Process Industry System Prediction Model Based on Cross-correlation Time-delay Grey Correlation Analysis
A grey relational analysis, process industry technology, applied in the field of process industry production, can solve the problems of inapplicable multi-variables, modeling errors, parameter perturbation noise and interference, etc., to achieve the effect of improving accuracy and optimizing model parameters
Active Publication Date: 2022-08-02
HANGZHOU DIANZI UNIV
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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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[0056] In the process industry, the prediction of key indicators can provide effective help for fault prediction and diagnosis analysis. After determining the indicators to be predicted and the relevant indicators, the process industry system prediction model based on the cross-correlation time-delay gray correlation analysis proposed by the present invention is used to determine the delay time between each indicator variable and the to-be-predicted indicators on the basis of completing the elimination of data errors, and selecting The appropriate index variables with strong correlation with the index to be predicted are selected, and the delay time is combined with the artificial neural network prediction model to remove irrelevant and redundant index variables with a progressive selection strategy, optimize the model parameters, and finally realize the index to be predicted. effective prediction.
[0057] like figure 1 As shown, the specific implementation steps of the pres...
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The invention relates to a process industry system prediction model based on cross-correlation time-delay grey correlation analysis. The present invention first calculates the correlation degree between each candidate variable and the target variable; sorts the variables in descending order, selects the variable whose correlation degree is greater than the correlation degree threshold, and obtains the characteristic variable set. The feature variable set is used as the input variable of the index prediction model, and its relative delay time is integrated into the process of model building. The artificial neural network is used to predict the change trend of the indicators, and the prediction model is trained. With the goal of minimizing the prediction error, the optimal input features are selected and the prediction model is established. The time series fusion delay time of the feature variables in the optimal input feature subset in different periods is used as the input of the index prediction model, the model is tested, the results are compared with the real value of the target variable, and the prediction performance is quantitatively evaluated. The invention improves the overall accuracy of the model, and finally realizes the effective prediction of the key indicators of the process industry.
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, metallurgy, electric power, pharmaceutical and other industries that occupy a dominant position in the national economy. Its production process generally contains a large number of indicators or variables. The monitoring of important indicators is the key to ensuring normal production, such as refining. Reactor temperature, column pressure and other important indicators in the hydrocracking unit of the chemical plant. However, process industrial production has the characteristics of large scale, complex and changeable processes, nonlinearity, strong coupling, and large lag. It is difficult for field operators to monitor individual key indicators with manual experience. Starting ...
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IPC IPC(8): G05B13/04
Inventor 郑松史佳霖罗单葛铭
Owner HANGZHOU DIANZI UNIV