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020 _aTHE0001919(Local)
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040 _aUMP
090 _aQA76.87 .D56 2008 rs Thesis
100 0 _aDinie Muhammad
245 1 0 _aFault detection using neural network /
_cDinie Muhammad
246 3 _aFault detection using neural network
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2008
300 _a92 p. :
_bill. ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008
520 3 _aThis thesis is about the application of Artificial Neural Network (ANN) as fault detection in the chemical process plant. At the present time, the process and development in chemical plants are getting more complex and hard to control. Therefore, the needs for a system that can help to supervise and control the process in the plant have to be accomplished in order to achieve higher performance and profitability. As the emergence of Artificial Neural Network application nowadays had help to solve problems in various fields had given a great significant effect as the system are reliable to be adapted in the chemical plant. Furthermore, this thesis will be focusing more on the application of Artificial Neural Network as fault detection scheme in term of estimator and classifier in the chemical plant. Fault detection is popular in the present time as a mechanism to detect early malfunction and abnormal process or equipment in the plant. By implementing such system, we can boost up the production and the safety level of the plant. For this thesis, the Vinyl Acetate Plant had been chosen as the case study to provide the necessary data and information to run the research. Vinyl Acetate Plant process will provides a dependable source of data and an appropriate test for alternative control and optimization strategies for continuous chemical processes. -Author
650 0 _aNeural networks (Computer science)
999 _aVIRTUA40
_c2659
_d2665
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*9992