Jump to Main Content
Assessing Waste Cooking Oils for the Production of Quality Biodiesel Using an Electronic Nose and a Stochastic Model
- Siqueira, Adriano F., Vidigal, Igor G., Melo, Mariana P., Giordani, Domingos S., Batista, Pollyanna S., Ferreira, Ana L. G.
- Energy & fuels 2019 v.33 no.4 pp. 3221-3226
- acidity, biodiesel, discriminant analysis, economic feasibility, electronic nose, frying oil, fuel production, iodine value, kitchen waste, laboratory experimentation, lipids, prediction, raw materials, stochastic processes, viscosity, Brazil
- Around 1% of waste cooking oil (WCO) is currently recycled to make biodiesel in Brazil, mainly because used oils can acquire physicochemical characteristics that render them unsuitable as raw materials. To make biofuel production from waste oils and fats more efficient and economically feasible, it is important to develop simple, rapid, and low-cost methods for testing the quality of WCOs. With the objective of establishing the applicability of stochastic modeling of e-nose profiles in assessing the suitability of WCO for biodiesel production, the synthesized biodiesel samples from 36 pre-used frying oils, obtained from domestic and commercial premises, were analyzed regarding ester content, acidity index, density, viscosity, and iodine index. Olfactory profiles of the WCO sources were obtained using a Cyranose chemical vapor-sensing instrument and interpreted by application of stochastic modeling and quadratic discriminant analysis. The predictive model obtained by stochastic analysis exclusively from the olfactory profiles of the samples of WCO allowed the latter to be classified according to their ability to generate biodiesel that would be compliant with standard specifications and with an overall accuracy greater than 80%. Our results demonstrated that stochastic modeling is a promising tool for predicting the quality of biodiesel based only on the WCO olfactory profiles and its origin, since it allows qualitative assessments of the principal biodiesel properties and eliminates the need for complex and time-consuming laboratory tests.