Soft sensor modeling method based on hybrid model of nearest neighbor and neural network

YANG Yijun; WANG Zhenlei; WANG Xin

doi:10.11949/0438-1157.20200401

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Soft sensor modeling method based on hybrid model of nearest neighbor and neural network

Process system engineering | 更新时间：2026-07-24

- Soft sensor modeling method based on hybrid model of nearest neighbor and neural network
- CIESC Journal Vol. 71, Issue 12, Pages: 5696-5705(2020)
- 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
  2.上海交通大学电工与电子技术中心，上海 200240
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20200401
  CLC： TP 273
- Received：16 April 2020，
  
  Revised：2020-07-11，
  
  Online First：17 December 2020，
  
  Published：05 December 2020
- 稿件说明：
移动端阅览
杨逸俊, 王振雷, 王昕. 基于最近邻与神经网络融合模型的软测量建模方法[J]. 化工学报, 2020, 71(12): 5696-5705

YANG Yijun, WANG Zhenlei, WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network[J]. CIESC Journal, 2020, 71(12): 5696-5705
杨逸俊, 王振雷, 王昕. 基于最近邻与神经网络融合模型的软测量建模方法[J]. 化工学报, 2020, 71(12): 5696-5705 DOI： 10.11949/0438-1157.20200401.

YANG Yijun, WANG Zhenlei, WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network[J]. CIESC Journal, 2020, 71(12): 5696-5705 DOI： 10.11949/0438-1157.20200401.

摘要

软测量建模能够有效地解决生产过程中在线分析仪表测量滞后大、价格昂贵、维护保养复杂等问题。目前，基于数据驱动的神经网络是软测量建模的主要工具之一。而在建模数据的采集过程中，主导变量的采集相对辅助变量要困难得多，由此产生了大量缺失标签的数据。但传统的软测量建模方法却忽视了这些无标签数据，只利用少量的有标签数据建模，从而影响了模型的预测精度。为了解决标签缺失的问题，采用最近邻算法对无标签数据进行伪标记，同时设计了由卷积操作与门限循环单元神经网络（GRU）结合的网络结构来进一步利用无标签数据，提取不同时刻数据中的动态特征，提高神经网络的预测精度。最后将该方法应用于丙烯精馏塔塔顶丙烷浓度的预测，实验结果表明该模型能有效处理非线性动态系统的标签缺失问题，具有更高的预测精度。

Abstract

Soft-sensing modeling can effectively solve the problems of large measurement lag

high price

and complex maintenance of online analytical instruments in the production process. At present

neural network based on data-driven is one of the main tools of soft sensor. In the process of modeling data collection

the collection of dominant variables is much more difficult than that of auxiliary variables

resulting in a large amount of unlabeled data. However

traditional soft sensor modeling methods ignore these unlabeled data and only use a small amount of labeled data for modeling

which has negative effect on the prediction accuracy of the model. To solve the problem of label missing

the nearest neighbor algorithm is used to pseudo label the unlabeled data. At the same time

a network structure is designed by combining convolution operation and gated recurrent unit neural network (GRU) to further utilize the unlabeled data

extract the dynamic feature from data at different time

and improve the prediction accuracy of the neural network. Finally

the method is applied to the prediction of propane concentration on the top of propylene distillation column. The results show that the model can solve the problem of label missing in the nonlinear dynamic system and has higher prediction accuracy.

关键词

Keywords

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