Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil.

Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R2 = 0.91) with a 45-sample training set and a good quality of prediction (R2 = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.

Non-edible babassu oil as a new source for energy production-a feasibility transesterification survey assisted by ultrasound.

Ethyl esters of babassu oil were synthesized by alkaline catalysis to make the green production of biodiesel feasible with simple methods and available technology. Babassu oil is a transparent, light yellow oil extracted from the seeds of the babassu palm (Orbinya sp), and due to its high saturated fatty acid composition (83%), it is considered a non-inedible oil. Transesterification using ethanol represents a valid alternative to using methanol because of ethanol's lower toxicity and the higher yield on weight compared to methanol. Statistical methodology was applied to optimize the transesterification reaction, which was promoted by ultrasonic waves and mechanical agitation. Nuclear magnetic resonance spectroscopy was used to quantify the conversion attained. Alkaline transesterification assisted by ultrasound produced the best results with respect to reaction time and the phase separation step. The model obtained showed that conversions higher than 97% may be achieved in 10min with correct tuning of the process variables.