Real-time optimization under parametric uncertainty: a probability constrained approach

Zhang, Yale; Monder, Dayadeep S.; Forbes, J. Fraser

doi:10.1016/s0959-1524(01)00047-6

Cited by 123 publications

(85 citation statements)

References 15 publications

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“…The first widely available RTO approach was the twostep approach that adapts the model parameters on the basis of differences between predicted and measured outputs and uses the updated process model to re-compute the optimal inputs 66,67 . However, in the presence of structural plant-model mismatch, this method is very unlikely to drive the plant to optimality 68,69 .…”

Section: Eq (29)mentioning

confidence: 99%

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Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches Keywords Batch control, predictive control, iterative learning control, run-to-run control, batch process optimization, dynamic optimization, optimizing control, run-to-run optimization.

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Section: Eq (29)mentioning

confidence: 99%

“…In the two-step approach, measurements are used to refine the model, which is then used to optimize the process 67 . The two-step approach has gained popularity over the past thirty years mainly because of its conceptual simplicity.…”

Section: Two-step Approach (Strategy 2)mentioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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“…Note that equation (8) is the same as equation (4). Assuming F x to be invertible, the adjoint variables can be computed from L x = 0, which gives…”

Section: Static Optimization Problem and Optimality Conditionsmentioning

confidence: 99%

Equivalence between Neighboring-Extremal Control and Self-Optimizing Control for the Steady-State Optimization of Dynamical Systems

François

Srinivasan

Bonvin

2014

Ind. Eng. Chem. Res.

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The problem of steering a dynamical system toward optimal steady-state performance is considered. For this purpose, a static optimization problem can be formulated and solved.However, because of uncertainty, the optimal steady-state inputs can rarely be applied directly in an open-loop manner. Instead, plant measurements are typically used to help reach the plant optimum. This paper investigates the use of optimizing control techniques for input adaptation. Two apparently different techniques of enforcing steady-state optimality are discussed, namely, neighboring-extremal control and self-optimizing control based on the nullspace method. These two techniques are compared for unconstrained real-time optimization in the presence of parametric variations. It is shown that, for the noise-free scenario, the two methods can be made equivalent through appropriate tuning. Note that both approach can use * To whom correspondence should be addressed 1 measurements that are taken either at successive steady-state operating points or during the transient behavior of the plant. Implementation of optimizing control is illustrated through a simulated CSTR example.

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“…More recently, [2] introduced a linear blending control algorithm which handles this type of uncertainties via an estimator of the components' properties. A more general method based on stochastic programming which covers various types of uncertainty is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Robust real-time optimization for the linear oil blending

Janaqi¹,

Aguilera²,

Chèbre³

2013

RAIRO-Oper. Res.

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In this paper we present a robust real-time optimization method for the online linear oil blending process. The blending process consists in determining the optimal mix of components so that the final product satisfies a set of specifications. We examine different sources of uncertainty inherent to the blending process and show how to address this uncertainty applying the Robust Optimization techniques. The polytopal structure of our problem permits a simplified robust approach. Our method is intended to avoid reblending and we measure its performance in terms of the blend quality giveaway and feedstocks prices. The difference between the nominal and the robust optimal values (the price of robustness) provides a benchmark for the cost of reblending which is difficult to estimate in practice. This new information can be used to adjust the level of conservatism in the model. We analyze the feasibility of a blend to be produced from a set of feedstocks when the heel of a previous blend is used in the composition of the new blend. Additional critical information for the control system is then produced.

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Real-time optimization under parametric uncertainty: a probability constrained approach

Cited by 123 publications

References 15 publications

Control and Optimization of Batch Chemical Processes

Control and Optimization of Batch Chemical Processes

Equivalence between Neighboring-Extremal Control and Self-Optimizing Control for the Steady-State Optimization of Dynamical Systems

Robust real-time optimization for the linear oil blending

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