A generalized chemical processing model for the investigation of computer control

Williams, Theodore J.; Otto, Robert E.

doi:10.1109/tce.1960.6367296

Cited by 108 publications

(106 citation statements)

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“…The reactor in the Williams-Otto plant [13], as modified by Roberts [14], is considered. This reactor example has also been used to illustrate model adequacy and RTO performance [15,16].…”

Section: Reactor and Modelmentioning

confidence: 99%

A dual modifier-adaptation approach for real-time optimization

Marchetti

Chachuat

Bonvin

2010

Journal of Process Control

121

142

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For good performance in practice, real-time optimization schemes need to be able to deal with the inevitable plant-model mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach does not require the model parameters to be estimated on-line. Instead, it uses information regarding the constraints and selected gradients to improve the plant operation. The dual modifier-adaptation approach presented in this paper drives the process towards optimality, while paying attention to the accuracy of the estimated gradients. The gradients are estimated from successive operating points generated by the optimization algorithm. The novelty lies in the development of an upper bound on the norm of the gradient errors, which is used as a constraint when determining the next operating point. The proposed approach is demonstrated via numerical simulation for both an unconstrained and a constrained problem. A Dual Modifier-Adaptation Approach forReal-Time Optimization AbstractFor good performance in practice, real-time optimization schemes need to be able to deal with the inevitable plant-model mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach does not require the model parameters to be estimated on-line. Instead, it uses information regarding the constraints and selected gradients to improve the plant operation. The dual modifier-adaptation approach presented in this paper drives the process towards optimality, while paying attention to the accuracy of the estimated gradients. The gradients are estimated from successive operating points generated by the optimization algorithm. The novelty lies in the development of an upper bound on the norm of the gradient errors, which is used as a constraint when determining the next operating point. The proposed approach is demonstrated via numerical simulation for both an unconstrained and a constrained problem.

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“…The reactor in the Williams-Otto plant [13], as modified by Roberts [14], is considered. This reactor example has also been used to illustrate model adequacy and RTO performance [15,16].…”

Section: Reactor and Modelmentioning

confidence: 99%

A dual modifier-adaptation approach for real-time optimization

Marchetti

Chachuat

Bonvin

2010

Journal of Process Control

121

142

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“…Solve the modified model-based optimization problem 28) where the modified cost and constraints are given by…”

Section: Algorithm 33: Methods Rrmentioning

confidence: 99%

“…Methods UR, UU and RR are illustrated on the Williams-Otto reactor [28]. We will use the model from [29], which has become a standard test problem for real-time optimization techniques [16].…”

Section: Simulated Example: Controlled Williams-otto Reactormentioning

confidence: 99%

Extension of modifier adaptation for controlled plants using static open-loop models

François

Costello

Marchetti

et al. 2016

Computers & Chemical Engineering

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Model-based optimization methods suffer from the limited accuracy of the available process models. Because of plant-model mismatch, model-based optimal inputs may be suboptimal or, worse, unfeasible for the plant. Modifier adaptation (MA) overcomes this obstacle by incorporating measurements in the optimization framework. However, the standard MA formulation requires that (1) the model satisfies adequacy conditions and (2) the model and the plant share the same degrees of freedom. In this article, three extensions of MA to problems where (2) does not hold are proposed. In particular, we consider the case of controlled plants for which the only a model of the open-loop plant is available. These extensions are shown to preserve the ability of MA to converge to the plant optimum despite disturbances and plant-model mismatch. The proposed methods are illustrated in simulation for the optimization of a CSTR.

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“…The Otto-Williams reactor, proposed by Williams & Otto (1960), was widely used in process optimization case studies. The model that we will use in this section is the one operating under the conditions proposed by Roberts (1979).…”

Section: Otto-williams Reactor Optimizationmentioning

confidence: 99%

“…On the other hand, the search for the best hybrid Otto-Williams reactor is accomplished with few minutes of a standard desktop computer. And a case study with full OttoWilliams process (Williams & Otto, 1960), having 93 variables and 83 equations, was solved with a couple of hours of desktop computer time.…”

Section: Approximate Mechanistic Modelmentioning

confidence: 99%

A semi-mechanistic model building framework based on selective and localized model extensions

Lima

Saraiva

2007

Computers & Chemical Engineering

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In the core of many process systems engineering tasks, like design, control, optimization and fault diagnosis, a mathematical model of the underlying plant plays a key role. Such models are so important that extensive studies are available, recommending different modeling techniques to be adopted for specific processes or goals. It is usual and practical to split modeling techniques under two main groups: mechanistic methods and empirical or statistical methods. Both paradigms have been adopted, but very few frameworks were developed to combine and integrate features from both of them. In this article we describe a framework for data-driven evolution of static mechanistic models with a selective inclusion of simple empirical terms. To illustrate its practical potential, our framework is applied to the identification of a non-ideal reactor and to the optimization of the Otto-Williams benchmark reactor.

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A generalized chemical processing model for the investigation of computer control

Cited by 108 publications

References 0 publications

A dual modifier-adaptation approach for real-time optimization

A dual modifier-adaptation approach for real-time optimization

Extension of modifier adaptation for controlled plants using static open-loop models

A semi-mechanistic model building framework based on selective and localized model extensions

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