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    Modeling and Simulation of a Heat Recovery Steam Generator using Partially Known Design Point Data

    Access Status
    Fulltext not available
    Authors
    Zewge, M.
    Lemma, T.
    Ibrahim, A.
    Debnath, Sujan
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Zewge, M. and Lemma, T. and Ibrahim, A. and Debnath, S. 2014. Modeling and Simulation of a Heat Recovery Steam Generator Using Partially Known Design Point Data. Advanced Materials Research. 845: pp. 596-603.
    Source Title
    Modeling and Simulation of a Heat Recovery Steam Generator usingPartially Known Design Point Data
    Source Conference
    1st International Materials, Industrial, and Manufacturing Engineering Conference, MIMEC 2013
    DOI
    10.4028/www.scientific.net/AMR.845.596
    ISSN
    1022-6680
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/22050
    Collection
    • Curtin Research Publications
    Abstract

    In a cogeneration or combined heat and power plant, a heat recovery steam generator (HRSG) helps achieve overall thermal efficiency as high as 80%. The purpose of this study is to model and simulate the HRSG given partial design point data. The pinch and approach temperatures are optimized within generally accepted range. In order to satisfy the energy conservation equation, tuning parameters are used for the overall heat transfer coefficients corresponding to the evaporator and economizer. For the off-design simulation, the values of pinch and approach temperatures are adjusted until the modeling error is within a set limit. The effect of mass flow rate on the heat transfer coefficient is accounted for & by employing empirical relations. A 12 Ton/hr natural circulation HRSG was considered as a case study. The validation test on inlet temperatures of the exhaust gas and feed water to the economizer demonstrated relative percentage errors of 0.4246% and 1.8776%, respectively. The model can be used for fault detection and diagnostic system design, performance optimization, and environmental load assessment.

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