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Transient-State Fault Detection System Based on Principal Component Analysis for Distillation Columns

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  • Gregorio Moreno-Sotelo

    (DEPI, TecNM, Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiaguito, Morelia 58120, Michoacán, Mexico
    These authors contributed equally to this work.)

  • Adriana del Carmen Téllez-Anguiano

    (DEPI, TecNM, Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiaguito, Morelia 58120, Michoacán, Mexico
    These authors contributed equally to this work.)

  • Mario Heras-Cervantes

    (DEPI, TecNM, Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiaguito, Morelia 58120, Michoacán, Mexico
    These authors contributed equally to this work.)

  • Ricardo Martínez-Parrales

    (DEPI, TecNM, Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiaguito, Morelia 58120, Michoacán, Mexico
    These authors contributed equally to this work.)

  • Gerardo Marx Chávez-Campos

    (DEPI, TecNM, Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiaguito, Morelia 58120, Michoacán, Mexico
    These authors contributed equally to this work.)

Abstract

This paper presents the design of a fault detection system (FDD) based on principal component analysis (PCA) to detect faults in the transient state of distillation processes. The FDD system detects faults due to changes in calorific power and pressure leaks that can occur during the heating of the mixture to be distilled (transient), mainly affecting the quality of the distilled product and the safety of the process and operators. The proposed FDD system is based on PCA with a T2 Hotelling statistical approach, considering data from a real distillation pilot plant process. The FDD system is evaluated with two fault scenarios, performing power changes and pressure leaks in the pilot plant reboiler during the transient state. Finally, the results of the FDD system are analyzed using Accuracy, Precision, Recall, and Specificity metrics to validate its performance.

Suggested Citation

  • Gregorio Moreno-Sotelo & Adriana del Carmen Téllez-Anguiano & Mario Heras-Cervantes & Ricardo Martínez-Parrales & Gerardo Marx Chávez-Campos, 2025. "Transient-State Fault Detection System Based on Principal Component Analysis for Distillation Columns," Mathematics, MDPI, vol. 13(11), pages 1-22, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1747-:d:1663900
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    References listed on IDEAS

    as
    1. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
    2. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
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