- The International Journal of Materials and Engineering Technology
- Vol: 2 Issue: 2
- ANALYSIS OF FACTORS THAT INFLUENCE RUBBER SEED OIL - BASED BIODIESEL PRODUCTION USING PRINCIPAL COMP...
ANALYSIS OF FACTORS THAT INFLUENCE RUBBER SEED OIL - BASED BIODIESEL PRODUCTION USING PRINCIPAL COMPONENT ANALYSIS AND KENDALL’S COEFFICIENT OF CONCORDANCE TECHNIQUES
Authors : Ayodeji Omotehinse, P. O. Akpaka
Pages : 46-53
View : 28 | Download : 15
Publication Date : 2019-12-31
Article Type : Research
Abstract :The inability of oils and hydroelectric sources to meet the ever growing demand experienced in global energy in recent years has generated a lot of concern. The continuous increase in the demand for energy and the dwindling tendency of petroleum resources has steered endless search for alternative renewable and sustainable fuel. This study adopts a novel combination of Principal Component Analysis (PCA) and Kendall’s Coefficient of Concordance (KCC) to analyze some factors that affect rubber seed oil-based biodiesel production which has been found to be a good substitute and most advantageous over petrol diesel because of its environmental friendliness. The KCC was used to analyze the data matrix generated by thirteen Judges who were requested to rank the thirty-one variables identified from relevant literature to influence biodiesel production in descending order of importance upon which basis an index of concordance in ranking among the judges was computed as W = 0.84. PCA was used to analyze the outcomes of the questionnaires crafted with thirty-one of the well-ordered variables, purposively selected, using statistiXL software. The results obtained by KCC p rovide basic insight into how consistence the Judges were in ranking the variables while the results by PCA shows that significant parsimony was achieved in factor reduction from thirty one variables to mere seven factors which represent the principal factors that influence rubber seed oil-based biodiesel production.Keywords : Rubber seed oil, Biodiesel, Principal component analysis, Parsimony