Search Results (332)

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

The insufficient octane ratings of commercial fuels limit the efficiency of spark-ignition (SI) engines and worsens emissions due to knock. One alternative approach to mitigate this phenomenon is the Octane-on-Demand (OOD) concept, which adjusts fuel properties onboard the vehicle. Although implementing this advanced technology can contribute to greenhouse-gas (GHG) emission reductions, few studies have examined its impact on combustion characteristics. Accordingly, the objective of this study was to conduct a comprehensive investigation combining fuel characterization and engine testing to evaluate the effectiveness of the OOD strategy in directly suppressing knock in an SI engine, an aspect not previously reported in the literature. The present study was divided into two parts. In the fuel study, optimal conditions for obtaining a candidate fuel—high-octane fractions (HOF)—from gasoline were determined based on chromatographic (GC–MS) analyses. During engine testing, commercial gasoline and blends containing HOF in several proportions were evaluated in a dual-fuel operation under knocking conditions. The maximum amplitude of pressure oscillations (MAPO) was used as the knock indicator. The results demonstrate that temporary fuel enrichment using only gasoline-derived fractions, without additional octane boosters, can effectively suppress knocking combustion. These findings highlight the influence of the OOD concept on the combustion process and provide guidance for optimizing fueling strategy design under knock-limited operation. The study contributes to the growing body of knowledge regarding OOD applications and underscores the need for comprehensive testing under real-world engine operating conditions. Full article

This study systematically quantified the carbon footprint generated by China’s consumption of eight major fossil energy sources (coal, coke, crude oil, petrol, kerosene, diesel, fuel oil, and natural gas), alongside the carbon carrying capacity of four vegetation ecosystems (forest, grassland, wetland, and crop), based on the IPCC inventory methodology. ArcGIS spatial analysis was employed to reveal the spatiotemporal distribution, while the STIRPAT model identified drivers of energy carbon footprint pressure (ECFP). Concurrently, the GM (1,1) model predicted evolution trends for both energy carbon footprint (ECF) and vegetation carbon carrying capacity. Results indicated that: (1) ECF increased from 12,039.89 million tons in 2015 to 13,896.41 million tons in 2022, representing a cumulative growth of 15.42%; (2) vegetation carbon carrying capacity increased from 4710.54 million tons in 2015 to 5300.76 million tons in 2022, representing a cumulative growth of 12.53%; (3) STIRPAT model analysis indicated that economic growth and technological progress were the dominant factors influencing ECFP; and (4) GM (1,1) predicted that the ECF would continue to grow at a slower pace by 2026, while vegetation carbon carrying capacity would steadily increase. It was concluded that optimizing the energy structure and strengthening vegetation conservation could effectively alleviate ECFP, providing crucial support for the carbon neutrality objectives of China. Full article

Biodiesel is a promising, sustainable alternative to fossil fuels such as petrol and diesel. Currently, biodiesel can be produced from edible plant oils and non-edible sources and wastes. Notably, fish waste oil is a sustainable resource for transesterification reactions to produce biodiesel. This research proposes a general process design methodology to investigate the potential of biodiesel production from fish waste oil as a pathway for waste-to-energy. The methodology integrates Pinch Analysis principles and process simulation to optimize the energy efficiency of a process design. Real data are collected on fish waste from fish industries in Egypt, focusing on three regions in northern Egypt with a total capacity of 7.5 tons per day (t/d). The research methodology is applied to the design of a biodiesel production plant with a fish waste oil capacity of 547.5 tons/year. The production process involves a transesterification reaction using methanol and NaOH as catalysts. The annual expected yields are 495.2 tons of biodiesel and 51.4 tons of glycerol. The base design indicates total heating and cooling energies of 6889.6 kW and 11,470.1 kW, respectively, and CO₂ emissions of 19,343 tons/year. An improved design using Pinch Analysis achieves substantial energy savings of 47% in heating, 69% in cooling, and, 9202 tons of CO₂ cut. The novelty of the work lies in developing and applying an integrated process design and energy minimization methodology. The work provides a transferable methodology that can be applied to other wastes. Full article

