Fuel Consumption Research Articles

Experimental Investigation of Indirect Aqueous Inoculated Diesel Engine Performance in Urban-Area Driving for Mechanical Performance, Fuel Consumption and NOx Emissions

Experimental Investigation of Indirect Aqueous Inoculated Diesel Engine Performance in Urban-Area Driving for Mechanical Performance, Fuel Consumption and NOx Emissions

Investigation and prediction on emissions and performance of CI engine employing binary and ternary blends with Karanja oil, used cooking oil, and Dunaliella salina oil.

Massive consumption of fossil fuels and alarming environmental degradation are motivating researchers to learn about alternative fuels. Straight vegetable oils are an alternative to fossil fuels to meet the standards. Microalgae is also a viable carbon-neutral alternative to depleting conventional fuel sources, a solution to the industrial requirement of organic consumables and an option for a green and sustainable economy for biofuels. In the present study, lipid was extracted from Karanja seeds and Dunaliella salina biomass. These were used to prepare different binary and ternary fuel blends with conventional reference diesel fuel with different proportions along with used cooking oil with their concentrations ranging from 10 to 20% (v/v). The influence of these blends on performance and emissions characteristics in CI engines has delved at varying engine loads from 0 to 100%. The binary blend with Dunaliella salina oil has increased the performance characteristics while decreasing all the major emission parameters compared to reference diesel fuel and shows a significant improvement among binary blends. Ternary blends with Dunaliella salina oil, on the other hand, have improved performance while lowering emission parameters when compared to reference diesel fuel and demonstrate a substantial improvement across ternary blends. For predicting the performance and emission characteristics of binary and ternary blends, an artificial neural network-based model was developed. The optimum blends, OB6 (90% RDF, 10% DO) and OB8 (80% RDF, 10% DO, 10% UCO), improved BSFC by 10.71%, BTE by 14.23%, and reduced BSEC by 12.45% at full load. Emissions were generally reduced, with CO2 decreasing by up to 39.39%. The simulation results demonstrated that the created 4-7-7 model was capable of accurately predicting the performance and emission characteristics of various alternative fuel blends and indicating a stronger correlation between the predicted and observed values, having a high correlation coefficient of 0.9974. Binary and ternary blends with straight vegetable oils improved CI engine performance and pollutants compared to reference diesel fuel, indicating they have the potential to replace conventional fuels for sustainable development.

LONG-TERM ENERGY DEMAND THROUGH BOTTOM-UP ANALYSIS AND PROJECTION FOR KUWAIT

In this study, an energy demand projection and analysis were conducted for the State of Kuwait, which has an oil-based economy. A bottom-up approach was used to conduct energy demand projection and analysis by utilizing the Model for Energy Demand Analysis (MAED), which is a software package developed by the International Atomic Energy Agency (IAEA). The energy demand projection was conducted until 2050, with 2019 as the base year. The total demand is expected to grow from 210.9 TWh in 2019 to 373.07 TWh in 2050. The industrial sector dominates final energy demand, with a percentage of 45.1% in 2019 and is expected to reach 55.8% by 2050. Reliance on fossil fuels for energy demand will persist with an annual growth rate of 4% throughout the projection period. The electricity demand in the household and service sectors has the highest share of space cooling during the long summer months. Policy implications in response to energy demand consumption patterns are discussed to achieve national sustainable economic growth and environmental requirements. The implementation of rules and regulations to deploy and use equipment and machinery with high energy efficiency ratings will assist in energy savings in the industrial sector. Providing incentives to use building materials with high insulation can significantly reduce the energy demand for space cooling. Setting high-efficiency rating standards for engine specifications for vehicles imported to Kuwait will help reduce the consumption of motor fuels in the transportation sector.

Two-stage hydrothermal liquefaction of Spirulina: Experimental observations, energy analysis and techno-economic assessment

