Complex industrial process running state evaluation method and application based on T-KPRM

Abstract

The invention discloses a complex industrial process running state evaluation method and application based on T-KPRM. Combined with advantages of PRM and T-KPLS, the method comprises the steps of: further decomposing a high-dimension principal component subspace and a residual error subspace of KPLS, separating the part relevant to output from the part irrelevant to output, separating the part having a relatively large residual error from the part of final noise, and accurately extracting variable information relevant to the output. By adopting the method, the field industrial process running state can be conveniently grasped. According to the invention, an off-line evaluation module of the operation state is established, a sliding window technology is introduced, and the similarity between an online data window and corresponding evaluation grades is utilized for carrying out complex industrial process operation state online evaluation; in addition, according to an Euclidean distance between the sliding data window and an optimal evaluation grade, contribution rates of corresponding variables are calculated, and non-optimum factors of the operation state are identified, so that filed operators can adjust and improve the production strategy in time, and the production efficiency is improved.

Description

technical field [0001] The invention relates to a T-KPRM-based industrial process operating state evaluation method and its application, belonging to the technical field of industrial production process operating state evaluation. Background technique [0002] Good industrial process operation status is an effective guarantee for enterprise product quality and economic benefits. However, in complex industrial processes, the operating state is often affected by various uncertain factors, which cause it to deviate from the optimal operating point. At the same time, data from industrial sites will also be subject to various disturbances (such as sensor failures, etc.) during the collection process, resulting in outliers in the sample data. Therefore, it is necessary to propose a robust online evaluation strategy for industrial operation status to grasp the operation status of complex industries in real time. The separation of raw coal using dense media is a complex industrial...

AI Technical Summary

Problems solved by technology

However, the data of industrial sites are often non-linear and contain a certain number of outliers. The evaluation models established by existing methods (such as T-KPLS, Fisher discriminant analysis) are easily affected by outliers and lose the outliers. The generalization ability of the method affects the accuracy of the evaluation results

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