Soft sensor technology has found widespread application in the real-time detection of
challenging variables like product quality and key process parameters. However, changes in …

Process nonlinearity and state shifting are two of the main factors that cause poor
performance of online soft sensors. Adaptive soft sensor is a common practice to ensure …

Soft sensing of quality parameters in process industries has been an active area of research
for the past two decades. To improve the performance of soft sensors in the scenario of time …

With the increasing availability of spectral, vibrational, thermal and other sensors, the
challenge of “Big Data” in chemical processing industry is not only to analyze the data …

Soft sensors have been extensively applied for predicting difficult-to-measure quality
variables. However, industrial processes are often characterized with the nonlinearity and …

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 …

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 …

In this paper, the moving window (MW) approach is introduced to update the soft sensor
model with the latest process information, which provides powerful efficiency of tracking the …

This paper proposes a novel adaptive soft sensor modeling method based on ensemble of
regularized local finite impulse response (FIR) models, which are estimated by using the …

An efficient nonlinear just-in-time learning (JITL) soft sensor method for online modeling of
batch processes with uneven operating durations is proposed. A recursive least-squares …