On the optimization of differential‐algebraic process systems

Cuthrell, J.E.; Biegler, Lorenz T.

doi:10.1002/aic.690330804

Cited by 424 publications

(214 citation statements)

References 25 publications

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“…Computationally, the DFBA approach involves opti-mization over a given time period to obtain time-resolved flux rates and metabolite levels. The optimization problem is rendered computationally tractable by parameterizing the dynamic equations with the help of orthogonal collocation on finite elements (39). To this end, the time period of interest is divided into a finite number of intervals, named finite elements.…”

Section: Methodsmentioning

confidence: 99%

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

Kleeßen

Araújo

Fernie

et al. 2012

Journal of Biological Chemistry

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Background: There are alternative substrates to the mitochondrial respiration. Results: Data-driven model-based analysis renders predictions of alternative substrates to the mitochondrial respiration. Conclusion: Metabolomics data in conjunction with flux-based models can discriminate among hypotheses based on enzymology alone. Significance: This analysis provides a basic framework for in silico studies of alternative pathways in metabolism.

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Section: Methodsmentioning

confidence: 99%

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

Kleeßen

Araújo

Fernie

et al. 2012

Journal of Biological Chemistry

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“…The approach is similar to the direct simultaneous method used for the solution of optimal control problems (Cuthrell and Biegler [1987]), where both the control variables and the state variables are discretized.…”

Section: General Formulationmentioning

confidence: 99%

A control system for a self-reacting point absorber wave energy converter subject to constraints

Bacelli

Ringwood

Gilloteaux

2011

IFAC Proceedings Volumes

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Abstract:The problem of the maximization of the energy produced by a self reacting point absorber subject to motion restriction is addressed. The main objective is to design a control system suitable for real-time implementation. The method presented for the solution of the optimization problem is based on the approximation of the motion of the device and of the force exerted by the power take off unit by means of a linear combination of basis functions. The result is that the optimal control problem is reformulated as a non linear program where the properties of the cost function and of the constraint are affected by the choice of the basis functions. An example is described where the motion and the force are approximated using Fourier series; an optimization algorithm for the solution of the non linear program is also presented. The control system is implemented and simulated using a real sea profile measured by a waverider buoy.

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“…The simultaneous method can be used to reformulate these DAE-constrained problems by discretizing the DAE system using collocation methods [18]. As an example of the simultaneous approach, we consider the formulation for robust NMPC with expected performance as the objective.…”

Section: Efficient Optimization Via the Simultaneous Approachmentioning

confidence: 99%

Parallel Solution of Robust Nonlinear Model Predictive Control Problems in Batch Crystallization

et al. 2016

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Abstract:Representing the uncertainties with a set of scenarios, the optimization problem resulting from a robust nonlinear model predictive control (NMPC) strategy at each sampling instance can be viewed as a large-scale stochastic program. This paper solves these optimization problems using the parallel Schur complement method developed to solve stochastic programs on distributed and shared memory machines. The control strategy is illustrated with a case study of a multidimensional unseeded batch crystallization process. For this application, a robust NMPC based on min-max optimization guarantees satisfaction of all state and input constraints for a set of uncertainty realizations, and also provides better robust performance compared with open-loop optimal control, nominal NMPC, and robust NMPC minimizing the expected performance at each sampling instance. The performance of robust NMPC can be improved by generating optimization scenarios using Bayesian inference. With the efficient parallel solver, the solution time of one optimization problem is reduced from 6.7 min to 0.5 min, allowing for real-time application.

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On the optimization of differential‐algebraic process systems

Abstract: simple optimal control problem is also solved to illustrate this strategy.

Cited by 424 publications

References 25 publications

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

A control system for a self-reacting point absorber wave energy converter subject to constraints

Parallel Solution of Robust Nonlinear Model Predictive Control Problems in Batch Crystallization

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