Fourier-Transform-Infrared-Spectroscopy-Based Approach to Predict Engine Fuel Properties of Biodiesel

Abstract

Robust models are useful to predict the properties of biodiesel to facilitate the careful choice of feedstock for producing biodiesel for automotive engine applications. In the present work, a Fourier transform infrared spectroscopy (FTIR)-based approach is used to predict the kinematic viscosity, density, cetane number, and the higher calorific value of biodiesels. Unlike the standard partial least-squares (PLS) regression performed over a complete infrared spectrum, a novel approach involving few independent variables based on the functional groups present in biodiesel and correlating them with the properties of biodiesel is explored in the present study. To mimic a wide range and the type of methyl esters present in biodiesels, five biodiesels of significantly different compositions, namely, camelina, coconut, karanja, linseed, and palm, are chosen and are mixed in different volumetric proportions to obtain 70 biodiesel blends. The peak absorbance ratios of 70 biodiesel blends obtained from FTIR are correlated with the measured kinematic viscosity, density, cetane number, and the higher calorific value. The property prediction models are developed using multilinear regression and an artificial neural network whose performance is compared with that of standard full spectrum PLS regression. The results obtained show that the proposed approach is simple, reliable, and direct and provides better prediction with mean absolute percentage errors of 4.62%, 1.04%, 2.75%, and 6.85%, respectively, for the kinematic viscosity, density, higher calorific value, and cetane number of biodiesels.

Biodiesel from Biomass Waste Feedstock Prosopis Juliflora as a Fuel Substitute for Diesel and Enhancement of Its Usability in Diesel Engines Using Decanol

“…The presence of the C–O–C stretch component in the fingerprint region indicates that JFME has good oxidative stability, a desirable biodiesel property. [ 38 ] The wave number 1168.86 cm −1 is a C–O stretching ester group, and 721.38 cm −1 is a C–H bending and rocking component. The C–H bending and rocking element can be related to the fuel's viscosity and its effect on engine performance.…”

Advancements in In-Cylinder and After-Treatment Strategies for Mitigating Pollutants from Diesel Engines: A Critical Review

“…53 The total fraction of fatty acids in the biodiesel corresponds to its chemical properties, and these can be further divided based on their dependency on the chain length and number of double and single bonds in and out of conjugation with cis or trans orientations. 54 The fuel bound oxygen species and lack of sulfur groups relatively improve emission quality. However, with long-term use, serious issues arise, such as those arising from oil thickening, injector depositions, and cold start in winter seasons, are observed.…”

“…These then have their own peculiar cetane number, density, kinematic viscosity, calorific value, etc . The total fraction of fatty acids in the biodiesel corresponds to its chemical properties, and these can be further divided based on their dependency on the chain length and number of double and single bonds in and out of conjugation with cis or trans orientations . The fuel bound oxygen species and lack of sulfur groups relatively improve emission quality.…”

Biodiesel composition based machine learning approaches to predict engine fuel properties

“…17 It is time-consuming, expensive, and laborious to measure these properties experimentally, thus necessitating property prediction models. [18][19][20][21] Numerical models to predict the biodiesel properties were developed using different data types, including electroanalytical, 22 chromatographic, 23 and spectroscopic. 23 Among these, chromatographic data is most widely used owing to its simplicity.…”