Hybrid model for fault detection and diagnosis in an industrial distillation column

Author:

Picabea Julia12,Maestri Mauricio12,Cassanello Miryan12,Horowitz Gabriel13

Affiliation:

1. ITAPROQ , Departamento de Industrias , FCEyN, Universidad de Buenos Aires , Int. Güiraldes 2620 , C1428BGA , Buenos Aires , Argentina

2. CONICET , Godoy Cruz 2290 , C1425FQB , Buenos Aires , Argentina

3. Y-TEC , Av. Del Petróleo s/n, 1923 , Berisso , Argentina

Abstract

Abstract The present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.

Funder

Universidad de Buenos Aires

Agencia Nacional de Promoción Científica y Tecnológica

Consejo Nacional de Investigaciones Científicas y Técnicas

Publisher

Walter de Gruyter GmbH

Subject

Modeling and Simulation,General Chemical Engineering

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