000 02188nam a2200277 a 4500
001 vtls000075155
003 KUKTEM
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008 131008t2013 my da f m 000 0 eng d
020 _aTHE0004995(Local)
039 9 _a201905140856
_bSHAHRILJ
_c201311181655
_dnabilah
_c201310221703
_dnabilah
_y201310081152
_znabilah
040 _aUMP
090 _aTS156.8 .Z35 2013 rs Bc.
100 0 _aZaidi Salim
245 1 0 _aApplication of process monitoring based on inferential measurement approach /
_cby Zaidi Salim
260 _aKuantan, Pahang :
_bUMP,
_c2013
300 _axiii, 44 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013
504 _aBibliography : p.40-42
520 3 _aIn this study, a new multivariate method to monitor continuous processes is developed based on the Process Control Analysis (PCA) framework. The objective of the study is to develop A new MSPM method and analyze the monitoring performance of system A and B. In industrial practice, monitoring process are usually performed based on an approximate model. As the number of variables increases, the fault detection performance tends to be slow in progression, as well as, introduce greater complexity in the later stages especially in fault identification and diagnosing. These research implements and analyzes Multiple Linear Regression (MLR) method to a continuous process which simplify the number of variables used. This research also based on the conventional MSPM technique. After that, the developed method was analyzed and finally, all the performance result of the developed method was compared with the conventional method. The monitoring results clearly demonstrate the superiority of the proposed method. The MLR methods show that the fault detection performance improved and better than the conventional method.
650 0 _aProcess control
_xStatistical methods
650 0 _aRegression analysis
650 0 _aMultivariate analysis
650 0 _aMathematical statistics
999 _aVIRTUA40
_c4214
_d4220
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*6503*9992