Research on intelligent traceability of abnormalities in complex chemical processes based on time series data analysis
Intelligent reasoning and tracing of abnormal working conditions in complex chemical processes is an effective way to achieve the forward movement of safety checkpoints and reduce the occurrence of catastrophic accidents.This study proposes an intelligent traceability analysis method for chemical process anomalies based on Spearman-Apriori,aiming to study the pre-causes of abnormal operating conditions in complex chemical processes and form an intelligent decision-making model.In light of the characteristics of strong coupling between chemical process parameters and great difficulty in correlation analysis,A Spearman correlation coefficient is introduced to analyze the correlation between process parameters in real-time online through Spearman and set a strong correlation threshold to associate Spearman correlation coefficient analysis with Apriori algorithm and use the support and confidence of Apriori algorithm to mine the super strong association rules between parameters in two dimensions.Applying this method to the intelligent tracing of abnormal working conditions in the synthesis section of the ammonia synthesis process,and selecting 8 key monitoring indicators such as hydrogen nitrogen ratio,pipeline process gas flow rate,and condensate flow rate in the feedwater heat exchanger,the study finds that an increase in hydrogen nitrogen ratio and a rise in condensate flow rate in the feedwater heat exchanger are the leading causes of two abnormal working conditions:overpressure at the inlet of the synthesis tower and low temperature in the first bed of the synthesis tower,The analysis results are consistent with the actual production process,proving that this method can effectively trace the causes of abnormal chemical processes and screen the main influencing factors.This study provides a theoretical basis for using production process big data to achieve intelligent traceability of chemical process anomalies and provides new ideas for further improving process risk refinement control.
safety engineeringtime series dataSpearman correlation coefficientApriori algorithmintelligent traceability
NETL
NSTL
万方数据
维普
