This study aims to investigate a CI diesel engine characteristic of diesel-biodiesel blend with oxygenated alcohols and nanoparticle fuel additives. Biodiesel was synthesized from a complementary palm-sesame oil blend using an ultrasound-assisted transesterification process. B30 was mixed with fuel additives as the base fuel to form ternary blends in different proportions before engine testing. The oxygenated alcohols (DMC and DEE) and nanoparticles

This research work focuses on investigating the lubricity and analyzing the engine characteristics of diesel–biodiesel blends with fuel additives (titanium dioxide (TiO2) and dimethyl carbonate (DMC)) and their effect on the tribological properties of a mineral lubricant. A blend of palm–sesame oil was used to produce biodiesel using ultrasound-assisted transesterification. B30 (30% biodiesel + 70% diesel) fuel was selected as the base fuel. The additives used in the current study to prepare ternary fuel blends were TiO2 and DMC. B30 + TiO2 showed a significant reduction of 6.72% in the coefficient of friction (COF) compared to B30. B10 (Malaysian commercial diesel) exhibited very poor lubricity and COF among all tested fuels. Both ternary fuel blends showed a promising reduction in wear rate. All contaminated lubricant samples showed an increment in COF due to the dilution of combustible fuels. Lub + B10 (lubricant + B10) showed the highest increment of 42.29% in COF among all contaminated lubricant samples. B30 + TiO2 showed the maximum reduction (6.76%) in brake-specific fuel consumption (BSFC). B30 + DMC showed the maximum increment (8.01%) in brake thermal efficiency (BTE). B30 + DMC exhibited a considerable decline of 32.09% and 25.4% in CO and HC emissions, respectively. The B30 + TiO2 fuel blend showed better lubricity and a significant improvement in engine characteristics.

The purpose of this study was the improvement of cold flow and lubricity characteristics of biodiesel produced from the palm-sesame oil blend. Extreme learning machine (ELM) and response surface methodology (RSM) techniques were used to model the production process and the input variables (time, catalyst amount, methanol to oil ratio, and duty cycle) were optimized using cuckoo search algorithm. The mean absolute percentage error (MAPE), coefficient of determination (R), mean square error (MSE), root mean square error (RMSE), and standard error of prediction (SEP) were calculated to evaluate the performance of RSM and ELM. The results showed that ELM model had better performance in prediction than RSM model. The optimum yield of P S biodiesel obtained was. % under operating parameters of time (. min), duty cycle (. %), methanol to oil ratio (V/V %) and catalyst amount (. wt%). The cold flow characteristics of P S biodiesel are significantly improved like cloud point (. °C), pour point (. °C), and cold filter plugging point (-. °C) with better oxidation stability. h. The average coefficient of friction P S biodiesel was lower than palm biodiesel (B) and B commercial diesel by. % and. % respectively. ©