Continuous stirred tank reactor operation state monitoring method based on time series data analysis

Abstract

The invention discloses a continuous stirred tank reactor operation state monitoring method based on time series data analysis, and aims to solve the problem of monitoring the operation state of a continuous stirred tank reactor by monitoring the time series abnormal change of the real-time sampling data of the continuous stirred tank reactor. The method comprises the steps of firstly deducing a time series correlation characteristic analysis algorithm according to the maximum time series characteristic typical correlation coefficient; secondly, further using an auto-regression model to describe a time sequence dynamic relation between time sequence related characteristics, and finally achieving the purpose of monitoring the running state of the continuous stirred tank reactor by monitoring the errors of the auto-regression model. Compared with a traditional method, the method has the advantages that the potential characteristic components, which are typically related to a time sequence, of the sampling data can be effectively extracted, and the superiority and effectiveness of the method in monitoring the running state of the continuous stirred tank reactor are verified through specific embodiments.

Description

technical field [0001] The invention relates to a method for monitoring a chemical process, in particular to a method for monitoring the operating state of a continuous stirring reactor based on time series data analysis. Background technique [0002] The real-time sampling data of the chemical process is the basis for the construction and application of "big data" in the chemical industry. The use of sampling data to monitor the operation status of the chemical process has become an important part of the chemical intelligent system. Data-driven chemical process monitoring After years of research and development, many process monitoring methods based on Principal Component Analysis (abbreviation: PCA) and Independent Component Analysis (abbreviation: ICA) algorithms have emerged. The core of the implementation of these mainstream process monitoring methods is mainly concerned with the mining of latent characteristics of data. In other words, the established data-driven mode...

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Problems solved by technology

[0004] However, although the covariance can reflect the correlation to a certain extent, the collinearity problem between the data will also lead to the maximization of the covariance index

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