A review on soft sensors for monitoring, control, and optimization of industrial processes
Over the past twenty years, numerous research outcomes have been published, related to
the design and implementation of soft sensors. In modern industrial processes, various types …
the design and implementation of soft sensors. In modern industrial processes, various types …
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
Data mining and analytics in the process industry: The role of machine learning
Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …
decision making/supports in the process industry over the past several decades. As a …
A novel soft sensor modeling approach based on difference-LSTM for complex industrial process
J Zhou, X Wang, C Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The main purpose of soft sensor modeling is to capture the dynamic nonlinear features
between the easy-to-measure auxiliary variables and the difficult-to-measure key variables …
between the easy-to-measure auxiliary variables and the difficult-to-measure key variables …
Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network
Industrial processes are often characterized with high nonlinearities and dynamics. For soft
sensor modelling, it is important to model the nonlinear and dynamic relationship between …
sensor modelling, it is important to model the nonlinear and dynamic relationship between …
Just-in-time based soft sensors for process industries: A status report and recommendations
Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …
available easy-to-measure variables. These sensors are typically developed using either …
Improving the performance of just-in-time learning-based soft sensor through data augmentation
Just-in-time learning (JITL) is a widely used method for online soft sensing. The limitation of
available data and the increase of sample dimensions will make the historical dataset …
available data and the increase of sample dimensions will make the historical dataset …
A probabilistic just-in-time learning framework for soft sensor development with missing data
Just-in-time learning (JITL) is one of the most widely used strategies for soft sensor modeling
in nonlinear processes. However, traditional JITL methods have difficulty in dealing with …
in nonlinear processes. However, traditional JITL methods have difficulty in dealing with …
Profitability related industrial-scale batch processes monitoring via deep learning based soft sensor development
Data-driven soft sensor technology has been widely developed to estimate quality-related
variables, while following difficulties still limit its application in batch processes, such as …
variables, while following difficulties still limit its application in batch processes, such as …
Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
Real time estimation of target quality variables using soft sensor relevant to time varying
process conditions will be a significant step forward in effective implementation of Industry …
process conditions will be a significant step forward in effective implementation of Industry …