000			02520nam a2200253 a 4500
001			vtls000031358
003			KUKTEM
005			20251114204450.0
008			080902t2008 my a f m 000 0 eng|d
020			_aTHE0001920(Local)
039		9	_a201905131528 _byusri _c201107132101 _dVLOAD _c200908141515 _dVLOAD _c200908141445 _dVLOAD _y200809021556 _zida84
040			_aUMP
090			_aQA76.87 .F35 2008 rs Thesis
100	0		_aFaizal Nazareth Ibrahim
245	1	0	_aController performance of P,PI and neral network control in vinyl acetate monomer process / _cFaizal Nazareth Ibrahim
246	3		_aController performance of P,PI and neral network control in vinyl acetate monomer process _h[electronic resource]
260			_aKuantan, Pahang : _bUMP, _c2008
300			_a45 p. : _bill. ; _c30 cm. + _e1 computer disc
502			_aProject paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008
520	3		_aThis research is about investigating the controller performance between P, PI and Neural Network control in Vinyl Acetate Monomer (VAC) Process. The manufacturing process is about vapor-phase reaction converting ethylene (C2H4), oxygen (O2) and acetic acid (HAc) into vinyl acetate (VAc) with water (H2O) and carbon dioxide (CO2) as byproducts. The data from the process are successfully generated and the simulation of the dynamic response is done with further analysis of P, PI control and Neural Network control. The study is focusing on the column section process as the clear view of the control performance is observed. The Proportional (P) and Proportional Integral (PI) control are type of controller that used in the process. The Neural Network control then is a control mechanism that has the similar system of human neurons for processing information data. It consists of network of neurons that have weight in each network and built generally in layers. As the analysis result of P and PI control showed some unsatisfying results, Neural Network Control is then developed to see the changes. In Neural Network control, the data has been trained and validate to get the better response before applied again to the process to see the improvement. At the end, Neural Network has visualized the better control performance as the unsatisfying responses of P and PI control have been improvised.-Author
650		0	_aNeural networks (Computer science)
650		0	_aPID controllers
999			_aVIRTUA40 _c2519 _d2525
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992