Laboratory test results for vehicle emissions, fuel economy, and driving range often fail to reflect real-world performance, undermining the effectiveness of sustainability policies and consumer guidance. This study provides the first integrated national assessment of real-world emissions and range outcomes for passenger vehicles in Australia. Using Portable Emissions Measurement Systems (PEMS) data from 114 petrol, diesel, hybrid, and battery-electric vehicles (BEVs) tested by the Australian Automobile Association (AAA), the analysis compares laboratory-certified values against on-road results and benchmarks them with international datasets from Europe and China. Real-world CO₂ emissions were, on average, 6.9% higher than laboratory ratings for petrol vehicles and 3.2% higher for diesel vehicles. Many diesel models exceeded Euro 6 NO_x limits by several multiples, while hybrids exhibited inconsistent CO₂ reductions under urban conditions. BEVs also displayed measurable divergence: real-world energy consumption was 1–20% higher than laboratory ratings, resulting in an average 16% reduction in effective driving range relative to WLTP values. These outcomes reveal a consistent tendency toward overstated laboratory performance across powertrains, highlighting systemic shortcomings in certification test cycles. The findings have direct implications for greenhouse gas mitigation, urban air quality, and consumer energy efficiency and support Australia’s active transition to WLTP and Euro 6 standards, institutionalisation of real-world testing, and inclusion of verified real-world energy use and range data in consumer labelling to enhance transparency and policy effectiveness. Full article

One of the major issues in the energy intensive industries (EIIs) operation is corrosion control. Particularly, in refineries, corrosion causes 33% of malfunctions, especially due to the deterioration of the metallic materials and, therefore, the shortening of the useful life of equipment and installations. To face this problem, novel polymer-derived ceramic (PDC) coatings have been formulated, developed and characterized by physical and chemical tests. Different formulations were analyzed on a lab-scale through accelerated corrosion tests under acidic environments using electrochemical impedance spectroscopy (EIS) to evaluate their corrosion performance when exposed to near-real operating conditions. The optimized formulation will be used as a barrier in stainless-steel pipelines to improve the energy performance of EIIs by reducing energy losses due to excess cooling of components, maximizing the thermal efficiency of equipment, increasing the service life of equipment and reducing operation and maintenance (O&M) costs and downtime. Full article

This study investigates the total carbon dioxide (CO₂) emissions from various types of passenger vehicles in five Latin American countries: Argentina, Brazil, Ecuador, Mexico, and Paraguay. The aim was to analyze to which degree CO₂ output can be reduced in Latin America by switching from petrol cars to electric cars. The vehicles analyzed include petrol-driven cars, short-, mid-, and long-range battery electric vehicles, fuel cell electric vehicles, plug-in hybrid electric vehicles, and hybrid electric vehicles. The study examines the total CO₂ emissions including battery production, vehicle manufacturing, and their operation, considering the energy grid mix of the selected countries for the year 2023. Using experimental data and considering production conditions yields more reliable results than previous studies. The results indicate that battery cars with the shortest cruising range using batteries produced in Europe and/or America generate the lowest levels of CO₂ emissions, regardless of the energy mix. However, the emission values vary across different countries. In countries with a predominant share of renewable energy for the electricity generation, such as Paraguay, Brazil, and Ecuador, battery cars are the most effective in reducing overall CO₂ emissions. Conversely, in countries like Argentina and Mexico, where renewable energy sources constitute a smaller share of the energy mix, the use of electric vehicles yields only a minor reduction in CO₂ output, while emissions of long-range vehicles with batteries produced in Asia even exceed those of internal combustion engine vehicles. Therefore, eco-friendly electricity generation is a prerequisite for eco-friendly use of electric cars and should therefore be the goal of every country. Full article

