Predicting fire consequences with the transformer model based on multimodal feature fusion.

Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industries. Traditional empirical and computational fluid dynamics models often struggle to balance computational efficiency with predictive accuracy, especially when considering the complex interplay between molecular properties and process conditions. To address this challenge, this study proposes a high-precision Transformer-based prediction model for estimating the radiation effect distance, a critical parameter for defining safety zones. The model is founded on a multimodal feature fusion approach, integrating SMILES encoding, molecular descriptors, and three-dimensional molecular structures to comprehensively represent chemical identity and hazard potential. A dataset was generated using industry-standard PHAST software simulations, covering diverse release scenarios for 40 flammable chemicals. The proposed multimodal Transformer model significantly outperforms traditional machine learning models. Compared with the random forest (RFR) benchmark model, its coefficient of determination (R