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What is Chemical Leak Source Tracking with AI about?

This study presents a machine learning-based emergency response system for tracking chemical leak sources in plants using fence monitoring data. By employing Deep Neural Networks and Random Forest classifiers trained on Computational Fluid Dynamics simulation data from 640 leak scenarios, the system achieved prediction accuracies of 75.43% and 86.33%, respectively. The proposed method aims to enhance emergency management by quickly identifying potential leak sources, thereby reducing secondary and tertiary

Author

Tati Franco

Language

EN