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Item type:Publication, Access status: without files , A note on efficient computation of privileged directions in modifier adaptationIn 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.Publicationtype: Journal Article15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Enforcing optimal operation in solid-oxide fuel-cell systems(Elsevier, 2019-08-15); 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Active directional modifier adaptation for real-time optimizationModifier adaptation is a real-time optimization (RTO) methodology that uses plant gradient estimates to correct model gradients, thereby driving the plant to optimality. However, obtaining accurate gradient estimates requires costly plant experiments at each RTO iteration. In directional modifier adaptation (DMA), the model gradients are corrected only in a small subspace of the input space, thus requiring fewer plant experiments. DMA selects the input subspace offline based on the local sensitivity of the Lagrangian gradient with respect to the uncertain model parameters. Here, we propose an extension, whereby the input subspace is selected at each RTO iteration via global sensitivity analysis, thus making the approach more reactive to changes and robust to large parametric uncertainties. Simulation results performed on the run-to-run optimization of two different semi-batch reactors show that the proposed approach finds a nice balance between experimental cost and optimality.Publicationtype: Journal Article9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modifier adaptation for real-time optimization : methods and applications(Multidisciplinary Digital Publishing Institute (MDPI), 2016-12-01); ; This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.Publicationtype: Review Article4