Investigate biodiesel products using heterogeneous catalysts in variations lauric acid feedstock through elemental analysis and emissions testing using diesel engines

An experimental evaluation of hydrogen, biodiesel and butanol blends on performance, combustion and exhaust characteristics in a diesel engine

This study investigates the comparative performance and emission characteristics of four fuels: diesel, liquefied petroleum gas (LPG), neat biodiesel (mustard-based) and gasoline, using a single-cylinder, four-stroke engine operated at a constant speed of 1500 rpm under eight load conditions (0.25 kW to 2.00 kW). Experiments were conducted both on the stock diesel engine and on the same engine modified for spark ignition (SI) operation. Key performance indicators such as thermal efficiency (TE), fuel consumption and emissions (hydrocarbons, carbon monoxide and nitrogen oxides (NOx)) were measured under steady-state conditions. Outcome indicated that LPG produced the lowest hydrocarbon and carbon monoxide emissions, highlighting its potential as a clean-burning fuel. Biodiesel exhibited moderate emissions but recorded the highest NOx levels, likely due to its higher oxygen content. Diesel demonstrated the best fuel economy (lowest specific fuel consumption (SFC)) but higher emissions compared to LPG and biodiesel. Gasoline achieved the highest TE but exhibited the highest hydrocarbon and carbon monoxide emissions, making it the least environmentally favourable option. The findings support the viability of LPG and biodiesel as cleaner alternatives to conventional diesel and gasoline, with trade-offs in fuel economy and NOx requiring further optimisation.

Comprehensive RSM-based optimization of intake charge and EGR strategies for minimizing pollutant emissions in diesel engines fueled with PODE/HVO/diesel ternary blend

A digital twin for diesel engines: Operator-infused physics-informed neural networks with transfer learning for engine health monitoring

The effects of microwave on the performance and emission characteristics of a diesel engine operating on pure hydrogen

An experimental and numerical investigation of HD diesel engine DOC efficiency in oxidizing NO to NO2

Multi-source data fusion for marine four-stroke diesel engine fault diagnosis: An adaptive weight transfer learning framework

Effect of nano additives and compression ratio on diesel engine performance and emission characteristics using co-pyrolytic fuel with diesel blends: An energy recovery approach towards circular economy

Comparative analysis of a diesel engine fueled with hydrogen-enriched nanoparticle-emulsified second-generation biodiesel

The association between occupational exposure to chlorinated solvents and lung cancer remains inconclusive. This study investigated this relationship using data from the internationally pooled SYNERGY study. Data from 14 case-control studies conducted in 13 European countries and Canada were pooled, including 28 048 participants (12 329 cases and 15 719 controls). Lifetime occupational exposure to chlorinated solvents was assessed using the ALOHA+job-exposure matrix. ORs and 95% CIs were estimated using unconditional logistic regression, adjusted for study centre, age, sex, smoking (pack-years and cessation), cumulative exposure to five occupational lung carcinogens (asbestos, hexavalent chromium, polycyclic aromatic hydrocarbons, respirable crystalline silica and diesel engine exhaust), cumulative benzene exposure and employment in high-risk occupations ('List A' jobs). Associations were estimated across categories of exposure levels, durations and analyses stratified by smoking status and lung cancer subtypes. We found no evidence of an association between ever exposure to chlorinated solvents and lung cancer risk (OR 1.03; 95% CI 0.96 to 1.10). Among exposed individuals, a positive trend with cumulative exposure was observed (p=0.031), but not when non-exposed individuals were included (p=0.173). Positive trends were found with exposure duration (p=0.005 for exposed; p=0.048 overall); risks were modestly elevated (OR 1.11) in those exposed for 20 or more years. No increased risk was observed across smoking strata or lung cancer subtypes. This pooled analysis provides limited evidence of an association between occupational exposure to chlorinated solvents and lung cancer, though exposure-response trends were noted among exposed individuals.

The increasing environmental challenges posed by fossil fuel dependence have accelerated global efforts to adopt renewable energy. Leveraging Egypt’s abundant solar and wind resources, this study develops an optimal framework for hybrid energy systems (HES) in the Shlateen region. The proposed HES integrates photovoltaic panels, wind turbines, diesel generators, and battery storage to ensure reliable and cost-effective electricity supply. An Improved Particle Swarm Optimization (IPSO) algorithm, coupled with the Point Estimate Method (PEM), is applied to address uncertainties in wind speed and solar irradiance. The Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE) and carbon dioxide (CO2) emissions are chosen as three simultaneous objective functions. The number of PVs, WTs, batteries, inverter power, and the diesel engine’s nominal capacity are among the decision variables that are taken into consideration as design parameters via a new meta-heuristic optimization algorithm. Because the design cost is increased and system reliability is degraded, the results highlight the possible consequences for educating industry executives and policymakers about the design and evaluation of HES under uncertain environmental conditions. In order to analyze the suggested methodology, many case studies are explored and presented. Software called MATLAB is used to implement and solve the optimization problem. According to the findings, The lowest LPSP achieved using the IPSO algorithm is 7.149 %, but this value rises to 7.211 % in the PSO alone. The PV/BESU/DG configuration ( scenario 1 ) delivers the lowest amount of losses with PV sizes of 45, 1.0856 BESU, and 4 DG, Cost of Energy (COE) of 0.65407 $/kWh, Loss of Power Supply Probability (LPSP) of 21.666%, and CO2 emissions of 2.4125e+05 tons/year. The COE falls between 0.24634 and 0.72586 dollars per kWh. The WT/BESU/DG configuration has the greatest COE at 0.72586 $/kWh, while the PV/WT/BESU/DG configuration ( scenario 3 ) has the lowest COE at 0.24634 $/kWh. This work demonstrates the application of computational intelligence and meta-heuristic algorithms as core computer science tools for solving real-world energy optimization problems.

