Publications associées à Validation of a Solution Model for the Optimization of a Binary Batch Distillation Column

Backtracking Dynamical Cavity Method

Freya Behrens, Lenka Zdeborová, Barbora Hudcová

The cavity method is one of the cornerstones of the statistical physics of disordered systems such as spin glasses and other complex systems. It is able to analytically and asymptotically exactly describe the equilibrium properties of a broad range of mode ...

American Physical Society2023

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Colin Neil Jones, Yuning Jiang, Wenjie Xu, Bratislav Svetozarevic

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

IEEE2023

On the relaxed maximum-likelihood blind MIMO channel estimation for orthogonal space-time block codes

Kamran Kalbasi

This paper concerns the maximum-likelihood channel estimation for MIMO systems with orthogonal space-time block codes when the finite alphabet constraint of the signal constellation is relaxed. We study the channel coefficients estimation subspace generate ...

ELSEVIER2020

OPF-based under frequency load shedding predicting the dynamic frequency trajectory

Mario Paolone, Quentin Walger, Asja Derviskadic, Guglielmo Frigo, Yihui Zuo

The paper describes a centralized Under Frequency Load Shedding (UFLS) method, where load shedding decisions are based on the solution of an optimization problem. The proposed approach anticipates the evolution of the grid frequency trajectory by means of ...

Elsevier2020

On reducing the number of decision variables for dynamic optimization

Dominique Bonvin, Diogo Filipe Mateus Rodrigues

This paper presents an input parameterization for dynamic optimization that allows reducing the number of decision variables compared to traditional direct methods. A small number of decision variables is likely to be beneficial for various applications su ...

2020

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...

EPFL2018

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming

Peyman Mohajerin Esfahani, Daniel Kuhn, John Lygeros, Tobias Sutter

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex programs ...

2018

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Damian Mateusz Straszak

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

EPFL2018

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dominique Bonvin, Predrag Milosavljevic, René Uwe Schneider

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Real-Time Optimization via Directional Modifier Adaptation, with Application to Kite Control

Sean Costello

The steady advance of computational methods makes model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate. The traditional remedy is to update the model parameters, but th ...

EPFL2015

On the Use of Transient Information for Static Real-Time Optimization

Dominique Bonvin, Grégory François, Alejandro Gabriel Marchetti

Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...

2015

Optimal energy dissipation in growing microorganisms and rectification columns

Urs von Stockar

This paper proposes a new point of view in analyzing the optimal Gibbs energy dissipation in growing microorganisms. Small Gibbs energy dissipation in growth would be of biological advantage because less resource is consumed and the biomass yield on these ...

De Gruyter2014

On the Role of the Necessary Conditions of Optimality in Structuring Dynamic Real-Time Optimization Schemes

Dominique Bonvin

In dynamic optimization problems, the optimal input profiles are typically obtained using models that predict the system behavior. In practice, however, process models are often inaccurate, and on-line adaptation is required for appropriate prediction and ...

Elsevier2013

On the Use of Models for Real-Time Optimization

Dominique Bonvin

The operation of dynamic processes can be optimized using models that predict the system behavior well, in particular its optimality features. In practice, however, process models are often structurally inaccurate, and on-line adaptation is typically requi ...

2012

On the Role of Constraints in Optimization under Uncertainty

Saurabh Anand Deshpande

This thesis addresses the problem of industrial real-time process optimization that suffers from the presence of uncertainty. Since a process model is typically used to compute the optimal operating conditions, both plant-model mismatch and process disturb ...

EPFL2012

Lennard-Jones binary mixture in disordered matrices: exploring the mode coupling scenario at increasing confinement

Pascal Gallo

We present results of molecular dynamics simulations performed on a Lennard-Jones liquid binary mixture confined in matrices of soft spheres at increasing packing fraction. We study the dynamical properties of the liquid at a given density upon supercoolin ...

2011

Modifier Adaptation for Run-to-Run Optimization of Transient Processes

Dominique Bonvin, Grégory François, Sean Costello

Dynamic optimization can be used to determine optimal input profiles for dynamic processes. Due to plant-model mismatch and disturbances, the optimal inputs determined through model-based optimization will, in general, not be optimal for the plant. Modifie ...

2011

A Globally Convergent Algorithm for the Run-to-Run Control of Systems with Sector Nonlinearities

Dominique Bonvin, Grégory François

Run-to-run control is a technique that exploits the repetitive nature of processes to iteratively adjust the inputs and drive the run-end outputs to their reference values. It can be used to control both static and finite-time dynamic systems. Although the ...

American Chemical Society2011

Neighboring-Extremal Control for Singular Dynamic Optimization Problems. II- Multiple-Input Systems

Sébastien Gros, Dominique Bonvin, Benoît Chachuat

Dynamic optimization provides a unified framework for improving process operations while taking operational constraints into account. In the presence of uncertainty, measurements can be incorporated into the optimization framework for tracking the optimum. ...

2009