Threat Zones Identification and Consequence Modeling Approach of Ammonia Dispersion via Machine Learning

The available ALOHA software for such simulations is time-consuming. Therefore, this study presents a new framework for ammonia dispersion using a multioutput artificial neural network (ANN) surrogate model trained on 4989 scenarios simulated via the readily available simulation software to save crucial time, enabling quick decisions.

The simulation is based on a real-world industrial site in Sheikhupura, Pakistan, and results in the generation of a comprehensive data set. The data set has taken into consideration various input variables and studied their effect on target output variables. Next, the ANN surrogate model is optimized via hyperparameter tuning, and its performance is evaluated using standard regression metrics, demonstrating excellent agreement with ALOHA simulation outputs, with <i>R</i>² values exceeding 0.98 and mean absolute percentage errors below 2.6% for all output parameters.