ORCID Profile
0000-0002-3672-3855
Current Organisations
Iranian Offshore Oil Company
,
VTT Technical Research Center of Finland Ltd.
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Publisher: Trans Tech Publications, Ltd.
Date: 12-2012
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.622-623.611
Abstract: Gas Turbines (GTs) are the beating heart of nearly all industrial plants and specifically play a vital role in oil and power industries. Significant research activities have been carried out to discover accurate dynamics and to approach to the optimal operational point of these systems. A variety of analytical and experimental system identification methods, models and control systems has been investigated so far for gas turbines. Artificial neural network (ANN) has been recognized as one of the successful approaches that can disclose nonlinear behaviour of such complicated systems. This paper briefly reviews major ANN-based research activities in the field of system identification, modelling and control of gas turbines. It can be used as a reference for those who are interested to work and study in this area.
Publisher: ASME International
Date: 09-01-2014
DOI: 10.1115/1.4026215
Abstract: This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation.
Publisher: CRC Press
Date: 16-10-2015
DOI: 10.1201/B18956
Publisher: Inderscience Publishers
Date: 2013
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-4225-6.CH014
Abstract: This chapter deals with research activities that have been carried out so far in the field of modelling and simulation of gas turbines for system optimization purposes. It covers major white-box and black-box gas turbine models and their applications to control systems.
Publisher: IEEE
Date: 12-2011
Publisher: Elsevier BV
Date: 2016
Publisher: Emerald
Date: 03-01-2017
DOI: 10.1108/AEAT-11-2014-0187
Abstract: The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices. It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller. This study aims at using artificial intelligence in gas turbine control systems. This paper provides a novel methodology for control of gas turbines.
Publisher: ASME International
Date: 31-07-2013
DOI: 10.1115/1.4024735
Abstract: During recent decades, artificial intelligence has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines (GT). In this study, a methodology based on artificial neural network (ANN) techniques was developed for offline system identification of a low-power gas turbine. The processed data was obtained from a SIMULINK model of a gas turbine in matlab environment. A comprehensive computer program code was generated and run in matlab for creating and training different ANN models with feed-forward multilayer perceptron (MLP) structure. The code consisted of various training functions, different number of neurons as well as a variety of transfer (activation) functions for hidden and output layers of the network. It was shown that the optimal model for a two-layer network with MLP structure consisted of 20 neurons in its hidden layer and used trainlm as its training function, as well as tansig and logsid as its transfer functions for the hidden and output layers. It was also observed that trainlm has a superior performance in terms of minimum mean squared error (MSE) compared with each of the other training functions. The resulting model could predict performance of the system with high accuracy. The methodology provides a comprehensive view of the performance of over 18,720 ANN models for system identification of the single-shaft gas turbine. One can use the optimal ANN model from this study when training from real data obtained from this type of GT. This is particularly useful when real data is only available over a limited operational range.
Location: Iran (Islamic Republic of)
No related grants have been discovered for Hamid Asgari.