This paper intends to explore the most affordable and environmentally friendly method for the synthesis of biodiesel. Substitute fuel is presently a significant topic all over the world, attributable to the efforts of reducing global warming, which is the result arising from the combustion of petroleum or petrol diesel fuel. Due to its advantages of being renewable and environmentally friendly, biodiesel production has the potential to become the major substitute of petrol diesel fuel. Biodiesel is non-toxic, biodegradable, is produced from renewable sources, and contributes a small amount of greenhouse gas (e.g., CO₂ and SO₂) emissions to the atmosphere. Research has established that one of the key obstacles to the commercialization of biodiesel is the high price of biodiesel production due to the shortage of suitable raw materials. However, waste-cooking-oil (WCO) is one of the most cost-effective sources of biodiesel synthesis, and can practically minimize the raw material cost. The research was carried out to produce biodiesel from waste cooking oil in order to reduce the cost, waste, and pollution associated with biodiesel production. The application of a microwave heating system towards enhancing the production of biodiesel from waste cooking oil has been given little consideration in the preceding research, particularly with the application of eggshell as a heterogeneous catalyst. However, the tentative results in this study show significant performance in terms of biodiesel production, as follows: (1) the increasing of the reaction time from 120 to 165 min considerably increased the biodiesel production, which declined with a further rise to 210 min; (2) the results of this study reveal that a methanol-to-oil molar ratio of nine is appropriate and can be used for the best production of biodiesel; (3) the production of biodiesel in this study demonstrated a significant increase in response to the further increasing of power; (4) a 120 min response time, a ratio of 9:1 methanol-to-oil molar fraction, 65 °C temperature; (5) and 5 wt % catalyst were found to be the most ideal reaction conditions during this study. In summary, recycled eggshell was re-used as a suitable catalyst to produce new biodiesel from waste cooking oil, applicable to diesel engines.

In this study, a nano-Ca(OH)₂/Fe₃O₄ catalyst was used to produce biodiesel from a 1:1 mixed jatropha-castor oil. By loading Ca(OH)₂ onto Fe₃O₄ nanoparticles, it increased the specific surface area by almost 40%, which improved the catalytic activity as it provided a larger area for the reactants to interact. The main purpose of mixing jatropha oil with castor oil was to lower the viscosity of the castor oil. The transesterification reaction was carried out at elevated temperature, using a microwave heating system. Moreover, it was shown that the preferred reaction conditions are using high temperature and short reaction duration. The optimized yield of methyl ester was 95%, achieved by using a catalyst with a Ca:Fe ratio of 7:1, temperature of 65 °C, methanol/oil ratio of 12:1, and reaction time of 35 min. The catalyst was shown to be reusable, easily recyclable, and its activity was very stable. Only 2% of the catalyst was lost, and the yield was 3% lower after ten successive applications. The solid, magnetic base catalyst could be easily separated from the reaction products, unlike homogeneous catalysts.

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.