Search Results (280)

Three TOP TIER^TM gasoline deposit control additives (DCAs) of differing chemistries were tested for their impact on particulate matter emissions in terms of particulate mass (PM) and particle number (PN) at operating conditions representative of road load, cold start, and high load on a 2.0 L, 4-cylinder, gasoline direct injection (GDI) spark ignition (SI) engine. The PM-PN emissions were measured using an Exhaust Emissions Particle Sizer (EEPS). Deposit control additives or detergents are gasoline additives used to prevent and clean combustion chamber and injector deposits in gasoline spark ignition (SI) engines. All three gasoline additives were tested at each operating condition at three different treatment rates. In addition, one of the additives was tested with a fuel-based friction modifier (FM). The results showed that of the treatment rates tested, the lowest allowable concentration (LAC) for all additives requires the least time for the emissions to settle. However, the impact of the gasoline additives on PM-PN emissions is not linear and changes with additive concentration depending on the additive chemistry and operating conditions. The additive with the friction modifier resulted in an increase of over 19% particle number and over 30% particulate mass at the road load operating condition, while the increase at high load was over 27% for particle number and 11% for particle mass. Full article

Internal combustion engines remain a predominant source of global energy consumption, contributing substantially to both operational costs and greenhouse gas emissions. This work evaluates a nanomaterial-based engine oil additive that reduces friction and wear and increases torque, horsepower, and fuel efficiency. This novel nano oil additive contains functionalized carbon nanotubes and hexagonal boron nitride nanosheets that are dispersed in base oil using a proprietary ultrasonication process. Block-on-ring tests performed by multiple testing facilities demonstrated up to a 17% decrease in coefficient of friction and up to a 78% decrease in wear compared to the base oil after treating with the nano oil additive. Thermal properties enhancement by the nano oil additive was evaluated and increases up to 17 °C in thermal stability were obtained. Additionally, the nano oil additive increased torque and horsepower by an average of 7% in motorcycles and 2.4% in pickup trucks. Most importantly, the nano oil additive demonstrated improvements in fuel economy in both gasoline and diesel engines, with laboratory tests reporting 3–5% increases and practical field tests on a commercial truck fleet reporting an average of a 6% increase. The improved engine efficiency leads to reduced turbo temperature in heavy diesel engines and prolonged engine life expectancy and will significantly improve global environmental sustainability. Full article

Due to the ever-increasing energy demand, Algeria’s sustainable energy crisis is a significant problem. Plant and crop residues can be a solution to this problem if they are used for bioethanol production, a viable alternative to fossil fuels. This study explores the potential of existing agricultural crop residues to overcome the sustainable energy crisis in Algeria. Agricultural residues such as cereals, roots and tubers, pulses, oil crops, vegetables, and fruits have great potential to solve the problem. The agricultural residues that are normally wasted can be utilized to produce bioethanol, which provides sustainable energy and also help to obtain a clean environment. It has been found that 1.65 million tons of bioethanol can be produced from Algeria’s available residues, which is equivalent to 44.10 petajoule of energy. Cereal and fruit residues contribute to most bioethanol generation, about 47.22% and 23.38%, respectively. In addition, bioethanol generated from residue can be used in Algeria’s transportation sector. Considering Algeria’s current energy condition, gasoline blended with ethanol such as E10 and E5 can be used in Algerian vehicles since no modification of vehicles is needed for utilizing these fuels. Research indicates that lignocellulosic biomass sources in Algeria, such as Alfa, olive pomace, and cereal straw, could provide up to 0.67 million tons of oil equivalent (Mtoe), representing approximately 4.37% of the energy consumption of the transport sector in Algeria. Algeria has the potential to produce up to 73.5 Mtoe and 57.9 Mtoe of renewable energy utilizing the energy crops. This study will also encourage relevant policymakers to develop sustainable energy policies that will enhance the renewable energy share in Algerian energy dynamics. Full article

