Process industry system prediction module based on crossed relevance time-lag gray correlation analysis

Abstract

The invention relates to a process industry system prediction module based on a crossed relevance time-lag gray correlation analysis. The process industry system prediction module based on the crossedrelevance time-lag gray correlation analysis comprises the steps that the degree of association between all candidate variables and target variables is calculated; the variables are arranged in an order descending mode, the variable with the degree of association greater than the degree of association threshold value is obtained, and feature variable set is obtained. The feature variable set is taken as an input variable of an index prediction module, and the relative delay time of the feature variable set is mixed into the process of mould establishment. The index change tendency is predicted through an artificial neural network, the prediction mould is trained, the minimum prediction error is taken as a target, an optimal input feature is selected, and a prediction module is established. The time series mixing delay time in different sections of the feature variables in an optimal input feature subset is taken as the input of the index prediction model, the model is tested, the result is compared with the real value of the target variable, and the predictive performance is quantitatively evaluated. According to the process industry system prediction module based on the crossed relevance time-lag gray correlation analysis, the precision of the whole model is improved, and the effective prediction on a process industry key index is finally realized.

Description

technical field [0001] The invention relates to the field of process industry production, and relates to a process industry system prediction model based on cross-correlation time-delay gray correlation analysis. Background technique [0002] The process industry mainly includes petroleum, chemical industry, metallurgy, electric power, pharmaceutical and other industries that occupy a dominant position in the national economy. The production process generally contains a large number of indicators or variables. The monitoring of important indicators is the key to ensure normal production. Various important indicators such as reactor temperature and tower body pressure in the hydrocracking unit of chemical plant. However, process industry production has the characteristics of large scale, complex and changeable process, nonlinearity, strong coupling, and large lag. It is difficult for field operators to monitor individual key indicators with manual experience to ensure that ab...

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Problems solved by technology

However, such models are very sensitive to modeling errors, parameter perturbations, noise and disturbances, and are not suitable for multivariate process industrial processes

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