to-hydrogen/natural gas and hydrogen-to-X system in Cameroon

In Sub-Saharan Africa (SSA), the capacity to generate energy faces significant hurdles. Despite efforts to integrate renewable energy sources and natural gas power plants into the energy portfolio, the desired reduction in environmental impact and alleviation of energy poverty remain elusive. Hence, exploring a spectrum of hybrid technologies, encompassing storage and hydrogen-based solutions, is imperative to optimize energy production while mitigating harmful emissions. To exemplify this necessity, the 216 MW Kribi gas power plant in Cameroon is the case study. The primary aim is to investigate cutting-edge emissions and energy schemes within the SSA. This paper assessed the minimum complaint load technique and four power-to-fuel options from technical, financial, and environmental perspectives to assess the viability of a natural gas fuel system powered with hydrogen in a hybrid mode. The system generates hydrogen by using water electrolysis, with photovoltaic electricity and gas power plant. This research also assesses process efficiency, storage capacity, annual costs, carbon avoided costs, and production prices for various fuels. Results showed that the LCOE from a photovoltaic solar plant is 0.19$/kWh, with the Power-to-Hydrogen process (76.2% efficiency) being the most efficient, followed by the ammonia and urea processes. The study gives a detailed examination of the hybrid hydrogen natural gas fuel system. According to the annual cost breakdown, the primary costs are associated with the acquisition of electrical energy and electrolyser CAPEX and OPEX, which account for 95% of total costs. Urea is the cheapest mass fuel. However, it costs more in terms of energy. Hydrogen is the most cost-effective source of energy. In terms of energy storage and energy density by volume, the methane resulted as the most suitable solution, while the ammonia resulted as the best H2 storage medium in terms of kg of H2 per m3 of storage (108 kgH2/m3). By substituting the fuel system with 15% H2, the environmental effects are reduced by 1622 tons per year, while carbon capture technology gathered 16,664 tons of CO2 for methanation and urea operations, yielding a total carbon averted cost of 21 $/ton.

Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal–organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.

Fuel Price Networks in the EU

This study deals with the evolution of fuels’ prices over time in the EU. The central research inquiry revolves around whether there exists any correlation among the trajectories followed by national prices in the gasoline and diesel markets. The EU, and more specifically the Euro-Area, by its construction, is treated as an OCA (Optimum Currency Area). In an OCA, certain conditions are met to ensure the smooth functioning of a common currency. The fuel price synchronization is essential because it contributes to the effective implementation of policies and promotes stability across the entire macroeconomy. The study covers the period of 2017–2022. For each type of fuel and year, we construct an individual network where network nodes represent the EU member states while the edges connecting these nodes represent strong temporal fuel price correlations among the member states. The properties of the resulting networks are analyzed within a Complex Network framework. Our goal is twofold: first, to detect any potential convergence or divergence in the trajectories of the prices, and second, to investigate the impact of tax and duty implementation on the co-movements of the prices. The empirical findings show that diesel markets have a more pronounced pattern of price co-movements compared to the gasoline markets. Moreover, the application of fuel taxation policies seems to adversely affect the co-movements of prices.

Impact of endogenous learning curves on maritime transition pathways

The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.

Capital Market Reaction To The Announcement Of Fuel Price Increase On Stock Returns Of Lq 45 Transportation Sub-Sector In 2023

The research was conducted to find out the reaction of the capital market to the rise in BBM in transportation and logistics sector companies listed on the Indonesian Stock Exchange. This study is an event study or event that arises directly from the rise in the price of oil (BBM) on October 1, 2023, against the market reaction based on the actions of transportation and logistics companies. Abnormal Return and Trading Volume Activity are variables used to analyze reactions. The data collection method used in this research is the Quantitative Data Method. Observations were conducted for 5 days before and 5 days after the BBM rise on October 1, 2023, using the Wilcoxon Signed Ranks test with a sample of 10 companies and a data test of 110. On the test results, there were abnormal differences in return and trading volume activity before and after the rise in BBM prices, the results could help market participants understand market dynamics, find investment opportunities, and manage risk. With a rise in sales, this means investors respond to events so that the markets react. The findings support the theory of signals because investors see the announcement of a BBM rise as a signal that can affect the stock price.

