Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools

Multivariate statistical process control (MSPC) has been widely used for quality prediction and monitoring in palm oil refinery processes. Currently, the refined, bleached deodorized palm oil (RBDPO) quality is determined based on the relationship between crude palm oil quality and process parameter...

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Main Author:	Rashid, Nor Adhihah
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Q Science (General) TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/101716/1/NorAdhihahRashidPSChE2021.pdf
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spelling	my-utm-ep.1017162023-07-09T01:13:24Z Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools 2021 Rashid, Nor Adhihah Q Science (General) TP Chemical technology Multivariate statistical process control (MSPC) has been widely used for quality prediction and monitoring in palm oil refinery processes. Currently, the refined, bleached deodorized palm oil (RBDPO) quality is determined based on the relationship between crude palm oil quality and process parameters, with the assumption that the process is static and not affected by the time-varying characteristic of the palm oil refinery process. However, the prediction is less accurate since the generated regression coefficients from static prediction models do not reflect the current process status and remain constant over time. Therefore, this study was conducted to introduce a new framework for regression coefficients improvement via dynamic prediction models. The dynamic prediction models were developed by integrating the MSPC prediction tool with time-series expansion methods where the prediction models were adapted to new process dynamics. Data collected from an industrial palm oil refining plant were used as the case study in this research. Four MSPC models, namely linear principal component regression (PCR), linear partial least squares (PLS), nonlinear principal component regression based on nonlinear iterative partial least squares algorithm (NIPALS-PCR) and nonlinear partial least squares based on nonlinear iterative partial least square algorithm (NIPALS-PLS) were used to determine the relationship between the quality and process variables. Time-series expansion methods were used to trace the dynamic behaviour based on five approaches, namely static, moving window (MW), recursive window (RW), exponentially weighted moving window (EWMW) and exponentially weighted recursive window (EWRW). The findings show that the combination of the linear prediction model with the time-series expansion method showed a more reliable prediction performance than the nonlinear prediction model. The performance of the PCR EWMW model in predicting the RBDPO quality is improved by 12.02 % (11.96 % for free fatty acid, 6.92 % for moisture content, 16.13 % for iodine value and 13.01 % for colour) compared to other prediction models. The sensitivity of the regression coefficients was also improved where the regression coefficients fluctuated very smoothly and showed high convergence to zero value when using the PCR EWMW model. This shows that the implementation of the linear dynamic prediction model was better than the static prediction model. Therefore, the linear dynamic prediction model for quality prediction was the best for it has the greatest prediction improvement and showed a better trend of the regression coefficient. 2021 Thesis http://eprints.utm.my/id/eprint/101716/ http://eprints.utm.my/id/eprint/101716/1/NorAdhihahRashidPSChE2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145694 phd doctoral Universiti Teknologi Malaysia, Faculty of Engineering - School of Chemical & Energy Engineering Faculty of Engineering - School of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General) TP Chemical technology
spellingShingle	Q Science (General) TP Chemical technology Rashid, Nor Adhihah Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
description	Multivariate statistical process control (MSPC) has been widely used for quality prediction and monitoring in palm oil refinery processes. Currently, the refined, bleached deodorized palm oil (RBDPO) quality is determined based on the relationship between crude palm oil quality and process parameters, with the assumption that the process is static and not affected by the time-varying characteristic of the palm oil refinery process. However, the prediction is less accurate since the generated regression coefficients from static prediction models do not reflect the current process status and remain constant over time. Therefore, this study was conducted to introduce a new framework for regression coefficients improvement via dynamic prediction models. The dynamic prediction models were developed by integrating the MSPC prediction tool with time-series expansion methods where the prediction models were adapted to new process dynamics. Data collected from an industrial palm oil refining plant were used as the case study in this research. Four MSPC models, namely linear principal component regression (PCR), linear partial least squares (PLS), nonlinear principal component regression based on nonlinear iterative partial least squares algorithm (NIPALS-PCR) and nonlinear partial least squares based on nonlinear iterative partial least square algorithm (NIPALS-PLS) were used to determine the relationship between the quality and process variables. Time-series expansion methods were used to trace the dynamic behaviour based on five approaches, namely static, moving window (MW), recursive window (RW), exponentially weighted moving window (EWMW) and exponentially weighted recursive window (EWRW). The findings show that the combination of the linear prediction model with the time-series expansion method showed a more reliable prediction performance than the nonlinear prediction model. The performance of the PCR EWMW model in predicting the RBDPO quality is improved by 12.02 % (11.96 % for free fatty acid, 6.92 % for moisture content, 16.13 % for iodine value and 13.01 % for colour) compared to other prediction models. The sensitivity of the regression coefficients was also improved where the regression coefficients fluctuated very smoothly and showed high convergence to zero value when using the PCR EWMW model. This shows that the implementation of the linear dynamic prediction model was better than the static prediction model. Therefore, the linear dynamic prediction model for quality prediction was the best for it has the greatest prediction improvement and showed a better trend of the regression coefficient.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rashid, Nor Adhihah
author_facet	Rashid, Nor Adhihah
author_sort	Rashid, Nor Adhihah
title	Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
title_short	Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
title_full	Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
title_fullStr	Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
title_full_unstemmed	Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
title_sort	refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Chemical & Energy Engineering
granting_department	Faculty of Engineering - School of Chemical & Energy Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/101716/1/NorAdhihahRashidPSChE2021.pdf
_version_	1776100754913755136

Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools

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