Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models
R Grbić, D Slišković, P Kadlec - Computers & chemical engineering, 2013 - Elsevier
Linear models can be inappropriate when dealing with nonlinear and multimode processes,
leading to a soft sensor with poor performance. Due to time-varying process behaviour it is …
leading to a soft sensor with poor performance. Due to time-varying process behaviour it is …
Online soft sensor design using local partial least squares models with adaptive process state partition
We propose a soft sensing method using local partial least squares models with adaptive
process state partition, referring to as the LPLS-APSP, which is capable of effectively …
process state partition, referring to as the LPLS-APSP, which is capable of effectively …
Application of online support vector regression for soft sensors
H Kaneko, K Funatsu - AIChE Journal, 2014 - Wiley Online Library
Soft sensors have been widely used in chemical plants to estimate process variables that
are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive …
are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive …
Auto-switch Gaussian process regression-based probabilistic soft sensors for industrial multigrade processes with transitions
Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …
Development of adaptive soft sensor based on statistical identification of key variables
MD Ma, JW Ko, SJ Wang, MF Wu, SS Jang… - Control Engineering …, 2009 - Elsevier
An adaptive data-driven soft sensor is derived based on a systematic key variables selection
of a process system. The key variables are captured using the statistical approach of …
of a process system. The key variables are captured using the statistical approach of …
Dynamic probabilistic latent variable model for process data modeling and regression application
Z Ge, X Chen - IEEE Transactions on Control Systems …, 2017 - ieeexplore.ieee.org
Dynamic and uncertainty are two main features of the industrial process data which should
be paid attention when carrying out process data modeling and analytics. In this paper, the …
be paid attention when carrying out process data modeling and analytics. In this paper, the …
Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants
H Kaneko, K Funatsu - Chemometrics and Intelligent Laboratory Systems, 2014 - Elsevier
A soft sensor predicts the values of some process variable y that is difficult to measure. To
maintain the predictive ability of a soft sensor model, adaptation mechanisms are applied to …
maintain the predictive ability of a soft sensor model, adaptation mechanisms are applied to …
Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size
H Kaneko, K Funatsu - Computers & chemical engineering, 2013 - Elsevier
Soft sensors have been widely used in chemical plants to estimate process variables that
are difficult to measure online. One crucial difficulty of soft sensors is that predictive accuracy …
are difficult to measure online. One crucial difficulty of soft sensors is that predictive accuracy …
Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models
W Shao, X Tian - Chemical Engineering Research and Design, 2015 - Elsevier
This paper proposes an adaptive soft sensing method based on selective ensemble of local
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …
Data-driven soft sensor approach for online quality prediction using state dependent parameter models
The goal of this paper is to design and implementation of a new data-driven soft sensor that
uses state dependent parameter (SDP) models to improve product quality monitoring. The …
uses state dependent parameter (SDP) models to improve product quality monitoring. The …