Convolution neural network with attention mechanism of input data for quality prediction of fluorine chemical products

被引：0
|
作者：
Li X. [1 ,2 ]
Chen Z. [3 ]
Wei Z. [4 ]
Li S. [4 ]
Chen X. [1 ]
Song K. [1 ]
机构：
[1] School of Chemical Engineering, Tianjin University, Tianjin
[2] Changzheng Engineering Co., Ltd., Beijing
[3] Zhejiang Juhua Qing'an Testing Technology Co., Ltd., Quzhou
[4] Zhejiang Juhua Co., Ltd., Quzhou
来源：
Huagong Jinzhan/Chemical Industry and Engineering Progress | 2022年 / 41卷 / 02期
关键词：
Attention mechanism; Convolution neural network; Fluorochemical process; Quality prediction;
D O I：
10.16085/j.issn.1000-6613.2021-0611
中图分类号：
学科分类号：
摘要：
For a complicated chemical process, i.e. the fluorochemical process, the simultaneous existence of the time-varying processes with different time characteristics makes regular machine learning methods unable to predict product quality precisely. In this study, a convolutional neural network with attention mechanism of input data (ACNN) was proposed to improve the prediction of the product quality. By introducing the adaptive attention mechanism on input data at different-time span, this method can simultaneously extract the characteristics of time-varying processes. Therefore, it can overcome the abovementioned drawbacks of the regular convolution neural network. This advantage allows the possibility for ACNN to accurately extract the features of strong and complicated time-varying fluorine chemical process, and to further precisely predict the quality of products to improve the performance of the industrial control system. The performance of ACNN was strongly proved by the application in quality prediction of the fluorine chemical process located in East China. The application of it in the TE (Tennessee Eastman) benchmark also proved its generalization in the applications on other chemical processes. The results showed that the accuracy of ACNN was higher for strong time-varying or long time-span fluctuations as compared to the conventional methods. © 2022, Chemical Industry Press Co., Ltd. All right reserved.
引用
收藏
页码：593 / 600
页数：7
相关论文
共 22 条
  • [1] ALBERGARIA J T, MARTINS F G, ALVIM-FERRAZ M C M, Et al., Multiple linear regression and artificial neural networks to predict time and efficiency of soil vapor extraction, Water, Air, & Soil Pollution, 225, 8, pp. 1-9, (2014)
  • [2] MEMARIAN A, VARANASI S K, HUANG B., Mixture robust semi-supervised probabilistic principal component regression with missing input data, Chemometrics and Intelligent Laboratory Systems, 214, (2021)
  • [3] SUGIYAMA M, IDE T, NAKAJIMA S, Et al., Semi-supervised local Fisher discriminant analysis for dimensionality reduction, Machine Learning, 78, 1, pp. 35-61, (2010)
  • [4] KANEKO H, ARAKAWA M, FUNATSU K., Erratum for "development of a new regression analysis method using independent component analysis, Journal of Chemical Information and Modeling, 53, 11, (2013)
  • [5] ADEBIYI O A, CORRIPIO A B., Dynamic neural networks partial least squares (DNNPLS) identification of multivariable processes, Computers & Chemical Engineering, 27, 2, pp. 143-155, (2003)
  • [6] WILSON D J H, IRWIN G W, LIGHTBODY G., Nonlinear PLS using radial basis functions, Transactions of the Institute of Measurement and Control, 19, 4, pp. 211-220, (1997)
  • [7] XIE Y, ZHANG Y W, JIA Q L, Et al., Fault detection based on probabilistic kernel partial least square regression for industrial processes, Journal of Chemical Engineering of Japan, 51, 1, pp. 89-99, (2018)
  • [8] HINTON G E, SALAKHUTDINOV R R., Reducing the dimensionality of data with neural networks, Science, 313, 5786, pp. 504-507, (2006)
  • [9] HINTON G E, OSINDERO S, TEH Y W., A fast learning algorithm for deep belief nets, Neural Computation, 18, 7, pp. 1527-1554, (2006)
  • [10] ZHANG W, LI C H, PENG G L, Et al., A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load, Mechanical Systems and Signal Processing, 100, pp. 439-453, (2018)
123