Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach

Jihao Shi, Junjie Li, Asif Sohail Usmani, Yuan Zhu, Guoming Chen, Dongdong Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Computational Fluid Dynamic (CFD) has been widely used for the gas release and dispersion modeling, which however could not support real-time emergency response planning due to its high computation overhead. Surrogate models offer a potential alternative to rigorous computational approaches, however, as the point-estimation alternatives, the existing neural network-based surrogate models are not able to quantify the uncertainty of the released gas spatial concentration. This study aims to fill a gap by proposing an advanced hybrid probabilistic Convolutional-Variational Autoencoder-Variational Bayesian neural network (Conv-VAE-VBnn). Experimental study based on a benchmark simulation dataset was conducted. The results demonstrated the additional uncertainty information estimated by the proposed model contributes to reducing the harmful effect of too ‘confidence’ of the point-estimation models. In addition, the proposed model exhibits competitive accuracy with R2 = 0.94 compared and real-time capacity with inference time less than 1s. Latent size Nz = 2, noise σz=0.1 and Monte Carlo sampling number m = 500 to ensure the model's real-time capacity, were also given. Overall, our proposed model could provide a reliable alternative for constructing a digital twin for emergency management during the exploration and exploitation of marine natural gas hydrate (NHG) in the near future.

Original languageEnglish
Article number119572
JournalEnergy
Volume219
DOIs
Publication statusPublished - 15 Mar 2021

Keywords

  • Convolution variational autoencoder
  • Digital twin of emergency management
  • Marine natural hydrate gas
  • Probabilistic dispersion modeling
  • Uncertainty estimation of spatial features
  • Variational Bayesian neural network

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Modelling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Energy(all)
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law
  • Electrical and Electronic Engineering

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