炼化企业仪表失真大数据分析及应用OA

As typical process industries,refining and petrochemical enterprises heavily rely on real-time measurement data from various instruments for their production operations.However,instrument data distortion is prevalent under extreme conditions,such as high temperatures and pressures,severely threatening production safety and process optimization.To address this issue,this study first investigated the primary causes and ad-verse impacts of instrument data distortion in refining facilities.Subsequently,a big data-driven intelligent diag-nosis and early warning system was constructed,proposing a comprehensive technical workflow that encompasses data collection and preprocessing,feature extraction and selection,distortion modeling and analysis,and early warning and decision support.By collecting data from the Distributed Control System(DCS)of refining units,variables were grouped based on local correlations to extract the multivariate spatiotemporal features of the in-strument data.A combined anomaly detection model integrating univariate feature thresholds and a centroid-based method was then developed,alongside a multi-dimensional anomaly index calculation method designed to quantitatively assess the risk of instrument distortion.Practical applications in actual refining units demonstrated that the proposed system accurately identified data distortion issues,with the predicted distortion probabilities highly aligning with on-site manual calibration results.Furthermore,the system successfully pinpointed typical abnormal instruments,such as level gauges and pressure transmitters,and effectively diagnosed the root causes of their distortion.Consequently,the implementation of this system reduced unit energy consumption and miti-gated the risk of unplanned shutdowns,providing reliable technical support for the safe production and efficient operation of refining and petrochemical enterprises.