Exploring the technical feasibility of carbon capture onboard ships

International shipping is crucial for freight transport, but it relies primarily on fossil fuels, contributing 3% of global greenhouse gas emissions. This calls for urgent action to decarbonize the shipping industry. While renewable-based e-fuels are a strong candidate for decarbonization of this sector in the long run, deploying these to the required scale will take significant time, considering technical modifications onboard the vessels, as well as the changes in fuel production and infrastructure for distribution. Carbon capture from ships is another route to emission reduction that can be implemented faster due to the relatively high maturity of the technology. Tankers, dry bulk carriers, and container vessels contribute a majority of global shipping emissions and are therefore prime candidates for carbon capture and storage. Solvent-based post-combustion capture is chosen for this study as it is mature and suitable for marine applications, though technical, economic, environmental, and practical challenges remain. This paper assesses the technical feasibility of the capture system for ships; both retrofit and newbuild vessels. While achieving zero-emissions presents a significant challenge, it is feasible to attain 70%–90% CO2 reduction through carbon capture in the near term. This reduction is crucial for transforming the industry into a more sustainable and environmentally friendly state. The limitation of space onboard is identified as a key factor in determining the viability, and the 70%–100% increase in energy consumption between existing ships and newbuild will be a substantial operational challenge. However, the high fuel consumption of the capture system could be economically acceptable if the price of alternative fuels remains high.

Fuel pricing rules: more competition by virtue of less volatility

ZusammenfassungDie 2013 eingerichtete Markttransparenzstelle für Kraftstoffe instruiert die Verbraucher über die aktuellen Preise an den Zapfsäulen, die sich lokal aber weitaus weniger unterscheiden als Verfechter des Informationsportals seinerzeit erwarteten. Anscheinend fördert ein erleichterter Konditionenvergleich das Parallelverhalten der Anbieter und vermindert die Rivalitätsbeziehung unter den Verkäufern. In einem recht allgemein formulierten Modell werden die beiden grundsätzlichen preispolitischen Alternativen gegenübergestellt, um eine Regulierungsvorschrift abzuleiten, die eine schärfere Konkurrenz zwischen den Tankstellenbetreibern erwarten lässt. Die damit verbundene temporäre Preisbindung verspricht, die Effektivität der Markttransparenzstelle zu fördern.

Distributed Generation (DG) system using COPRAS method

Distributed Generation (DG) system. Distributed Generation (DG) power systems, Expansion of power systems in remote areas, and Very recently as a sustainable way of electrification popular. Depletion of conventional fossil fuels, Fuel price volatility, and emissions Awareness environment about reduction, Due to this its demand is increasing. DG systems have additional power quality challenges. There are many types of DG power sources that produce electrical power with different voltages at various frequencies. RES-Based Distributed Generation (DG) Systems are conventional energy systems In finding new modern solutions for planning have contributed Widespread use of DG is old and Defines new views, Current delivery system The only way to deal with the project Calling for more complex defenses. COPRAS (Complex Proportionality Assessment) is very Multiple criteria used in decision-making methods is one. This technique is different Decision making by researchers Used to solve problems. Investment cost, operating cost, Primary energy consumption, CO2 emissions. Energy storage system, fuel system, Wind Solar Hybrid System, Conventional System, PV energy storage system. Involvement of Distributed Generation (DG) system PV energy storage system ranked 1st, and the Conventional system also got 5th position.

Converting the threats of fossil fuel-based energy generation into opportunities for renewable energy development in India

Many researchers and non-governmental organizations have raised concerns about environmental degradation in developing nations. India is also one of the developing nations primarily dependent upon conventional energy resources to meet the energy demand of the exponentially increasing population. India has a tremendous reserve of renewable energy, which can satisfy the increasing energy demand of the nation. Being at an emerging stage, India needs to identify the significant enablers that can empower renewable energy development. Interpretive Structural Modeling (ISM) has been employed to represent the interrelationships among the enablers by developing a hierarchy structural model. The driving and dependence powers of the enablers are calculated by integrating the fuzzy concept with the Matrix Impact of Cross Multiplication Applied to Classification (MICMAC) technique. Global pressure for cleaner energy, increasing price of fossil fuels, and rising energy demand with the population can act as the most significant enablers. The present study is one of the preliminary studies that has converted current fossil fuel-based energy generation threats into opportunities for renewable energy development. The developed structural model of the enablers would represent hierarchical strategies at different stages for developing renewable energy in developing nations.

Assessing the Relationship between Fuel and Charcoal Prices in Uganda

Charcoal is a dominant energy source in urban areas of Uganda, and increases in retail prices in the past have led to social unrest. This paper assesses the relationship between charcoal and fuel prices to determine whether fuel prices influence the retail price of charcoal. We specify a transportation cost model for charcoal supply and derive the reduced-form equilibrium price function. We estimate an error-correction model for the equilibrium price with monthly data from July 2010 to January 2021 to determine whether there are long-term and/or short-term relationships between the retail and supply prices of charcoal and the prices of diesel and other fuel types. As the price data are integrated of orders zero and one, the autoregressive distributed lag (ARDL) bounds test is used. The results show that there is a long-term relationship (cointegration) between the retail price of charcoal and the supply price of charcoal and the price of kerosene, which is a substitute energy source for the end users. The prices of firewood and diesel are not statistically significant in the model. The long-term equation includes a positive trend, indicating that the retail price of charcoal is increasing more over time than implied by the supply price of charcoal and the price of kerosene. The increasing demand from a growing urban population and the reduced supply from deforestation are trends that will increase the equilibrium price of charcoal, as observed.