The intensification of environmental pollution and significant consumption of fossil fuels has led to widespread attention on developing renewable resources. As a carbon-containing renewable energy source, Spirulina is characterized by its fast growth cycle and wide distribution. Currently, one of the research hotspots is the conversion of Spirulina into bio-oil through hydrothermal liquefaction. However, studies on the two-stage hydrothermal liquefaction of Spirulina predominantly concentrate on the product yield or composition. There is relatively little research on thermodynamic (energy) analysis and techno-economic assessment during the reaction process. The main bio-oil components from two-stage hydrothermal liquefaction of Spirulina are ketones and acids with 44.9 % and 34.1 %, respectively. Compared to no-catalysis, two-stage hydrothermal liquefaction of Spirulina over HZSM-5, alkili-modified HZSM-5, and Ni@alkali-modified HZSM-5 increase the relative content of hydrocarbons to 6.6 %, 32.9 % and 48.1 %, respectively. Energy analysis and techno-economic assessment are conducted with Aspen Plus simulation, revealing a exergy efficiency of 56.4 % for the catalytic two-stage hydrothermal liquefaction of Spirulina compared with 43.6 % over no-catalyst. The primary losses occur in the first stage of the reaction process (248.6 kW) and the cooling process (10.7 kW) for catalytic two-stage hydrothermal liquefaction of Spirulina. The hourly price of producing 1 kg bio-oil with a catalyst is $3.67, while without a catalyst, it is $4.54 kg−1; obtaining a unit of chemical exergy costs $0.14×10−3 kJ−1 through catalysis. Moreover, the fluctuations in the prices of sulfuric acid and catalysts are most sensitive to the cost price of bio-oil with or without catalysts in the two-stage hydrothermal liquefaction process, with the values being 10.06 % and 8.10 %. This study has important significance for efficient transformation and energy utilization of Spirulina.

Daily Goal-Disruptive Events Increase Truck Drivers’ Fuel Consumption via Anger and Happiness

Daily Goal-Disruptive Events Increase Truck Drivers’ Fuel Consumption via Anger and Happiness

Metal-organic frameworks (MOFs) for photoreduction of CO2

In recent decades, human industrialisation has become increasingly advanced, and the quality of life has also improved. Subsequently, the excessive consumption of fossil fuels has caused a series of problems, for example, global environmental warming caused by excessive CO2 emissions. Among numerous CO2 reduction technologies, using solar energy as an energy source for the CO2 photoreduction is considered one of the most prospective technologies. This method not only reduces the amount of CO2, but also enables to converse CO2 into other valuable chemicals, thereby achieving carbon cycling. Metal-organic frameworks (MOFs) are composed of metal ions and organic ligands as three-dimensional porous materials. MOFs have many advantages, including sizeable specific surface area, excellent CO2 absorption capacity and modifiable pore structure, leading to broad application potential in photocatalytic reduction of CO2. This article mainly introduces three aspects: functionalisation and modification of pure MOFs, MOF-based composite materials, and MOF-derived materials, which further improve the CO2 photocatalytic efficiency of MOFs. Furthermore, it includes the analysis of reported examples, discussion and outlook on reaction mechanisms and challenges. At present, the main difficulties faced by MOFs-based photocatalysts lie in their economic feasibility and poor material stability. Therefore, this review aims to help stimulate in-depth research in this area as well as better exert the advantages of MOFs.

From low-cost mineral to high-performance Li4SiO4 for solar energy storage and CO2 capture

The huge consumption of fossil fuels results in excessive CO2 emissions and its reduction has become a common concern. The combination of renewable energies (i.e., solar energy) and carbon capture, utilization, and sequestration are well recognized as two major routes to achieving carbon neutrality. Lithium orthosilicate (Li4SiO4) has been identified as one of the most promising candidates for solar energy storage and CO2 capture. However, the cost reduction and performance enhancement remain to be the main obstacles for its practical application. In this work, high-performance Li4SiO4 heat carriers have been synthesized using low-cost mineral as silicon source for solar energy storage and CO2 capture. Li4SiO4 derived from diatomite exhibited cyclic energy storage densities above 500 kJ/kg. The performance was further improved by doping alkali carbonates, particularly doping 3 wt% K2CO3 (LD-P-3K), which exhibited high and stable energy storage density of approximately 700 kJ/kg in the tested 10 cycles. Then, the mechanism of alkali doping was revealed through experiments and DFT calculations. The DSC results demonstrated that the improved performance was attributed to the formation of a eutectic melt which reduced the CO2 diffusion resistance. The DFT calculations showed that K-doped Li4SiO4 had lower adsorption energy and more significant charge migration, which meant stronger interaction. Additionally, diatomite-derived Li4SiO4 material is also a promising candidate for high-temperature CO2 capture, evidenced by its relatively high cyclic CO2 capture capacity (∼0.25 g/g). In general, satisfactory density/capacity and stability endows diatomite-derived Li4SiO4 with great prospects for both thermochemical energy storage and high-temperature CO2 capture.

