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Lakhdar Aggoune1, Yahya Chetouani1, Tarek Raïssi1

  • 1Laboratoire d'Automatique de Sétif, Département d'Electrotechnique, Université de Sétif 1, Cité Maabouda, Route de Béjaia, 19000 Sétif, Algeria; Université de Rouen, Département Génie Chimique, Rue Lavoisier, 76821 Mont Saint Aignan Cedex, France; Conservatoire National des Arts et Métiers, Département EASY, Cedric-laetitia, 292, Rue St-Martin, case 2D2P10, 75141 Paris Cedex 03, France.

ISA Transactions
|March 30, 2016
PubMed
Summary
This summary is machine-generated.

This study explores using black-box modeling and Kullback Leibler divergence (KLD) for fault detection in chemical plants. The method effectively identifies process anomalies, providing early warnings for improved safety and reliability.

Keywords:
Dynamic processesFault detectionKullback Leibler divergenceNARMAX modelSafety

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Area of Science:

  • Chemical Engineering
  • Process Control
  • System Identification

Background:

  • Chemical plants require robust fault detection for safety and quality.
  • Distillation columns are critical but complex industrial processes.
  • Existing fault detection methods may struggle with nonlinear dynamics.

Purpose of the Study:

  • To investigate the feasibility of black-box modeling and KLD for fault detection in distillation columns.
  • To develop and validate a nonlinear model for process behavior estimation.
  • To assess the effectiveness of KLD in identifying abnormal operating conditions.

Main Methods:

  • Development of a Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model.
  • Application of Kullback Leibler divergence (KLD) for anomaly detection.
  • Experimental validation using a laboratory-scale distillation plant with induced faults.

Main Results:

  • The NARMAX model accurately captured the nonlinear dynamics of the distillation column.
  • KLD effectively distinguished between normal and abnormal operating modes.
  • The integrated fault detection method demonstrated high sensitivity and provided early alarms.

Conclusions:

  • The proposed fault detection scheme using black-box modeling and KLD is effective for distillation columns.
  • This approach enhances process safety, reliability, and product quality.
  • Early fault detection enables timely operator intervention, preventing potential issues.