Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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Abstract
The quest for renewable, cost-effective, environmentally friendly and sustainable alternative fuels to run Compression Ignition (CI) engines has escalated the tempo of research in biodiesel in recent decades. Investigations aimed at improving combustion, engine performance and emission characteristics of CI engines fuelled with Fatty Acid Methyl Esters (FAME) have increased substantially in recent years. Properties of biodiesel are key parameters in relation to engine performance, emission characteristics and its suitability as CI engine fuel; these properties are influenced by the Fatty Acid (FA) composition of the biodiesel. In order to overcome the complexities related to real-time experimental determination of biodiesel properties, various prediction techniques have been used. This current effort explores multiple linear regression to formulate linear correlations for the prediction of the density, Cetane Number (CN), Calorific Value (CV) and Kinematic Viscosity (KV) of biodiesel using the five commonest FAs (palmitic, stearic, oleic, linoleic and linolenic acids). Input data were sourced from literature to formulate linear relations for these FAME fingerprints and the outcome subjected to statistical analysis. The predictive capabilities of the models were verified using other experimental data mined from various sources. The outcomes of analysis shows that the adjusted R2 and maximum absolute errors are 83 and 0.35% for density, 84.3 and 1.72% for CN, 43 and 0.98% for CV and 68.3 and 4.33% for KV. It is evident that linear correlations established from five FAs are highly successful in predicting density, CN, CV and KV of biodiesel from a wide range of feedstocks.
How to cite this article:
Onuh Emmanuel Idoko, Inambao Freddie and Awogbemi Omojola. Application of Multiple Linear Regression for the Prediction of Some Properties of Biodiesel
using Fatty Acid Compositions.
DOI: https://doi.org/10.36478/jeasci.2020.1951.1961
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.1951.1961