Batch process quality prediction based on denoising autoencoder-spatial temporal convolutional attention mechanism fusion network

被引:0
作者
Zhang, Yan [1 ,2 ]
Cao, Jie [1 ,4 ]
Zhao, Xiaoqiang [1 ,2 ,3 ]
Hui, Yongyong [1 ,2 ,3 ]
机构
[1] Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou, Peoples R China
[2] Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou, Peoples R China
[3] Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou, Peoples R China
[4] Mfg Informatizat Engn Res Ctr Gansu Prov, Lanzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Batch processes; Quality prediction; Denoising-Autoencoder; Maximum Information Coefficient; Spatiotemporal convolutional attention; MANUFACTURING PROCESS; PRODUCT QUALITY; MODEL; DIAGNOSIS;
D O I
10.1007/s10489-025-06368-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In batch processes, the accurate prediction of quality variables plays a crucial role in smooth production and quality control. However, various sources of noise in the production environment cause abnormal data fluctuations that deviate from the real value. Coupled with the dynamic nonlinearity of batch processing and the complex spatiotemporal relationship of variables, which greatly increase the difficulty of prediction and pose a severe challenge to prediction performance. Therefore, a denoising autoencoder-Spatial Temporal Convolution Attention Fusion Network (DAE-STCAFN) prediction method is proposed. Firstly, combining DAE and maximum information coefficient (MIC), multi-level data features are extracted to prepare high-quality input data for the quality prediction model. DAE is used to denoise the original data, and relevant variables are selected through MIC. Then, an augmented matrix is constructed to eliminate the autocorrelation of the selected variables in the time series. Secondly, a spatial temporal convolutional attention fusion mechanism is created to extract the spatial temporal fusion features between the input and output variable sequences. Thirdly, to further enhance the learning ability of the model, a batch attention module is constructed to automatically learn the relationship among sample in small batch. Finally, experiments were carried out on the simulation platform of penicillin fermentation and hot tandem rolling process. In the prediction process of penicillin concentration, RMSE and MAE of the proposed method were 0.0099 and 0.0077, respectively. In the prediction of strip thickness, the RMSE and MAE are 0.0008 and 0.0003 respectively. The results show that the proposed method is effective both in simulation experiment and in actual industrial production in terms of prediction accuracy, stability and generalization ability.
引用
收藏
页数:20
相关论文
共 42 条
[1]  
Akande T, 2022, InArtificial Intelligence and Applications
[2]   A modular simulation package for fed-batch fermentation:: penicillin production [J].
Birol, G ;
Ündey, C ;
Çinar, A .
COMPUTERS & CHEMICAL ENGINEERING, 2002, 26 (11) :1553-1565
[3]   Semi-Supervised Adaptive Pseudo-Label Feature Learning for Hyperspectral Image Classification in Internet of Things [J].
Chen, Huayue ;
Ru, Jie ;
Long, Haoyu ;
He, Jialin ;
Chen, Tao ;
Deng, Wu .
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (19) :30754-30768
[4]   Batch process monitoring using multiway Laplacian autoencoders [J].
Gao, Xuejin ;
Xu, Zidong ;
Li, Zheng ;
Wang, Pu .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2020, 98 (06) :1269-1279
[5]   Using regression models for predicting the product quality in a tubing extrusion process [J].
Garcia, Vicente ;
Salvador Sanchez, J. ;
Alberto Rodriguez-Picon, Luis ;
Carlos Mendez-Gonzalez, Luis ;
de Jesus Ochoa-Dominguez, Humberto .
JOURNAL OF INTELLIGENT MANUFACTURING, 2019, 30 (06) :2535-2544
[6]   Batch process monitoring based on WGNPE-GSVDD related and independent variables [J].
Hui, Yongyong ;
Zhao, Xiaoqiang .
CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2018, 26 (12) :2549-2561
[7]   Profitability related industrial-scale batch processes monitoring via deep learning based soft sensor development [J].
Ji, Cheng ;
Ma, Fangyuan ;
Wang, Jingde ;
Sun, Wei .
COMPUTERS & CHEMICAL ENGINEERING, 2023, 170
[8]   Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares [J].
Jiang, Qingchao ;
Yan, Xuefeng ;
Yi, Hui ;
Gao, Furong .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (05) :4098-4107
[9]  
Lebedev IS., 2024, Emerg Sci J, V8, P355, DOI [10.28991/ESJ-2024-08-01-025, DOI 10.28991/ESJ-2024-08-01-025]
[10]  
Lee L, 2006, INT TEST CONF P, P41