The quality of groundwater is essential to sustain human and environmental activities now and in the future. However, the current intensification of anthropogenic activities has increased the magnitude of contaminant sources. When those contaminants reach a saturated zone (groundwater), their levels of presence may make their use for various purposes unfeasible. Therefore, research into the vulnerability degree is essential for estimations of potential for contamination and possible risks. This manuscript presents the results obtained by applying a parametric procedure for mapping groundwater vulnerability based on a set of attributes related to contaminant sources, transport, and natural attenuation of contaminants. In addition to vulnerability zoning, the set of attributes supports the adoption of measures and recommendations related to territorial and environmental planning guidelines and orientations about land uses. The open source Geographical Information System—QGIS open source version 3.22.4 was used for spatially integrating different attribute maps and obtaining partial indices for contaminant introduction, transport, and attenuation; hence, the specific vulnerability index. The results promoted the division of the region into six classes of specific vulnerability, namely, extremely high, accounting for around 23% vulnerability, very high (20%), moderate (24%), very low (23%), and high and low together accounting for 10%. Such categories were associated with measures and recommendations aimed at territorial and environmental planning and protection and control of environmental functions. Approximately 50% of the study area requires restrictive measures regarding buildings, sustainable drainage systems, waste disposal, chemical storage, and petrol stations, and other measures are necessary for the protection of wells and natural springs. The method employed can produce results that enable areas to be categorized and ranked in terms of specific vulnerability; however, it requires a large quantity of data and spatial details according to the scale adopted. The specific vulnerability map produced will help planners make more appropriate territorial and environmental planning decisions and risk management, avoiding groundwater contamination. Full article

The accelerating integration of electromobility into renewable-based power systems necessitates a comprehensive understanding of vehicle life cycles and their interactions with emerging smart grid infrastructures. This study employs a Life Cycle Assessment (LCA) approach to evaluate the environmental performance of materials and components used in A- and B-segment passenger vehicles, within the framework of sustainable energy system development. Four propulsion technologies—petrol, diesel, compressed natural gas (CNG), and battery electric vehicles (BEVs)—were analyzed across two technological horizons (2020 and 2050), considering both landfilling and recycling end-of-life scenarios. The results demonstrate that while BEVs offer the lowest operational emissions and the greatest potential for supporting grid flexibility and renewable energy integration, they also exhibit the highest environmental burdens during production, primarily due to battery manufacturing. Nevertheless, the adoption of advanced recycling technologies significantly mitigates these impacts by reducing resource depletion, global warming potential, and cumulative energy demand. The findings highlight that circular material management and high-efficiency recycling are critical enablers of sustainable electromobility. By linking vehicle charging, energy storage, and recycling strategies, the integration of transport and energy systems can enhance grid stability, improve resource efficiency, and accelerate progress toward a decarbonized, resilient, and smart energy future. Full article

This study presents a systematic comparison of five crossover operators used in genetic algorithms (GA) for the Traveling Salesman Problem (TSP). Partially Mapped Crossover (PMX), Order Crossover (OX), Cycle Crossover (CX), Edge Recombination (ERX), and Alternating Edges (AEX) are evaluated within an identical GA framework using tournament selection, inversion mutation, generational replacement, and elitism. Experiments were conducted on seven datasets, including three TSPLIB benchmarks, a clustered synthetic instance, a uniformly random instance, and two real-world Croatian city sets of 50 and 100 cities. Thirty independent GA runs per operator were analyzed using the Friedman test followed by Holm-corrected Wilcoxon pairwise comparisons. The Friedman test shows highly significant global performance differences. After applying Holm correction, the top four operators (PMX, OX, CX, and ERX) are statistically comparable on most datasets, as the correction eliminates most pairwise differences among them. All pairwise comparisons involving AEX remain significant across every dataset, confirming its consistently inferior performance. OX achieves the best average ranks across all datasets consistently, while PMX, CX, and ERX exhibit comparable mid-range performance. To illustrate practical relevance, optimized routes for Croatian instances were used to estimate fuel consumption and CO₂ emissions for petrol, diesel, and electric vehicles. The results demonstrate meaningful sustainability benefits achievable through optimized routing. Full article

