Abstract Model predictive control requires the solution of a sequence of continuous
optimization problems that are nonlinear if a nonlinear model is used for the plant. We …

Modeling and control of prototypical industrial polymerization processes are presented in
the presence of disturbances and plant‐model mismatch. The process model consists of the …

The primary purpose of this study is to develop a computationally efficient, numerically
robust method for implementing model predictive control in real-time using nonlinear …

The parameter estimation problem and its application to industrial data obtained from
polymerization processes are analyzed. The polymerization process model consists of the …

This paper discusses an algorithm for efficiently calculating the control moves for
constrained nonlinear model predictive control. The approach focuses on real-time …

Given a complex first principles model of a process, a strategy for model complexity
reduction is developed, such that the model obtained is suitable for process control. The …

We implement and test a nonlinear model predictive control (NMPC) setup for first principles
models, at the example of a copolymerization process. We address the important question of …

O processo de copolimerização do metacrilato de metila/acetato de vinila ocorre pelo
mecanismo cinético de radicais livres. Este sistema de copolimerização é interessante pois …

While models of high complexity are very useful for simulation and prediction purposes in
the process industry, they may not be adequate for controller design. This paper proposes a …

We implement and test a nonlinear model predictive control (NMPC) setup for first principles
models, at the example of a copolymerization process. We address the important question of …