Designing plant experiments for real-time optimization systems

Yip, Wai San; Marlin, Thomas E.

doi:10.1016/s0967-0661(02)00213-7

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…Regarding (ii), the use of multiple data sets has been suggested to increase the number of identifiable parameters [89]. How to select the additional data sets based on the design of plant experiments has also been addressed [90].…”

Section: Real-time Optimization With Model Updatementioning

confidence: 99%

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Modifier-Adaptation Methodology for Real-Time Optimization

Marchetti

Chachuat

Bonvin

2009

Ind. Eng. Chem. Res.

255

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: Real-time Optimization With Model Updatementioning

confidence: 99%

“…From (4.89) and (B.5), one has 90) where l AC is the distance between the two complement affine subspaces. From all possible complement subsets S A and S C , ǫ n (u) max occurs for the complement subsets that correspond to the nearest complement affine subspaces, i.e.…”

mentioning

confidence: 99%

Modifier-Adaptation Methodology for Real-Time Optimization

Marchetti

Chachuat

Bonvin

2009

Ind. Eng. Chem. Res.

255

331

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“…However, this approach will only resolve the problem if the model is structurally correct and the parameters are identifiable. Under structural plant-model mismatch, it has been demonstrated that the two-step approach can lead to suboptimal operation. , Dedicated online experiments for the estimation of model parameters can involve significant costs in large scale processes but not lead to a better operation because of the structural mismatch …”

Section: Introductionmentioning

confidence: 99%

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) has gained growing attention during the past few years as a useful approach to boost process performance while safety and environmental constraints are satisfied. Despite the increasing acceptance of RTO in traditional industries such as petrochemical and refineries, its application to novel chemical processes remains limited. This can be partially explained by the fact that only inaccurate models are available and the performance of the traditional RTO scheme suffers in the presence of plant-model mismatch. During the past few years, the so-called modifier-adaptation schemes for real-time optimization have been gaining popularity as an efficient tool to handle plant-model mismatch. So far, there are only few published works regarding experimental implementations. In this contribution, a reliable RTO scheme that is able to deal with model uncertainty and measurement noise is applied to a novel transition metal complex catalyzed process that is performed in a continuously operated miniplant. The experimental results show that the proposed scheme is able to drive the process to an improved operation despite significant plant-model mismatch demonstrating the applicability of the method to real processes.

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“…1 In a large-scale plant the implementation of RTO has proved to be profitable. 2,3,4 RTO drives operating condition towards the actual plant optimum in spite of model mismatch by adjusting selected optimization variables using measurement data. Problems face in RTO arise due to the inability to develop and adopt accurate models and the following are three different strategies which have been classified on how measurements are used to compensate the model uncertainty: model-parameter adaptation, modifier adaptation and input adaptation.…”

Section: Introductionmentioning

confidence: 99%

Neural network adaptive real-time optimizing control of industrial processes

Abdullah

Razali

Ahmed

et al. 2017

Chem. Eng. Res. Bull.

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Real-time optimization (RTO) has attracted considerable interest among researchers and industries for being able to optimise the plant economics such as product efficiency, product quality and process safety in the wake of increasing global competitions. The success of RTO depends much on the quality of model being used in the optimisation. The present study was carried out to explore the use of artificial neural network (ANN) to improve the quality of the model being used in the modified two step (MTS) technique. The MTS is a real-time optimising control algorithm of the modifier adaptation scheme which is used to determine the optimum steadystate control set-points. The proposed new version of MTS technique will be using process model based on ANN. A laboratory scale process of a two continuous stirred tank heat exchanger in series (2CSTHEs) is used as a case study. The multilayer feed forward ANN architecture 4-10-6 with linear function was used to model the 2CSTHEs and then integrates into the MTS technique, the resulted algorithm will be known as Iterative Neural Network Modified Two Step (INNMTS) technique. Simulation studies were conducted to test the performance of the INNMTS technique on the 2CSTHEs process. The results show that the overall value for the coefficient of determination ( 2 )is equal to one, which indicates adequacy of the model proposed for the prediction of the behavior of 2CSTHEs system. When NN model of 2CSTHEs is applied to the INNMTS technique, the model-plant mismatch is greatly reduced to almost zero, which indicates by significant reduction in the number of iterations to 5which requires by INNMTS compared to 16 iterations by the MTS technique to converge to optimal real solution.

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Designing plant experiments for real-time optimization systems

Cited by 21 publications

References 11 publications

Modifier-Adaptation Methodology for Real-Time Optimization

Modifier-Adaptation Methodology for Real-Time Optimization

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Neural network adaptive real-time optimizing control of industrial processes

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