Hybrid intelligent soft-measurement method of Mooney viscosity of rubber

Abstract

The invention relates to a hybrid intelligent soft-measurement method of Mooney viscosity of rubber, comprising the steps of collecting the technological parameters that affect Mooney viscosity in the field at first, then analyzing the principle component and determining the necessary auxiliary variable to normalize; establishing hybrid intelligent algorithm model formed by combining mechanical analysis, wavelet neural network and least squares support vector machine, then collecting data in real time, and calculating input data through the established model to obtain real time predicted value of Mooney viscosity. With the advantages of a plurality of modeling methods, not only predictive effect is better than the single soft-measurement method, but also the model has good robustness and generalization ability suitable for different working conditions. Computation speed and precision of the soft-measurement predicted model meet the production requirement, and are beneficial to stabilize production, guide operation and promote the quality of the products.

Description

technical field [0001] The invention relates to an intelligent soft measurement method for rubber Mooney viscosity mixing in the production of industrial synthetic rubber. Background technique [0002] In the production of industrial synthetic rubber, the Mooney viscosity of rubber is the main production control quality index. For the monitoring of this indicator, although there is an analyzer for analysis at present, due to the low measurement accuracy, large lag, expensive equipment investment and difficult maintenance, the online monitoring of the analyzer is not used in production, but the method of manual sampling and testing is used. The results of manual sampling and laboratory analysis lag far behind the production, generally it takes at least 8 hours to get the analysis results, and the operators formulate the production formula to control the production according to the analysis results. When substandard products appear, they can only be resolved after the fact. ...

AI Technical Summary

Problems solved by technology

The topology of the support vector machine is determined by the support vectors, and its optimal solution is based on the idea of ​​structural risk minimization. At the same time, it seeks the best compromise between the complexity of the model and the learning ability according to the limited sample information. The approximation method has a stronger generalization ability; as a new form of SVM developed in recent years, the least squares support vector machine (LS-SVM) can better solve the problems of small samples, nonlinearity, high dimensionality, and local minimum points. and other practical problems, but if the model structure is huge, it will increase the calculation amount of online estimation of the model