期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 181, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2023.108508
关键词
Chemical accident; Surrogate model; Differentiable physics; Optimization; Process safety; Deep learning; Super resolution
This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, mitigating the risk of major industrial disasters, and providing faster and higher-resolution results.
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 higherresolution 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.
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