A Review of the Development and Application of ArtificialIntelligence in the Safety Inspection of Chemical Storage Tanks

Zhaojiu Zhou

doi:10.62677/IJETAA.2605149

Authors

Zhaojiu Zhou Sinopec Tenth Construction Co., Ltd., Qingdao, Shandong, China Author

DOI:

https://doi.org/10.62677/IJETAA.2605149

Keywords:

Artificial intelligence, Deep learning, Chemical storage tank, Safety inspection, Corrosion recognition, Acoustic emission, Leak detection

Abstract

Chemical storage tanks undergo long-term corrosion, fatigue, and medium attack; failure of their floors, shells, or welds can readily trigger leakage, fire, and explosion, making safety inspection a core element of process safety. In recent years, artificial intelligence (AI), and deep learning in particular, has profoundly reshaped the inspection paradigm. This paper systematically reviews the latest progress from 2021 to 2026 along several directions: corrosion and surface-defect recognition based on computer vision and deep learning; intelligent diagnosis from acoustic-emission and non-destructive-testing signals; intelligent sensing of leakage and hazardous gases; multi-source fusion, structural health monitoring, and risk assessment; digital twins and predictive maintenance; and unmanned-platform inspection. Key methods, representative results, and performance boundaries are summarized, and challenges such as data scarcity, model interpretability, edge deployment, and standardization are discussed. AI is shown to be moving tank-safety inspection from single-point defect detection toward a multimodal, full-lifecycle, online assurance system, while engineering trustworthiness and large-scale deployment remain to be addressed.

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