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Real-time spatiotemporal forecast of natural gas jet fire from offshore platform by using deep probability learning.
- Source :
-
Ocean Engineering . Feb2024, Vol. 294, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Blow-outs occurred on offshore platform and associated fires have been recurrent during the previous few decades, and poses a potential safety hazard to humans, property and the surrounding environment. Although the real-time forecast based on deep learning have shown promise in the fields of fire modelling and hazardous area evaluations, jet fire spatio-temporal modelling has not yet undergone sufficient investigation in complex ocean engineering cases like offshore platforms. This research therefore proposes a deep learning-based framework for jet fire spatio-temporal probabilistic real-time forecast by developing the Hybrid-VB-ConvSTnn model, which integratesConvGRU and variational Bayesian inference. And the significant hyperparameters were locally optimized through sensitivity analysis and finally identified as Monte Carlo (MC) sampling number m = 100 and dropout probability p = 0.1. By performance comparison with different models, the Hybrid-VB-ConvSTnn model shows competitive spatio-temporal forecasting capabilities in terms of both real-time (Inference time = 0.83s) and accuracy (R 2 = 0.982). Moreover, the Hybrid-VB-ConvSTnn model could provide the additional uncertainty inferences based on the probability density of the Bernoulli distribution, which avoids the inherent shortcomings of "overconfidence" for traditional point-estimate models and lends credibility to flame boundary identification. The proposed framework could support the digital twin-based fire emergency management on offshore platforms by more comprehensive and robust risk evaluation. • Deep learning-based advanced approach framework for natural gas jet fire spatiotemporal probabilistic forecast is developed. • Uncertainty inference in flame boundary forecast can be determined with variational Bayesian inference. • Hyperparameters that determine model performance are locally optimized through sensitivity analysis. • Real-time forecast provides an alternative for digital twin model of jet fire accidents management on offshore platform. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NATURAL gas
*DEEP learning
*DIGITAL twins
*BINOMIAL distribution
*FIRE management
Subjects
Details
- Language :
- English
- ISSN :
- 00298018
- Volume :
- 294
- Database :
- Academic Search Index
- Journal :
- Ocean Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 175413333
- Full Text :
- https://doi.org/10.1016/j.oceaneng.2023.116658