The aim of this study was to develop a diagnostic algorithm for assessing the technical condition of the lubrication system in KAMAZ diesel engines based on the analysis of used oil parameters. Experimental measurements were conducted to determine discrepancies between the actual and permissible (according to technical documentation) oil levels, as well as statistical calculations to evaluate the diagnostic value of specific parameters. The findings revealed that the maximum permissible oil volume in the KAMAZ-740 engine exceeds the nominal level by 30%, and in the KAMAZ-EURO engine by 24%. Statistical analysis identified the most informative diagnostic parameters as oil volume, viscosity, total base number, and flash point. Using the permissible values of the identified indicators, schematic plots were developed, each reflecting the current oil condition and enabling the identification of complex and combined faults. The results led to the development of a diagnostic algorithm for diesel engine lubrication system evaluation and a finite state machine method to predict critical oil levels and prevent typical problems and critical engine breakdowns. The projected annual economic benefit from implementing the developed diagnostic system amounted to USD 144,000 for a fleet of 2000 vehicles, based on an average annual mileage of 60,000 km.

Optimizing injector nozzle configuration for high efficiency and low emissions in diesel engines fueled with biodiesel and n-butanol blends.

Abstract The growing demand for carbon‐neutral fuels has driven increased research into hydrogen (H 2 )‐assisted biodiesel combustion. Engine performance, combustion, and emissions were studied using algae biodiesel blends with H 2 enrichment at 3 and 6 LPM. A graph neural network (GNN) model was also developed to link experimental dual‐fuel data with engine behavior predictions. Experiments of six biodiesel blend ratios and two H 2 flow rates were performed at five different loads (0–100%), evaluating performance, combustion, and emissions. Due to the lower calorific value of the fuel, the brake thermal efficiency (BTE) reduced by 6.1% with a higher biodiesel mixture, and 6 LPM H 2 enhanced the engine performance by 3.7% and compensated for the thermal energy loss. The H 2 enrichment enhanced peak pressure and heat release rate (HRR) by 6–6.4%, compensating for losses from biodiesel usage. Overall, nitrogen oxides (NO x ) emissions increased by 23.1% with B100 and 3.6% for 6 LPM H 2 addition. Hydrocarbons (HC) were reduced by 87.5%, carbon monoxide (CO) by 28.8%, and the total amount of smoke decreased by 27.1% with the B100 + 6 LPM H 2 condition. A 90‐node heterogeneous GNN using 38 physics‐informed features achieved R 2 >0.95 and RMSE <5% for five simultaneous outputs, effectively capturing nonlinear interactions between hydrogen and biodiesel. Overall, H 2 enhances the performance and clean‐burning potential of algae biodiesel, significantly reducing key pollutants while causing a modest increase in NO x . The developed GNN framework provides an efficient predictive tool for optimizing H 2 biofuel dual‐fuel engines and supports the advancement of low‐carbon combustion technologies.

In this study, the effects of fusel oil quantity on engine combustion and exhaust emissions were investigated. Fusel oil was injected into the intake air in varying amounts (4, 6, 10, 12, and 16 g/min). Reactivity-controlled compression ignition strategy was used in a four-cylinder, four-stroke diesel engine. The experiments were done under 40, 60, 80, and 100 Nm loads and 1750 rpm constant speed. The specific fuel consumptions, maximum cylinder pressures, first peaks of heat release rates and pressure rise rates obtained from the fusel oil tests showed an increase compared to the diesel fuel tests. While ignition delays were seen to be close to each other, the combustion duration decreased with increasing alcohol content. On the other hand, compared to diesel fuel tests, the largest increase in HC emissions was observed with the DF16 at 40 Nm load, at 137.67%. NO emissions were reduced by 13.54% with the DF16 at 80 Nm load, and CO2 emissions were reduced by 3.12% with the DF4 at 40 Nm load. Although fusel oil had a negative effect on HC emissions, it was observed to have a positive effect on NO and CO2 emissions.

