Process control has become increasingly important for the food industry since the last
decades due to its capability of increasing yield, minimizing production cost, and improving …

In this work, we present a novel, data‐driven, quality modeling, and control approach for
batch processes. Specifically, we adapt subspace identification methods for use with batch …

A data-driven predictive control methodology based on reduced Hankel matrix is proposed
in this paper. Undersome assumptions, the properties of a system can be simply and visually …

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented
for trajectory tracking in batch processes. Different from the iterative learning model …

In this work, a domain-adaption joint-Y partial least squares (JYPLS) is proposed to solve
the problem of transfer learning for end-product quality prediction of batch processes. The …

Data-driven methods have been successfully used in modern industrial production. The
sufficient data is the basis for implementing these methods. However, it is often impossible to …

This manuscript considers batch process operations and addresses the challenge of
identifying a model that synergistically captures the dynamic input–output behavior of …

This paper addresses the problem of subspace-based model identification and predictive
control of particulate process subject to uncertainty and time-varying parameters. To this …

Batch process reactors are often used for products where quality is of paramount
importance. To this end, this work addresses the problem of direct, data-driven, quality …

This paper addresses the problem of capturing the multiphase nature of a rotational molding
process using subspace identification (SSID) to enable improved control. Existing SSID …