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

SHUANG Yifan; GU Xingsheng

doi:10.11949/j.issn.0438-1157.20151854

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

更新时间：2025-12-10

- Multi-model soft sensor for hydrogen purity in catalytic reforming process based on improved fast search clustering algorithm and Gaussian processes regression
- CIESC Journal Issue 3, Pages: 765-772(2016)
- 作者机构：
  
  华东理工大学化工过程先进控制与优化技术教育部重点实验室,上海,200237
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151854
  CLC： P274
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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SHUANG Yifan, GU Xingsheng. Multi-model soft sensor for hydrogen purity in catalytic reforming process based on improved fast search clustering algorithm and Gaussian processes regression[J]. CIESC Journal, 2016, (3): 765-772.
DOI：

SHUANG Yifan, GU Xingsheng. Multi-model soft sensor for hydrogen purity in catalytic reforming process based on improved fast search clustering algorithm and Gaussian processes regression[J]. CIESC Journal, 2016, (3): 765-772. DOI： 10.11949/j.issn.0438-1157.20151854.

摘要

氢气是催化重整反应的重要副产物之一

建立氢气纯度软测量模型有助于指导生产。针对催化重整过程工况复杂多变、单一软测量模型难以满足精度要求

提出了一种基于改进的快速搜索聚类算法和高斯过程回归的多模型软测量建模方法。首先

针对快速搜索聚类算法中截断距离是由人为设定的问题

提出了一种截断距离确定方法。并用该改进算法对历史数据进行自动分类

建立各个数据子集的高斯过程回归模型

使各子模型在最大程度上反映不同工况点。然后

针对聚类后得到的带有类别标签的历史数据

建立类别辨识模型

与各子模型相结合

形成开关模式的组合模型。最后

将该建模方法应用于连续催化重整装置

建立了脱氯前氢气纯度的在线计算模型。结果表明

该多模型建模方法具有较高的预测精度

优于传统的单一模型

有一定的实用价值。

Abstract

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.

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Related Institution

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