天然气脱水装置生产过程多工况时变数据异常识别OA

The complexity of natural gas production processes,together with the large number of process variables and diverse operating conditions,poses significant challenges for process anomaly identification.To address this issue,this paper proposes an anomaly identification method for natural gas dehydration processes based on an adaptive Gaussian mixture model(AGMM)and principal component analysis(PCA),which is referred to as anomaly identification for multi-condition time-varying data(AIMT).In the proposed approach,sliding-window PCA is employed to continuously update the monitoring model,while AGMM is used to characterize the dynamic process behavior under varying operating conditions.The parameters of each Gaussian component are adaptively optimized using large volumes of historical monitoring data,enabling effective representation of complex multimodal operating states.Process anomalies are detected using the squared prediction error(SPE)and Hotelling's T2 statistics.The effectiveness of the proposed method is verified using datasets collected under single-condition,variable-condition,and multi-condition operating scenarios,as well as real fault events from an expanded TEG dehydration unit with a processing capacity of one million cubic meters per day at the Chongqing Gas Field of PetroChina Southwest Oil and Gas Field Company.The results demonstrate that the proposed method can effectively identify process anomalies under time-varying and multiple operating conditions.