Efficient Gas Leak Simulation Surrogate Modeling and Super Resolution for Gas Detector Placement Optimization

Abstract

This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, enabling a timely and appropriate initial response. Consequently, it mitigates the risk of major industrial disasters. This framework integrates a surrogate model based on differentiable physics with a sensor placement optimization tool. Particularly noteworthy is that the surrogate model provides faster results compared to traditional CFD tools and seamlessly integrates with a CNN-based deep learning model, yielding higher-resolution outputs. This enhances the feasibility of practical application compared to conventional methods. Sensor placement optimization employs MINLP, considering various constraints to achieve more precise optimization. The proposed framework offers an innovative approach to address industrial safety concerns by enabling early chemical leak detection and facilitating timely responses, ultimately reducing risks like fires, explosions, and toxic incidents.