A hybrid data-driven and mechanistic model soft sensor for estimating CO2 concentrations for a carbon capture pilot plant
Integrating post-combustion carbon capture and storage (CCS) facilities into fossil fuel
power plants is considered an important step for reaching global carbon emission reduction …
power plants is considered an important step for reaching global carbon emission reduction …
Local parameter optimization of LSSVM for industrial soft sensing with big data and cloud implementation
Due to the advantages of high prediction accuracy, least squares support vector machine
(LSSVM) has been widely utilized for soft sensor developments in industrial processes. The …
(LSSVM) has been widely utilized for soft sensor developments in industrial processes. The …
[HTML][HTML] Data-driven soft sensors targeting heat pump systems
The development of smart sensors, low cost communication, and computation technologies
enables continuous monitoring and accumulation of tremendous amounts of data for heat …
enables continuous monitoring and accumulation of tremendous amounts of data for heat …
Noise-tolerant co-trained semisupervised soft sensor model for industrial process
Q Lei, H Wang - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Soft sensors are often used to obtain variables that are difficult to directly measure in an
industrial process. In this article, a co-training-based semisupervised soft sensor model is …
industrial process. In this article, a co-training-based semisupervised soft sensor model is …
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 …
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 …
Soft Sensor Modeling Method Considering Higher-Order Moments of Prediction Residuals
Traditional data-driven soft sensor methods can be regarded as an optimization process to
minimize the predicted error. When applying the mean squared error as the objective …
minimize the predicted error. When applying the mean squared error as the objective …
Soft sensor modeling based on multi-state-dependent parameter models and application for quality monitoring in industrial sulfur recovery process
Soft sensors have gained wide popularity in the industrial processes for online quality
prediction in the recent years. In the case of online deployment, it is important to incorporate …
prediction in the recent years. In the case of online deployment, it is important to incorporate …
Dynamic data reconciliation and model validation of a MEA-based CO2 capture system using pilot plant data
This work focuses on development of a “gold standard” process model for a MEA-based post-
combustion CO 2 capture process. The steady-state model includes a comprehensive …
combustion CO 2 capture process. The steady-state model includes a comprehensive …
Study of soft sensor modeling based on deep learning
Y Lin, W Yan - 2015 American Control Conference (ACC), 2015 - ieeexplore.ieee.org
Soft sensor are widely used to estimate process variables which are difficult to measure
online in industrial process control. This paper proposes a new soft sensor modeling method …
online in industrial process control. This paper proposes a new soft sensor modeling method …