Several sediment core studies have been performed on Utah Lake over the past century, with recent studies providing detailed depositional history based on shallow core samples. To offer additional coverage, we collected 10 deeper sediment cores that extended at least 140 cm below the sediment–water interface from various locations across the lake and analyzed them for ICP-OES detectable elements, fractional calcium carbonate, and loss on ignition (as a proxy for fractional organic matter). Despite high water levels and equipment limitations restricting us to near-shore areas, our samples effectively represented the lake. Our findings revealed significant chemostratigraphic variability, indicating non-homogeneous lakebed sediment. Elements with higher min–max normalized mean concentrations showed strong correlations. Depth trends in the sediments indicated positive correlations for Mn, Al, Fe, K, and V, and negative correlations for Ba, Cu, Pb, Sr, and Zn, with P showing variable correlations. Some of our multidimensional scaling results exhibited geochemical shifts at 30–40 cm, supporting claims that this depth marks the onset of European settlement. Elevated Pb levels in the upper sediment layers are likely the result of mid-20th century leaded gasoline pollution. Sediment P is linked to Ca, Fe, and trace metal pollutants, suggesting both natural processes and human activities influence elemental distribution, though only a few cores showed P changes aligning with European settlement. Full article

The sulfur content in crude oil varies between 1000 and 30,000 ppm (parts per million), meaning that its removal from fuels requires significant technical and economic effort. Growing concern about pollution, accompanied by stricter environmental regulations, have led to the development of strategies to mitigate the negative effects of sulfur-containing compounds in petroleum, which can cause malfunctions in manufacturing plants and refineries, such as causing catalyst poisoning in catalytic reforming equipment and sulfur dioxide emissions that have been generated through the use of fuels in vehicles, vessels, furnaces, etc. Sulfur is one of the main pollutants found in diesel and gasoline. The hydrodesulfurization method removes sulfur and nitrogen-containing compounds from diesel and gasoline, ensuring compliance with current environmental regulations established for the import and export of fuels. In addition, hydrodesulfurization contributes to reducing sulfur dioxide and nitrogen dioxide emissions into the environment and prevents corrosion, which increases safety for both manufacturing plants and end consumers. This situation is analyzed in this paper, considering Mexican legislation about fuels and their usage. Sulfur is an important pollutant contained in diesel and gasoline fuels; it exhibits lubricant properties, helping to reduce the maintenance intervals of the machines and increase engine life. Therefore, its removal from fuel blends is a topic of great scientific interest as researchers look for different lubricant alternatives, which are relevant to motor vehicle engines. Full article

In the context of increasingly stringent vehicle emission regulations, computer-aided engineering has been indispensable for optimizing the design and the operational strategies of emission control systems. This paper proposes a new filtration model for particulate filters that enables the accurate estimation of solid particle number emissions above 10 and 23 nm in diameter (SPN10 and SPN23, respectively). The model incorporates a persistent slip factor and a linear filtration efficiency of cake layers into the unit collector model proposed by Konstandopoulos and Johnson. This enhancement captures PM escape phenomena, such as a passage through interconnected large pores in filter walls. Simulations using a 1D + 1D two-channel framework with the proposed model successfully reproduced experimental results of SPN10 and SPN23 emissions downstream of a miniature gasoline particulate filter (GPF) tested with a synthetic particle generator. The model was also able to represent the observed continuous emissions during a cake filtration mode. Additional simulations using the same model parameters showed good agreement with experimental data of SPN10 and SPN23 emissions downstream of a full-size GPF tested with a gasoline direct injection (G-DI) engine under 5 steady-state operating conditions. The simulations revealed that particles in the 10–100 nm size range dominated the downstream SPN emissions despite their high filtration efficiency, whereas particles in the 100–200 nm size range were less significant. The proposed model is expected to contribute to the GPF developments to comply with the stringent emission regulations of the upcoming Euro 7. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