STUDENT PERSPECTIVE TOWARD ENERGY CONSERVATION IN UNIVERSITI UTARA MALAYSIA

Climate change and global warming are crucial problems due to carbon dioxide (CO₂) emissions cause by fossil fuels consumption. Malaysia has a sustainable power mix but the industry and transportation sectors rely on fossil fuels to generate electricity. Energy conservation are important to improve or change the behavior of people in order to minimize energy usage because humans cannot longer rely on conventional energy sources. Due to the impact of climate change and global warning problems. The objectives of this study are (1) to determine the current practices of energy conservation among UUM students and (2) to investigate the relationship between current practices and energy conservation perspectives among UUM students. This study focuses on Universiti Utara Malaysia (UUM) students' academic session A221, which is semester 1 to semester 7 students. Researchers have used quantitative methods, the questionnaire was distributed using Google form that uploaded on social media and will receiving answers. A survey was conducted with 377 understudies to get information. The analysis was using statistical software package (SPSS) of software for using in data analysis. The results show the mean of attitude with total mean 31.87 is higher than the level of awareness, energy conservation knowledge and subjective norms, which mean that attitude is most influences perspective of students to energy conservation. Besides, the relationship between level of awareness, energy conservation knowledge, attitude and subjective norms are positive correlated. Based on the results and discussion, energy saving intervention strategies are proposed for student accommodation. Government should implement educational programs and campaigns to raise awareness toward energy conservation practice among students.

Sectoral carbon dioxide emissions and environmental sustainability in Pakistan

Global greenhouse gases and carbon dioxide emissions have escalated to concerning levels. Given the growing urbanization, industrialization, and energy consumption, it is crucial to understand how carbon dioxide emissions from various sectors influence environmental sustainability in Pakistan. The prime objective of this study is to examine the nexus between sectoral carbon dioxide emissions and environmental sustainability in Pakistan, analyzing data from 1971 to 2014. The study employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) method and the Autoregressive Distributive Lag (ARDL) model to analyze patterns and relationships, providing insights into how each sector's emissions contribute to the overall environmental impact. The results highlight that the independent variables – economic growth, population growth, and energy consumption - are the most significant contributors to overall carbon dioxide emissions, driven by the high consumption of fossil fuels. At an aggregate/disaggregate level, various models show mixed associations between dependent variables such as overall carbon dioxide emissions, carbon dioxide emissions from gaseous fuel consumption, carbon dioxide emissions from liquid fuel consumption, carbon dioxide emissions from solid fuel consumption, carbon dioxide emissions from residential buildings, commercial and public services, and carbon dioxide emissions from the transportation sector with the independent variables. Pairwise Granger causality confirms a unidirectional causality among various pairs of relationships. The study suggests that policymakers in Pakistan adopt a multi-sectoral approach to achieve environmental sustainability. It also recommends accelerating the transition to renewable energy sources such as solar, wind, and hydropower to reduce dependence on fossil fuels.

Advancing disaster management through biofuel integration: A comprehensive review

Scientific evidence has demonstrated that biofuels have the ability to reduce environmental contamination, hence addressing the underlying causes of potential disasters. Renewable fuels produced from biological resources provide flexible solutions for several aspects of crisis management, such as supplying energy for emergency generators, fuelling transportation fleets, and offering heating and cooking options. Empirical evidence supports the notion that biofuels effectively reduce carbon and sulphur emissions. Notably, certain biofuels, such as algal biodiesel, have the capacity to mitigate 0.6% of carbon dioxide output. Cities, such as Delhi, that are contaminated by the consumption of fossil fuels have a scarcity of breathing oxygen during the winter and elevated ambient temperatures during the summer. These pioneers cause inundation and tempests in vulnerable regions. The lessons acquired from various locations highlight the importance of cooperation, enhancing capabilities, and providing governmental support in order to replicate and expand successful models. To optimize the advantages of biofuels in crisis management, it is crucial to prioritize research and development projects, involve stakeholders, and raise awareness. The role of national and international policy is vital in providing incentives for the production and use of biofuels, while technical advancements are key in shaping the future of sustainability and efficiency. Communities may utilize the ideas of sustainability and resilience to leverage the transformational potential of biofuels in order to construct societies that are safer, more environmentally friendly, and better equipped to withstand challenges.

