A Hybrid Approach to Fault Detection and Diagnosis in a Diesel Fuel Hydrotreatment Process
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.
[1] R. P. Lippmann, "An introduction to computing with neural nets." IEEE
ASSP Mag.4, pp. 4-22, 1987.
[2] J. C. Hoskins, D. M. Himmelblau, "Artificial neural network models of
knowledge representation in chemical engineering" Computers and
Chemical Engineering, vol. 12., pp. 881-890, March 1988.
[3] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part III: Process history
based methods" Computers and Chemical Engineering, vol. 27, pp.
327-346., April 2002.
[4] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part I: Quantitative
model- based methods" Computers and Chemical Engineering, vol. 27,
pp. 293-311, April 2003.
[5] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part II: Qualitative
models and search strategies" Computers and Chemical Engineering,
vol. 27., pp. 313-326, April 2003
[6] H. L. Pinheiro, "Controle robusto de reator químico de leito fixo" M.S.
thesis, COPPE, UFRJ, Rio de Janeiro, Brazil, 1992.
[7] V. Hlavacék, "Fixed bed reactors, flow and chemical reaction residence
time distribution theory in chemical engineering", (Petho, Arpad,
Richard D. Noble, eds, Verlag Chemie, Weihein, 1982, pp.103-111
[8] M. Morari, E. Zafiriou , "Robust process control" , Prentice Hall,
Englewood Cliffs, New Jersey, U.S.A.,1989.
[9] S. Haykin, "Neural networks - A comprehensive foundation", 2nd
edition, Ed. Prentice Hall, 1999.
[1] R. P. Lippmann, "An introduction to computing with neural nets." IEEE
ASSP Mag.4, pp. 4-22, 1987.
[2] J. C. Hoskins, D. M. Himmelblau, "Artificial neural network models of
knowledge representation in chemical engineering" Computers and
Chemical Engineering, vol. 12., pp. 881-890, March 1988.
[3] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part III: Process history
based methods" Computers and Chemical Engineering, vol. 27, pp.
327-346., April 2002.
[4] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part I: Quantitative
model- based methods" Computers and Chemical Engineering, vol. 27,
pp. 293-311, April 2003.
[5] V. Venkatasubramanian, R. Renghunathan, S. N. Kavuri, K. Yin,"A
review of process fault detection and diagnosis Part II: Qualitative
models and search strategies" Computers and Chemical Engineering,
vol. 27., pp. 313-326, April 2003
[6] H. L. Pinheiro, "Controle robusto de reator químico de leito fixo" M.S.
thesis, COPPE, UFRJ, Rio de Janeiro, Brazil, 1992.
[7] V. Hlavacék, "Fixed bed reactors, flow and chemical reaction residence
time distribution theory in chemical engineering", (Petho, Arpad,
Richard D. Noble, eds, Verlag Chemie, Weihein, 1982, pp.103-111
[8] M. Morari, E. Zafiriou , "Robust process control" , Prentice Hall,
Englewood Cliffs, New Jersey, U.S.A.,1989.
[9] S. Haykin, "Neural networks - A comprehensive foundation", 2nd
edition, Ed. Prentice Hall, 1999.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:52341", author = "Salvatore L. and Pires B. and Campos M. C. M. and De Souza Jr M. B.", title = "A Hybrid Approach to Fault Detection and Diagnosis in a Diesel Fuel Hydrotreatment Process", 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.", keywords = "Fault detection, hydrotreatment, hybrid systems,neural networks.", volume = "1", number = "12", pages = "132-6", }