[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 …
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 …
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 …
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 variable selection method for soft sensor development through mixed integer quadratic programming
W Jian, L Zhu, Z Xu, X Chen - Chemometrics and Intelligent Laboratory …, 2017 - Elsevier
Soft sensors are widely employed in industry to predict quality variables, which are difficult to
measure online, by using secondary variables. To build an accurate soft sensor, a proper …
measure online, by using secondary variables. To build an accurate soft sensor, a proper …
Input selection methods for soft sensor design: A survey
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-
time estimation of hard-to-measure variables as a function of available data obtained from …
time estimation of hard-to-measure variables as a function of available data obtained from …
Applicability domain of soft sensor models based on one‐class support vector machine
H Kaneko, K Funatsu - AIChE Journal, 2013 - Wiley Online Library
Soft sensors are widely used to estimate process variables that are difficult to measure
online. By using soft sensors, analyzer faults can be detected by estimation errors. However …
online. By using soft sensors, analyzer faults can be detected by estimation errors. However …
A systematic approach for soft sensor development
B Lin, B Recke, JKH Knudsen, SB Jørgensen - Computers & chemical …, 2007 - Elsevier
This paper presents a systematic approach based on robust statistical techniques for
development of a data-driven soft sensor, which is an important component of the process …
development of a data-driven soft sensor, which is an important component of the process …
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 …