Convergence and optimality of modified two-step algorithm for integrated system optimization and parameter estimation

Brdyś, M.; Roberts, P.D.

doi:10.1080/00207728708967111

Cited by 30 publications

(16 citation statements)

References 6 publications

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“…For instance, the original ISOPE formulation, which does not consider the constraints, modifies only the gradient of the cost function [72,74]. Optimality and convergence analysis for the original ISOPE algorithm have been studied [11]. ISOPE has later been extended to include process-dependent constraints [10,94].…”

Section: Isopementioning

confidence: 99%

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Modifier-Adaptation Methodology for Real-Time Optimization

Marchetti

Chachuat

Bonvin

2009

Ind. Eng. Chem. Res.

256

331

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The ability of a model-based real-time optimization (RTO) scheme to converge to the plant optimum relies on the ability of the underlying process model to predict the plant’s necessary conditions of optimality (NCO). These include the values and gradients of the active constraints, as well as the gradient of the cost function. Hence, in the presence of plant−model mismatch or unmeasured disturbances, one could use (estimates of) the plant NCO to track the plant optimum. This paper shows how to formulate a modifed optimization problem that incorporates such information. The so-called modifiers, which express the difference between the measured or estimated plant NCO and those predicted by the model, are added to the constraints and the cost function of the modified optimization problem and are adapted iteratively. Local convergence and model-adequacy issues are analyzed. The modifier-adaptation scheme is tested experimentally via the RTO of a three-tank system.

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Section: Isopementioning

confidence: 99%

“…The output-matching requirement (2.33) represents a model-qualification condition that is found throughout the ISOPE literature [11,13,74,94]. The new operating point is determined by filtering the inputs using a first-order exponential filter, similar to (2.23).…”

Section: Isopementioning

confidence: 99%

Modifier-Adaptation Methodology for Real-Time Optimization

Marchetti

Chachuat

Bonvin

2009

Ind. Eng. Chem. Res.

256

331

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“…The ISOPE approach was originally proposed by Roberts [6]. The optimality and convergence properties for the basic version were ®rst established by Brdys and Roberts [7]. The improved, augmented version of the algorithm was published by Brdys, Ellis and Roberts [8], together with thorough optimality and convergence analysis.…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling in optimisation of continuous fermentation processes

Lednický

Mészáros

1998

Bioprocess Engineering

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The capability of self-recurrent neural networks in dynamic modeling of continuous fermentation is investigated in this simulation study. In the past, feedforward neural networks have been successfully used as onestep-ahead predictors. However, in steady-state optimisation of continuous fermentations the neural network model has to be iterated to predict many time steps ahead into the future in order to get steady-state values of the variables involved in objective cost function, and this iteration may result in increasing errors. Therefore, as an alternative to classical feedforward neural network trained by using backpropagation method, self-recurrent multilayer neural net trained by backpropagation through time method was chosen in order to improve accuracy of longterm predictions. Prediction capabilities of the resulting neural network model is tested by implementing this into the Integrated System Optimisation and Parameter Estimation (ISOPE) optimisation algorithm. Maximisation of cellular productivity of the baker's yeast continuous fermentation was used as the goal of the proposed optimising control problem. The training and prediction results of proposed neural network and performances of resulting optimisation structure are demonstrated.

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“…This condition represents a model-qualification condition that is present throughout the ISOPE literature [7,10,23,24].…”

Section: Isope Algorithmmentioning

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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Convergence and optimality of modified two-step algorithm for integrated system optimization and parameter estimation

Cited by 30 publications

References 6 publications

Modifier-Adaptation Methodology for Real-Time Optimization

Modifier-Adaptation Methodology for Real-Time Optimization

Neural network modeling in optimisation of continuous fermentation processes

Modifier Adaptation for Real-Time Optimization—Methods and Applications

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