Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar

Degradable plastic is produced by combining different percentages of additives such as oil palm biomass (OPB), palm oil (PO) and starch with polyethylene (PE). Currently, experiments are carried out in laboratories to determine the formulation of degradable plastics with the most bioactive component...

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Main Author:	Abu Bakar, Syamsiah
Format:	Thesis
Language:	English
Published:	2012
Online Access:	https://ir.uitm.edu.my/id/eprint/79013/1/79013.pdf
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spelling	my-uitm-ir.790132023-09-12T06:43:56Z Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar 2012 Abu Bakar, Syamsiah Degradable plastic is produced by combining different percentages of additives such as oil palm biomass (OPB), palm oil (PO) and starch with polyethylene (PE). Currently, experiments are carried out in laboratories to determine the formulation of degradable plastics with the most bioactive components and desirable physical properties measured by melt How index (MFI), melting point (MP) and density. The procedure is time consuming and costly. Therefore, a different approach is required to minimize the time consumption, production cost and labour cost. In this research, an Adaptive Neuro-Fuzzy Inference System (ANFIS) and linear regression (LR) models have been developed and utilized to predict the physical properties of degradable plastics. The prediction accuracy of ANFIS and LR models are assessed by comparing simulated results with the actual lab results using root mean square error (RMSE), correlation coefficient (/?), coefficient of determination (R2 ) and adjusted R square (R2 ). ANFIS and LR models are found to have compatible prediction performances. The findings show that ANFIS model is capable of determining the desirable input-output relationships in degradable plastic production as reflected by the small RMSE values, high R and R2 values. Furthermore the difference between R2 and R2 values is small which indicates that the addition of new variable does not contribute to the over fitting of ANFIS model. It was also found that different membership functions used in ANFIS has an impact on ANFIS prediction performance. The developed ANFIS model can be used as a guide for the production of degradable plastic with some intended physical requirements. 2012 Thesis https://ir.uitm.edu.my/id/eprint/79013/ https://ir.uitm.edu.my/id/eprint/79013/1/79013.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Mohd Dom, Rosma
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohd Dom, Rosma
description	Degradable plastic is produced by combining different percentages of additives such as oil palm biomass (OPB), palm oil (PO) and starch with polyethylene (PE). Currently, experiments are carried out in laboratories to determine the formulation of degradable plastics with the most bioactive components and desirable physical properties measured by melt How index (MFI), melting point (MP) and density. The procedure is time consuming and costly. Therefore, a different approach is required to minimize the time consumption, production cost and labour cost. In this research, an Adaptive Neuro-Fuzzy Inference System (ANFIS) and linear regression (LR) models have been developed and utilized to predict the physical properties of degradable plastics. The prediction accuracy of ANFIS and LR models are assessed by comparing simulated results with the actual lab results using root mean square error (RMSE), correlation coefficient (/?), coefficient of determination (R2 ) and adjusted R square (R2 ). ANFIS and LR models are found to have compatible prediction performances. The findings show that ANFIS model is capable of determining the desirable input-output relationships in degradable plastic production as reflected by the small RMSE values, high R and R2 values. Furthermore the difference between R2 and R2 values is small which indicates that the addition of new variable does not contribute to the over fitting of ANFIS model. It was also found that different membership functions used in ANFIS has an impact on ANFIS prediction performance. The developed ANFIS model can be used as a guide for the production of degradable plastic with some intended physical requirements.
format	Thesis
qualification_level	Master's degree
author	Abu Bakar, Syamsiah
spellingShingle	Abu Bakar, Syamsiah Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
author_facet	Abu Bakar, Syamsiah
author_sort	Abu Bakar, Syamsiah
title	Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
title_short	Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
title_full	Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
title_fullStr	Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
title_full_unstemmed	Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar
title_sort	prediction of the physical properties of degradable plastics using anfis / syamsiah abu bakar
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/79013/1/79013.pdf
_version_	1783736263614922752

Prediction of the physical properties of degradable plastics using ANFIS / Syamsiah Abu Bakar

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