Soft sensor model for dynamic processes based on multichannel convolutional neural network
Soft sensors have been extensively used to predict the difficult-to-measure key quality
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …
Dynamic soft sensor development based on convolutional neural networks
In industrial processes, soft sensor models are commonly developed to estimate values of
quality-relevant variables in real time. In order to take advantage of the correlations between …
quality-relevant variables in real time. In order to take advantage of the correlations between …
Deep learning based soft sensor and its application on a pyrolysis reactor for compositions predictions of gas phase components
In this work, we proposed a data-driven soft sensor based on deep learning techniques,
namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only …
namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only …
Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
Soft sensors have been extensively developed to estimate the difficult-to-measure quality
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
Novel deep learning based on data fusion integrating correlation analysis for soft sensor modeling
Accurate soft sensing modeling of complex industrial processes can provide operation
guidance for improving the product quality. However, most modeling methods cannot mine …
guidance for improving the product quality. However, most modeling methods cannot mine …
Graph convolutional network soft sensor for process quality prediction
The nonlinear time-varying characteristics of the process industry can be modeled using
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
Probabilistic sequential network for deep learning of complex process data and soft sensor application
Q Sun, Z Ge - IEEE Transactions on Industrial Informatics, 2018 - ieeexplore.ieee.org
Soft sensing of quality/key variables is critical to the control and optimization of industrial
processes. One of the main drawbacks of data-driven soft sensors is to deal with the …
processes. One of the main drawbacks of data-driven soft sensors is to deal with the …
Quality prediction modeling for industrial processes using multiscale attention-based convolutional neural network
Soft sensors have been increasingly applied for quality prediction in complex industrial
processes, which often have different scales of topology and highly coupled spatiotemporal …
processes, which often have different scales of topology and highly coupled spatiotemporal …
Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes
Z Geng, Z Chen, Q Meng, Y Han - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Industrial process data are usually time-series data collected by sensors, which have the
characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor …
characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor …
Modeling of soft sensor for chemical process
P Cao, X Luo - Ciesc Journal, 2013 - hgxb.cip.com.cn
In the commercial chemical process, many primary product variables cannot be measured
online, and soft sensor is an important means to solve this problem. Soft sensing modeling is …
online, and soft sensor is an important means to solve this problem. Soft sensing modeling is …