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Recurrent Autoencoder for Fault Detection in a Polymerization Reactor Train.

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Title: Recurrent Autoencoder for Fault Detection in a Polymerization Reactor Train.
Authors: Perdomo, Mariano M.1,2 (AUTHOR), Clementi, Luis A.2,3 (AUTHOR), Vega, Jorge R.1,2 (AUTHOR) jvega@santafe-conicet.gov.ar
Source: Macromolecular Reaction Engineering. Jun2026, Vol. 20 Issue 3, p1-18. 18p.
Subjects: Polymerization reactors, Autoencoders, Nonlinear dynamical systems, Fault diagnosis, Outlier detection, Continuous processing
Abstract: A fault detection system based on a recurrent autoencoder is proposed, suitable for continuous processes with complex nonlinear dynamics. The system consists of three modules: 1) a recurrent autoencoder; 2) a fault state detection module; and 3) an interpretation module. The training strategy only requires process data obtained under normal operating conditions. The fault detection module analyzes the total reconstruction error of the measured variables obtained by the recurrent autoencoder. The interpretability module examines the reconstruction errors of the individual variables and highlights variables potentially responsible for the detected anomaly. The developed system is applied to a computationally simulated Styrene‐Butadiene rubber latex production process. The latex is synthesized in a train of continuous stirred tank reactors that may be affected by various types of faults. Simulation results show a satisfactory performance of the proposed fault detection system. Failures whose data patterns differ significantly from those of normal operation are effectively detected. In contrast, some subtle faults only cause minor deviations in the data patterns and are therefore difficult to detect rapidly, leading to delays in detection. In most cases, the interpretability module can correctly identify the variable more strongly associated with the detected fault. [ABSTRACT FROM AUTHOR]
Copyright of Macromolecular Reaction Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 194642009
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  Data: Recurrent Autoencoder for Fault Detection in a Polymerization Reactor Train.
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  Data: <searchLink fieldCode="AR" term="%22Perdomo%2C+Mariano+M%2E%22">Perdomo, Mariano M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clementi%2C+Luis+A%2E%22">Clementi, Luis A.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vega%2C+Jorge+R%2E%22">Vega, Jorge R.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jvega@santafe-conicet.gov.ar</i>
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  Data: <searchLink fieldCode="JN" term="%22Macromolecular+Reaction+Engineering%22">Macromolecular Reaction Engineering</searchLink>. Jun2026, Vol. 20 Issue 3, p1-18. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Polymerization+reactors%22">Polymerization reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+dynamical+systems%22">Nonlinear dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+processing%22">Continuous processing</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: A fault detection system based on a recurrent autoencoder is proposed, suitable for continuous processes with complex nonlinear dynamics. The system consists of three modules: 1) a recurrent autoencoder; 2) a fault state detection module; and 3) an interpretation module. The training strategy only requires process data obtained under normal operating conditions. The fault detection module analyzes the total reconstruction error of the measured variables obtained by the recurrent autoencoder. The interpretability module examines the reconstruction errors of the individual variables and highlights variables potentially responsible for the detected anomaly. The developed system is applied to a computationally simulated Styrene‐Butadiene rubber latex production process. The latex is synthesized in a train of continuous stirred tank reactors that may be affected by various types of faults. Simulation results show a satisfactory performance of the proposed fault detection system. Failures whose data patterns differ significantly from those of normal operation are effectively detected. In contrast, some subtle faults only cause minor deviations in the data patterns and are therefore difficult to detect rapidly, leading to delays in detection. In most cases, the interpretability module can correctly identify the variable more strongly associated with the detected fault. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Macromolecular Reaction Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mren.70022
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      – Code: eng
        Text: English
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        PageCount: 18
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      – SubjectFull: Polymerization reactors
        Type: general
      – SubjectFull: Autoencoders
        Type: general
      – SubjectFull: Nonlinear dynamical systems
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Continuous processing
        Type: general
    Titles:
      – TitleFull: Recurrent Autoencoder for Fault Detection in a Polymerization Reactor Train.
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            NameFull: Perdomo, Mariano M.
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            NameFull: Clementi, Luis A.
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            NameFull: Vega, Jorge R.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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            – TitleFull: Macromolecular Reaction Engineering
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