This analysis demonstrates that a new modeling framework improves temperature predictions in process safety applications, indicating better emergency response capabilities.
Modern safety information technologies increasingly rely on accurate modeling of emergency scenarios, particularly the depressurization of process equipment (vessels, process & storage tanks, reactors) containing hazardous multi-component mixtures. However, current approaches often employ oversimplified pseudo-component models, lacking application agility as well as automation of data transfer required for solving modern complex problems of risk analysis. Therefore, significant predictive discrepancies occur when estimating flow rate, temperature, and composition of accidental releases-critical inputs for consequences modeling of fires, explosions, and toxic dispersion. This study identifies methodological limitations across existing literature and commercial software, including the absence of multi-component mixtures' behavior modeling, failure to simulate additive effects from multiple simultaneous emission sources. Therefore, a novel computational framework for dynamic depressurization modeling has been developed encompassing more realistic modeling approaches, modularity, inter-module data exchange, and the possibility of integration within safety information technologies. By enabling realistic simulations of vessels partial depressurization scenarios, the proposed framework enhances forecasting accuracy and supports informed decision-making in emergency response and adaptive risk management. Proposed approach allows advanced modeling of accidents propagation, offering an improvement both in the accuracy and speed of depressurization modeling for process safety applications.
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Smalii et al. (2025) studied this question.
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