The machine learning life cycle in chemical operations–status and open challenges
M Gärtler, V Khaydarov, B Klöpper… - Chemie Ingenieur …, 2021 - Wiley Online Library
Artificial intelligence (AI) has received a lot of attention with many publications in recent
years. Interestingly related projects in the industry are mostly still in their early stages. We …
years. Interestingly related projects in the industry are mostly still in their early stages. We …
Attention-based interval aided networks for data modeling of heterogeneous sampling sequences with missing values in process industry
In complex process industries, multivariate time sequences are omnipresent, whose
nonlinearities and dynamics present two major challenges for soft sensing of important …
nonlinearities and dynamics present two major challenges for soft sensing of important …
A multimode mechanism-guided product quality estimation approach for multi-rate industrial processes
Discrete and delayed laboratory analyses of product quality restrict the operational
optimization of industrial processes. However, it is challenging to build an accurate online …
optimization of industrial processes. However, it is challenging to build an accurate online …
Fuzzy stochastic configuration networks for nonlinear system modeling
K Li, J Qiao, D Wang - IEEE Transactions on Fuzzy Systems, 2023 - ieeexplore.ieee.org
This article proposes a novel randomized neuro-fuzzy model called fuzzy stochastic
configuration networks (F-SCNs), which integrates the Takagi–Sugeno (T–S) fuzzy inference …
configuration networks (F-SCNs), which integrates the Takagi–Sugeno (T–S) fuzzy inference …
A deep residual PLS for data-driven quality prediction modeling in industrial process
Partial least squares (PLS) model is the most typical data-driven method for quality-related
industrial tasks like soft sensor. However, only linear relations are captured between the …
industrial tasks like soft sensor. However, only linear relations are captured between the …
ConvLSTM and self-attention aided canonical correlation analysis for multioutput soft sensor modeling
The polymerization process produces industrially important products; hence, its monitoring
and control are of paramount importance. However, the nonavailability of real-time (on …
and control are of paramount importance. However, the nonavailability of real-time (on …
Active fault isolation of over-actuated systems based on a control allocation approach
F Cao, Z Zhang, X He - IEEE Transactions on Instrumentation …, 2022 - ieeexplore.ieee.org
Active fault diagnosis is one of the latest frontiers in the field of fault diagnosis, which can
improve fault diagnosis performance by redesigning the control input for specific faults …
improve fault diagnosis performance by redesigning the control input for specific faults …
Distributed robust process monitoring based on optimized denoising autoencoder with reinforcement learning
S Chen, Q Jiang - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
Global monitoring for complex large-scale chemical processes is often challenging because
of complex correlations among variables. This article proposes an optimized denoising …
of complex correlations among variables. This article proposes an optimized denoising …
Multirate-former: an efficient transformer-based hierarchical network for multi-step prediction of multirate industrial processes
Due to the limitations of measurement technology and cost in industrial processes, it is
difficult to obtain measured values of variables with different properties, such as flow rate …
difficult to obtain measured values of variables with different properties, such as flow rate …
Deep nonlinear dynamic feature extraction for quality prediction based on spatiotemporal neighborhood preserving SAE
Complex industrial process data often exhibit nonlinear static and dynamic characteristics.
Traditional deep learning methods such as stacked autoencoder (SAE) have excellent …
Traditional deep learning methods such as stacked autoencoder (SAE) have excellent …