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 …

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 …

We report the use of a soft sensor ensemble based on recursive partial least squares with a
large number of overlapping models. The proposed method uses process memory …

Local learning based soft sensing methods succeed in coping with time-varying
characteristics of processes as well as nonlinearities in industrial plants. In this paper, a …

To improve the predictive ability of soft sensors in chemical and industrial plants, the
selection of process variables and consideration of dynamics in the processes have been …

The predictive ability of soft sensors, which estimate values of an objective variable y online,
decreases due to process changes in chemical plants. To reduce the decrease of predictive …

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 …

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 …

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 …

Most traditional soft sensors are built offline and only to be used online. In modern industrial
processes, the operation condition is changed frequently. For these time-varying processes …