Dynamic early warning method for major hazard source of chemical industrial park

Abstract

The invention relates to a dynamic early warning method for a major hazard source of a chemical industrial park. The dynamic early warning method is characterized by adopting parallel coordinates to perform correlation analysis on correlated variables of key link parameters in the major hazard source, using screened variables with strong correlation as input variables, performing dimensionality reduction on the variables with strong correlation through a principal component analysis method before prediction; dynamically predicting a key production link variable through a recurrent neural network; and performing danger level evaluation by using dynamic prediction results of the key parameters. The dynamic early warning method of the invention is provided for solving some problems in dynamicearly warning of the hazard source of the chemical industrial park, and has the advantages of openness, robustness, parallelism, flexibility, no special requirement on mathematical form of the problem, and so on.

Description

technical field [0001] The invention belongs to the technical field of information and control, and relates to automation technology, in particular to a dynamic early warning method for a chemical industry park. Background technique [0002] In recent years, with the improvement of people's demand for chemical products, the chemical production process is developing in the direction of large-scale, comprehensive and complex. Large-scale chemical parks have become a new type of chemical production mode in my country. The chemical production process has the characteristics of high complexity, strong correlation, long production process, and numerous equipment. It is a process system with multiple variables, strong system coupling, time-varying, and large lag. Due to equipment failure, aging of production equipment, personnel operation errors, raw material characteristics and changes in the external environment, industrial process variables will deviate from the target value, ca...

AI Technical Summary

Problems solved by technology

[0005] At present, the common problems of dynamic early warning in chemical industry parks are: 1. The prediction speed is slow. Traditional methods usually do not consider the correlation between key chemical parameters. When performing data analysis, all variables in the production system are statistically analyzed together. , which will inevitably increase the computational complexity

2. Traditional prediction methods mostly use support vector machines, BP neural networks, and probability models as prediction methods. These methods can only perform static predictions, and are not suitable for dynamic early warning of industrial processes, because the dynamic time modeling problem is transformed into static The problem of spatial modeling will inevitably lead to the decline of prediction accuracy

The prediction model of this method adopts the Elman neural network, which has a feedback link inside, which can remember the state information of the relevant variables in the chemical production process of the input system at the last moment, and can express the time delay between input and output, so that the system has adaptability. The ability of time-varying characteristics 3. Traditional prediction methods usually only consider the prediction of key parameters, and do not link the prediction of key parameters with the dynamic early warning of major hazards

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