Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization

Würth, Lynn; Hannemann, Ralf; Marquardt, Wolfgang

doi:10.1016/j.jprocont.2009.02.001

Cited by 78 publications

(37 citation statements)

References 38 publications

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“…The idea is to recompute the optimal sensitivities F (t) online after solving the current NLP and then apply the approach shown in Fig 8.1 in between two successive optimizations. Similar idea has been published in Würth et al (2009) where the authors proposed to use sensitivity based neighbouring-extremal updates combined with real-time optimization. In this way, the frequency of optimizations can be greatly reduced.…”

Section: Discussionmentioning

confidence: 89%

“…(2011). Similar idea has been published in Würth et al (2009) where the authors proposed to use sensitivity based neighbouring-extremal updates combined with real-time optimization. In this way, the frequency of optimizations can be greatly reduced.…”

Section: Remarkmentioning

confidence: 89%

“…Remark 5 The optimal sensitivity has been used recently to compute online fast optimal control updates in the context of nonlinear model predictive control (as for example in Zavala and Biegler (2009)) and real time dynamic optimization as in Würth et al (2009). However, in both cases the proposed methods required measurement or estimation of the disturbance/model uncertainty ∆d.…”

Section: Remarkmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic online optimization of a house heating system in a fluctuating energy price scenario

Oliveira

Jäschke

Skogestad

2013

IFAC Proceedings Volumes

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SummaryThe main focus of this thesis is to find implementation strategies for the optimal operation of processes during transients. That is, we do not focus on the algorithms to solve a given dynamic optimization problem, but on how to implement the solution in practice using control. The underlining theme is based on the general idea of finding feedback policies that give acceptable performance even in the presence of model uncertainties and disturbances. By 'acceptable' we mean that important constraints should always be satisfied and the economic performance should be near the optimal. In this thesis we considered different classes of applications, each one with their own particularities and challenges.The first part of the work deals with the optimal operation of thermal energy storage systems. We consider the optimal operation of energy storage in buildings with focus on the optimization of an electric water heating system. The optimization objective is to minimize the energy costs of heating the water, with the requirement that we should satisfy the uncertain demand at any time. The main complications in this problem are the time varying nature of the electricity price and the unpredictability of the future water demand. First, we present a detailed problem formulation which may also be suitable similar problems. Many insights into the optimization problem formulation are given and guidelines on implementation strategies including feedback control structures are proposed.Next, we use the hot water system as an example to illustrate our proposed implementation strategy based on hierarchical decomposition of the optimization-control problem. In our approach, economic objectives and control objectives are decoupled using a two-layer cascade feedback structure. We show that the decomposed optimization problem can be written as a simple linear program (LP) which can be solved very efficiently. The main result is that great economical benefits can be obtained at a very low computational cost and suitable for low cost embedded hardware.Part two of the thesis is dedicated to an intelligent anti-slugging coni Summary trol system for offshore oil production maximization. Existing anti-slug control systems are not robust and tend to become unstable after some time, because of inflow disturbances or plant dynamic changes, thus, requiring constant supervision and retuning. A second problem is the fact that the ideal setpoint is unknown and we could easily choose a suboptimal or infeasible operating point. Here we present a method to tackle these problems. Our complete control solution is composed of an autonomous supervisor that seeks to maximize production by manipulating a pressure setpoint and a robust adaptive controller that is able to quickly identify and adapt to changes in the plant. Our proposed solution has been tested in a experimental rig and the results are very encouraging. An analysis of the robustness and optimality of different linear controllers for slug mitigation is also carried out in this part of ...

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Section: Discussionmentioning

confidence: 89%

Section: Remarkmentioning

confidence: 89%

Section: Remarkmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic online optimization of a house heating system in a fluctuating energy price scenario

Oliveira

Jäschke

Skogestad

2013

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…We test the different MA-based algorithms discussed in the previous sections on the problem of run-to run optimization of the Williams-Otto semi-batch reactor described in Würth et al (2009). The following reactions take place:…”

Section: Simulation Studymentioning

confidence: 99%

“…The manipulated variables for this process are the time-varying profiles F Bin (t) and T w (t). The model equations and the parameter values used in this study can be found in Würth et al (2009). The economic objective is to maximize the yield of the desired products at the final time t f .…”

Section: Simulation Studymentioning

confidence: 99%

Improved Directional Derivatives for Modifier-Adaptation Schemes

et al. 2017

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Abstract:Modifier 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.

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Real‐time economic model predictive control of nonlinear process systems

Ellis

Christofides

2014

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com) Closed-loop stability of nonlinear systems under real-time Lyapunov-based economic model predictive control (LEMPC) with potentially unknown and time-varying computational delay is considered. To address guaranteed closed-loop stability (in the sense of boundedness of the closed-loop state in a compact state-space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite-time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed-loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed-loop stability under the real-time LEMPC strategy is analyzed and specific stability conditions are derived. The real-time LEMPC scheme is applied to a chemical process network example to demonstrate closed-loop stability and closed-loop economic performance improvement over that achieved for operation at the economically optimal steady state. V C 2014 American Institute of Chemical Engineers AIChE J, 61: [555][556][557][558][559][560][561][562][563][564][565][566][567][568][569][570][571] 2015

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Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization

Cited by 78 publications

References 38 publications

Dynamic online optimization of a house heating system in a fluctuating energy price scenario

Dynamic online optimization of a house heating system in a fluctuating energy price scenario

Improved Directional Derivatives for Modifier-Adaptation Schemes

Real‐time economic model predictive control of nonlinear process systems

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