Journal of Chemistry and Chemical Research

Abnormal operating conditions in chemical processes can severely affect production safety and product quality. Timely identification and effective control of these conditions are crucial for ensuring the safe operation of production systems. This study explores intelligent identification methods for abnormal operating conditions in chemical processes and their corresponding safety control strategies. It proposes an identification system based on multi-source data fusion and machine learning algorithms, and designs intelligent control strategies to reduce the risk of abnormal operating conditions. Through case studies, the effectiveness of this method in actual production environments is verified, which significantly improves the safety and stability of the reaction system. The research results provide theoretical support and practical guidance for enhancing the automation and safety of chemical processes.

Chemical processes; Abnormal operating conditions; Intelligent identification; Safety control; Machine learning