Fatty acid based prediction models for biodiesel properties incorporating compositional uncertainty. (15th May 2017)

• Record Type: Journal Article
• Authors: Caldeira, Carla; Freire, Fausto; Olivetti, Elsa A.; Kirchain, Randolph
• Is Part Of: Fuel. Volume 196(2017)
• Page Start: 13
• Page End: 20
• Publication Date: 2017-05-15
• Subjects: Biodiesel properties -- Compositional uncertainty -- Prediction models; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2017.01.074
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: Uncertainty was integrated in prediction models for biodiesel properties. 3 virgin oils and a waste oil were used to assess the models. The models results present lower variation than the reference values. Prediction models should report information on model uncertainty. Abstract: Biodiesel is globally produced by transesterification of vegetable oils. Each vegetable oil possesses a typical fatty acid (FA) profile that will influence the final properties of the biodiesel. Models have been developed to express the relationship between the FA composition and the fuel properties. However, as the FA sources are variable and because the chemical composition of a FA source are not always fully characterized, this variability translates into uncertainty for the production planner. This paper explores the underlying variability associated with the FA composition and assesses the results of these models incorporating FA compositional uncertainty. Models for viscosity, density, cetane number, iodine value, cold filter plugging point and oxidative stability were considered. The potential range of properties given by the models was compared with values reported in the literature. The main goal is to assess the influence of compositional uncertainty and the potential existence of systematic deviations in the results provided by these models. This assessment can be used to improve production plans with tools that account for compositional uncertainty and variability, allowing the biodiesel producer planner to determine blends that minimize the risk of noncompliance with the technical requirements.