Publications by Tafarel de Avila Ferreira

Modifier Adaptation as a Feedback Control Scheme

Dominique Bonvin, Alejandro Gabriel Marchetti, Sean Costello, Tafarel de Avila Ferreira

As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative techn ...

AMER CHEMICAL SOC2020

Enforcing optimal operation in solid-oxide fuel-cell systems

Dominique Bonvin, Jan Van Herle, Christophe Salzmann, Timm Faulwasser, Tafarel de Avila Ferreira, Zacharie Wuillemin

This paper describes an optimization strategy for operating solid-oxide fuel-cell systems at optimal efficiency. Specifically, we present the experimental validation of a real-time optimization (RTO) strategy applied to a commercial solid-oxide fuel-cell s ...

2019

Real-Time Optimization of Solid Oxide Fuel Cell Systems : Methodology and Implementation

Tafarel de Avila Ferreira

Optimization of industrial processes aims at minimizing operating cost or maximizing economic profit while respecting plant constraints. In process industry, real-time optimization (RTO) is often considered to ensure optimal plant operation and constraint ...

EPFL2019

Real-Time Optimization of Uncertain Process Systems via Modifier Adaptation and Gaussian Processes

Dominique Bonvin, Colin Neil Jones, Timm Faulwasser, Harsh Ambarishkumar Shukla, Tafarel de Avila Ferreira

In the context of static real-time optimization, the use of measurements allows dealing with uncertainty in the form of plant-model mismatch and disturbances. Modifier adaptation (MA) is a measurement-based scheme that uses first-order corrections to the m ...

IEEE2018

Use of Transient Measurements for Static Real-Time Optimization

Dominique Bonvin, Grégory François, Alejandro Gabriel Marchetti, Tafarel de Avila Ferreira

Modifier adaptation (MA) is a real-time optimization (RTO) method characterized by its ability to enforce plant optimality upon convergence despite the presence of model uncertainty. The approach is based on correcting the available model using gradient es ...

2017