02520nam a2200253 a 4500 vtls000031358 KUKTEM 20251114204450.0 080902t2008 my a f m 000 0 eng|d THE0001920(Local) 201905131528 yusri 201107132101 VLOAD 200908141515 VLOAD 200908141445 VLOAD 200809021556 ida84 UMP QA76.87 .F35 2008 rs Thesis Faizal Nazareth Ibrahim Controller performance of P,PI and neral network control in vinyl acetate monomer process / Faizal Nazareth Ibrahim Controller performance of P,PI and neral network control in vinyl acetate monomer process [electronic resource] Kuantan, Pahang : UMP, 2008 45 p. : ill. ; 30 cm. + 1 computer disc Project paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008 This 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 Neural networks (Computer science) PID controllers 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .F35 2008 rs Thesis 0000031369 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 10000 10000 2019-09-04 0 CD 2748 | QA76.87 .F35 2008 rs Thesis 0000031370 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2519 2525 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992