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000			02857ntm a2200265 a 4500
001			vtls000084859
003			KUKTEM
005			20251117113240.0
008			150114t2014 my a f m 000 0 eng d
020			_aTHE0004729(Local)
039		9	_a201905141046 _bsmfh _c201712061119 _dhuda _y201501141216 _zfawwaz
040			_aUMP
090			_aTP370.9.M38 H35 2014 r Bc.
100	0		_aWan Hairani Wan Na’aman
245	1	0	_aUtilizing mlr technique in mspm system / _cWan Hairani Wan Na’aman
260			_aKuantan, Pahang : _bUMP, _c2014
300			_axiii, 47 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. 41-44
520	3		_aThe main purpose of this research is to propose a new MSPM technique, where the original variables are modelled into linear composites in order to reduce the number of variables in monitoring, where eventually it may also monitoring the performances. Hence, the objectives are to develop the conventional MSPM method for the original set of variables (System A), to develop the conventional MSPM method, which applies Multiple Linear Regression (MLR) technique (System B) and last but not least is to analyse the monitoring performances between System A and System B. By doing this research, it was proved that, it is able to justify that the developed and upgraded MSPM method is comparatively better than conventional PCA-based MSPM method in monitoring the multivariate of non-linear process. The research also show the development of advanced multivariate way of process monitoring in terms of variables points besides of samples scores. The complete procedures of fault detection and identification comprise of two main phases namely as off-line modelling and monitoring (Phase I) and on-line monitoring (Phase II) where MLR technique is applies in phase I. All the mathematical model were developed into coding of matlab platform of version 7. The results were presented in the form of graphs or Shewart Chart of 95% and 99% confident limit with the Hotelling T-Squared Distribution and Squared Prediction Errors was considered has the statistical tools for observing. Both method which are PCA-based MSPM System and MLR-based MSPM System were compared it performance and had been analysed. From the results, MLR-based MSPM Sytem gives better performance interm of faster detection of fault compared with PCA-based MSPM System. As the conclusion, all the three objectives were achieved successfully
650		0	_aChemical engineering _xMathematical models
856	4	0	_uhttp://ecollib.ump.edu.my/18400/ _zAccess in library only
999			_aVIRTUA40 _c5416 _d5422
999			_aVTLSSORT008002000400090010002450260030005000502050405200650085609992