Melt flow index estimation using neural network models for propylene polymerization process

Thesis (Sarjana Pendidikan (Pengajaran Bahasa Inggeris sebagai Bahasa Kedua)) - Universiti Teknologi Malaysia, 2013One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as melt flow index (MFI). As an alternative to the o...

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Main Author:	Jumari, Nur Fazirah
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/41907/5/NurFazirahJumariMFKK2013.pdf
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spelling	my-utm-ep.419072017-06-20T08:32:29Z Melt flow index estimation using neural network models for propylene polymerization process 2013-12 Jumari, Nur Fazirah TP Chemical technology Thesis (Sarjana Pendidikan (Pengajaran Bahasa Inggeris sebagai Bahasa Kedua)) - Universiti Teknologi Malaysia, 2013One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated by using a model based-soft sensor. This research presents models for soft sensors to measure MFI in industrial polypropylene loop reactors by using the artificial neural network (ANN), hybrid FP-ANN (HNN) and stacked neural network (SNN) models. The ANN model of the two loop reactors was developed by employing the concept of Feed-Forward Back Propagation (FFBP) network architecture using Levenberg-Marquardt training method. Serial hybrid FP-ANN (HNN) models were developed in this study. The error between actual MFI and simulation MFI from FP model was fed into the HNN model as one of the input variables. To construct the stacked neural network (SNN) model, two layers were needed: 1) level-0 generalizer output comes from a number of diverse ANN models and 2) level-1 generalizer was developed using the results of level-0 generalizer with additional input variables. All models were developed and simulated in MATLAB 2009a environment. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). When these three models (ANN, HNN, and SNN) were compared, the SNN model shows the lower RMSE for each type of MFI studied. 2013-12 Thesis http://eprints.utm.my/id/eprint/41907/ http://eprints.utm.my/id/eprint/41907/5/NurFazirahJumariMFKK2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Chemical Engineering Faculty of Chemical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Jumari, Nur Fazirah Melt flow index estimation using neural network models for propylene polymerization process
description	Thesis (Sarjana Pendidikan (Pengajaran Bahasa Inggeris sebagai Bahasa Kedua)) - Universiti Teknologi Malaysia, 2013One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated by using a model based-soft sensor. This research presents models for soft sensors to measure MFI in industrial polypropylene loop reactors by using the artificial neural network (ANN), hybrid FP-ANN (HNN) and stacked neural network (SNN) models. The ANN model of the two loop reactors was developed by employing the concept of Feed-Forward Back Propagation (FFBP) network architecture using Levenberg-Marquardt training method. Serial hybrid FP-ANN (HNN) models were developed in this study. The error between actual MFI and simulation MFI from FP model was fed into the HNN model as one of the input variables. To construct the stacked neural network (SNN) model, two layers were needed: 1) level-0 generalizer output comes from a number of diverse ANN models and 2) level-1 generalizer was developed using the results of level-0 generalizer with additional input variables. All models were developed and simulated in MATLAB 2009a environment. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). When these three models (ANN, HNN, and SNN) were compared, the SNN model shows the lower RMSE for each type of MFI studied.
format	Thesis
qualification_level	Master's degree
author	Jumari, Nur Fazirah
author_facet	Jumari, Nur Fazirah
author_sort	Jumari, Nur Fazirah
title	Melt flow index estimation using neural network models for propylene polymerization process
title_short	Melt flow index estimation using neural network models for propylene polymerization process
title_full	Melt flow index estimation using neural network models for propylene polymerization process
title_fullStr	Melt flow index estimation using neural network models for propylene polymerization process
title_full_unstemmed	Melt flow index estimation using neural network models for propylene polymerization process
title_sort	melt flow index estimation using neural network models for propylene polymerization process
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical Engineering
granting_department	Faculty of Chemical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/41907/5/NurFazirahJumariMFKK2013.pdf
_version_	1747816645998411776

Melt flow index estimation using neural network models for propylene polymerization process

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