Studies were conducted on the hydrogen-free processing of model alkanes, straight-run gasoline, and catalytic cracking gasoline using a new synthesized Co-Mo-Ce/ZSM + Al₂O₃ nanocatalyst, which demonstrated high activity in desulfurization. Thus, the mass fraction of sulfur in the resulting gasoline was reduced by almost three times compared to the initial value of 0.0776% to 0.0354% as a result of hydrogen-free processing of straight-run gasoline. The amount of sulfur in the resulting product was reduced by almost an order of magnitude with hydrogen-free processing of catalytic cracked gasoline: from 0.1650 in the original gasoline to 0.0123%. The octane number of the refined straight-run gasoline was 77.9–80.9 according to the research method (RM) and 61.13–65.8 with the motor method (MM). Physical and chemical methods of analysis (BET, TPD-NH₃, TEM, SEM, and XRD) revealed that nano-structured acid sites coexist with nano-dispersed metallic sites on the surface of the Co-Mo-Ce/ZSM + Al₂O₃ catalyst. The functioning of these two types of nano-active sites (metallic and acidic) ensures the polyfunctionality of the catalytic action of the nanoparticles. The following reactions occur simultaneously in the hydrogen-free processing: isomerization, dehydrogenation, dehydrocyclization. Hydrogen-free processing of low-octane gasoline fractions on nanosized zeolite-containing catalysts is one of the most promising methods to obtain high-octane motor gasoline. Full article

In the context of growing data availability and increasing complexity of demand patterns in retail fuel distribution, selecting effective forecasting models for large collections of time series is becoming a key operational challenge. This study investigates the effectiveness of a hybrid forecasting approach combining ARIMA models with dynamically updated Markov Chains. Unlike many existing studies that focus on isolated or small-scale experiments, this research evaluates the hybrid model across a full set of approximately 150 time series collected from multiple petrol stations, without pre-clustering or manual selection. A comprehensive set of statistical and structural features is extracted from each time series to analyze their relation to forecast performance. The results show that the hybrid ARIMA–Markov approach significantly outperforms both individual statistical models and commonly applied machine learning methods in many cases, particularly for non-stationary or regime-shifting series. In 100% of cases, the hybrid model reduced the error compared to both baseline models—the median RMSE improvement over ARIMA was 13.03%, and 15.64% over the Markov model, with statistical significance confirmed by the Wilcoxon signed-rank test. The analysis also highlights specific time series features—such as entropy, regime shift frequency, and autocorrelation structure—as strong indicators of whether hybrid modeling yields performance gains. Feature-conditioning analyses (e.g., lag-1 autocorrelation, volatility, entropy) explain when hybridization helps, enabling a feature-aware workflow that selectively deploys model components and narrows parameter searches. The greatest benefits of applying the hybrid model were observed for time series characterized by high variability, moderate entropy of differences, and a well-defined temporal dependency structure—the correlation values between these features and the improvement in hybrid performance relative to ARIMA and Markov models reached 0.55–0.58, ensuring adequate statistical significance. Such approaches are particularly valuable in enterprise environments dealing with thousands of time series, where automated model configuration becomes essential. The findings position interpretable, adaptive hybrids as a practical default for short-horizon demand forecasting in fuel supply chains and, more broadly, in energy-use applications characterized by heterogeneous profiles and evolving regimes. Full article

