Development of dissimilarity-based mspm system

Nurul Saidatulhaniza, Zahari (2014) Development of dissimilarity-based mspm system. Faculty of Chemical & Natural Resources Engineering, Universiti Malaysia Pahang.

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This research is about development of dissimilarity matrix based on Multivariate Statistical Process Monitoring (MSPM) system. MSPM is an observation system to validate whether the process is happening according to its desired target. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional Principal Component Analysis (PCA) which applied based on MSPM system is less effective because it only valid for the linear relationships between measured variables. In order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process monitoring as alternative technique which models the non-linear process which simultaneously can improve the process monitoring performance. The procedures in MSPM system consists of two main phases basically for model development and fault detection. This research focused on converting dissimilarity matrix to minor product moment before proceeding to PCA process which runs by using Matlab software. The monitoring performance in both techniques were compared and analysed to achieve the aims of this research. The findings of this study are illustrated in the form of Hotelling’s T2 and Squared Prediction Errors (SPE) monitoring statistics to be analysed. As a conclusion, the dissimilarity system is comparable to the conventional method. Thus, it can be the other alternative method in the process monitoring performance. Finally, it is recommended to use data from other chemical processing systems for more concrete justification of the new technique

Item Type: Undergraduates Project Papers
Additional Information: Faculty of Chemical & Natural Resources Engineering Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2014
Uncontrolled Keywords: Multivariate anlaysis; Principal component analysis
Subjects: Q Science > QA Mathematics
Faculty/Division: Faculty of Chemical & Natural Resources Engineering
Depositing User: Mr. Nik Ahmad Nasyrun Nik Abd Malik
Date Deposited: 12 Oct 2015 00:03
Last Modified: 03 Aug 2023 01:54
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