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Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model

With the development of the chemical industry, fault detection of chemical process has become hard challenge due to the high-dimensional data and complex chemical process and increasing number of equipment. The standard feedforward neural network is not particularly effective at solving these issues...

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Main Author: Zambri, Muhammad Ridzuan
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2022
Subjects:
T Technology
TP155-156 Chemical engineering
Online Access:http://eprints.usm.my/55494/1/Nonlinear%20Chemical%20Process%20Monitoring%20And%20Fault%20Detection%20Based%20On%20Modified%20Lstm%20Model.pdf
http://eprints.usm.my/55494/
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spelling my.usm.eprints.55494 http://eprints.usm.my/55494/ Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model Zambri, Muhammad Ridzuan T Technology TP155-156 Chemical engineering With the development of the chemical industry, fault detection of chemical process has become hard challenge due to the high-dimensional data and complex chemical process and increasing number of equipment. The standard feedforward neural network is not particularly effective at solving these issues. This study proposed a fault detection model based on modified Long Short-Term Memory (LSTM) model. The simulation experiment of the Tennessee Eastman (TE) chemical process for modified LSTM model will be using MATLAB software. The investigation the performance between the LSTM model with the Artificial Neural Network (ANN). The modification of the LSTM will be made by comparing different type of faults that will be used for the fault detection. The percentage of the training and validation also has a great influence towards the accuracy of the fault detection. The link to determining the optimum number of hidden layer nodes by manipulated the value of each hidden layers on the LSTM network is added since the number of hidden layer nodes in the LSTM network impacts the diagnosis outcome. Then, the optimized LSTM model will be obtained in order to get higher accuracy of the fault detection in chemical process. Finally, through the simulation in the MATLAB software, the results show that the modified LSTM model has a better performance in chemical fault detection than ANN and the higher accuracy that can be achieved by the LSTM model is 99.69%. Universiti Sains Malaysia 2022-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/55494/1/Nonlinear%20Chemical%20Process%20Monitoring%20And%20Fault%20Detection%20Based%20On%20Modified%20Lstm%20Model.pdf Zambri, Muhammad Ridzuan (2022) Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Kimia. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TP155-156 Chemical engineering
spellingShingle T Technology
TP155-156 Chemical engineering
Zambri, Muhammad Ridzuan
Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
description With the development of the chemical industry, fault detection of chemical process has become hard challenge due to the high-dimensional data and complex chemical process and increasing number of equipment. The standard feedforward neural network is not particularly effective at solving these issues. This study proposed a fault detection model based on modified Long Short-Term Memory (LSTM) model. The simulation experiment of the Tennessee Eastman (TE) chemical process for modified LSTM model will be using MATLAB software. The investigation the performance between the LSTM model with the Artificial Neural Network (ANN). The modification of the LSTM will be made by comparing different type of faults that will be used for the fault detection. The percentage of the training and validation also has a great influence towards the accuracy of the fault detection. The link to determining the optimum number of hidden layer nodes by manipulated the value of each hidden layers on the LSTM network is added since the number of hidden layer nodes in the LSTM network impacts the diagnosis outcome. Then, the optimized LSTM model will be obtained in order to get higher accuracy of the fault detection in chemical process. Finally, through the simulation in the MATLAB software, the results show that the modified LSTM model has a better performance in chemical fault detection than ANN and the higher accuracy that can be achieved by the LSTM model is 99.69%.
format Monograph
author Zambri, Muhammad Ridzuan
author_facet Zambri, Muhammad Ridzuan
author_sort Zambri, Muhammad Ridzuan
title Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
title_short Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
title_full Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
title_fullStr Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
title_full_unstemmed Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model
title_sort nonlinear chemical process monitoring and fault detection based on modified lstm model
publisher Universiti Sains Malaysia
publishDate 2022
url http://eprints.usm.my/55494/1/Nonlinear%20Chemical%20Process%20Monitoring%20And%20Fault%20Detection%20Based%20On%20Modified%20Lstm%20Model.pdf
http://eprints.usm.my/55494/
_version_ 1748703969001078784
score 13.211869

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