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    A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section

    Source: Journal of Engineering for Gas Turbines and Power:;2001:;volume( 123 ):;issue: 002::page 371
    Author:
    C. Boccaletti
    ,
    G. Cerri
    ,
    B. Seyedan
    DOI: 10.1115/1.1361062
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The objective of the paper is to assess the feasibility of the neural network (NN) approach in power plant process evaluations. A “feed-forward” technique with a back propagation algorithm was applied to a gas turbine equipped with waste heat boiler and water heater. Data from physical or empirical simulators of plant components were used to train such a NN model. Results obtained using a conventional computing technique are compared with those of the direct method based on a NN approach. The NN simulator was able to perform calculations in a really short computing time with a high degree of accuracy, predicting various steady-state operating conditions on the basis of inputs that can be easily obtained with existing plant instrumentation. The optimization of NN parameters like number of hidden neurons, training sample size, and learning rate is discussed in the paper.
    keyword(s): Gas turbines , Artificial neural networks , Industrial plants , Heat recovery AND Optimization ,
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      A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/125213
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    • Journal of Engineering for Gas Turbines and Power

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    contributor authorC. Boccaletti
    contributor authorG. Cerri
    contributor authorB. Seyedan
    date accessioned2017-05-09T00:04:52Z
    date available2017-05-09T00:04:52Z
    date copyrightApril, 2001
    date issued2001
    identifier issn1528-8919
    identifier otherJETPEZ-26803#371_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/125213
    description abstractThe objective of the paper is to assess the feasibility of the neural network (NN) approach in power plant process evaluations. A “feed-forward” technique with a back propagation algorithm was applied to a gas turbine equipped with waste heat boiler and water heater. Data from physical or empirical simulators of plant components were used to train such a NN model. Results obtained using a conventional computing technique are compared with those of the direct method based on a NN approach. The NN simulator was able to perform calculations in a really short computing time with a high degree of accuracy, predicting various steady-state operating conditions on the basis of inputs that can be easily obtained with existing plant instrumentation. The optimization of NN parameters like number of hidden neurons, training sample size, and learning rate is discussed in the paper.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section
    typeJournal Paper
    journal volume123
    journal issue2
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.1361062
    journal fristpage371
    journal lastpage376
    identifier eissn0742-4795
    keywordsGas turbines
    keywordsArtificial neural networks
    keywordsIndustrial plants
    keywordsHeat recovery AND Optimization
    treeJournal of Engineering for Gas Turbines and Power:;2001:;volume( 123 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian