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基于改进模糊C均值聚类算法的乙烯裂解原料识别

ISSN号：0438-1157
期刊名称：《化工学报》
时间：0
分类：TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]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
相关基金：国家重点基础研究发展计划项目（2012CB720500）;国家自然科学基金项目（U1162202,61222303,21206037）;上海市科技启明星跟踪计划项目（13QH1401200）;教育部新世纪优秀人才计划（NCET10-0885）;十二五国家科技支撑计划项目（2012BAF05BOO）;上海市重点学科建设项目（B504）.

关键词：算法, 优化, 模型, 模糊C均值, 混合概率模型, EM算法, 原料识别, 乙烯裂解, algorithm, optimization, model, fuzzy C-means, mixture probabilistic model, EM algorithm, feed property identification, ethylene cracking

中文摘要：

乙烯裂解过程中原料变化种类多，其原料分析仪因价格昂贵工业现场很少配备，为此实现油品属性的在线识别对实现裂解过程在线优化具有重要意义。由于传统模糊c均值算法隶属度的求取是基于欧氏距离，其算法只包含均值中心，带来聚类效果的单一性。为了充分利用裂解原料的有效信息，提出了基于混合概率模型的模糊隶属度设置方法，即通过建立混合高斯模型实现对聚类样本隶属关系的概率分布描述，并利用EM算法进行模型参数的极大似然估计。该算法可在考虑样本均值中心的前提下，进一步有效利用样本协方差与权重系数信息进行模式判别。最后，以经典IRIS数据聚类、乙烯裂解原料识别为仿真实例，验证了本文所述方法在Dunn指标和Xiebieni指标上明显优于模糊c均值聚类算法，表明了该方法的有效性。

英文摘要：

In ethylene cracking process, the changes of feed have many kinds, and due to its expensive feed analyzer, little industrial site equips with it, so online recognition of oil property is important to achieve cracking online optimization. As the traditional fuzzy C-means algorithm is based on the membership of the strike Euclidean distance, the algorithm contains only the mean center, bringing the unity of clustering results. To take full advantage of effective information of cracking feed, this paper proposes a fuzzy membership set method based on hybrid probabilistic model, namely through the establishment of Gaussian mixture model to achieve describing the probability distribution of clustering sample＇ s affiliation, and use EM algorithm to estimate the model parameter＇s pole maximum likelihood. The algorithm can not only consider mean center of the sample, but also effectively use samplecovariance and the weight coefficient information for mode discrimination. Finally, the simulation is based on classic IRIS data clustering and ethylene cracking feedstock identification, verifying the method described in this paper in the index of dunn and Xiebieni is better than fuzzy C-means clustering algorithm, showing that the method is effective.