Penicillin fermentation process failure monitoring method based on recursive kernel principal component analysis

Abstract

The invention relates to a penicillin fermentation process failure monitoring method based on recursive kernel principal component analysis (RKPCA), which belongs to the technical field of failure monitoring and diagnosis. The method comprises the following steps: acquiring the ventilation rate, stirrer power, substrate feed rate, substrate feed temperature, generated heat quantity, concentrationof dissolved oxygen, pH value and concentration of carbon dioxide; and establishing an initial monitoring model by using the first N numbered standardized samples, updating the model by a RKPCA method, and computing the characteristic vectors to detect and diagnose the failure in the process of continuous annealing, wherein when the T2 statistics and SPE statistics exceed the respective control limit, judging that a failure exists, and otherwise, judging that the whole process is normal. The method mainly solves the problems of data nonlinearity and time variability; and the RKPCA method is used for updating the model by carrying out recursive computation on the characteristic values and characteristic vectors of the training data covariance. The result indicates that the method can greatly reduce the false alarm rate and enhance the failure detection accuracy.

Description

technical field [0001] The invention belongs to the technical field of fault monitoring and diagnosis, and proposes a method for fault monitoring of penicillin fermentation process based on recursive kernel principal component analysis. Background technique [0002] Batch and semi-batch processes have been widely used in chemical industry, fermentation, pharmacy, food production and many other fields. As a kind of antibiotic, penicillin has a wide range of clinical medical value, and its production equipment is a typical non-linear, dynamic, multi-stage semi- Batch production process. The operation of the batch process is complicated. Minor changes in operating conditions, impurities mixed in raw materials and other abnormal conditions will affect the output and quality of the final product. If the batch process can be monitored and diagnosed online, it will help the operator to eliminate the fault in time or temporarily stop it. Production to reduce waste of raw materials ...

AI Technical Summary

Problems solved by technology

[0003] At present, Nonmikos and Macgregor proposed a multivariate statistical monitoring method for batch processes, mainly based on multiway principal component analysis (MPCA) and multiway partial least squares (MPLS). Both methods assume that the relationship between process variables is linear, and they are more effective for monitoring simple batch processes. However, most batch processes have multi-stage nature due to changes in operating conditions or reaction processes. The dynamic characteristics of the data are different, and the variables in the same operation stage are often highly nonlinear. At this time, the information of the original data cannot be well represented by a single statistical model, and it may lead to the lack of some important information, resulting in missed failures. In fact, there is no steady-state operating point in the batch process, and the trajectory of the process variable shows a nonlinear trend with time, which is a typical nonlinear dynamic operation process. Aiming at the nonlinear characteristics of the batch process, Lee et al. A nonlinear algorithm based on kernel function: multiway kernel principal component analysis (MKPCA), which extracts the nonlinear characteristics of the batch process, expands the 3D data matrix of the batch process into a 2D matrix vertically and standardizes it , establish a process model and use it for online monitoring of the process, kernel principal component analysis (kernel principal component analysis, KPCA) maps nonlinear data to a high-dimensional feature space through a nonlinear kernel function, and then performs linear PCA in the feature space to extract features, KPCA performs PCA in a high-dimensional feature space, so there is no need to solve nonlinear optimization problems, and compared to other nonlinear methods, it does not need to specify the number of pivots before modeling

However, the traditional KPCA algorithm has shortcomings, that is, the KPCA model is time-invariant, and most of the actual industrial processes have time-varying characteristics, which limits the application of KPCA in nonlinear batch processes

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