To maintain optimum economic process performance, a good process model is the
cornerstone of an optimal scheduling strategy and controller design. Up to now, approaches …

The availability of an accurate model can facilitate tasks such as design or monitoring in
chemical industries. A well-constructed model is invaluable but it can be costly to construct …

Model-based control plays an important role in batch process control. With more data
collected online, real-time model updates using Gaussian process regression (GPR) are …

In chemical industries, many important tasks such as process design and monitoring rely on
the availability of a good model. A high-performance data-driven prediction model is desired …

Process data have a number of characteristics, such as noise, nonlinearity, process
dynamics, and autocorrelation. Ideally, adaptive soft sensors would be used to solve model …

The advantages of maximally transferring similar process data for modeling make the
process transfer model attract increasing attention in quality prediction and optimal control …

In industrial processes, quality variable prediction is important for process control and
monitoring. Deep learning (DL) methods offer excellent prediction performance and …

Abstract 1In the context of process design, control, and management, it has become
common to utilize time-series data measured in industrial plants to construct mathematical …

In this work, we generalize a previously developed data-based modeling methodology for
batch processes to account for time-varying dynamics by incorporating online learning …

In real industrial processes, new process “excitation” patterns that largely deviate from
previously collected training data will appear due to disturbances caused by process inputs …