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门尼粘度是合成橡胶生产的主要质量指标,如何在线监测门尼粘度,并实现质量的自动监控是橡胶生产工业亟待解决的问题。本文应用主元分析和最小二乘支持向量机法建立生产过程门尼粘度预测模型。结合工艺机理分析,找出影响橡胶门尼粘度的主要参数并做主元分析,确定最少辅助变量,简化支持向量机结构,建立基于PCA LS-SVM的门尼粘度预测模型。仿真结果,门尼粘度预测值与实际值最大相对误差为5.78%,预测模型精度高,泛化能力强,运行速度快,可以指导生产。
Mooney viscosity is the main quality index of synthetic rubber production. How to monitor the Mooney viscosity on line and realize the automatic quality control is a problem to be solved urgently in the rubber production industry. In this paper, principal component analysis and least square support vector machine method to establish the production process Mooney viscosity prediction model. Based on the analysis of the process mechanism, the main parameters influencing the Mooney viscosity of rubber were found out and analyzed by principal component analysis (PCA). The least auxiliary variables were determined and the structure of support vector machine was simplified. The Mooney viscosity prediction model based on PCA LS-SVM was established. The simulation results show that the maximum relative error between the predicted and the actual Mooney viscosity is 5.78%. The prediction model has high precision, strong generalization ability and fast running speed, which can guide the production.