Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques.

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Influence of varying concentrations of TiO2 nanoparticles and engine speed on the performance and emissions of diesel engine operated on waste cooking oil biodiesel blends using response surface methodology.

For a few decades now fast depleting fossil fuels has been a major challenge. Fast expanding population and increased rate of urbanization has increased energy demand. This makes the current scenario worse. Fossil fuels' emissions are another challenge. Apart from fossil fuel emissions, the untreated disposal of waste cooking oil presents another environment's sustainability challenge. The treatment of waste cooking oil as fuel presents a tangible solution to challenge. In this research article, impact of the engine speed and the concentration of titanium dioxide (TiO2) nanoparticles (NPs) in diesel-biodiesel blended fuels on the engine's performance. The emission characteristics of a single-cylinder four-stroke diesel engine has also been examined. TiO2 NPs were produced by a sol-gel methodology. The diesel-biodiesel combination was fortified with TiO2 NPs at 40, 80 and 120 ppm. These mixtures were used to power the diesel engine, which was then run at 1150, 1400, 1650, 1900 and 2150 RPM. Interaction between engine speeds and nanoparticle concentrations and investigation of their combined effect on engine performance and emissions was done using response surface methodology. The minimum BSFC of 0.33994 kg/kWh and maximum BTE of 25.90% were found for B30 + 120 ppm biodiesel blend at 2150 rpm as compared to all other tested fuels. The emissions including CO and HC emissions were recorded as 25.61486 kg/kWh and 0.05289kg/kWh respectively at 2150 rpm for B30 + 120 ppm biodiesel blend while NOx on the contrary side exhibits a slight escalation with increasing engine speed and nanoparticles concentration. The findings of the experiments demonstrated that adding TiO2 nanoparticles to diesel-biodiesel blends is an effective way to enhance the performance of diesel engines while simultaneously reducing the emissions. It was also discovered that the mathematical model that was built can efficiently estimate the performance of the engine and the emission levels.

Effect of flanged diffuser divergence angle on wind turbine: A numerical investigation.

Power augmentation in a small-scale horizontal axis wind turbine, with its rotor encased in a flanged diffuser is explored. The power output of the wind turbine varies with changes in the diffuser design and the resulting back pressure. Reduction in this back pressure also results in early flow separation at the diffuser surface, which hinders turbine performance. The main aim of this study is to numerically investigate the local configuration of the wind turbine location inside the diffuser by varying diffuser angles and wind speeds. Therefore, shroud and flange were modeled and analyzed using the computational fluid dynamic (CFD) analyses and experiments were performed at two wind speeds 6 m/s and 8 m/s with and without the diffuser for model validation. The divergence angle of 4° was found to have no flow separation, thus maximizing flow rate. The proposed design shows wind speed improvement of up to 1.68 times compared to the baseline configuration. The corresponding optimum flange height was found to be 250 mm. However, increasing the divergence angle had a similar output. The dimensionless location of wind turbine was found to be between 0.45 and 0.5 for 2° and 4° divergence angle respectively. Furthermore, the maximum augmentation location varies with wind speed and diffuser's divergence angle as described by dimensionless location of wind turbine, thus presenting a noteworthy contribution to the horizontal axis wind turbine area with the flanged diffuser.