Development of soft-sensors for polymerization processes

Polymerization processes are important industrial processes where the polymer product can be made into many household and laboratory products. One limitation to this process is that there is no online indication of an important polymer quality, melt flow rate (MFR), as at least 2 hours of laboratory...

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Main Author: Kng, Madeline Wan Rong
Other Authors: Chen Tao
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/61991
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-619912023-03-03T15:39:44Z Development of soft-sensors for polymerization processes Kng, Madeline Wan Rong Chen Tao School of Chemical and Biomedical Engineering DRNTU::Engineering::Chemical engineering::Polymers and polymer manufacture Polymerization processes are important industrial processes where the polymer product can be made into many household and laboratory products. One limitation to this process is that there is no online indication of an important polymer quality, melt flow rate (MFR), as at least 2 hours of laboratory analysis is required to determine the polymer quality. To enable real-time monitoring of MFR, various works have been done to conduct prediction of the polymer quality. One approach is modeling based on fundamental principles governing the process which will require good knowledge of the polymerization process and much effort and time due to the complexity of the process. The other approach is to develop an artificial intelligence model using data mining tools such as artificial neural network, which has an advantage over the mechanistic approach due to its excellent ability to model nonlinear relationships. In this project, a software sensor using artificial neural network built-in in MATLAB is developed to predict the MFR of an industrial polymerization plant. A suitable network function, newrb, is recommended in this report. Historical data is found to have an influence on the model and update of model is necessary when training input gets outdated. The normalization method using the mean and standard deviation of the process variables can be further improvised by other univariate and multivariate analysis in order to removing outliers, reduced and classified to gain better prediction results. Bachelor of Engineering (Chemical and Biomolecular Engineering) 2015-01-05T01:32:48Z 2015-01-05T01:32:48Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/61991 en Nanyang Technological University 82 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Chemical engineering::Polymers and polymer manufacture
spellingShingle DRNTU::Engineering::Chemical engineering::Polymers and polymer manufacture
Kng, Madeline Wan Rong
Development of soft-sensors for polymerization processes
description Polymerization processes are important industrial processes where the polymer product can be made into many household and laboratory products. One limitation to this process is that there is no online indication of an important polymer quality, melt flow rate (MFR), as at least 2 hours of laboratory analysis is required to determine the polymer quality. To enable real-time monitoring of MFR, various works have been done to conduct prediction of the polymer quality. One approach is modeling based on fundamental principles governing the process which will require good knowledge of the polymerization process and much effort and time due to the complexity of the process. The other approach is to develop an artificial intelligence model using data mining tools such as artificial neural network, which has an advantage over the mechanistic approach due to its excellent ability to model nonlinear relationships. In this project, a software sensor using artificial neural network built-in in MATLAB is developed to predict the MFR of an industrial polymerization plant. A suitable network function, newrb, is recommended in this report. Historical data is found to have an influence on the model and update of model is necessary when training input gets outdated. The normalization method using the mean and standard deviation of the process variables can be further improvised by other univariate and multivariate analysis in order to removing outliers, reduced and classified to gain better prediction results.
author2 Chen Tao
author_facet Chen Tao
Kng, Madeline Wan Rong
format Final Year Project
author Kng, Madeline Wan Rong
author_sort Kng, Madeline Wan Rong
title Development of soft-sensors for polymerization processes
title_short Development of soft-sensors for polymerization processes
title_full Development of soft-sensors for polymerization processes
title_fullStr Development of soft-sensors for polymerization processes
title_full_unstemmed Development of soft-sensors for polymerization processes
title_sort development of soft-sensors for polymerization processes
publishDate 2015
url http://hdl.handle.net/10356/61991
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