In the context of global warming and China’s “dual carbon” goals, Shanxi, as China’s main coal-producing region (accounting for 28.4% of the country’s coal production), is facing the dual challenges of carbon emission reduction and economic development. Based on the data from 1990 to 2019, this study quantitatively analysed the carbon emission driving mechanisms of seven major energy sources in Shanxi, including coal, coke, and gasoline, through the coupling analysis of the Kaya identity and the LMDI model, and explored the climate change mitigation pathways. The results show that the total carbon emissions of Shanxi’s energy sector increased significantly from 1990 to 2019, with coal being the most important emission source. Through the decomposition of the LMDI model, it is found that the effect of economic activity is the core driving force of carbon emission growth, and the improvement of energy intensity is the key inhibitor. It is worth noting that the demographic effect turned negative after 2010, which had a dampening effect on the growth of carbon emissions. In addition, the adjustment of energy structure shows the characteristics of stages: the structural effect of coal has turned from negative to positive after 2010, while the proportion of clean energy, such as natural gas, has increased, indicating that the optimisation of energy structure has achieved initial results. Based on the above findings, the study proposes three major paths for climate mitigation in Shanxi’s energy industry: (1) promote low-carbon upgrading of the industry and reduce the economy’s dependence on high-carbon energy; (2) Strengthen energy efficiency and continuously reduce energy consumption per unit of GDP through technological innovation; (3) accelerate the transformation of the energy structure and expand the proportion of clean energy such as natural gas and renewable energy. This paper innovatively provides an empirical reference for the model-based, coupling-based carbon emissions-driven analysis and climate mitigation strategy design in resource-based areas. Full article

This paper presents the design, development, and experimental validation of a low-cost, modular, and scalable Advanced Driver Assistance System (ADAS) platform intended for research and educational purposes. The system integrates embedded computer vision and electronic control using an FPGA for accelerated real-time image processing and an STM32 microcontroller for sensor data acquisition and actuator management. The YOLOv3-Tiny model is implemented to enable efficient pedestrian and vehicle detection under hardware constraints, while additional vision algorithms are used for lane line detection, ensuring a favorable trade-off between accuracy and processing speed. The platform is deployed on a 1:5 scale gasoline-powered vehicle, offering a safe and cost-effective testbed for validating ADAS functionalities, such as lane tracking, pedestrian and vehicle identification, and semi-autonomous navigation. The methodology includes the integration of a CMOS camera, an FPGA development board, and various sensors (LiDAR, ultrasonic, and Hall-effect), along with synchronized communication protocols to ensure real-time data exchange between vision and control modules. A wireless graphical user interface (GUI) enables remote monitoring and teleoperation. Experimental results show competitive detection accuracy—exceeding 94% in structured environments—and processing latencies below 70 ms per frame, demonstrating the platform’s effectiveness for rapid prototyping and applied training. Its modularity and affordability position it as a powerful tool for advancing ADAS research and education, with high potential for future expansion to full-scale autonomous vehicle applications. Full article

Understanding the differential impacts of emission sources of volatile organic compounds (VOCs) on formaldehyde (HCHO) levels is pivotal to effectively mitigating key photochemical radical precursors, thereby enhancing the regulation of atmospheric oxidation capacity (AOC) and ozone formation. This investigation systematically selected and analyzed year-long VOC measurements across three urban zones in Shenzhen, China. Photochemical age correction methods were implemented to develop the initial concentrations of VOCs before source apportionment; then Positive Matrix Factorization (PMF) modeling resolved six primary sources: solvent usage (28.6–47.9%), vehicle exhaust (24.2–31.2%), biogenic emission (13.8–18.1%), natural gas (8.5–16.3%), gasoline evaporation (3.2–8.9%), and biomass burning (0.3–2.4%). A machine learning (ML) framework incorporating Shapley Additive Explanations (SHAP) was subsequently applied to evaluate the influence of six emission sources on HCHO concentrations while accounting for reaction time adjustments. This machine learning-driven nonlinear analysis demonstrated that vehicle exhaust nearly always emerged as the primary anthropogenic contributor in diverse functional zones and different seasons, with gasoline evaporation as another key contributor, while the traditional reactivity metric method, ozone formation potential (OFP), tended to underestimate the role of the two sources. This study highlights the primacy of strengthening emission reduction of transportation sectors to mitigate HCHO pollution in megacities. Full article

