Prediction of physical properties of oil palm biomass reinforced polyethylene: Linear regression approach

In recent years, there has been an increasing interest on renewable resources for consumer products and biodegradable materials.Traditional polymeric materials derived from petro-chemical sources do not degrade and disposal of such materials is a major concern in minimizing the environmental proble...

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Bibliographic Details
Main Authors: Abu Bakar, Syamsiah, Mohd Dom, Rosma, Akbarally, Ajab Bai, Wan Hassan, Wan Hasamudin
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
Online Access:http://repo.uum.edu.my/13634/1/123.pdf
http://repo.uum.edu.my/13634/
http://www.icoci.cms.net.my
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Summary:In recent years, there has been an increasing interest on renewable resources for consumer products and biodegradable materials.Traditional polymeric materials derived from petro-chemical sources do not degrade and disposal of such materials is a major concern in minimizing the environmental problems. Currently, experiments are carried out in laboratories to determine the physical properties of degradable plastics which include melt flow index (MFI), melting point (MP) and Density.Oil palm biomass (OPB) is used as bio-active components in the formulation with Polyethylene (PE).Alternatively, a different approach is required as to minimize the time consume, the cost of production and the cost of labor.In this study, Linear Regression model has been developed and used to predict the physical properties of degradable plastics.The ability of Linear Regression model is assessed by comparing the theoretical results with the actual lab results using correlation coefficient (r) and coefficient of determination (R2).The result showed that the percentage prediction accuracy for MFI is 93%, 71% for the prediction of MP and 24% for the prediction of Density respectively using linear regression.The study proves that the use of Linear Regression model for predicting the physical properties of degradable plastics is highly feasible.