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Accessible Unlicensed Requires Authentication Published by De Gruyter August 3, 2020

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

Julia Picabea, Mauricio Maestri, Miryan Cassanello and Gabriel Horowitz

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.


Corresponding author: Gabriel Horowitz, ITAPROQ, Departamento de Industrias, FCEyN, Universidad de Buenos Aires, Int. Güiraldes 2620, C1428BGA, Buenos Aires, Argentina; and Y-TEC, Av. Del Petróleo s/n, 1923, Berisso, Argentina, E-mail:

Funding source: Universidad de Buenos Aires

Award Identifier / Grant number: UBACyT20020130100544BA

Funding source: Agencia Nacional de Promoción Científica y Tecnológica

Award Identifier / Grant number: PICT2014-0704

Funding source: Consejo Nacional de Investigaciones Científicas y Técnicas

Award Identifier / Grant number: PIP1122015-0100902CO

Acknowledgments

This work was supported by ANPCyT [PICT2014-0704]; Universidad de Buenos Aires [UBACyT 20020130100544BA] and CONICET [PIP1122015-0100902CO]. The authors would also like to acknowledge the operations staff of the methanol plant of YPF (Plaza Huincul).

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was funded by the ANPCyT [PICT2014-0704]; Universidad de Buenos Aires [UBACyT 20020130100544BA] and CONICET [PIP1122015-0100902CO].

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-01-19
Accepted: 2020-05-01
Published Online: 2020-08-03

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