NARX NETWORK BASED DATA-DRIVEN ALGORITHM FOR DETECTION OF TRAY FAULTS IN NONLINEAR DYNAMIC DISTILLATION COLUMN

Authors

  • Syed Ali Ammar Taqvi NED University of Engineering and Technology Karachi, Pakistan Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610, Perak, Malaysia
  • Haslinda Zabiri Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610, Perak, Malaysia
  • Lemma Dendena Tufa School of Chemical and Bioengineering, Addis Ababa Institute of Technology, King George VI St Addis Ababa 1000, Addis Ababa, Ethiopia
  • Fahim Uddin NED University of Engineering and Technology Karachi, Pakistan
  • Syeda Anmol Fatima Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610, Perak, Malaysia
  • Abdulhalim Shah Maulud Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610, Perak, Malaysia

DOI:

https://doi.org/10.11113/jt.v82.14350

Keywords:

NARX network, data driven, fault detection, distillation column, Aspen Plus

Abstract

Efficient monitoring of highly complex process industries is essential for better management, safer operations and high-quality production. Timely detection of various faults helps to improve the performance of the complex industries, prevent various unfavorable consequences and reduce the maintenance cost. Fault Detection and Diagnosis (FDD) for process monitoring and control has been an active field of research for the past two decades. Distillation columns are inherently nonlinear, and thus to have an accurate and robust performance, the fault detection methods should be based on nonlinear dynamic methods. The paper presents a robust data-driven fault detection approach for realistic tray upsets in the distillation column. The detection of tray faults in the distillation column is conducted by Nonlinear AutoRegressive with eXogenous Input (NARX) network with Tapped Delay Lines (TDL). Aspen Plus® Dynamic simulation has been used to generate normal and faulty datasets. The study shows that the proposed method can be used for the detection of tray faults in distillation column for dynamic process monitoring. The performance of the proposed method has been evaluated by the Missed Detection Rate (MDR) and the Detection Delay (DD).

Author Biography

  • Haslinda Zabiri, Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610, Perak, Malaysia

    Associate Professor,

    Chemical Engineering Department

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Published

2020-07-20

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Section

Science and Engineering

How to Cite

NARX NETWORK BASED DATA-DRIVEN ALGORITHM FOR DETECTION OF TRAY FAULTS IN NONLINEAR DYNAMIC DISTILLATION COLUMN. (2020). Jurnal Teknologi, 82(5). https://doi.org/10.11113/jt.v82.14350