The establishment of carbon capture, utilization, and storage supply chains at the national level is crucial for meeting global decarbonization targets: they have been suggested as a solution to maintain the global temperature rise below 2 °C relative to preindustrial levels. Optimizing these systems requires a balance of economic viability with environmental impact, but this is a challenge due to diverse operational limitations. This paper introduces an optimization framework that integrates life cycle assessment with a source-sink model while combining the geographical storage and conversion pathways of carbon dioxide into high-value chemicals. This study explores the economic and environmental outcomes of national carbon capture, utilization, and storage networks, considering several constraints, such as carbon dioxide reduction goals, product market demand, and renewable hydrogen availability. The framework is utilized in Germany as a case study, presenting three case studies to maximize overall annual profit and life cycle greenhouse gas reduction. In all analyzed scenarios, the results indicate a clear trade-off between profitability and emission reductions: profit-driven strategies are characterized by increased emissions, while environmental strategies have higher costs despite the environmental benefit. In addition, cost-optimal cases prefer high-profit utilization routes (e.g., gasoline through methane reforming) and cost-effective capture technologies, leading to significant profitability. On the other hand, climate-optimal approaches require diversification, integrating carbon dioxide storage with conversion pathways that exhibit lower emissions (e.g., gasoline, acetic acid, methanol through carbon dioxide hydrogenation). The proposed method significantly contributes to developing and constructing more sustainable, large-scale carbon projects. Full article

This paper analyzes the influence of the number of ignition coils and spark discharge energy on the Coefficient of Variation of Indicated Mean Effective Pressure (COV_IMEP) of an SI internal combustion piston engine. A modern electronically controlled induction ignition system is used during the test. Two fuels are used in the experiment. The reference fuel is gasoline and the tested fuel is ammonia. For the traditional fuel, using an additional ignition coil does not improve COV_IMEP. This parameter for gasoline has an almost constant value for different ignition system charging times. The situation is different for ammonia. This fuel requires high ignition energy. The use of one ignition coil demands a long charging time. For short charging times, unrepeatability of the engine cycles is unacceptable. The use of an additional ignition coil allowed to the charging coil timing to be shortened and the unrepeatable engine cycles to be reduced. This paper determined the maximum charging time of the used ignition coil, above which the spark parameters are worse. In addition, the influence of charging time and number of ignition coils on total spark energy, spark discharge duration, maximum spark power, and voltage during spark discharge for ammonia is presented. The data presented in this paper are developed based on measurements of current and voltage in the secondary winding of the ignition coil. A self-developed electronic device enabling the change in spark energy is used to control the ignition system. This paper also presents the construction of modern ignition systems, describes the functions of selected components, and briefly discusses their diagnostics. Full article

