A semi-supervised integrated real-time learning method for soft measurement of Mooney viscosity of industrial rubber compounds

Abstract

The invention discloses a semi-supervised integrated real-time learning method for soft measurement of Mooney viscosity of industrial mixing rubber. The present invention aims at the problem of poor prediction performance of the traditional soft-sensing method caused by the lack of marked samples and sufficient non-marked samples in the process of industrial rubber mixing. Based on the Gaussian process regression model, combined with the real-time learning method, a diverse JITGPR sub-model is constructed. Adaptive ensemble prediction is performed on selected unlabeled samples, and high-confidence pseudo-labels are selected to augment the training sample set. Finally, the final predicted value of Mooney viscosity is obtained through the fusion of the expanded training set, diverse JITGPR sub-models and limited mixing mechanism. The invention overcomes the problems of less marked samples, sufficient non-marked samples, increased cost, and difficulty in improving product quality due to the lag in obtaining the Mooney viscosity value during the rubber mixing process, realizes the online real-time prediction of the Mooney viscosity, and effectively improves the traditional Predictive performance of soft-sensing modeling of compound Mooney viscosity.

Description

technical field [0001] The invention relates to the field of soft measurement modeling and application of industrial batch processes, in particular to a semi-supervised integrated real-time learning soft measurement method for Mooney viscosity of industrial mixing rubber. Background technique [0002] With the development of the automobile industry, tires are playing an increasingly important role as an integral part of vehicles. In the tire manufacturing process, rubber mixing is the first and key link. The process is a typical short cycle intermittent process. In this process, raw materials such as natural rubber or synthetic rubber and additives are mixed together and processed in a closed mixer. After 2-5 minutes, a mixing batch run is complete. Therefore, rubber mixing is a nonlinear and complex batch-wise process, and the quality of rubber products is very dependent on the important quality index reflecting the viscoelastic behavior of elastomers, that is, Mooney vi...

AI Technical Summary

Problems solved by technology

[0006] The main technical problem to be solved by the present invention is: the present invention aims at the problem that the Mooney viscosity parameter is difficult to measure on-line in real time during the industrial rubber mixing process, and aims at the deficiencies of the prior art, and provides an industrial mixing based on semi-supervised integrated real-time learning Soft Measurement Method of Glue Mooney Viscosity (SSEJITGPR) to Realize Online Estimation of Mooney Viscosity Parameters During Rubber Compounding

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