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Item type:Publication, Access status: without files , Optimal load sharing for serial compressors via modifier adaptation(IEEE, 2018-11-27); Mercangöz, MehmetThis paper investigates the optimal load-sharing problem of serial gas compressors in the presence of plant- model mismatch. The problem is formulated as a static real- time optimization task that is solved via modifier adaptation for interconnected systems. The proposed approach guarantees optimal operation of the plant upon convergence. Furthermore, it is shown how the specific problem structure can be exploited during process operation for the efficient estimation of plant gradients with respect to local inputs. A simulated case study demonstrates the effectiveness of the proposed real-time optimization approach.Publicationtype: Conference Paper9 - 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 , Distributed modifier adaptation using a coordinator and measured interconnection variablesWe propose a distributed real-time optimization scheme for structurally uncertain systems consisting of multiple interconnected subsystems. In particular, we investigate a novel distributed variant of modifier adaptation that exploits a coordinator and knowledge of interconnection model. This way, each subsystem can compute its optimal inputs using only local information in terms of model, gradients and measurements. A coordinator ensures that the local optimal inputs computed by each subsystem are consistent with the interconnection model. In addition to ensuring confidentiality of local data, the scheme also guarantees plant optimality upon convergence. The analysis is illustrated via a simulation example.Publicationtype: Conference Paper4 - 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 , Time-optimal path-following operation in the presence of uncertaintyPath-following tasks, which refer to dynamic motion planning along pre-specified geometric references, are frequently encountered in applications such as milling, robot-supported measurements, and trajectory planning for autonomous vehicles. Different convex and non-convex optimal control formulations have been proposed to tackle these problems for the case of perfect models. This paper analyzes path-following problems in the presence of plant-model mismatch. The proposed adaptation strategies rely on concepts that are well known in the field of real-time optimization. We present conditions guaranteeing that, upon convergence, a minimumtime solution is attained despite the presence of plant-model mismatch. We draw upon a simulated robotic example to illustrate our results.Publicationtype: Conference Paper6