IoT Integrated Electric Car Energy Monitoring System: Case Study of IMEI TEAM UMSIDA

The increasingly rapid development of technology accompanied by the ever-increasing consumption of fossil fuels has a negative impact on environmental sustainability and provides challenges related to innovation regarding these problems. Electric fuel vehicles are one of the relevant innovations to reduce air pollution and minimize exhaust emissions from fossil fuel vehicles on global warming. IMEI TEAM UMSIDA continues to contribute to the development of vehicles powered by electric energy by participating in energy-saving vehicle competitions such as KMHE and Shell Eco Marathon. In developing electric cars, the IMEI TEAM UMSIDA experienced problems related to obtaining information on the car's energy consumption in real time or when the car was on the track. This problem prompted researchers to create a tool that is capable of monitoring car energy consumption in real time and can be accessed remotely. The experimental method is the right method for this research, because with this method the researcher carries out many experiments to ensure that the tool system works well. The parameters produced in this research are voltage, current, power and energy values in units of time. This tool is equipped with an IoT-based communication system with an internet network as a link between the user, hardware system and software system which functions to carry out remote monitoring in real time. The test results show that this tool functions well and is able to carry out voltage measurements with an error percentage of less than 0.1% and current measurements with an error percentage of less than 0.2%.

Optimization of Fuel Consumption by Controlling the Load Distribution between Engines in an LNG Ship Electric Propulsion Plant

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Fuel Consumption of Adaptive Cruise Control Platoons: Bench Experiments and Data-Driven Estimation for Real Traffic

Fuel Consumption of Adaptive Cruise Control Platoons: Bench Experiments and Data-Driven Estimation for Real Traffic

Harnessing artificial intelligence for enhanced bioethanol productions: a cutting-edge approach towards sustainable energy solution

Abstract The adoption of biofuels as an energy source has experienced a substantial increase, exceeding the consumption of fossil fuels. The shift can be ascribed to the availability of renewable resources for energy production and the ecological advantages linked to their utilisation. Nevertheless, due to its intricate characteristics, the process of producing ethanol fuel from biomass poses difficulties in terms of administration, enhancement, and forecasting future results. To tackle these difficulties, it is crucial to utilise modelling techniques like artificial intelligence (AI) to create, oversee, and improve bioethanol production procedures. Artificial Neural Networks (ANN) is a prominent AI technique that offers significant advantages for modelling bioethanol production systems’ pretreatment, fermentation, and conversion stages. They are highly flexible and accurate, making them particularly well-suited. This study thoroughly examines several artificial intelligence techniques used in bioethanol production, specifically focusing on research published in the past ten years. The analysis emphasises the importance of using AI methods to address the complexities of bioethanol production and shows their role in enhancing efficiency and sustainability in the biofuel industry.

High Fuel Consumption Driving Behavior Causal Analysis Based on LightGBM and SHAP

High Fuel Consumption Driving Behavior Causal Analysis Based on LightGBM and SHAP

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

Energy Transition for rural development: A preliminar case study in Colombia for improving artisanal fishing

According to the Food and Agriculture Organization of the United Nations (FAO), artisanal fishing relates to the use of small fishing vessels for short fishing trips devote to capture fish for their consumption and also for local marketing. This is a process that requires the use of energy in its different phases, from the boat for navigation to the refrigeration center for storage. The energy used for this process normally comes from fossil fuels, which generates an impact from the environmental point of view by the footprint left by the boats, added by the lack of economic resources for the purchase of fuel required by the engines, hampering the daily work of fishermen. The International Renewable Energy Agency (IRENA) defines energy transition as the pathway towards the transformation of the global energy sector from fossil-based to zero-carbon by the second half of this century. The energy transition seeks the decarbonization of the economy and provides opportunities to reduce dependence on fossil fuels. This paper reviews some basic concepts necessary to understand the process to reduce the consumption of fossil fuels in fishing operations from the perspective of the energy transition. Technological, environmental, and socio-economic factors are considered to promote responsible fishing in rural areas coastlines in Colombia. A case study in the municipality of Guapi (province of Cauca, Colombia) is considerate.

