In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst concentration (1 wt %), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R²) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively.

The development of environmentally benign silicone composites from sugar palm fibre and silicone rubber was carried out in this study. The mechanical, physical, and morphological properties of the composites with sugar palm (SP) filler contents ranging from 0% to 16% by weight (wt%) were investigated. Based on the uniaxial tensile tests, it was found that the increment in filler content led to higher stiffness. Via dynamic mechanical analysis (DMA), the viscoelastic properties of the silicone biocomposite showed that the storage modulus and loss modulus increased with the increment in filler content. The physical properties also revealed that the density and moisture absorption rate increased as the filler content increased. Inversely, the swelling effect of the highest filler content (16 wt%) revealed that its swelling ratio possessed the lowest rate as compared to the lower filler addition and pure silicone rubber. The morphological analysis via scanning electron microscopy (SEM) showed that the sugar palm filler was evenly dispersed and no agglomeration could be seen. Thus, it can be concluded that the addition of sugar palm filler enhanced the stiffness property of silicone rubber. These new findings could contribute positively to the employment of natural fibres as reinforcements for greener biocomposite materials.

This study emphasizes the performance of two-dimensional electrically non-conducting Oldroyd-B fluid flowing across a stretching sheet with thermophoretic particle deposition. The heat and mass transfer mechanisms are elaborated in the presence of a magnetic dipole, which acts as an external magnetic field. The fluid possesses magnetic characteristics due to the presence of ferrite particles. The gyrotactic microorganisms are considered to keep the suspended ferromagnetic particles stable. Cattaneo–Christov heat flux is cogitated instead of the conventional Fourier law. Further, to strengthen the heat transfer and mass transfer processes, thermal stratification and chemical reaction are employed. Appropriate similarity transformations are applied to convert highly nonlinear coupled partial differential equations into non-linear ordinary differential equations (ODEs). To numerically solve these ODEs, an excellent MATLAB bvp4c approach is used. The physical behavior of important parameters and their graphical representations are thoroughly examined. The tables are presented to address the thermophoretic particle velocity deposition, rate of heat flux, and motile microorganisms’ density number. The results show that the rate of heat transfer decreases as the value of the thermal relaxation time parameter surges. Furthermore, when the thermophoretic coefficient increases, the velocity of thermophoretic deposition decreases.

Alcoholic fuels have recently come to light as a sustainable source for powering today’s vehicles. Various studies have investigated the effects of alcoholic fuels on engine efficiency and emission characteristics. However, scarce literature is available for their effects on lubricant. Therefore, propanol-gasoline fuel mixtures, with concentrations of 9% (P9) and 18% (P18) propanol, were made to compare their engine characteristics and lubricating oil condition with that of pure gasoline (0 percent propanol (P0)). To determine the rate of deterioration, the characteristics of the lubricating oil were evaluated after 100 h of engine operation, as suggested by the manufacturer. When compared with unused lube oil, P18 showed reductions in flash point temperature and kinematic viscosity of 14% and 36%, respectively, at 100 °C. For P18, which contains Fe (27 PPM), Al (11 PPM), and Cu (14 PPM), the highest wear element concentrations in the lubricating oil were found. The moisture in the degraded oil was well within the allowable limit for the three fuel mixtures. With the increase in propanol percentage in the propanol-gasoline blend, the engine performance was increased. Compared to P9 and P0, P18 had the partially unburned emissions.

Biodiesel is gaining recognition as a good replacement for typical diesel owing to its renewability, sustainability, and eco-friendly nature. Transesterification is the leading route for biodiesel generation, which occurs during homogeneous/heterogeneous/enzymatic catalysis. Besides this, the usage of heterogeneous catalysts is considered more advantageous over homogeneous catalysts due to the easy catalyst recovery. Consequently, numerous heterogeneous catalysts have been synthesized from multiple sources with the intention of making the manufacturing process more efficient and cost-effective. Alongside this, numerous researchers have attempted to improve the biodiesel yield using heterogeneous catalysts by introducing cosolvents, such that phase limitation between oil and alcohol can be minimized. This short review is aimed at examining the investigations performed to date on heterogeneously catalyzed biodiesel generation in the presence of different cosolvents. It encompasses the techniques for heterogeneous catalyst synthesis, reported in the literature available for heterogeneous catalyzed biodiesel generation using cosolvents and their effects. It also suggests that the application of cosolvent in heterogeneously catalyzed three-phase systems substantially reduces the mass transfer limitation between alcohol and oil phases, which leads to enhancements in biodiesel yield along with reductions in values of optimized parameters, with catalyst weight ranges from 1 to 15 wt. %, and alcohol/oil ratio ranges from 5.5 to 20. The reaction time for getting the maximum conversion ranges from 10 to 600 min in the presence of different cosolvents. Alongside this, most of the time, the biodiesel yield remained above 90% in the presence of cosolvents.

This study involved the application of artificial neural network (ANN) as a new approach for thermoacoustic refrigerators to predict the temperature difference across the stack under some operating conditions. One ANN model for a standing wave thermoacoustic refrigerator, had been developed based on the experimental data from other literature. Temperature difference across the stack was chosen as a response to the input parameters, mean pressure and frequency in the proposed ANN model. A multi-layer feed-forward neural network with a back propagation algorithm had been proposed for predicting the temperature difference across the stack of the thermoacoustic refrigerator. This proposed ANN model has three layers with configuration 2-12-1, namely, input layer with two neurons representing the two operating parameters, one hidden layer with an optimal 12 hidden neurons, and output layer with one neuron representing the temperature difference across the stack, as response. The high ability of ANN for data prediction was proven in this study through achieving an average prediction error of 0.2% and a regression coefficient (R) of 0.99979 during testing phase. This research work provides a new approach based on ANN technique to solve complex thermoacoustic problems with linear or nonlinear nature through either modeling, optimization or system identification.