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 …
Stable soft sensor modeling based on causality analysis
Data-driven soft sensors, aiming to estimate and predict hard-to-measure quality variables
using easy-to-measure process variables, have now become the key foundation for …
using easy-to-measure process variables, have now become the key foundation for …
Adaptive soft sensor modeling based on weighted supervised latent factor analysis with selectively integrated moving windows
An adaptive soft sensor modeling method based on weighted supervised latent factor
analysis is proposed. In conventional moving window based adaptive soft sensor, predictive …
analysis is proposed. In conventional moving window based adaptive soft sensor, predictive …
Soft sensor model maintenance: A case study in industrial processes
One of the challenges of utilizing soft sensors is that their prediction accuracy deteriorates
with time due to multiple factors, including changes in operating conditions. Once soft …
with time due to multiple factors, including changes in operating conditions. Once soft …
Refining data-driven soft sensor modeling framework with variable time reconstruction
Due to the difference of variable positions brought by process structure, time-delay exists
between process variables and quality variables. In this paper, this commonly overlooked …
between process variables and quality variables. In this paper, this commonly overlooked …
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 …
Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models
Data-driven soft sensors have been widely applied to a broad range of process industries for
virtually sensing difficult-to-measure but of-great-concern variables. However, it is still …
virtually sensing difficult-to-measure but of-great-concern variables. However, it is still …
A design methodology of a soft sensor based on local models
SJ Hong, JH Jung, C Han - Computers & Chemical Engineering, 1999 - Elsevier
A soft sensor is an empirical model, which estimates variables that is infeasible to measure
on-line from other correlated variables. Because constructing a soft sensor is a process of …
on-line from other correlated variables. Because constructing a soft sensor is a process of …
Adaptive soft sensor design using a regression neural network and bias update strategy for non-linear industrial processes
SV Vijayan, HK Mohanta, BK Rout… - … Science and Technology, 2023 - iopscience.iop.org
Soft sensing of quality parameters in process industries has been an active area of research
for the past two decades. To improve the performance of soft sensors in the scenario of time …
for the past two decades. To improve the performance of soft sensors in the scenario of time …
Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data
Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …