Development of surrogate model using CFD and deep neural networks to optimize gas detector layout

Abstract

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.