An intelligent non-optimality self-recovery method based on reinforcement learning with small data in big data era
Batch processes have attracted extensive attention as a crucial manufacturing way in
modern industries. Although they are well equipped with control devices, batch processes …
modern industries. Although they are well equipped with control devices, batch processes …
Enhanced process comprehension and statistical analysis for slow-varying batch processes
Under the influence of various exterior factors, batch processes commonly involve normal
slow variations over batches, in which the changing underlying behaviors make their …
slow variations over batches, in which the changing underlying behaviors make their …
Online quality prediction of industrial terephthalic acid hydropurification process using modified regularized slow-feature analysis
Purified terephthalic acid (PTA) is an important product for the polyester and textile industry.
In the industrial PTA-production process, 4-carboxybenzaldehyde (4-CBA) is a detrimental …
In the industrial PTA-production process, 4-carboxybenzaldehyde (4-CBA) is a detrimental …
A dynamic active safe semi-supervised learning framework for fault identification in labeled expensive chemical processes
X Jia, W Tian, C Li, X Yang, Z Luo, H Wang - Processes, 2020 - mdpi.com
A novel active semi-supervised learning framework using unlabeled data is proposed for
fault identification in labeled expensive chemical processes. A principal component analysis …
fault identification in labeled expensive chemical processes. A principal component analysis …
Dual learning-based online ensemble regression approach for adaptive soft sensor modeling of nonlinear time-varying processes
H Jin, X Chen, L Wang, K Yang, L Wu - Chemometrics and Intelligent …, 2016 - Elsevier
Soft sensors have been widely used to estimate difficult-to-measure variables in the process
industry. However, the nonlinear nature and time-varying behavior of many processes pose …
industry. However, the nonlinear nature and time-varying behavior of many processes pose …
Quality prediction in complex batch processes with just-in-time learning model based on non-Gaussian dissimilarity measure
In modern batch processes, soft sensors have been widely used for estimating quality
variables. However, they do not show superior prediction performance owing to the self …
variables. However, they do not show superior prediction performance owing to the self …
Transfer learning for nonlinear batch process operation optimization
This paper concerns with the JY-KPLS model based transfer learning for the operation
optimization of nonlinear batch processes. Due to problems of data insufficiency and …
optimization of nonlinear batch processes. Due to problems of data insufficiency and …
Mutual information–dynamic stacked sparse autoencoders for fault detection
J Yin, X Yan - Industrial & Engineering Chemistry Research, 2019 - ACS Publications
Data-based process monitoring is gaining increasing attention, especially deep learning
modeling methods. Given that process data are inherently dynamic, the dynamic …
modeling methods. Given that process data are inherently dynamic, the dynamic …
Adaptive soft sensor development for multi-output industrial processes based on selective ensemble learning
Soft sensors are vital for online predictions of quality-related yet difficult-to-measure
variables in process industry. In this paper, an adaptive soft sensing approach based on …
variables in process industry. In this paper, an adaptive soft sensing approach based on …
Online learning based Fisher discriminant analysis and its application for fault classification in industrial processes
J Liu, C Song, J Zhao, P Ji - Chemometrics and Intelligent Laboratory …, 2019 - Elsevier
For the industrial fault classification, there are still two important issues ignored by the typical
Fisher discriminant analysis (FDA). Firstly, because of inevitable process change, online …
Fisher discriminant analysis (FDA). Firstly, because of inevitable process change, online …