文献类型：期刊文献

中文题名：Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

作者：Zhi Li[1,2,3,4];Yuchong Xia[2];Jian Long[2];Chensheng Liu[2];Longfei Zhang[2]

机构：[1]State Key Laboratory of Industrial Control Technology,East China University of Science and Technology,Shanghai 200237,China;[2]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[3]Huzhou Institute of Industrial Control Technology,Huzhou 313099,China;[4]Engineering Research Center of Process System Engineering,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2025

卷号：81

期号：5

起止页码：241

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报(英文版)

基金：supported by the National Key Research and Development Program of China(2023YFB3307800);National Natural Science Foundation of China(62394343,62373155);Major Science and Technology Project of Xinjiang(No.2022A01006-4);State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26);Fundamental Research Funds for the Central Universities.

语种：英文

中文关键词：Multi-scale feature fusion;Soft sensors;Stacked autoencoders;Computational chemistry;Chemical processes;Parameter estimation

摘要：Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.