02188nam a2200277 a 4500 vtls000075155 KUKTEM 20251114204550.0 131008t2013 my da f m 000 0 eng d THE0004995(Local) 201905140856 SHAHRILJ 201311181655 nabilah 201310221703 nabilah 201310081152 nabilah UMP TS156.8 .Z35 2013 rs Bc. Zaidi Salim Application of process monitoring based on inferential measurement approach / by Zaidi Salim Kuantan, Pahang : UMP, 2013 xiii, 44 p. : ill. ; 30 cm. + 1 CD-ROM Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013 Bibliography : p.40-42 In 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. Process control Statistical methods Regression analysis Multivariate analysis Mathematical statistics 0 0 0 1 10000 10000 2019-09-04 0 TS156.8 .Z35 2013 rs Bc. 0000074837 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 10000 10000 2019-09-04 0 CD 7118 | TS156.8 .Z35 2013 rs Bc. 0000074838 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 4214 4220 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*6503*9992