Surrogate-based prediction of pressure limit violations in drum boilers: A failure-prevention perspective
Journal
Results in Engineering
Series/Report No.
Results in Engineering
Journal Volume
29
Start Page
109213
ISSN
25901230
Date Issued
2026-03
Author(s)
Abstract
Drum-boiler pressure regulation plays a critical role in ensuring operational safety and reliability in industrial energy systems. This study develops a lightweight surrogate framework trained using first-principles data from an enhanced four-state Åström–Bell drum-boiler model. The surrogate achieves '1 % steady-state prediction error on pressure and drum level and '3 % error in the energy-storage constant. Transient validation further shows a maximum drum-level deviation of only ±0.27 m under startup ramp conditions with stable recovery. The model executes within '5 ms, enabling real-time computation of pressure contours and safety envelopes for decision support. Parametric analysis reveals that doubling drum volume from V to 2V (V = 67 m³) expands the safe region by ∼65 % in the heat–steam–water design space. These results demonstrate that the proposed framework effectively bridges high-fidelity thermodynamic modeling with rapid surrogate prediction, supporting risk-informed design, operational guidance, and predictive reliability assessment for pressure-boiler systems.
Subjects
Drum-boiler
Failure analysis
Machine learning
Safety assessment
Surrogate modeling
Publisher
Elsevier B.V.
Type
journal article