批次加权软化分的多阶段AR-PCA间歇过程监测

Multiphase AR-PCA monitoring for batch processes based on the batch weighted soft classifying

由于多阶段间歇过程在每个操作阶段主导变量和过程特性的巨大差异性,也为了降低传统方法只进行阶段硬划分以及过程建模不考虑过程动态性导致的漏报率和误报率,提出了基于批次加权软划分的多阶段自回归主元分析(AR-PCA)间歇过程监测方法。方法引入了反距离加权(IDW)和单变量控制图对仿射传播聚类(AP)进行改进,避免以单批次作为AP输入不能表征整个生产过程阶段特性的局限性,并且解决了AP不能辨识过渡阶段的缺陷。然后针对过渡阶段和稳定阶段分别建立ARPCA模型和MPCA模型,较传统方法以整批次数据建立唯一模型具有更高的模型精度,同时消除了过渡阶段的动态性,可有效降低误报和漏报。实验设计由青霉素发酵仿真平台和重组大肠杆菌实际生产过程完成,结果显示了该方法的可行性和有效性。

Due to the huge difference of multiphase batch processes in the dominant variables and process characteristics of each operation phase,meanwhile,in order to reduce the leaking alarm rate and false alarm rate of traditional methods in phase hard Classifying and process modeling ignoring dynamic,a multiphase auto regression-principal component analysis（ AR-PCA） monitoring method for batch progress based on the batch weighted soft classifying is proposed. inverse distance weighted（ IDW） and single variable control charts are introduced to improve affinity propagation clustering（ AP）,which avoids the limitation of a single batch as the input of AP cannot represent the stage characteristics of the entire production process,and the defect of AP unrecognizing the transition stage can be addressed. After AR-PCA and PCA models are established for the transition phase and the stable phase respectively,higher precision than the traditional method to establish a unique model with entire batch data can be achieved,while eliminating the dynamic of transition phase. Leaking alarm and false alarm can be effectively reduced. Design of experiments is carried out by the penicillin fermentation simulation platform and the actual production process of recombinant E. coli,and results indicate the feasibility and effectiveness of the proposed method.