Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …

With decades of successful application of model predictive control (MPC) to industrial
processes, practitioners are now focused on ease of commissioning, monitoring, and …

Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

In industrial process control, some product qualities and key variables are always difficult to
measure online due to technical or economic limitations. As an effective solution, data …

Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …

The nonlinear time-varying characteristics of the process industry can be modeled using
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …

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 …

In many industrial plants, development and implementation of advanced monitoring and
control techniques require real-time measurement of process quality variables. However, on …