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Sterculia foetida derived biodiesel is a potential fuel for a diesel engine. The Sterculia foetida biodiesel required a pre-refining process called degumming and an acid pretreatment process before converting them to methyl ester using the transesterification process. This study blended fuel from Sterculia foetida biodiesel and diesel with different volume ratios (5% to 30% of biodiesel blend with 95% to 70% diesel fuel). Sterculia foetida biodiesel and blended fuels met the ASTM D6751 and EN 14214 standards. The blended fuel is examined to obtain its influences on the performance and emission when operating at a diesel engine (1300 rpm to 2400 rpm). From the outcome, the engine performance of the SFB5 blend shows better performance than diesel fuel in terms of BTE (28.84%) and BSFC (5.86%). Artificial neural networks and extreme learning machines were employed to predict engine performance and exhaust emissions. The developed models gave excellent results, where the coefficient of determination is more than 99% and 98% for BSFC and BTE, respectively. When the engine is operated with SFB5, there is a significant reduction in CO, HC, and smoke opacity emissions by 8.26%, 2.08%, and 3.08%, respectively, and at the same time, an increase in CO2 by 3.53% and NOXby 22.39%. The comparison is made with diesel fuel. The extreme learning machine modelling is powerful for predicting engine performance and exhaust emission compared to artificial neural networks in terms of prediction accuracy. Sterculia foetida biodiesel–diesel blends of 5% show its capability to replace diesel fuel by providing engine peak performance than diesel fuel.

Global warming and the depletion of fossil fuels have spurred many efforts in the quest for finding renewable, alternative sources of fuels, such as biodiesel. Due to its auxiliary functions in areas such as carbon dioxide sequestration and wastewater treatment, the potential of microalgae as a feedstock for biodiesel production has attracted a lot of attention from researchers all over the world. Major improvements have been made from the upstream to the downstream aspects related to microalgae processing. One of the main concerns is the high cost associated with the production of biodiesel from microalgae, which includes drying of the biomass and the subsequent lipid extraction. These two processes can be circumvented by applying direct or in situ transesterification of the wet microalgae biomass, hence substantially reducing the cost. In situ transesterification is considered as a significant improvement to commercially produce biodiesel from microalgae. This review covers the methods used to extract lipids from microalgae and various in situ transesterification methods, focusing on recent developments related to the process. Nevertheless, more studies need to be conducted to further enhance the discussed in situ transesterification methods before implementing them on a commercial scale.

In this study, coconut oils have been transesterified with ethanol using microwave technology. The product obtained (biodiesel and FAEE) was then fractional distillated under vacuum to collect bio-kerosene or bio-jet fuel, which is a renewable fuel to operate a gas turbine engine. This process was modeled using RSM and ANN for optimization purposes. The developed models were proved to be reliable and accurate through different statistical tests and the results showed that ANN modeling was better than RSM. Based on the study, the optimum bio-jet fuel production yield of 74.45 wt% could be achieved with an ethanol–oil molar ratio of 9.25:1 under microwave irradiation with a power of 163.69 W for 12.66 min. This predicted value was obtained from the ANN model that has been optimized with ACO. Besides that, the sensitivity analysis indicated that microwave power offers a dominant impact on the results, followed by the reaction time and lastly ethanol–oil molar ratio. The properties of the bio-jet fuel obtained in this work was also measured and compared with American Society for Testing and Materials (ASTM) D1655 standard.

Pyrolyzed waste plastic-based green fuel has been reported to be used as an alternate fuel for diesel engines. Some of the main challenges for implementing this in current automotive technology include evaluating engine performance, emission, noise vibration harshness (NVH), and knock characteristics of this fuel. This study focuses on the engine performance of poly-ethylene terephthalate (PET)-based waste plastic oil (WPO) at varying engine speed conditions. The pyrolysis of mixed-waste plastic was carried out at 300 °C in a fixed-bed reactor. Physicochemical properties such as viscosity, density, calorific value, sulfur, and research octane number (RON) of the plastic fuel and its blends with gasoline were analyzed using ASTM standard test methods. The WPO was blended with two different types of gasoline (RON88 and RON90) at 10, 20, and 30%, and was tested in a spark-ignition (SI) engine. The experimental results showed that different WPO–gasoline blends can be used in an SI engine without any engine modifications, and the performance indicators for different blends were found to be close to that of pure gasoline. The brake power and brake specific fuel consumption (BSFC) were found to be 4.1 kW and 0.309 kg/kW h, respectively. The 10% WPO and 90% RON90 blend produced optimal engine performance at 3500 rpm.

The production of biodiesel using vegetable oil is an effective way to meet growing energy demands, which could potentially reduce the dependency on fossil fuels. The aim of this study was to evaluate grape seed (Vitis vinifera), Philippine tung (Reutealis trisperma), and kesambi (Schleichera oleosa) oils as potential feedstocks for biodiesel production to meet this demand. Firstly, biodiesels from these oils were produced and then their fatty acid methyl ester profiles and physicochemical properties were evaluated and compared with palm biodiesel. The results showed that the biodiesel produced from grape seed oil possessed the highest oxidation stability of 4.62 h. On the other hand, poor oxidation stability was observed for Philippine tung biodiesel at 2.47 h. The poor properties of Philippine tung biodiesel can be attributed to the presence of α-elaeostearic fatty acid. Furthermore, synthetic antioxidants (pyrogallol) and diesel were used to improve the oxidation stability. The 0.2 wt.% concentration of pyrogallol antioxidant could increase the oxidation stability of grape seed biodiesel to 6.24 h, while for kesambi and Philippine tung, biodiesels at higher concentrations of 0.3% and 0.4 wt.%, respectively, were needed to meet the minimum limit of 8 h. The blending of biodiesel with fossil diesel at different ratios can also increase the oxidation stability.

Optimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Bioethanol is known as a viable alternative fuel to solve both energy and environmental crises. This study used response surface methodology based on the Box-Behnken experimental design to obtain the optimum conditions for and quality of bioethanol production. Enzymatic hydrolysis optimization was performed with selected hydrolysis parameters, including substrate loading, stroke speed, α-amylase concentration and amyloglucosidase concentration. From the experiment, the resulting optimum conditions are 23.88% (w/v) substrate loading, 109.43 U/g α-amylase concentration, 65.44 U/mL amyloglucosidase concentration and 74.87 rpm stroke speed, which yielded 196.23 g/L reducing sugar. The fermentation process was also carried out, with a production value of 0.45 g ethanol/g reducing sugar, which is equivalent to 88.61% of ethanol yield after fermentation by using Saccharomyces cerevisiae (S. cerevisiae). The physical and chemical properties of the produced ethanol are within the specifications of the ASTM D4806 standard. The good quality of ethanol produced from this study indicates that Manihot glaziovii (M. glaziovii) has great potential as bioethanol feedstock.