Lauret P., Heymes F., Aprin L., Johannet A., Dusserre G., Munier L., Lapebie E., 2013, Near Field Atmospheric Dispersion Modeling on an Industrial Site Using Neural Networks, Chemical Engineering Transactions, 31, 151-156.
Assessment of likely consequences of a potential accident is a major concern of loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites may imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially in near field of the gas source. The present work aims to study if Neural Networks and Cellular Automata could be relevant to overcome these gaps. These tools were investigated on steady state and dynamic state. A database was designed from RANS k-E CFD and Gaussian plume models. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.