This study presents a tri-state modeling-based FTA-BN hybrid diagnostic framework for assessing the reliability of diesel powertrains in heavy-duty railway maintenance machinery. Recognizing the critical role of these power units in project timeline control and construction efficiency, the framework addresses the challenge of multi-phase fault evolution under harsh operating conditions. A tri-state model—comprising fully operational, performance degradation, and functional failure states—is introduced to enable dynamic, quantitative evaluation of system degradation, overcoming the limitations of traditional binary-state models in characterizing performance degradation processes. The methodology employs a modular failure decomposition strategy and constructs a multi-level fault tree model based on the functional topology of the Deutz BF12L513C powertrain. By integrating a Dynamic Bayesian Network (DBN), the framework achieves three objectives: (1) probabilistic fusion of long-term operational data, (2) quantification of expert-based conditional probabilities using the Delphi method, and (3) uncertainty propagation among coupled failure modes. Analysis of 3 years of field data yields a system reliability of 0.9330, with a 6.25% probability of performance degradation and a 0.44% probability of functional failure. Fault path analysis identifies hydraulic circuit integrity (node X67) and the exhaust energy recovery subsystem (node X73) as key reliability bottlenecks. This framework offers a scalable approach for preventive maintenance and life-cycle reliability management in complex engineering systems.

With the progression of urban renewal, the functional transformation of numerous old industrial heritage buildings has imposed new demands on their indoor physical environments. This paper focuses on the adaptive renovation of thermal environments in old industrial buildings, using two case studies: Welding Workshop (Before Renovation) and the Cylinder Casting Workshop (After Renovation) of Hefei Motor Factory and Diesel Engine Factory. By integrating on-site thermal environment measurements and subjective thermal sensation questionnaires, we employs statistical regression methods to analyze the relationship between operative temperature and actual thermal sensation (MTS) and subjective thermal discomfort. The study identifies the acceptable temperature range and duration proportion in old industrial buildings, and further compares objective and subjective differences in human thermal comfort between summer and transitional seasons in the same workshop. Based on the acceptable duration proportion, a quantitative relationship between subjective sensations and operative temperature is established. These findings offer theoretical and empirical support for green renovation strategies of existing industrial buildings and design optimization of new constructions. Practical application This study provides empirical, decision-support evidence for the green renovation of industrial heritage. At its core, it establishes operative temperature as a critical design parameter and adopts the acceptable duration proportion of thermal comfort as a quantifiable target—thereby translating comfort needs into actionable design language. The data support a practical approach combining enhanced building envelope performance with flexible indoor environmental adjustments to balance heritage preservation and thermal comfort improvements. This research framework can be integrated into the design justification, scheme comparison, and post-occupancy evaluation processes of similar projects, offering a scientific and operational reference for enhancing environmental performance in the adaptive reuse of industrial heritage.

This research investigates the effects of Neem biodiesel and hydrogen-enriched air on the emissions and performance of a common-rail direct-injection diesel engine operating under variable load conditions of 25%, 50%, 75%, and 100%. The aim is to improve engine efficiency and promote sustainable energy solutions. Several Neem biodiesel blends (B10-B30) were initially evaluated, and B15 was selected for comprehensive analysis due to its optimal performance. Hydrogen as a gaseous fuel was subsequently inducted into the inlet air at rates of 3.34 to 9.27 liters per minute to assess its influence on engine behavior. Key parameters, including Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), and emissions of carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx), were analyzed. The B15 blend exhibited a BSFC of 0.27 kg/kWh and a BTE of 31.38% at full load. With hydrogen supplementation at 5.19 liters per minute, BTE increased to 33.31% and BSFC decreased to 0.25 kg/kWh. NOx and CO emissions were reduced to 488 ppm and 0.04%, respectively, while HC emissions remained unchanged. Hydrogen’s high flame speed and broad flammability range contributed to emission reductions; however, higher hydrogen levels led to higher NOx emissions, necessitating ongoing monitoring to comply with regulations. The ANN model, trained on experimental data, was very good at predicting performance and emissions, suggesting it could be used for real-time combustion diagnostics and fuel optimization. In summary, adopting dual-fuel systems utilizing hydrogen and Neem biodiesel offers significant potential to reduce the environmental impact of diesel engines.

This study experimentally investigated the effects of carbon nanotubes additives on the performance, combustion and emission characteristics of a diesel engine with an oxygenated ternary blend. The base fuel blend consisted of 45% diesel, 45% biodiesel, and 10% propanol, while single-walled (SWCNT), double-walled (DWCNT), and multi-walled carbon nanotubes (MWCNT) were added at a concentration of 100 ppm. Engine experiments were conducted under varying load conditions using a one-cylinder diesel engine. The results showed that the carbon nanotubes addition improved combustion behavior by enhancing the heat release rate and stabilizing in-cylinder pressure development. Compared to neat diesel, thermal efficiency increased by 1.74% with DWCNT and by 2.08% with MWCNT on average, while specific fuel consumption increased for all oxygenated blends due to their lower heating value. Significant emission reductions were achieved with the nano-enhanced ternary blends, including average reductions of 35.26% in hydrocarbon emissions and up to 34.30% in nitrogen oxides. Carbon monoxide emissions were also reduced across most operating conditions. The findings demonstrate that carbon nanotube morphology plays a critical role in balancing combustion intensity, engine efficiency, and emission reduction, with DWCNT and MWCNT providing a more favorable tradeoff than SWCNT in oxygenated ternary fuel blends.