Development of dissimilarity-based mspm system / Nurul Saidatulhaniza Zahari
Material type:
TextPublication details: Kuantan, Pahang : UMP, 2014Description: xiii, 41 p. : ill. ; 30 cm. + 1 CD-ROMISBN: - THE0002017(Local)
| Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
Final Year Report
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UMPLIB GAMBANG | QA278 .S25 2014 r Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000091487 | ||
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Final Year Report
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UMPLIB GAMBANG | CD 8517 | QA278 .S25 2014 r Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000091488 |
Faculty of Chemical & Natural Resources Engineering
Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2014
Bibliography : p. 39-41
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