Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach.

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NO x emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide ( CO 2 ) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance.

Exergy-energy, sustainability, and emissions assessment of Guizotia abyssinica (L.) fuel blends with metallic nano additives.

This study extensively examined the impact of aluminium oxide (Al2O3) and titanium dioxide (TiO2) nanoparticles addition in the biodiesel fuel derived from Guizotia abyssinica (L.) oil. The assessment of fuel blends, which were created by combining nanoparticles and biodiesel was conducted using energy, exergy, and sustainability indices. The highest recorded power output of 2.81 kW was observed for the GAB20A engine operating at 1800 rpm. The experimental results revealed that the GAB20A exhibited the lowest fuel consumption, with a recorded value of 203 g/kWh, when operated at 1600 rpm among all the tested blend fuels. The blend GAB20A exhibited the highest level of energy efficiency at 1600 rpm of 29.5%, as determined by the study. Simultaneously, it was observed that GAB20 exhibited the lowest energy efficiency at 1200 rpm among all the blend fuels at 25%. The emission levels of nitrogen oxides (NOx) and carbon monoxide (CO) were observed to be quite low, although a little rise in carbon dioxide (CO2) was detected. For validation of results the artificial neural network (ANN) was used and an average of 1.703% difference in energy efficiency, 2.246% decrease in exergy efficiency, and 1.416% difference in sustainability index was found.

Effects of different biofuels and their mixtures with diesel fuel on diesel engine performance and exhausts.

In this study, a compression ignition engine that ran on recycled used cooking oil (RUCO), Jatropha curcas (JC), Pongamia Pinnata (PP), and petroleum diesel fuel (PDF) was investigated for its energy, performance, and exhaust emissions. The 20 % by volume RUCO, JC, and PP biofuel mix with PDF is taken. According to the American Society for Testing and Material (ASTM) standard, the blend qualities are evaluated. Viscosity, density, flash point, and heating value have all been tested for the 20 % blend. The outcome indicated that for a 20 % mix, the viscosity, density and flash point were all greater than in the PDF but heat value lower. Because studies have demonstrated that diesel engines can operate on 20 % replacement without any modifications, this study focused on 20 % blend. The engine was tested with loads (Ls) ranging from 0 % to 100 % of its entire capacity while the compression ratios (CRs) was varied. The experimental result demonstrated that the thermal efficiency, as measured by the PDF, was much greater than that of the DRUCO20, DJC20, and DPP20 blends. After the addition of RUCO, JC, and PP to PDF, the temperature of the exhaust gases reduced, and the engine used more gasoline as a result. It was discovered that an engine emissions of hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) were lower than those of PDF. Even though it produced a greater amount of carbon dioxide (CO2) emissions, the DRUCO20 was superior to both the DCJ20 and the DPP20.

Optimization of wear behavior of heat-treated Ti-6Al-7Nb biomedical alloy by response surface methodology.

Titanium-based metals are used most often in biomedical implant studies because they have good qualities like being biocompatible, not being poisonous, Osseo-integration, high specific properties, wear resistance, etc. The main goal of this work is to improve the wear resistance of Ti-6Al-7Nb biomedical metal by using a mix of Taguchi, ANOVA, and Grey Relational Analysis. The effect of changeable control process factors like applied load, spinning speed, and time on wear reaction measures like wear rate (WR), coefficient of friction (COF), and frictional force. The optimal combinations of wear rate, COF, and frictional force minimise wear characteristics. The L9 Taguchi orthogonal array was used to plan the experiments, which were done on a pin-on-disc set-up according to ASTM G99. To find the best set of control factors, Taguchi, ANOVA, and Grey relationship analysis were used. The results show that a load of 30 N, a speed of 700 rpm, and a time of 10 min are the best control settings.

Experimental and empirical investigation of a CI engine fuelled with blends of diesel and roselle biodiesel.

The continuous rise in demand, combined with the depletion of the world's fossil fuel reserves, has forced the search for alternative fuels. The biodiesel produced from Roselle is one such indigenous biodiesel with tremendous promise, and its technical ability to operate with compression ignition engines is studied in this work. To characterize the fuel blends, researchers used experimental and empirical approaches while operating at engine loads of 25, 50, 75, and 100%, and with fuel injection timings of 19°, 21°, 23°, 25°, and 27° before top dead center. Results indicate that for 20% blend with the change of injection timing from 19° bTDC to 27° bTDC at full load, brake specific fuel consumption and exhaust gas temperature was increased by 15.84% and 4.60% respectively, while brake thermal efficiency decreases by 4.4%. Also, an 18.89% reduction in smoke, 5.26% increase in CO2, and 12.94% increase in NOx were observed. In addition, an empirical model for full range characterization was created. With an r-squared value of 0.9980 ± 0.0011, the artificial neural network model constructed to characterize all 10 variables was able to predict satisfactorily. Furthermore, substantial correlation among specific variables suggested that empirically reduced models were realistic.