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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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058723Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
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