Empirical strategies for open‐loop on‐line optimization

McFarlane, Randall C.; Bacon, D. W.

doi:10.1002/cjce.5450670421

Cited by 18 publications

(17 citation statements)

References 15 publications

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“…Perturbation-based methods for RTO system studied in this work include: (1) the Integrated System Optimization and Parameter Estimation (ISOPE) approach (Roberts, 1979); (2) a Linear Adaptive On-line Optimization (LAOO) approach (McFarlane and Bacon, 1989); and (3) a Quadratic Adaptive Online Optimization (QAOO) approach (Golden and Ydstie, 1989). A conventional RTO method, the Two-Phase approach (Chen and Joseph, 1987), which is widely used in current commercial RTO systems, is considered as a benchmark in this work.…”

Section: Perturbation-based Methods For Rto Systemmentioning

confidence: 99%

“…One empirical strategy for determining the real plant inputoutput sensitivities ∇ x û during transitions between RTO cycles, proposed by McFarlane and Bacon (1989), is based on on-line identifi cation of a linear ARX model (Söderström and Stoica, 1989) and does not require any prior knowledge of process (i.e., fi rst-principles models are unnecessary). In this approach, Equation (5) is modifi ed to an unconstrained steady-state optimization problem by using a desirability function (McFarlane and Bacon, 1989).…”

Section: Linear Adaptive On-line Optimization Approachmentioning

confidence: 99%

“…In recent research, this augmentation procedure was extended to develop an iterative technique for solving dynamic optimal control problems (Becerra and Roberts, 1996). Bamberger and Iserman (1978), Garcia and Morari (1981), and McFarlane and Bacon (1989) have extended adaptive control techniques to RTO systems. These approaches use steady-state information extracted from linear or non-linear dynamic models, which have been updated using process measurements, to determine the direction of improving process performance.…”

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confidence: 98%

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Performance Analysis of Perturbation-Based Methods for Real-Time Optimization

Zhang¹,

Forbes²

2008

Can. J. Chem. Eng.

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are the base on which any RTO system is built. A number of researchers have investigated the design and interaction of these two subsystems, particularly with respect to plant/model mismatch. The Two-Phase approach (Chen and Joseph, 1987) is the most widely used method for model updating and modelbased optimization in RTO. In this approach the optimization problem and parameter estimation problem are solved separately. Although this Two-Phase approach attempts to solve the RTO problem by updating the imperfect model, it will not necessarily converge to the correct optimum (Durbeck, 1965;Biegler et al., 1985;Forbes et al., 1994). To address this issue, methods have been proposed to deal explicitly with plant/model mismatch. These methods fall into two distinct classes: (1) those that modify the RTO problem directly; and (2) those that use adaptive control ideas modifi ed to suit RTO applications.This paper provides a comprehensive performance analysis approach for Real-Time Optimization (RTO) technologies, which incorporates systematic approaches to estimating bounds on the convergence behaviour and performance effects of on-line experiments used by a given RTO approach. The performance analysis method is illustrated by an investigation of the conventional two-phase approach and representative techniques drawn from the three main classes of perturbation-based RTO methods which attempt to directly compensate for plant/model mismatch through adaptation. The proposed approach is applied to two simulation-based case studies: a heat exchanger system and a continuous bioreactor.On présente dans cet article une méthode complète d'analyse de performance pour les technologies d'optimisation en temps réel (RTO), qui comporte des approches systématiques pour l'estimation des bornes de convergence et les effets de performance sur des expériences en ligne utilisées dans une approche RTO donnée. L'analyse de performance est illustrée par une étude de l'approche conventionnelle à deux phases et des techniques représentatives issues des trois catégories principales de méthodes RTO basées sur des perturbations et qui tentent de compenser directement l'incompatibilité usine/modèle par l'adaptation. La méthode proposée est appliquée à deux études de cas basées sur des simulations : un système d'échangeur de chaleur et un bioréacteur continu.

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Section: Perturbation-based Methods For Rto Systemmentioning

confidence: 99%

Section: Linear Adaptive On-line Optimization Approachmentioning

confidence: 99%

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confidence: 98%

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Performance Analysis of Perturbation-Based Methods for Real-Time Optimization

Zhang¹,

Forbes²

2008

Can. J. Chem. Eng.

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“…Indeed, the static gain of a transfer function represents the sensitivity (or gradient) of the output with respect to the input. McFarlane and Bacon [70] proposed to identify a linear ARX model and used the estimated static gradient for online optimizing control. A pseudo-random binary sequence (PRBS) was superimposed on each of the inputs to identify the ARX model.…”

Section: Dynamic Model Identificationmentioning

confidence: 99%

“…The same approach was also used by Golden and Ydstie [32] for estimating the first-and second-order derivatives of a SISO plant. Zhang and Forbes [60] compared the optimizing controllers proposed in [70] and [32] with ISOPE and the two-step approach.…”

Section: Dynamic Model Identificationmentioning

confidence: 99%

Modifier Adaptation for Real-Time Optimization—Methods and Applications

et al. 2016

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This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.

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Model‐free real‐time optimization of process systems using safe Bayesian optimization

Krishnamoorthy

Doyle²

2023

AIChE Journal

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Conventional real‐time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model‐free RTO methods are an attractive alternative to circumvent the challenge of developing accurate models. Most model‐free RTO methods are based on estimating the steady‐state cost gradient with respect to the decision variables and driving the estimated gradient to zero using integral action. However, accurate gradient estimation requires clear time scale separation from the plant dynamics, such that the dynamic plant can be assumed to be a static map. For processes with long settling times, this can lead to prohibitively slow convergence to the optimum. To avoid the need to estimate the cost gradients from the measurement, this article uses Bayesian optimization, which is a zeroth order black‐box optimization framework. In particular, this article uses a safe Bayesian optimization based on interior point methods to ensure that the setpoints computed by the model‐free steady‐state RTO layer are guaranteed to be feasible with high probability (i.e., the safety‐critical constraints will not be violated at steady‐state). The proposed method can thus be seen as a model‐free variant of the conventional two‐step steady‐state RTO framework (with steady‐state detection), which is demonstrated on a benchmark Williams‐Otto reactor example.

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Empirical strategies for open‐loop on‐line optimization

Cited by 18 publications

References 15 publications

Performance Analysis of Perturbation-Based Methods for Real-Time Optimization

Performance Analysis of Perturbation-Based Methods for Real-Time Optimization

Modifier Adaptation for Real-Time Optimization—Methods and Applications

Model‐free real‐time optimization of process systems using safe Bayesian optimization

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