Data-driven modelling and optimization of palm oil refining process

Bleaching earth and citric acid are expensive chemicals utilized in the palm oil refining process. Both chemicals are important in removing impurities such as colour pigments and other breakdown products in crude palm oil. The purpose of this study was to successfully develop refined oil quality pre...

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Main Author:	Sulaiman, Nurul Sulaiha
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/102146/1/NurulSulaihaPSChE2021.pdf.pdf
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spelling	my-utm-ep.1021462023-08-07T08:11:19Z Data-driven modelling and optimization of palm oil refining process 2021 Sulaiman, Nurul Sulaiha TP Chemical technology Bleaching earth and citric acid are expensive chemicals utilized in the palm oil refining process. Both chemicals are important in removing impurities such as colour pigments and other breakdown products in crude palm oil. The purpose of this study was to successfully develop refined oil quality prediction model and to optimize the dosage of bleaching earth and citric acid utilized in a palm oil refining process. First, response surface methodology (RSM) was used to develop an empirical model to relate the removal rate of free fatty acid (FFA) and red colour pigments in the refined oil to the dosage of both bleaching earth and citric acid. Next, artificial neural networks (ANNs) models were developed based on the pilot-scale experimental data and actual palm oil refining plant data as input and output data for the models. The quality parameters of crude oil (FFA and colour pigment content) as well as process parameters (bleaching earth and citric acid dosage, pressure and temperature of the deodorizer) were the input data while the quality of refined oil (FFA and red colour pigments content) were the output data. For comparison purpose, three types of ANN models were developed, which were multi-layer perceptron (MLP), stacked neural network (SNN) models and radial basis function neural network (RBFNN) models. All the ANN models developed were able to predict the quality of the refined palm oil. The developed models were compared and the best model, the SNN model, was chosen as the model for refined palm oil quality prediction. Finally, optimization frameworks to optimize the palm oil refining process were developed based on the multi-objective genetic algorithms approach using RSM and ANN models. The RSM models were validated by comparing the predicted value with the actual pilot-scale data, while all ANN models were validated by a new set of actual palm oil refining plant data. From the findings, bleaching earth dosage was found to be the major contributor to the impurities removal rate affecting the palm oil refining performance. Optimizing the dosage of bleaching earth and citric acid enabled significant savings in raw material usage, thus creating a more sustainable and cost-effective palm oil refining process. The models developed from this study were able to predict the quality of the refined oil accurately and quickly, gave the optimum dosage of the bleaching earth and citric acid, as well as provided optimum point of pressure and temperature of the deodorization process. 2021 Thesis http://eprints.utm.my/id/eprint/102146/ http://eprints.utm.my/id/eprint/102146/1/NurulSulaihaPSChE2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145703 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Sulaiman, Nurul Sulaiha Data-driven modelling and optimization of palm oil refining process
description	Bleaching earth and citric acid are expensive chemicals utilized in the palm oil refining process. Both chemicals are important in removing impurities such as colour pigments and other breakdown products in crude palm oil. The purpose of this study was to successfully develop refined oil quality prediction model and to optimize the dosage of bleaching earth and citric acid utilized in a palm oil refining process. First, response surface methodology (RSM) was used to develop an empirical model to relate the removal rate of free fatty acid (FFA) and red colour pigments in the refined oil to the dosage of both bleaching earth and citric acid. Next, artificial neural networks (ANNs) models were developed based on the pilot-scale experimental data and actual palm oil refining plant data as input and output data for the models. The quality parameters of crude oil (FFA and colour pigment content) as well as process parameters (bleaching earth and citric acid dosage, pressure and temperature of the deodorizer) were the input data while the quality of refined oil (FFA and red colour pigments content) were the output data. For comparison purpose, three types of ANN models were developed, which were multi-layer perceptron (MLP), stacked neural network (SNN) models and radial basis function neural network (RBFNN) models. All the ANN models developed were able to predict the quality of the refined palm oil. The developed models were compared and the best model, the SNN model, was chosen as the model for refined palm oil quality prediction. Finally, optimization frameworks to optimize the palm oil refining process were developed based on the multi-objective genetic algorithms approach using RSM and ANN models. The RSM models were validated by comparing the predicted value with the actual pilot-scale data, while all ANN models were validated by a new set of actual palm oil refining plant data. From the findings, bleaching earth dosage was found to be the major contributor to the impurities removal rate affecting the palm oil refining performance. Optimizing the dosage of bleaching earth and citric acid enabled significant savings in raw material usage, thus creating a more sustainable and cost-effective palm oil refining process. The models developed from this study were able to predict the quality of the refined oil accurately and quickly, gave the optimum dosage of the bleaching earth and citric acid, as well as provided optimum point of pressure and temperature of the deodorization process.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sulaiman, Nurul Sulaiha
author_facet	Sulaiman, Nurul Sulaiha
author_sort	Sulaiman, Nurul Sulaiha
title	Data-driven modelling and optimization of palm oil refining process
title_short	Data-driven modelling and optimization of palm oil refining process
title_full	Data-driven modelling and optimization of palm oil refining process
title_fullStr	Data-driven modelling and optimization of palm oil refining process
title_full_unstemmed	Data-driven modelling and optimization of palm oil refining process
title_sort	data-driven modelling and optimization of palm oil refining process
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Chemical & Energy Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/102146/1/NurulSulaihaPSChE2021.pdf.pdf
_version_	1776100856790253568

Data-driven modelling and optimization of palm oil refining process

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