Angle similarity stage division and monitoring method in microbial pharmacy process

Abstract

The invention discloses an angle similarity stage division and monitoring method in a microbial pharmacy process. In order to better process multi-stage characteristics in the penicillin fermentationprocess, an effective fault monitoring model based on a multi-stage division method is established. The method comprises two stages of off-line modeling and on-line monitoring. The off-line modeling comprises the following steps: firstly, expanding three-dimensional data of a fermentation process along a time axis; dividing the data into C0 sub-periods; and then establishing respective KECA modelsby using the sub-period data, finally calculating T2 and SPE statistics of the data, and determining the control limit of the statistics in each period. The on-line monitoring comprises the steps ofprocessing newly collected data according to a model, dividing the data into sub-periods, calculating the statistics of the data, and comparing the statistics with a control limit to judge whether theproduction process is faulty or not. According to the method, the multi-stage characteristics of the intermittent process are fully considered, and the fault monitoring accuracy is satisfactory.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis based on industrial processes, in particular to a period division and fault diagnosis technology for intermittent processes. The data-driven method of the present invention is a specific application in fault monitoring of a typical batch process—penicillin fermentation process. Background technique [0002] At present, there are a large number of batch processes in the industrial production process. However, its mechanism is complicated, its operation complexity is high, and its product quality is easily affected by uncertain factors. Multi-stage characteristics are inherent characteristics of batch processes, each stage has its specific and unique operating mode and potential process characteristics, and has different key process variables and specific control objectives. In order to reduce the false alarm rate and false alarm rate of a batch process (such as a fermentation process), it...

AI Technical Summary

Problems solved by technology

However, the current multi-stage batch process monitoring has the following two deficiencies: 1) The input of the clustering data is the load matrix decomposed by MPCA, and MPCA is a linearization method that cannot deal with the nonlinearity of the batch process. It will inevitably lose the nonlinear characteristics, and nonlinearity is an inherent characteristic of the batch process, resulting in the loss of nonlinear data

2) The K-means or FCM clustering algorithm used needs to specify the number of division stages in advance. Once the number of stages is not selected properly, the division result will not conform to the real structure of the data set, that is, it will not conform to the actual operation of the process. mechanism, its monitoring of the process will cause a large number of false alarms and missed alarms

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