Efficient JITL framework for nonlinear industrial chemical engineering soft sensing based on adaptive multi-branch variable scale integrated convolutional neural networks-Reference-Cited by-同舟云学术

Efficient JITL framework for nonlinear industrial chemical engineering soft sensing based on adaptive multi-branch variable scale integrated convolutional neural networks

Published:2023-10 Issue: Volume:58 Page:102199
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Chen Yifan,Li Anlan,Li Xiangyang,Xue Dong,Long Jian^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference65 articles.

1. A supervised adaptive resampling monitoring method for quality indicator in time-varying process;Tao;IEEE Trans. Instrum. Meas.,2021

2. Variable-weighted FDA combined with t-SNE and multiple extreme learning machines for visual industrial process monitoring;Lu;ISA Trans.,2022

3. Data-driven multi-period modeling and optimization for the industrial steam system of large-scale refineries;Xu;Chem. Eng. Sci.,2023

4. Adaptive Data Dimensionality Reduction for Chemical Process Modeling Based on the Information Criterion Related to Data Association and Redundancy;Luo;Ind. Eng. Chem. Res.,2022

5. Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes;Chen;Inf. Sci.,2022

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology;Advanced Engineering Informatics;2024-10

2. Just-in-time learning method based on two kinds of local samples combined with two-stage training parallel learner for nonlinear chemical process soft sensing;Measurement;2024-10

3. Efficient prediction framework for large-scale nonlinear petrochemical process based on feature selection and temporal-attention LSTM: Applied to fluid catalytic cracking;Chemical Engineering Science;2024-09

4. Soft sensor development for mixed oil interface tracking in multi-product pipelines based on knowledge-informed semi-supervised Variational Bayesian Gaussian mixture regression;Energy;2024-08

5. Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems;Processes;2024-06-30