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To reduce damage arising from accidents in chemical processing plants, detection of the incident must be rapid to mitigate the danger. In the case of the gas leaks, detectors are critical. To improve efficiency, leak detectors must be installed at locations after considering various factors like the characteristics of the workspace, processes involved, and potential consequences of the accidents. Thus, the consequences of potential accidents must be simulated. Among various approaches, computational fluid dynamics (CFD) is the most powerful tool to determine the consequences of gas leaks in industrial plants. However, the computational cost of CFD is large, making it prohibitively difficult and expensive to simulate many scenarios. Thus, a deep-neural-network-based surrogate model has been designed to mimic FLACS (FLame ACceleration Simulator), one of the most important programs in the modeling of gas leaks. Using the simulated results of a proposed surrogate model, a sensor allocation optimization problem was solved using mixed integer linear programming (MILP). The optimal solutions produced by the proposed surrogate model and FLACS were compared to verify the efficacy of the proposed surrogate model.

This work uses computational fluid dynamics (CFD) to model the disastrous Gumi hydrogen fluoride gas leak of 2012. To ensure the model's validity, an anhydrous hydrogen fluoride leak field test was validated using a single-phase and two-phase dispersion model, and then compared with other dispersion models. When combined with the anhydrous ammonia leak test, the overall mean relative bias is 0.135 and mean relative square error is 0.068, which is within the valid range for dispersion simulation. The Gumi hydrogen fluoride gas leak disaster is then simulated and compared with the actual impacts. Human fatality occurred only in the plant and probability of death via toxic exposure shows a similar result. Toxic dose measurement by post-accidental vegetation fluoride concentration shows a similar range with CFD results for the downwind side.