For predicting the melt index (MI) in industrial polymerization processes, traditional data-
driven empirical models do not utilize the information in a large amount of the unlabeled …

For multi-grade chemical processes, often, limited labeled data are available, resulting in an
insufficient construction of reliable soft sensors for several modes. Additionally, the current …

Industrial processes with multiple operating grades have become increasingly important in
satisfying the requirements of agile manufacturing and a diversified market. However …

Multi-grade processes have played an important role in the fine chemical and polymer
industries. An integrated nonlinear soft sensor modeling method is proposed for online …

Batch processes are often characterized by inherent nonlinearity, multiplicity of operating
phases, and batch-to-batch variations, which poses great challenges for accurate and …

Data-driven techniques in industrial processes have been continually attended during the
past decades. However, there are many challenging issues in this field when the collected …

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 …

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 …

Many estimation, prediction, and learning applications have a dynamic nature. One of the
most important challenges in machine learning is dealing with concept changes. Underlying …

In industrial production, soft sensors play very important roles in ensuring product quality
and production safety. Traditionally, global modeling methods, which use historical data to …