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020 _aTHE0002017(Local)
039 9 _a201905131605
_byusri
_c201712060946
_dhuda
_y201501160939
_zfawwaz
040 _aUMP
090 _aQA278 .S25 2014 r Bc.
100 0 _aNurul Saidatulhaniza Zahari
245 1 0 _aDevelopment of dissimilarity-based mspm system /
_cNurul Saidatulhaniza Zahari
260 _aKuantan, Pahang :
_bUMP,
_c2014
300 _axiii, 41 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
500 _aFaculty of Chemical & Natural Resources Engineering
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2014
504 _aBibliography : p. 39-41
520 3 _aThis 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
650 0 _aMultivariate anlaysis
650 0 _aPrincipal component analysis
856 4 0 _uhttp://ecollib.ump.edu.my/18296/
_zAccess in library only
999 _aVIRTUA40
_c5654
_d5660
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