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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 , 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 , Adaptation strategies for tracking constraints under plant-model mismatchOptimal operating conditions for a process plant are typically obtained via model-based optimization. However, due to modeling errors, the operating conditions found are often sub-optimal or, worse, they can violate critical process constraints. Hence, model corrections become a necessity and are done by exploiting measured process data. To this end, either model parameters are adapted and/or correction terms are added to the model-based optimization problem. The modifier-adaptation methodology does the latter by adding bias and gradient correction terms that are called modifiers. The role of modifiers and model parameters are often seen as competing, and which one of the two is better suited to track the optimality conditions is an open problem. This paper attempts to shed light on finding a synergy between the model parameters and the modifiers in the case when tracking constraints is sufficient for near-optimal performance. We demonstrate through the simulation study of a batch-to-batch optimization problem that a set of model parameters can be selected that mirror the role of modifiers. The modifiers are then added only when there is insufficient number of mirror parameters for independent constraint tracking.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Improved directional derivatives for modifier-adaptation schemesModifier adaptation enables the real-time optimization (RTO) of plant operation in the presence of considerable plant-model mismatch. For this, modifier adaptation requires the estimation of plant gradients, which is experimentally expensive as this might involves several online experiments. Recently, a directional modifier-adaptation approach has been proposed, which uses the process model to compute offline a subset of input directions that are critical for plant optimization. This allows estimating directional derivatives only in the critical directions instead of full gradients, thereby reducing the burden of gradient estimation. However, in certain cases such as change of active constraints and large parametric uncertainties, directional modifier adaptation may lead to significant suboptimality. Here, we propose an extension to directional modifier adaptation, whereby, at each RTO iteration, we compute a set of critical directions that are robust to large parametric perturbations. We draw upon a simulation study of the run-to-run optimization of the Williams-Otto semi-batch reactor to illustrate the performance of the proposed extension.Publicationtype: Conference Paper5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Real-Time optimization based on adaptation of surrogate modelsRecently, different real-time optimization (RTO) schemes that guarantee feasibility of all RTO iterates and monotonic convergence to the optimal plant operating point have been proposed. However, simulations reveal that these schemes converge very slowly to the plant optimum, which may be prohibitive in applications. This note proposes an RTO scheme based on second-order surrogate models of the objective and the constraints, which enforces feasibility of all RTO iterates, i.e., plant constraints are satisfied at all iterations. In order to speed up convergence, we suggest an online adaptation strategy of the surrogate models that is based on trust-region ideas. The efficacy of the proposed RTO scheme is demonstrated in simulations via both a numerical example and the steady-state optimization of the Williams-Otto reactor.Publicationtype: Conference Paper8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On handling cost gradient uncertainty in real-time optimizationThis paper deals with the real-time optimization of uncertain plants and proposes an approach based on surrogate models to reach the plant optimum when the plant cost gradient is imperfectly known. It is shown that, for processes with only box constraints, the optimum is reached upon convergence if the multiplicative gradient uncertainty lies within some bounded interval. For the case of general constraints, conditions are derived that guarantee plant feasibility and, in principle, allow enforcing cost decrease at each iteration.Publicationtype: Conference Paper7