Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …

Data-driven fault detection plays an important role in industrial systems due to its
applicability in case of unknown physical models. In fault detection, disturbances must be …

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 …

Although the use of chemometric methods to predict fuel quality properties has received
wide attention over the past three decades, as seen from the review included with this …

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 …

Locally weighted partial least squares (LWPLS) is a widely used just-in-time learning (JITL)
modeling algorithm for adaptive soft sensor development. In LWPLS, spatial variable …

Spectroscopic methods play an instrumental role in the implementation of the US Food and
Drug Administration outlined process analytical technology for biopharmaceutical …

Just-in-time learning (JIT) has recently gained growing popularity for soft sensor
development of nonlinear processes. However, traditional JIT methods aim to pursue a …

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 …

This work presents a new method for adaptive soft sensor development by further exploiting
just-in-time modeling framework. In the presented method, referred to as online local …