Modelling and long-term simulation of a heat recovery steam generator

Chaibakhsh, Ali

doi:10.1080/13873954.2012.698623

Cited by 12 publications

(8 citation statements)

References 47 publications

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“…By increasing the inlet process flow, the rate of flow will increase in the tubes and time of heating will decrease and hence the outlet temperature will decrease. Also, in order to show the accuracy of the developed model, sensitivity analyses are perfumed by perturbing the model's inputs from nominal operating conditions [48]. Fig.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Crude oil direct fired furnace model

Chaibakhsh

Ensansefat

Jamali

et al. 2015

Applied Thermal Engineering

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Section: Simulation Experimentsmentioning

confidence: 99%

Crude oil direct fired furnace model

Chaibakhsh

Ensansefat

Jamali

et al. 2015

Applied Thermal Engineering

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“…In this case, despite the good performances of LNFM models, increasing the number of variables and the size of training data may cause some problems in training the fuzzy part, which refers to the so-called "curse of dimensionality" or "fuzzy rule explosion" problems [16,21]. For high-dimensional datasets, the conventional ANFIS structure is much more suffering such problems due to high number of tunable parameters [22]. Employing hierarchical fuzzy systems (HFS) as the structure of the model's nonlinear part is an appropriate approach to deal with these problems [23].…”

Section: A Laguerre-based Network Fuzzy Systemmentioning

confidence: 99%

“…Employing hierarchical fuzzy systems (HFS) as the structure of the model's nonlinear part is an appropriate approach to deal with these problems [23]. Furthermore, a data clustering technique can be used to reduce the number of fuzzy rules and corresponding tunable parameters [22,24].…”

Section: A Laguerre-based Network Fuzzy Systemmentioning

confidence: 99%

“…In order to find the optimal membership functions, an optimization approach based on genetic algorithm (GA) is employed to solve a non-linear optimization problem by minimizing the fitness function (13) and with respect to constraints (14) [22]. While no further improvement is observed in J m (Μ,Q), the optimization process would be terminated.…”

Section: B Neuro-fuzzy Systemmentioning

confidence: 99%

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Temperature sensor fault diagnosing in heavy duty gas turbines using Laguerre network-based hierarchical fuzzy systems

Chaibakhsh

Amirkhani

Piredeir

2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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This study present an application of Laguerre network-based hierarchical fuzzy modeling approach in fault diagnosis of the temperature sensors in industrial heavy duty gas turbines. The recorded experimental data from the performances of a V94.2 gas turbine unit were employed in modeling stage. A comparison between the responses of the models and real data indicate the capability of the model for long-term prediction of the turbine outlet temperature at different operating conditions. The differences between the models and measured values were defined as the residuals. To deal with uncertainties and disturbances, the thresholds bounds were considered for the residuals. The residuals deviations with respect to threshold boundaries yield to symptoms, which were analyzed in a Takagi-Sugeno fuzzy inference expert system. The performances of fault detection and fault diagnosis system were evaluated by subjecting the sensors to faults. The obtained results show that the faults are successfully detected and diagnosed.

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“…An optimization approach-based genetic algorithm (GA) was proposed by Chaibakhsh to solve the nonlinear optimization problem by minimizing the objective function [29]. e best possible cluster centers, }, can be captured with respect to the following constraints on the membership values:…”

Section: The Training Algorithmsmentioning

confidence: 99%

A Model-Based Coordinated Control Concept for Steam Power Plants

Chaibakhsh

Ghaffari

2013

Journal of Engineering

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An application of model-based coordinated control concept for improving the performance and maneuverability of once-through power plants is presented. In this structure, neuro-fuzzy-based Hammerstein models are employed as the core of feedforward (FF) controller to generate the reference trajectories for the plant’s subsystems. The setpoints for fuel, electrical power, and steam pressure are provided by FF controller with respect to load demands. In order to diminish the effect of disturbances and to compensate deviations between variables and corresponding feedforward setpoints, feedback controllers are considered. The error signal defined by the deviation of desired boiler output pressure from its actual value is used to compensate the demand signals for turbine and boiler. The performance of the proposed control system is compared with that of a conventional turbine-follower and also turbine-follower coordinated control modes during load changes. The results indicate the effectiveness and load tracking capability of the proposed control system at different operating conditions.

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Modelling and long-term simulation of a heat recovery steam generator

Cited by 12 publications

References 47 publications

Crude oil direct fired furnace model

Crude oil direct fired furnace model

Temperature sensor fault diagnosing in heavy duty gas turbines using Laguerre network-based hierarchical fuzzy systems

A Model-Based Coordinated Control Concept for Steam Power Plants

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