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| 001 | vtls000084802 | ||
| 003 | KUKTEM | ||
| 005 | 20251117113252.0 | ||
| 008 | 150109t2014 my a f m 000 0 eng d | ||
| 020 | _aTHE0002012(Local) | ||
| 039 | 9 |
_a201905131603 _byusri _c201712041151 _dhuda _y201501091107 _zfawwaz |
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| 040 | _aUMP | ||
| 090 | _aQA278 .F37 2014 r Bc. | ||
| 100 | 0 | _aMohd Farid Mohd Na’aim | |
| 245 | 1 | 0 |
_aUtilising multiple linear regression (mlr) technique in multivariate statistical process monitoring (mspm) system / _cMohd Farid Mohd Na’aim |
| 260 |
_aKuantan, Pahang : _bUMP, _c2014 |
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| 300 |
_axiv, 55 p. : _bill. ; _c30 cm. + _e1 CD-ROM |
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| 500 | _aFaculty of Chemical & Natural Resources Engineering | ||
| 502 | _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2014 | ||
| 504 | _aBibliography : p. 41-43 | ||
| 520 | 3 | _aNowadays, modern process plants are following the trend of highly integrated and complex processes and highly instrumented chemical processes. In the most chemical processes, the need of monitoring various process variables is driven by the large amount of data produced by the instruments. Eventually, data will overload and wasted. Therefore, something needs to be done to monitor process data in lesser dimension as well as retain the most variations present. Principal Component Analysis (PCA) based MSPM technique is introduced to help us monitor and control processes. However, we have to retain too much principal component (PC) scores which complicate the fault detection operation. Thus, new MSPM technique, Multiple Linear Regression (MLR) is introduced to be utilized together with PCA to monitor predictor variables from criterion variables by equation that relates both variables. Hence, we only need to monitor criterion variables. The hypothesis for this study is if MLR is implemented, the lesser the number of variables need to be monitored. This proposed method is applied to the on-line monitoring of a simulated continuous stirred tank reactor with recycle (CSTRwR) from case study of Zhang, Martin, & Morris(1995). MATLAB software is utilized in this study. The general framework of fault detection comprises Phase I and Phase II. Phase I starts from normalisation of NOC data, PC scores formulated, monitoring statistics SPE and T2 are calculated and lastly 95% and 99% control limits developed. Phase II starts from standardisation of fault data with respect to NOC data, PC scores developed, monitoring statistics SPE and T2 are developed and lastly fault detection using control limits developed in Phase I. System A is the CSTRwR monitored by PCA-based MSPM system for the original set of variables. System B is the CSTRwR monitored by new MLR-PCA based MSPM system for the criterion variables which are the main product variables. Dynamic model was developed in Phase I. Then, fault data was introduced in the Phase II for fault detection. For System A, using abrupt fault data No.1 (F01a), faults were detected successfully by monitoring five variables out of 13 variables meanwhile System B only monitor two variables out of three variables with almost identical outcomes. Hence, new MSPM technique, MLR was successfully proven to be an efficient monitoring tool with quick detection and isolability while retaining as much as possible variations in lesser dimension | |
| 650 | 0 | _aMultivariate anlaysis | |
| 650 | 0 | _aPrincipal component analysis | |
| 856 | 4 | 0 |
_ahttp://ecollib.ump.edu.my/9711/ _zAccess in library only |
| 999 |
_aVIRTUA40 _c5802 _d5808 |
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