HazardClassTransformer: Transformer-Based Model for Reactive Chemical Hazard Classification in Industrial Processes

The article introduces the HazardClassTransformer, a Transformer-based model designed for classifying reactive chemical hazards in industrial processes. This model is trained on a Globally Harmonized System of Classification and Labeling of Chemicals (GHS) dataset, ensuring its classifications align with global safety standards. The HazardClassTransformer leverages the Transformer architecture's ability to handle complex, high-dimensional data and long-range dependencies, making it well-suited for cheminformatics. The model consists of an input embedding layer for converting chemical properties into vectors, a multi-head self-attention mechanism for capturing interactions between chemical properties, position-wise feed-forward networks for learning rich representations, and an output layer for mapping data to hazard classifications. The architecture also incorporates data augmentation and hyperparameter tuning to improve model performance. Experiments demonstrate the HazardClassTransformer's superiority over traditional machine learning models, with accuracy, precision, recall, and F1-score showing significant improvements. The model benefits from data augmentation, and an ablation study confirms the importance of the multi-head self-attention mechanism and positional encodings. Hyperparameter tuning further refines the model's performance. The HazardClassTransformer contributes to industrial safety management by providing a scalable, efficient, and accurate solution for reactive chemical hazard classification. It aligns with GHS standards, enhancing practical applicability in real-world industrial settings. Future work could extend the model to other chemical safety domains and incorporate real-time data and multi-modal inputs.