Recent advances on hydrogen generation based on inorganic metal oxide nano-catalyst using water electrolysis approach

Abstract Today world is looking for a cheap, environment friendly and efficient substitute of fossil fuel. Because due to large consumption of the fossil fuels on daily basis in whole world, emission of hazardous gases have produced lethal effects on human being. In this scenario hydrogen energy has emerged in form of clean, renewable and more efficient energy. Now the key challenge is that efficient production of the green hydrogen at commercial scale to meet demand of hydrogen. The electrolysis of water is the best pathway to achieve efficient hydrogen production. For this purpose the synthesis and improvement of low cast, active as well as stable catalysts or electrolysis is prerequisite for hydrogen production by electro-catalytic method for splitting of water. Main focus of this review is that, how we can perform the electrolysis of water by various techniques using novel methods especially electro-catalysts in term of activity, efficiency, large surface area, porosity, and stability. This will be performed by the method of two-half cell reaction one is the Hydrogen Evolution Reaction (HER) other one Oxygen Evolution Reaction (OER), where reaction proceeded in both medium acidic as well as alkaline phases. Particular attention is given to produce green clean hydrogen production from usable water and its physical and chemical storages for further uses for the support of human sustainability. Basically the recent strategy is to prepare, design and development of nanoscale materials/composite with non-noble metals and with also nanostructured with noble-metals will be discussed in this approach. The increased efficiency and utility have been the focal points of the use of diverse materials from different classes. To increase the electro-catalytic efficiency in OER and HER, we will discuss about new analyses methods and insights into studying the chemical compositions, shapes, surface area, porosity, and synergy of catalysts and the active sites of nanostructured electro-catalysts. This review will further provide the picture of current state of developments as well as recent progress for mechanized efficient production of clean hydrogen (i.e., HER) from water by electrocatalytic method using various nanoscale materials in a broad scale.

Response Surface Optimization of Brake Thermal Efficiency and Specific Fuel Consumption of Spark-Ignition Engine Fueled with Gasoline–Pyrooil and Gasoline–Pyrooil–Ethanol Blends

<div>The present study explores the performance of high-density polyethylene (HDPE) pyrooil and ethanol blends with gasoline in SI engine using statistical modeling and analysis using response surface methodology (RSM) and the Anderson–Darling (AD) residual test. The pyrooil was extracted from HDPE through pyrolysis at 450°C and then distilled to separate the liquid fraction. Two blends were prepared by combining pyrooil and gasoline, and pyrooil–ethanol mixture (volume ratio of 9:1) and gasoline, both at volumetric concentrations ranging from 2% to 8% to evaluate brake thermal efficiency (BTE) and specific fuel consumption (SFC) in a SI engine. An experimental matrix containing speed, torque, and blend ratio as independent variables for both blends were designed, analyzed, and optimized using the RSM. The results show that a 4% blend of pyrooil with gasoline (P4) and a 6% blend of pyrooil–ethanol mixture with gasoline (P6E) were optimum for an SI engine. Also, the experimental findings show that the P6E blend exhibits 11% higher BTE and 11.82% lower SFC compared to base fuel (pure gasoline), and 7.55% higher BTE and 6% lower SFC than P4. From the AD test, the residuals for BTE and SFC follow a normal distribution. The results conclude that distilled HDPE pyrooil could be used in SI engines at concentrations of P4 and P6E without requiring engine modification.</div>

Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions

The widening gap between real-world vehicle energy consumption and modeled predictions can be attributed to discrepancies between actual ambient temperatures and assumptions made in laboratory tests. This study collected a detailed, extensive dataset comprising 25,640,666 records of real-world vehicle operating (speed, acceleration, etc.) and fuel consumption data alongside 124,938 hourly meteorological profiles (temperature, relative humidity, etc.). High-resolution fuel consumption rates (FCRs) based on ambient temperature were developed, and adjustment factors were introduced based on ambient temperature and vehicle specific power (VSP) binning. Fuel consumption factors (FCFs) were compared across different temperatures by incorporating VSP distributions and the adjusted FCRs, revealing larger FCFs at extreme temperatures compared to moderate ones. Fuel consumption inventories, both with and without temperature adjustments, were evaluated. The results indicated a 6% underestimation of annual fuel consumption in Beijing when disregarding temperature adjustments. The variation was observed across months (in July and August, underestimations can reach 11%) and across VSP bins (larger impact in low VSP bins). The relationship between FCR and ambient temperature is similar to a quadratic curve, with the lowest consumption occurring at 10 °C–20 °C. The FCF adjustment factor does not vary across speed intervals in cold weather and remains stable at approximately 1.15 at −10 °C, but it drops from 1.25 to 1 as speed increases from 5 to 100 km/h in hot weather. This study underscores the importance of considering ambient temperature in vehicle energy consumption modeling and the necessity of temperature-adjusted approaches for accurate fuel consumption estimations.