Impact of Rising Fuel Prices on Taxi Drivers

Impact of Rising Fuel Prices on Taxi Drivers

Empirical Analysis of Mining Costs Amid Energy Price Volatility for Secondary Deposits in Quarrying

The outlined methodology for calculating operating costs in open-cast mines and quarries not only facilitates the selection of optimal mining equipment and systems for working lower-grade secondary deposits but also adds significant value in navigating the challenges of fluctuating prices of energy carriers and fuels. Moreover, the study rigorously assesses the impact of mining operations on the performance of deployed mining equipment and the overall viability of the rock mining project. The selection procedure relies on a comprehensive analysis of the technical and economic parameters of selected solutions, providing critical insights to guide decisions regarding the continuation or discontinuation of mining operations. We analyse, based on empirical data, the technical and economic parameters of several variants of mining equipment to be used for the extraction of rocks and stones from secondary deposits in conditions of fluctuation depending on the level of energy prices, in order to find the best configuration in terms of operating costs and potential revenue. In addition to analysing the structure of operating costs, the article presents their correlation with the required profit from the sale of raw materials using the linear correlation method. The results clearly demonstrate the economic viability of mining secondary deposits, taking into account the actual costs incurred by mining companies.

Impacts of Fuel Prices and Regulations on Electricity Generation Emissions and Urban Air Quality

Impacts of Fuel Prices and Regulations on Electricity Generation Emissions and Urban Air Quality

How the fuel prices, interest rates, and exchange rate affect inflation and economic growth

The objective of this study is to examine the impact of fuel costs, interest rates, and exchange rates on both inflation and economic growth in the province of Central Kalimantan. The research use path analysis as the methodology. Path analysis is a technique employed to ascertain the direct and indirect association between the independent factors and the dependent variable while considering the presence of a mediating variable. The data utilized in this investigation is classified as secondary data. The data are sourced from publications by the Central Statistics Agency (BPS), Bank Indonesia (BI), and Pertamina. The research employed the Path Analysis methodology. The findings of this study suggest that fuel prices exert a direct, positive, and statistically significant impact on inflation. Interest rates exert a direct and substantial impact on inflation. The Exchange Rate has a direct and negligible impact on Inflation. Fuel prices exert a direct and substantial impact on economic growth. Interest rates exert a direct and negligible impact on economic growth. The Exchange Rate exerts a direct and substantial adverse impact on Economic Growth. Inflation exerts a direct and substantial adverse impact on economic growth. Fuel prices have little direct impact on economic growth via inflation. Interest rates do not have a direct impact on economic growth by means of inflation. The exchange rate has an indirect impact on economic growth via influencing inflation.

PERFORMANCE CHARACTERIZATION, PRICE EVALUATION, AND ESTIMATION OF EMISSIONS OF INTERNAL COMBUSTION ENGINES WITH FUEL ADDITIVES

Fuel prices have been escalating day by day due to resources being limited. The key focus of this research is to evaluate the performance of internal combustion engines by the inverse correlation between fuel prices and engine efficiency and to encourage future advancements in the development of efficient fuels for automobiles. Investigations indicate the impact of various fuel additives, such as butane, ethane, propane, cracking liquid (0X11257), and XAG 818, on the fuel's characteristics. This work attempts to analyze existing data on fuel prices using mathematical tools like MATLAB and the various factors affecting them over the years. The analysis shows that there is about a 14% increase in values of engine parameters, such as brake thermal efficiency. The heat energy generated with suitable fuel additives was 12.69% more than low-quality fuel additives. Therefore, using excellent high-quality fuel additives benefits engine health and performance.

Fuel Price Increase: Unveiling the Torments and Agonies of the Transportation Sector — Basis for Policy Formation

This study aimed to investigate the impact of oil price hikes on the transportation sector in Padada, Davao del Sur. Utilizing a phenomenological approach and qualitative design, purposive sampling was conducted among fourteen participants from various transportation sectors. Data was analyzed through Data Reduction, Data Display, and Conclusion Verification, with assistance from a data analyst. The findings indicate that rising fuel prices significantly affect livelihoods and lifestyles, highlighting the broader impact of global issues on local communities. Additionally, increased oil prices have led to fare adjustments, impacting passengers' monthly budgets. The study suggests that government programs to support local transportation owners and drivers and initiatives for domestic oil exploration could mitigate these effects. Budget constraints and fuel price stabilization policies were also identified as relevant considerations for public administration. Consequently, it is recommended that the Local Government Unit of Padada, Davao del Sur, propose policies to address the impact of oil price increases on the transportation sector. Additionally, revising and strengthening Municipality Ordinance 003, Series of 2009, which allows for passenger fare adjustments based on daily oil prices, is advised.

Impact of Changes in Fuel Prices on Inflation and Economic Growth in Mozambique

Impact of Changes in Fuel Prices on Inflation and Economic Growth in Mozambique