Investigation of risk-aware dynamic accident monitoring and early warning technologies for chemical production processes - Chen Lv; Xiaolu Wang; Sheng Xue; Shuang Wang (Scientific Reports)
Abstract There are numerous hazardous factors in the production processes of chemical enterprises, posing significant risks. This study overcomes the limitations of traditional safety early warning methods that rely on single-dimensional fault parameter monitoring. By innovatively integrating cusp catastrophe theory and risk entropy analysis, it constructs a dual-mode collaborative early warning system of ‘Conventional parameter monitoring-risk situation awareness’ based on multi-source information fusion. This approach objectively evaluates the current operational state and anticipates dynamic changes in risk situations, providing a solid foundation for early accident warnings. The case study demonstrates that precise calculation of risk entropy value changes, $$Δ = 8μ^{3} + 27v^{2} = 0.03460$$ is obtained, indicating that the current chemical production process system is safe and stable, with a low likelihood of system risk escalation. The analysis results align with actual conditions. The research outcomes achieve a three-dimensional dynamic coupling of ‘condition monitoring—mutation warning—trend prediction’ under complex working conditions, offering an engineering application value by providing a technical pathway for transforming chemical process safety from ‘passive response’ to ‘active defense’.