[HTML][HTML] Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit
HK Mohanta, AK Pani - Petroleum Science, 2021 - Elsevier
Prediction of primary quality variables in real time with adaptation capability for varying
process conditions is a critical task in process industries. This article focuses on the …
process conditions is a critical task in process industries. This article focuses on the …
Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
HK Mohanta, AK Pani - Applied Soft Computing, 2022 - Elsevier
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 …
A comparative study of adaptive soft sensors for quality prediction in an industrial refining hydrocracking process
X Yuan, J Zhou, Y Wang - 2018 IEEE 7th Data Driven Control …, 2018 - ieeexplore.ieee.org
Soft sensors have played indispensable roles in modern refining industry, which can provide
significant information for process modeling, control, monitoring and optimization. However …
significant information for process modeling, control, monitoring and optimization. However …
Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes
J Wang, K Qiu, Y Guo, R Wang… - The Canadian Journal of …, 2021 - Wiley Online Library
The online measurement of key quality variables based on soft sensors plays a critical role
in ensuring the safety and stability of batch processes. Recently, the relevant vector machine …
in ensuring the safety and stability of batch processes. Recently, the relevant vector machine …
A unified just-in-time learning paradigm and its application to adaptive soft sensing for nonlinear and time-varying chemical process
In traditional just-in-time learning (JITL)-based adaptive soft sensors for complex chemical
process, relevant sample selection and local model construction are carried out separately …
process, relevant sample selection and local model construction are carried out separately …
A deep learning just-in-time modeling approach for soft sensor based on variational autoencoder
F Guo, R Xie, B Huang - Chemometrics and Intelligent Laboratory Systems, 2020 - Elsevier
This paper presents a variational autoencoder-based just-in-time (JIT) learning framework
for soft sensor modeling. Just-in-Time learning is often applied for soft sensor modeling in …
for soft sensor modeling. Just-in-Time learning is often applied for soft sensor modeling in …
Soft sensor framework based on semisupervised just-in-time relevance vector regression for multiphase batch processes with unlabeled data
K Qiu, J Wang, X Zhou, Y Guo… - Industrial & Engineering …, 2020 - ACS Publications
Soft sensors using just-in-time learning (JITL) have attracted much attention in the
application of online prediction in batch processes because of the ability to perform adaptive …
application of online prediction in batch processes because of the ability to perform adaptive …
A mutual information-based Variational Autoencoder for robust JIT soft sensing with abnormal observations
F Guo, B Huang - Chemometrics and Intelligent Laboratory Systems, 2020 - Elsevier
Considering industrial process with high-dimensional, intrinsic nonlinearities and possibly
abnormal observations, a robust deep learning soft sensor model is developed under the …
abnormal observations, a robust deep learning soft sensor model is developed under the …
Variable-scale probabilistic just-in-time learning for soft sensor development with missing data
Just-in-time learning (JITL) has been widely applied to data-driven modeling to deal with the
nonlinearity problems in industrial processes. To mitigate the effects of noise existing in JITL …
nonlinearity problems in industrial processes. To mitigate the effects of noise existing in JITL …
Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes
Abstract Just-in-time (JIT) learning based soft sensors have been widely used for predicting
product quality variables of nonlinear processes. They dynamically build online local models …
product quality variables of nonlinear processes. They dynamically build online local models …