ArticlePloS one2026
Analysis of vapor cloud explosion behavior in LPG spherical tanks using multi-energy modeling and machine-learning-based factor assessment.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Vapor cloud explosions (VCEs) associated with liquefied petroleum gas (LPG) storage systems represent a significant hazard in refinery operations. Accurate estimation of explosion distance is essential for safety setback design and quantitative risk assessment. This study developed an integrated framework that combines physics-based and data-driven techniques to study VCE behavior resulting from LPG releases from a 20,000-barrel pressurized spherical tank under realistic refinery conditions. A total of 336 leakage scenarios were simulated using DNV PHAST 2022 and the Multi-Energy method. Seven influencing parameters were considered, including leak diameter, LPG composition (propane-butane ratio), leak location, atmospheric category, seasonal period, day/night conditions, and three overpressure thresholds (0.02, 0.14, and 0.21 bar). The results revealed a wide dispersion in explosion distance across scenarios, with maximum distances exceeding 2,100 m for catastrophic full-bore ruptures. Feature-importance analysis using a Random Forest regression model showed that leak diameter was the dominant controlling parameter, accounting for approximately 74% of the predictive importance, while LPG composition contributed an additional 24-25%. Operational and atmospheric parameters had comparatively minor effects. The three investigated overpressure thresholds produced nearly identical explosion distances across the evaluated scenarios, indicating that within the low-pressure range, explosion extent is primarily governed by the released fuel mass and flammable cloud geometry rather than the threshold value. A classical power-law correlation was used to describe the geometric scaling between leak diameter and explosion distance; however, its predictive capability was limited (RMSE ≈ 337 m) due to its single-variable formulation. In contrast, the Random Forest model captured multi-factor interactions within the PHAST-generated dataset with very high predictive accuracy (R² ≈ 0.9997, RMSE ≈ 9 m). This hybrid framework provides a transparent approach for analyzing LPG vapor cloud explosions and offers practical insights for safety-distance determination, refinery layout optimization, and risk-based inspection planning.
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