Intuitionistic set theory based multiple kernel fuzzy clustering and its application of ethylene raw material properties

CUI Xinghua; DU Wenli; ZHAO Liang; LI Jiangli; CHI Liang

doi:10.11949/j.issn.0438-1157.20161069

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Intuitionistic set theory based multiple kernel fuzzy clustering and its application of ethylene raw material properties

更新时间：2025-12-10

- Intuitionistic set theory based multiple kernel fuzzy clustering and its application of ethylene raw material properties
- CIESC Journal Issue 2, Pages: 739-745(2017)
- 作者机构：
  
  1. 化学工程联合国家重点实验室, 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237
  2. 中国石油天然气股份有限公司吉林石化分公司,吉林市,吉林省,132000
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161069
  CLC： P227
- Online First：05 February 2017，
  
  Published：05 February 2017
- 稿件说明：
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CUI Xinghua, DU Wenli, ZHAO Liang, et al. Intuitionistic set theory based multiple kernel fuzzy clustering and its application of ethylene raw material properties[J]. CIESC Journal, 2017, (2): 739-745.
DOI：

CUI Xinghua, DU Wenli, ZHAO Liang, et al. Intuitionistic set theory based multiple kernel fuzzy clustering and its application of ethylene raw material properties[J]. CIESC Journal, 2017, (2): 739-745. DOI： 10.11949/j.issn.0438-1157.20161069.

摘要

随着乙烯裂解原料种类的日益增多，原料分析仪价格昂贵，因此根据乙烯裂解原料属性进行在线聚类，对实现乙烯收率建模，优化乙烯产率、节能减耗具有重要现实意义。为了提高原料在聚类的准确性，提出了一种基于直觉模糊集理论的核聚类算法。即在定义直觉模糊集隶属度时通过引入犹豫度来表征数据的不确定信息，同时利用直觉模糊熵对多核聚类算法的损失函数重新定义，使类簇中的数据点最优化；进一步地，使用随机森林对裂解原料属性进行特征选择，依据对乙烯产率的贡献度选取聚类的主要特征属性。最后根据实际工业裂解的石脑油数据验证了所述算法的有效性。

Abstract

Along with the increasing types of ethylene cracking materials and expensive feed analyzer

clustering of ethylene cracking materials which is to improve ethylene yield modeling

ethylene yield and energy consumption has very important practical significance. In order to improve the accuracy of online identification of raw materials

an intuitionistic fuzzy kernel clustering algorithm based on the theory of intuitionistic fuzzy sets is presented. In the definition of membership

membership considers uncertain information which is the hesitation degree. At the same time

intuitionistic fuzzy entropy is incorporated in the loss function of multiple kernel clustering algorithm. That is to optimize the data points in the class. Further

the cracking material attribute feature selection using random forest

based on the main attributes of contribution of ethylene yield. Finally

the actual ethylene cracking naphtha data of industry is used to verify the effectiveness and superiority of the algorithm.

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