TY  - JOUR
T1  - Application of Multiple Linear Regression for the Prediction of Some Properties of Biodiesel
using Fatty Acid Compositions
AU - Omojola, Awogbemi AU - Emmanuel Idoko, Onuh AU - Freddie, Inambao 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 8
SP  - 1951
EP  - 1961
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.1951.1961
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.1951.1961
KW  - biodiesel
KW  -property prediction
KW  -linear
correlations
KW  -fatty acid compositions
KW  -FAME
AB  - 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.
ER  -