Dominique Bonvin

Biography

Dominique Bonvin is Professor and Director of the Automatic Control Laboratory of EPFL. He received his Diploma in Chemical Engineering from ETH Zürich, and his Ph.D. degree from the University of California, Santa Barbara. He worked in the field of process control for the Sandoz Corporation in Basel and with the Systems Engineering Group of ETH Zürich. He joined the EPFL in 1989, where his current research interests include modeling, control and optimization of dynamic systems. He served as Director of the Automatic Control Laboratory for the periods 1993-97, 2003-2007 and again since 2012, Head of the Mechanical Engineering Department in 1995-97 and Dean of Bachelor and Master Studies at EPFL for the period 2004-2011.

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https://people.epfl.ch/dominique.bonvin

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A note on efficient computation of privileged directions in modifier adaptation

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Martand Singhal

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially perturbed in all input directions. To ease this gradient estimation task, it has been proposed to exploit the sensitivity of the model gradient with respect to the model parameters to find a reduced input subspace that is spanned by a few privileged directions for gradient estimation. The computation of gradient sensitivities to parametric variations requires the evaluation of double derivatives with respect to both the inputs and the parameters. In this short note, we propose an approach for computing the privileged directions using only single derivatives with respect to the inputs. We show that this approach results in significant reduction in computational costs without compromising the quality of the privileged directions. (C) 2019 Elsevier Ltd. All rights reserved.

PERGAMON-ELSEVIER SCIENCE LTD2020

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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 technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.

AMER CHEMICAL SOC2020

Real-time optimization of load sharing for gas compressors in the presence of uncertainty

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

This paper investigates the problem of load-sharing optimization of gas compressors in the presence of uncertainty. The objective is to operate a set of compressor units in an energy-efficient way, while at the same time meeting a varying load demand. The main challenge is the fact that the available models, and in particular the compressor efficiency maps, carry a significant amount of uncertainty. For this task, real-time optimization (RTO) techniques that rely on plant measurements and correct the model are available in the literature. This paper is tailored to the application of RTO to the compressor load-sharing optimization problem. An adaptive optimization approach that guarantees optimal plant operation upon convergence is used. To this end, we use appropriate measurements to estimate plant gradients and correct the model in such a way that it exhibits the same optimality conditions as the plant. This way, the challenge is shifted from having an accurate model to being able to estimate experimental gradients accurately. We show how the specific problem structure can be exploited for the purpose of efficient estimation of plant gradients. We consider both parallel and serial compressor configurations as well as operation close to surge constraints. The simulation of an industrial case study demonstrates the efficiency of the proposed approach.

ELSEVIER SCI LTD2020