Intelligent identification of abnormal operating conditions and safety control strategies in chemical processes
化工过程非正常工况智能识别与安全控制策略

摘要

化工过程中的非正常工况会严重影响生产安全和产品质量，及时识别并有效控制这些工况对保障生产系统的安全运行至关重要。本研究探讨了化工过程非正常工况的智能识别方法及其安全控制策略，提出了一种基于多源数据融合和机器学习算法的识别系统，并设计了智能控制策略以降低非正常工况发生的风险。通过案例研究，验证了该方法在实际生产环境中的有效性，显著提高了反应系统的安全性和稳定性。研究结果为提升化工过程自动化和安全性提供了理论支持与实践指导。

Abstract

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.