Operational Digital Twin for Coke Drum Skirt Thermal Stress: Thermocouple-Informed FEA and PINN Transfer Learning Approach 核心 · 已核验

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"This paper presents a practical framework for an operational digital twin for continuous thermal stress evaluation of coke drum skirts. Finite Element Analysis (FEA) delivers accurate predictions but is too computationally intensive and slow for real-time use. Physics-Informed Neural Networks (PINNs), by embedding governing physical laws into their training, provide a faster, physics-consistent alternative. We calibrate FEA models with thermocouple data to generate a spectrum of synthetic cycles, then apply transfer learning to train a PINN surrogate. Case studies on simulated coke drum cycles demonstrated that the PINN reduced computation time from 4-6 hours per FEA transient run to under 20 seconds per cycle while maintaining prediction errors within 3-5% relative to calibrated FEA outputs and within + 5 \u00b0C agreement with thermocouple measurements. The framework reduces computational effort by more than two orders of magnitude, enables continuous monitoring, and supports early anomaly detection, enhancing reliability and predictive maintenance. Operationally, this reduces failure risk and unplanned outages while improving process safety and unit uptime. "

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落地页

https://ieee-dataport.org/documents/operational-digital-twin-coke-drum-skirt-thermal-stress-thermocouple-informed-fea-and

国内可访问性

国内直连:可达 (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

PHM 任务

anomaly_detection condition_monitoring

传感器