Article Details

Abstract

Refinery units are continuously exposed to atmospheric environments characterized by high humidity, salt laden aerosols, pollutant deposition, and alternating wet-dry cycles, making metallic equipment susceptible to atmospheric corrosion and the resulting risks of leakage, failure, and safety incidents. With advances in online monitoring, sensor networks, and machine learning, corrosion research is shifting from empirical assessment toward data-driven prediction. This review examines four aspects: the atmospheric corrosion environment and monitoring data foundation of refinery units, corrosion data augmentation, corrosion-rate prediction modeling, and few-shot transfer prediction. It analyzes the effects of multiple environmental factors on corrosion evolution and summarizes progress in conventional and generative augmentation, machine learning, deep temporal networks, and transfer learning. Future research should focus on physics-aware augmentation adapted to target-domain environments, mechanism-integrated prediction, cross-domain transfer, interpretable uncertainty assessment, and closed-loop systems integrating monitoring, prediction, early warning, and maintenance.

Keywords

refinery units; atmospheric corrosion; data augmentation; corrosion prediction; transfer learning

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Funding

2024 Jieyang City Social Development Science and Technology Plan Project (SHFZ2024020)

About the authors

Lin Zhuangdun (December 1972-), male, Han ethnicity, from Jieyang, Guangdong, senior engineer, bachelor's degree, research interests include special equipment inspection, safety management and assessment; Yuan Wufei (February 1978-), male, Han ethnicity, from Jieyang, Guangdong, senior engineer, bachelor's degree, research interests include special equipment inspection, safety management and assessment; Wu Haihong (August 1974-), male, Han ethnicity, from Heyuan, Guangdong, senior engineer, master's student, research interests include special equipment inspection and safety assessment; Huang Chenhao (November 2001-), male, Han ethnicity, from Nanning, Guangxi, master's student, research interests include intelligent detection and instrumentation; Corresponding author: Liu Guixiong (October 1968-), male, Han ethnicity, from Jieyang, Guangdong, professor, doctoral supervisor, doctoral student, research interests include advanced sensing and instrumentation.