化工学报 ›› 2013, Vol. 64 ›› Issue (5): 1717-1722.DOI: 10.3969/j.issn.0438-1157.2013.05.029

• 过程系统工程 • 上一篇    下一篇

基于GMC(1,N)的聚丙烯熔融指数预报

王明旭, 刘兴高   

  1. 浙江大学控制系, 工业控制技术国家重点实验室, 浙江 杭州 310027
  • 收稿日期:2012-09-21 修回日期:2012-11-18 出版日期:2013-05-05 发布日期:2013-05-05
  • 通讯作者: 刘兴高
  • 作者简介:王明旭(1987-),男,硕士研究生。
  • 基金资助:

    中国石油天然气集团公司石油化工联合基金项目(U1162130);国家高技术研究发展计划项目(2006AA05Z226);浙江省杰出青年科学基金项目(R4100133)。

Melt index prediction of polypropylene based on GMC(1,N)model

WANG Mingxu, LIU Xinggao   

  1. State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
  • Received:2012-09-21 Revised:2012-11-18 Online:2013-05-05 Published:2013-05-05
  • Supported by:

    supported by the High-tech Research and Development Program of China(2006AA05Z226)and Science Fund for Distinguished Young Scholars of Zhejiang Province(R4100133).

关键词: 灰色预测, GMC(1,N)模型, 灰色神经网络模型, 熔融指数预报

Abstract: Melt index(MI)is the most important parameter in determining polypropylene’s specifications and also the key of quality control.Considering the continuity of the polypropylene production process and the characteristics of gray prediction model, a new GMC(1,N)melt index prediction model is proposed in this paper to infer the MI of polypropylene from other process variables, which shows good applicability. Furthermore, a back propagation network is built to correct the residual of the model and the GMC(1,N)-BP model is improved with better precision.Standard SVM(support vector machines), LS-SVM and weighted LS-SVM model are taken for comparison.A practical polypropylene industrial process is taken as case study.The results show that the proposed improved model achieves good results in practical melt index prediction.

Key words: gray prediction, GMC(1,N)model, gray neural network, melt index prediction

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