Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - II. Implementation Issues

The idea of iterative process optimization based on collected output measurements, or "real-time optimization" (RTO), has gained much prominence in recent decades, with many RTO algorithms being proposed, researched, and developed. While the essential goal of these schemes is to drive the process to its true optimal conditions without violating any safety-critical, or "hard", constraints, no generalized, unified approach for guaranteeing this behavior exists. In this two-part paper, we propose an implementable set of conditions that can enforce these properties for any RTO algorithm. This second part examines the practical side of the sufficient conditions for feasibility and optimality (SCFO) proposed in the first and focuses on how they may be enforced in real application, where much of the knowledge required for the conceptual SCFO is unavailable. Methods for improving convergence speed are also considered.Comment: 56 pages, 15 figure

Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - I. Theoretical Foundations

The idea of iterative process optimization based on collected output measurements, or "real-time optimization" (RTO), has gained much prominence in recent decades, with many RTO algorithms being proposed, researched, and developed. While the essential goal of these schemes is to drive the process to its true optimal conditions without violating any safety-critical, or "hard", constraints, no generalized, unified approach for guaranteeing this behavior exists. In this two-part paper, we propose an implementable set of conditions that can enforce these properties for any RTO algorithm. The first part of the work is dedicated to the theory behind the sufficient conditions for feasibility and optimality (SCFO), together with their basic implementation strategy. RTO algorithms enforcing the SCFO are shown to perform as desired in several numerical examples - allowing for feasible-side convergence to the plant optimum where algorithms not enforcing the conditions would fail.Comment: Working paper; supplementary material available at: http://infoscience.epfl.ch/record/18807

Quels ingénieurs pour la Suisse de demain ?

Cet article décrit l’évolution de la profession d’ingénieur de part le monde, et en Suisse en particulier. A partir de l’observation d’une société en mutation, on étudie les nouveaux défis qui se présentent à l’ingénieur et on en déduit l’impact sur le métier et la formation d’ingénieur. On aborde ensuite la relation entre la formation et le monde du travail et présente quelques actions concrètes propres à améliorer le recrutement et la formation de la prochaine génération d’ingénieurs

Optimisation en temps réel de procédés industriels, Maîtrise et optimisation de procédés industriels complexes

On aborde le problème de l’optimisation de procédés continus et discontinus en présence d’incertitudes sous forme d’erreurs de modèle et de perturbations inconnues. L’idée consiste à se baser sur des mesures du procédé pour venir ajuster les entrées. On propose d’abord, pour les procédés discontinus, un paramétrage des entrées qui permet de ramener le problème d’optimisation dynamique à un problème d’optimisation statique. On présente ensuite trois façons distinctes d’utiliser des mesures pour venir ajuster les conditions opératoires du procédé et l’amener ainsi de manière itérative en temps réel vers l’optimalité. La première approche est très intuitive et consiste à utiliser les mesures disponibles pour identifier les paramètres du modèle et calculer ensuite les entrées optimales à partir du modèle ajusté. On verra que cette façon de procéder ne permet en général pas d’amener le processus vers l’optimalité. La deuxième approche propose d’estimer certaines grandeurs expérimentales qui sont liées à l’optimalité du procédé et de corriger le modèle de manière à ce que le modèle corrigé et le procédé partagent les mêmes conditions d’optimalité. La troisième approche enfin utilise directement ces mêmes grandeurs expérimentales pour amener par rétroaction, c’est-à-dire sans optimisation numérique, le processus vers l’optimalité. Ces méthodologies sont illustrées expérimentalement sur un procédé continu (un système de piles à combustible) et un procédé discontinu (un réacteur de polymérisation batch)

Measurement-based Run-to-run Optimization of a Batch Reaction-distillation System

Measurement-based optimization schemes have been developed to deal with uncertainty and process variations. One of the methods therein, labeled NCO tracking, relies on appropriate parameterization of the input profiles and adjusts the corresponding input parameters using measurements so as to satisfy the necessary conditions of optimality (NCO). The applicability of NCO-tracking schemes has been demonstrated on several academic-size examples. The goal of this paper is to show that it can be applied with similar ease to more complex real-life systems. Run-to-run optimization of a batch reaction-separation system with propylene glycol is used for illustration

Implementation techniques for the SCFO experimental optimization framework

The material presented in this document is intended as a comprehensive, implementation-oriented supplement to the experimental optimization framework presented in a companion document. The issues of physical degradation, unknown Lipschitz constants, measurement/estimation noise, gradient estimation, sufficient excitation, and the handling of soft constraints and/or a numerical cost function are all addressed, and a robust, implementable version of the sufficient conditions for feasible-side global convergence is proposed.Comment: supplementary document; 66 page

On linear and quadratic Lipschitz bounds for twice continuously differentiable functions

Lower and upper bounds for a given function are important in many mathematical and engineering contexts, where they often serve as a base for both analysis and application. In this short paper, we derive piecewise linear and quadratic bounds that are stated in terms of the Lipschitz constants of the function and the Lipschitz constants of its partial derivatives, and serve to bound the function's evolution over a compact set. While the results follow from basic mathematical principles and are certainly not new, we present them as they are, from our experience, very difficult to find explicitly either in the literature or in most analysis textbooks.Comment: 3 pages; supplementary documen