Recursive Gaussian process regression model for adaptive quality monitoring in batch processes
In chemical batch processes with slow responses and a long duration, it is time‐consuming
and expensive to obtain sufficient normal data for statistical analysis. With the persistent …
and expensive to obtain sufficient normal data for statistical analysis. With the persistent …
Recursive Gaussian mixture models for adaptive process monitoring
Gaussian mixture models (GMM) have recently been introduced and widely used for
process monitoring. This paper intends to develop a new recursive GMM model for adaptive …
process monitoring. This paper intends to develop a new recursive GMM model for adaptive …
Online predictive monitoring and prediction model for a periodic process through multiway non-Gaussian modeling
C Yoo, M Kim, S Hwang, Y Jo, J Oh - Chinese Journal of Chemical …, 2008 - Elsevier
A new on-line predictive monitoring and prediction model for periodic biological processes
is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is …
is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is …
Nonlinear multiphase batch process monitoring and quality prediction using multi-way concurrent locally weighted projection regression
Y Zhang, J Cao, X Zhao, Y Hui - Chemometrics and Intelligent Laboratory …, 2023 - Elsevier
The batch process has the characteristics of nonlinear and multiphase due to variation
operation conditions. Nonlinear and multiphase modeling of the batch process is very …
operation conditions. Nonlinear and multiphase modeling of the batch process is very …
A supervised adaptive resampling monitoring method for quality indicator in time-varying process
Y Tao, H Shi, B Song, X Zhou… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In real-world industrial process, the system state is time-varying due to the changeable
operating environment, and thus, a steady monitoring model is unsuitable in such a context …
operating environment, and thus, a steady monitoring model is unsuitable in such a context …
Identification of robust Gaussian Process Regression with noisy input using EM algorithm
In the traditional formulation of Gaussian Process Regression (GPR), the input data is
assumed to be noise-free. However, this assumption is not always realistic in many practical …
assumed to be noise-free. However, this assumption is not always realistic in many practical …
Sequential local-based Gaussian mixture model for monitoring multiphase batch processes
To address the incapability of using a single model to monitor multiphase batch processes
with varying characteristics in different phases, a sequential local-based Gaussian mixture …
with varying characteristics in different phases, a sequential local-based Gaussian mixture …
Adaptive soft sensor development based on online ensemble Gaussian process regression for nonlinear time-varying batch processes
H Jin, X Chen, L Wang, K Yang… - Industrial & Engineering …, 2015 - ACS Publications
Traditional soft sensors may be ill-suited for batch processes because they cannot efficiently
handle process nonlinearity and/or time-varying changes as well as provide the prediction …
handle process nonlinearity and/or time-varying changes as well as provide the prediction …
Enhanced process comprehension and statistical analysis for slow-varying batch processes
Under the influence of various exterior factors, batch processes commonly involve normal
slow variations over batches, in which the changing underlying behaviors make their …
slow variations over batches, in which the changing underlying behaviors make their …
Transfer learning based on incorporating source knowledge using Gaussian process models for quick modeling of dynamic target processes
To maintain optimum economic process performance, a good process model is the
cornerstone of an optimal scheduling strategy and controller design. Up to now, approaches …
cornerstone of an optimal scheduling strategy and controller design. Up to now, approaches …