Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization

Faraz Qasim, Doug Hyung Lee, Jongkuk Won, Jin-Kuk Ha and Sang Jin Park
Additional contact information
Faraz Qasim: Department of Research & Development, Advanced Process Solutions & Technology Inc., 209, Gonghang-daero, Seoul 07802, Korea
Doug Hyung Lee: Department of Research & Development, Advanced Process Solutions & Technology Inc., 209, Gonghang-daero, Seoul 07802, Korea
Jongkuk Won: Department of Advanced Materials and Chemical Engineering, Hannam University, Daedeok-gu, Hannam-ro 70, Daejeon 34430, Korea
Jin-Kuk Ha: Department of Research & Development, Advanced Process Solutions & Technology Inc., 209, Gonghang-daero, Seoul 07802, Korea
Sang Jin Park: Department of Chemical Engineering, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea

Energies, 2021, vol. 14, issue 21, 1-24

Abstract: As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

Keywords: failure mode; FMEA; FTA; RCA; cause and effect; decision support system; KPI; fired furnace; performance monitoring; abnormal conditions; SQL database (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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