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 …

This work aims at comparing several features of Principal Component Analysis (PCA) and
Partial Least Squares Regression (PLSR), as techniques typically utilized for modeling …

The purpose of this paper is to provide a summary of the current state of the application of
process systems engineering tools in the pharmaceutical industry. In this paper, we present …

REVIEW an earlier paper of special significance is referenced. The key aspects of this
review include advances in measurement technologies that are applicable for at-line or …

This work addresses one aspect of the overparameterization problem in using
artificial/recurrent neural networks (ANN/RNN) based dynamic models for model predictive …

This work addresses the problem of implementing model predictive control (MPC) in
situations where the training data available for modeling contains possible correlations, and …

In this paper we propose a parallel coordinate descent algorithm for solving smooth convex
optimization problems with separable constraints that may arise, eg in distributed model …

Background: Adaptive model predictive control (MPC) algorithms that recursively update the
glucose prediction model are shown to be promising in the development of fully automated …

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 …