Multi-model soft sensor for hydrogen purity in catalytic reforming process based on improved fast search clustering algorithm and Gaussian processes regression

被引：0

作者：

Shuang Y. ^{[1
]}

Gu X. ^{[1
]}

机构：

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education), East China University of Science and Technology, Shanghai

来源：

Huagong Xuebao/CIESC Journal | 2016年 / 67卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Algorithm; Catalytic reforming; Clustering by fast search; Gaussian processes regression; Hydrogen; Model; Soft sensor;

D O I：

10.11949/j.issn.0438-1157.20151854

中图分类号：

学科分类号：

摘要：

Hydrogen is one of the most important by-products in catalytic reforming process, a hydrogen purity soft sensor will contribute to guiding production. However, the working condition of catalytic reforming process is complex and changeable, a single model soft sensor is hard to ensure the prediction accuracy. Aiming at this problem, this paper present a combined soft sensor model based on modified fast search clustering algorithm and Gaussian processes regression (GPR). The history sample are classified by the novel clustering algorithm and then each sub-model is built through GPR with the classified sub sample. Meanwhile the class identification model has been built by GPR as well. Finally, the combined model soft sensor is established in a switcher form. The combined is applied to a catalytic reformer and the result indicates that the proposed method has a good result and has certain practical value. © All Right Reserved.

引用

页码：765 / 772

页数：7

共 20 条

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