Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models
One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these propertie...
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Jurnal Teknologi, Universiti Teknologi Malaysia
2016
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oai:www.myjurnal.my:982482018-09-20T00:00:00Z Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models Farah Nadhirah Mathematics & statistics One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). 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). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C) Jurnal Teknologi, Universiti Teknologi Malaysia Nur Fazirah Jumari 2016-00-00 Journal application/pdf 98248 www.myjurnal.my/filebank/published_article/4794714.pdf www.myjurnal.my/public/article-view.php?id=98248 |
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Digital Repository |
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Local Institution |
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Online Access |
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Mathematics & statistics |
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Mathematics & statistics Farah Nadhirah Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| description |
One of the major challenges in polymerization industry is the lack of online instruments to
measure polymer end-used properties such as xylene soluble, particle size distribution and
melt flow index (MFI). As an alternative to the online instruments and conventional
laboratory tests, these properties can be estimated using model based-soft sensor. This
paper presents models for soft sensors to measure MFI in industrial polypropylene loop
reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN)
model and stacked neural network (SNN) model. All models were developed and
simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN
models were compared and analyzed. The MFI was divided into three grades, which are
A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). 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). The
SNN model is the best model when tested with each grade of the MFI. It has shown lowest
RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for
MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for
MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C) |
| author2 |
Nur Fazirah Jumari |
| author_facet |
Nur Fazirah Jumari Farah Nadhirah |
| format |
Journal |
| author |
Farah Nadhirah |
| author_sort |
Farah Nadhirah |
| title |
Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| title_short |
Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| title_full |
Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| title_fullStr |
Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| title_full_unstemmed |
Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| title_sort |
comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models |
| publisher |
Jurnal Teknologi, Universiti Teknologi Malaysia |
| publishDate |
2016 |
| url |
http://www.myjurnal.my/public/article-view.php?id=98248 |
| first_indexed |
2018-09-20T14:01:56Z |
| last_indexed |
2018-09-20T14:01:56Z |
| _version_ |
1612242861365919744 |