The paper assesses the change in air pollutant emissions from a petrol passenger vehicle with changing mileage. The search for solutions enabling the assessment of the change in air pollutant emissions, considering the phenomenon of vehicle ageing, justifies the need to verify the actual air pollutant emissions from used vehicles. The fleet of vehicles used in Poland has an operational age exceeding 12 years, and the number of vehicles imported from Western Europe each year reaches almost 1 million. The research method used in the paper included conducting road tests, known as real driving emissions (RDE) tests of air pollutant emissions for a single vehicle, at different times and with various mileages. The petrol vehicle was operated by one driver whose driving style and routes were comparable and constant throughout the year. The RDE results were compared with data specifying the vehicle’s operating age and mileage to verify the research hypothesis, assuming increased emissions with increasing vehicle mileage. The emissions of basic air pollutants were determined as part of the research conducted using specialist equipment. The research results were obtained for one vehicle, and the experiment was carried out over several years. The results show differences in the emissions of selected chemical compounds depending on the petrol vehicle’s mileage and operating age while ensuring comparable driving technique and operation of one vehicle over a longer time period of 8 years. The vehicle’s age and mileage influence air pollutant emissions. The obtained results show a change in the emission of selected chemical compounds depending on the mileage, thereby confirming the validity of the adopted hypothesis. Full article

Soil contamination with petroleum-derived substances, including petrol, is one of the most serious environmental issues of the modern era. These products are characterised by their durability and stability in the environment, their capacity for bioaccumulation and their toxicity to many organisms, including plants. This study aimed to evaluate the impact of petrol contamination on trace element content in the above-ground parts of oats (Avena sativa L.) and to determine the effectiveness of in situ stabilisation methods using compost, bentonite and calcium oxide in reducing bioaccumulation of these elements. Petrol contamination of the soil significantly altered the biomass yield and the concentration of trace elements in the plants. It caused a decrease in the dry matter yield and an increase in the content of most trace elements in the above-ground parts of oats. The most pronounced effects were observed for Cd, Ni, Fe, Co, Cr and Mn, whose concentrations in the plants increased across the entire range of petrol doses. Petrol had a similar effect on Zn and Pb content in the above-ground parts of oats, but only up to a medium level of contamination (5 cm³ kg⁻¹). In contrast to the aforementioned elements, soil contamination with petrol contributed to a decrease in the copper content of the above-ground parts of oats. The materials applied to the soil had a beneficial effect on the biomass and the concentration of certain trace elements in plants. The compost and especially calcium oxide had a positive influence on plant yield. Compared to the series without their application to the soil, all materials reduced the content of Cr, Fe, Cd and, especially, Mn in the above-ground parts of plants. Compost also reduced the content of Pb, while bentonite and calcium oxide reduced the content of Co. Calcium oxide also reduced the content of Cu in the above-ground parts of oats. However, bentonite had a weaker effect than compost and calcium oxide. Changes in the content of other elements in plants after application of the aforementioned materials were often opposite (and dependent on the type of material), with the clearest effect being on nickel content. The materials used in the study produced good results in limiting the impact of minor soil contamination with petrol on the content of certain trace elements in plants. Full article

The Northern Tibetan Plateau Basin is the most extensive and least explored Mesozoic marine basin in China and shows considerable potential for oil and gas exploration. This study systematically analyzed the abundance, type, hydrocarbon generation potential, and conversion rate of organic matter within three Jurassic drill core samples from the Biloucuo area of the Northern Tibetan Plateau Basin. The total organic carbon (TOC) content of these Jurassic source rocks was >4%, on average, permitting their classification as excellent source rocks. The average contents of sapropelinite, exinite, vitrinite, and inertinite in kerogen were 74%, 4%, 18%, and 4%, respectively. The H/C and O/C ratios of the kerogen mainly ranged from 0.8 to 1.3 and 0.06 to 0.11, respectively, indicative of type II₁ kerogen. The average S₁ + S₂ content was 15.0 mg/g rock, indicating a high hydrocarbon generation potential. On the basis of the relationship between the quantity of soluble hydrocarbons remaining in the strata and the S₂ and TOC contents, it can be inferred that the hydrocarbon generation conversion rate of these Jurassic source rocks was between 25% and 50%, and partial hydrocarbon expulsion has taken place. It is estimated that the maximum oil generation potential of the formation will reach 20 kg/t rock at a greater depth, which equates to good exploration potential. Full article