Abstract
The production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk values of MHIs and improve accident prevention ability of CIP, we need a method that can combine dynamic prediction and assessment. Quantitative risk assessment (QRA) is not capable of modelling risk variations during the operation of a process. Therefore, this paper adopts the data-driven approach. Inspired by visual qualitative analysis and quantitative analysis, a dynamic early warning method is proposed for MHIs. We can get the future trend of these key parameters by using strongly correlation variables to predict key parameters. Fuzzy evaluation analysis is performed on the risk levels of key parameters, and the dynamic evaluation index of these MHIs is obtained. This method can be applied to the dynamic evaluation of MHIs system in CIP. It can contribute to the safety of CIP in some aspects.
Highlights
The demand for everyday life materials has stimulated the vigorous development of the chemical industry
In the early warning of chemical industrial park (CIP), conducting a reasonable analysis of big data and fully considering the correlation between key parameters of various process industries is essential for prediction analysis [21]
CIP is composed of many major hazard installations (MHIs); the air separation distillation section is one of the typical representatives of MHIs
Summary
The demand for everyday life materials has stimulated the vigorous development of the chemical industry. Aven and Ylonen [13] proposed a closer integration of the risk analysis and management approach and the sociotechnical perspective on safety can be used to improve risk and safety regulations These methods take human factors, social factors, and domino effect into account, but they cannot dynamically assess the risk changes of hazards caused by abnormal parameter changes. Other fields have been applied to investigate parameter prediction models [14,15,16] These traditional methods face the background of big data with large computational complexity, multiple processes, and multivariable chemical processes [17]. In the early warning of CIP, conducting a reasonable analysis of big data and fully considering the correlation between key parameters of various process industries is essential for prediction analysis [21].
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