Hydrogen–gasoline dual-fuel spark ignition (SI) engines represent a promising transitional solution toward cleaner combustion and reduced carbon emissions. In a previous study, a predictive engine model was developed to simulate the performance and combustion characteristics of such systems; however, its accuracy was constrained by the use of estimated combustion parameters. This study presents an experimental validation based on high-resolution in-cylinder pressure measurements performed on a naturally aspirated SI engine operating with a 20% hydrogen energy share. The objectives are twofold: (1) to refine the combustion model using empirically derived combustion metrics, and (2) to evaluate the feasibility of moderate hydrogen enrichment in a stock engine configuration. To facilitate a more accurate understanding of how key combustion parameters evolve under different operating conditions, Vibe function was fitted to the ensemble-averaged heat release rate curves computed from 100 consecutive engine cycles at each static full-load operating point. This approach enabled the extraction of stable and representative metrics, including the mass fraction burned at 50% (MFB50) and combustion duration, which were then used to recalibrate the predictive combustion model. In addition, cycle-to-cycle variation and combustion duration were also investigated in the dual-fuel mode. The combustion duration exhibited a consistent and substantial reduction across all of the examined operating points when compared to pure gasoline operation. Furthermore, the cycle-to-cycle variation difference remained statistically insignificant, indicating that the introduction of 20% hydrogen did not adversely affect combustion stability. In addition to improving model accuracy, this work investigates the occurrence of abnormal combustion phenomena—including backfiring, auto-ignition, and knock—under enriched conditions. The results confirm that 20% hydrogen blends can be safely utilized in standard engine architectures, yielding faster combustion and reduced burn durations. The validated model offers a reliable foundation for further dual-fuel optimization and supports the broader integration of hydrogen into conventional internal combustion platforms. Full article

Municipal solid waste management (MSWM) on islands involves several challenges relating to politics, society, the environment, and technology. This paper addresses the potential for producing biogas and biomethane from food waste on Tenerife, including waste from households, with the aim of reducing landfill and primary fossil energy consumption. The study also introduces the European and Regional policy framework and requirements. Effective microorganisms have been studied as proposals to stabilise the food waste from households, avoiding odours and decomposition during storage. The trials show positive results in terms of the preservation of organic matter until the food waste is transported to the biogas plant. In addition, a new concept for a small biogas plant made of textile materials, which are suited to the municipalities of Tenerife, is presented to provide an easy-to-build solution, with ranges of up to 75 kW in electrical power. With a theoretical potential of 299,012 tons of food waste being available per year (based on 2022), preliminary laboratory experiments with real samples of the island showed a theoretical potential of 28.97 × 10⁶ Nm³ for biogas and 264,612 tons for digestate, which can be used as fertilisers, with potential savings of 18.15 × 10⁶ L of gasoline and 42.66 × 10³ equivalent CO₂ tons. Full article

The sustainability of the natural gas-to-methanol (NGTM) and methanol-to-gasoline (MTG) processes are assessed in this systematic review as a potential substitute in the global energy transition. Methanol offers itself as a versatile and less carbon-intensive substitute for conventional gasoline in light of growing environmental concerns and the demand for cleaner fuels. This review’s rationale is to assess MTG’s ability to lessen environmental impact while preserving compatibility with current fuel infrastructure. The goal is to examine methanol and gasoline’s effects on the environment, society, and economy throughout their life cycles. This review used a two-phase systematic literature review methodology, filtering and evaluating studies that were indexed by Scopus using bibliometric and thematic analysis. A total of 25 documents were reviewed, in which 22 documents analyzed part of this study, and 68% employed LCA or techno-economic analysis, with the U.S. contributing 35% of the overall publications. A comparative analysis of the reviewed literature indicates that methanol-based fuels offer significantly lower greenhouse gas (GHG) emissions and life cycle environmental impacts than gasoline, particularly when combined with carbon capture and renewable feedstocks. This review also highlights benefits, such as improved safety and energy security, while acknowledging challenges, including high production costs, infrastructure adaptation, and toxicity concerns. Several drawbacks are high manufacturing costs, the necessity to adjust infrastructure, and toxicity issues. The report suggests investing in renewable methanol production, AI-driven process optimization, and robust legislative frameworks for integrating green fuels. The life cycle sustainability assessment (LCSA) of NGTM and MTG systems should be investigated in future studies, particularly in light of different feedstock and regional circumstances. The findings emphasize NGTM and MTG’s strategic role in aligning with several UN Sustainable Development Goals (SDGs) and add to the worldwide conversation on sustainable fuels. A strong transition necessitates multi-stakeholder cooperation, innovation, and supporting policies to fully realize the sustainability promise of cleaner fuels like methanol. Full article