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 …

This article presents an improved batch-to-batch optimisation technique that is shown to be
able to bring the yield closer to its set-point from one batch to the next. In addition, an …

The intuitive and simple ideas that support model predictive control (MPC) along with its
capabilities have been the key to its success both in industry and academia. The …

This paper presents an innovative optimisation technique, which utilises an adaptive
Multiway Partial Least Squares (MPLS) model to track the dynamics of a batch process from …

In this work, the problem of driving a batch process to a desired final product quality using
data-driven model based midcourse correction (MCC) is described. Specifically, we adapt a …

The performance of the batch trajectory tracking algorithm is affected by batch-to-batch
variation, which includes irregular phase transitions and batch durations. The latent variable …

In this paper, the design of predictive functional control (PFC) based on non-minimal state
space models is proposed for nonminimum phase processes referred to as inverse …

In order to meet tight product quality specifications for chemical batch processes, it is vital to
monitor and control product quality throughout the batch duration. However, the frequent …

Abstract The University of Manchester: Carlos Alberto Duran Villalobos: Doctor of
Philosophy in the Faculty of Engineering and Physical Sciences: December 2015This thesis …

A new methodology to identify models in a pseudo-state space form for batch/fed-batch
processes is proposed. The methodology employs historical data from previous batch runs …