@article{(Open Science Index):https://publications.waset.org/pdf/3224,
	  title     = {A Hybrid Approach to Fault Detection and Diagnosis in a Diesel Fuel Hydrotreatment Process},
	  author    = {Salvatore L. and  Pires B. and  Campos M. C. M. and  De Souza Jr M. B.},
	  country	= {},
	  institution	= {},
	  abstract     = {It is estimated that the total cost of abnormal
conditions to US process industries is around $20 billion dollars in
annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum
refineries is a conversion process that leads to high profitable
economical returns. However, this is a difficult process to control
because it is operated continuously, with high hydrogen pressures
and it is also subject to disturbances in feed properties and catalyst
performance. So, the automatic detection of fault and diagnosis plays
an important role in this context. In this work, a hybrid approach
based on neural networks together with a pos-processing
classification algorithm is used to detect faults in a simulated HDT
unit. Nine classes (8 faults and the normal operation) were correctly
classified using the proposed approach in a maximum time of 5
minutes, based on on-line data process measurements.},
	    journal   = {International Journal of Chemical and Molecular Engineering},
	  volume    = {1},
	  number    = {12},
	  year      = {2007},
	  pages     = {155 - 160},
	  ee        = {https://publications.waset.org/pdf/3224},
	  url   	= {https://publications.waset.org/vol/12},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 12, 2007},
	}