Journal of Engineering Research

Risk early warning in chemical processes has always been an important topic in industrial safety management. With the rapid development of artificial intelligence technology, traditional risk assessment methods have gradually exposed problems such as low efficiency and poor accuracy. This study proposes a dynamic risk early warning model for chemical processes based on artificial intelligence. By integrating machine learning and data mining technologies, the model monitors abnormal behaviors in chemical processes in real time and predicts potential risks in advance. It can effectively improve the accuracy and response speed of risk early warning, reduce the interference of human factors, and enhance the level of safety management. Experimental results show that the artificial intelligence model has high prediction accuracy in multiple chemical scenarios and can provide effective support for safety decision-making.

Chemical process; Dynamic risk early warning; Artificial intelligence; Machine learning; Safety management