Robust data-driven model to study dispersion of vapor cloud in offshore facility

This study aims to investigate the suitability of BRANN and LMANN in estimating dimension of flammable cloud in congested offshore platform. A large number of numerical simulations are conducted using FLACS. Part of these simulations results are used to training the network. The trained network is subsequently used to predict the vapor cloud dimension and compared against remaining simulation results. The predictive abilities of these network along with Response Surface Method and Frozen Cloud Approach (FCA) are studied. The comparative results indicate BRANN model with 20 hidden neurons is the most robust and precise. The developed BRANN would serve an effective and tool for quick Explosion Risk Analysis ERA.