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    Performance Prediction of a Transport Gasifier–Based IGCC System with CO2 Capture under Uncertainties

    Source: Journal of Energy Engineering:;2016:;Volume ( 142 ):;issue: 003
    Author:
    Jinling Chi
    ,
    Shijie Zhang
    ,
    Yun Yang
    ,
    Bo Wang
    ,
    Yunhan Xiao
    DOI: 10.1061/(ASCE)EY.1943-7897.0000302
    Publisher: American Society of Civil Engineers
    Abstract: In this study, a transport gasifier–based integrated gasification combined cycle (IGCC) with 90% CO2 capture rate was analyzed to predict possible net power output and net efficiency distributions in the system under uncertainties. Moreover, the influence of each parameter on the total model output uncertainty was quantified and displayed using Pareto charts. Uncertainties in parameters of the gasifier, Selexol-CO2 capture process, hydrogen-fueled gas turbine, compressors, steam turbines, and pumps were considered and quantitatively represented in uniform, triangle, or normal distributions. Monte Carlo simulation was adopted to propagate the parameters through process modeling. Results showed that the predicted median net plant efficiency of the system was 37.71% with a standard deviation of 0.41%. The unconverted carbon rate in the gasifier and the gas turbine combustor temperature (gtCMBT) exhibited the greatest effect on predicting uncertainties in efficiency and power output, respectively. Thus, future work should focus on reducing these parameter uncertainties. Comparisons are conducted between the transport gasifer–based system and the dry-feed entrained flow gasifier–based system with similar configuration. The results indicate that, even in the worst case scenario, IGCC with the oxygen-blown gasifier has a similar net efficiency to IGCC with the dry-feed entrained flow gasifier.
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      Performance Prediction of a Transport Gasifier–Based IGCC System with CO2 Capture under Uncertainties

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4245735
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    contributor authorJinling Chi
    contributor authorShijie Zhang
    contributor authorYun Yang
    contributor authorBo Wang
    contributor authorYunhan Xiao
    date accessioned2017-12-30T13:06:37Z
    date available2017-12-30T13:06:37Z
    date issued2016
    identifier other%28ASCE%29EY.1943-7897.0000302.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4245735
    description abstractIn this study, a transport gasifier–based integrated gasification combined cycle (IGCC) with 90% CO2 capture rate was analyzed to predict possible net power output and net efficiency distributions in the system under uncertainties. Moreover, the influence of each parameter on the total model output uncertainty was quantified and displayed using Pareto charts. Uncertainties in parameters of the gasifier, Selexol-CO2 capture process, hydrogen-fueled gas turbine, compressors, steam turbines, and pumps were considered and quantitatively represented in uniform, triangle, or normal distributions. Monte Carlo simulation was adopted to propagate the parameters through process modeling. Results showed that the predicted median net plant efficiency of the system was 37.71% with a standard deviation of 0.41%. The unconverted carbon rate in the gasifier and the gas turbine combustor temperature (gtCMBT) exhibited the greatest effect on predicting uncertainties in efficiency and power output, respectively. Thus, future work should focus on reducing these parameter uncertainties. Comparisons are conducted between the transport gasifer–based system and the dry-feed entrained flow gasifier–based system with similar configuration. The results indicate that, even in the worst case scenario, IGCC with the oxygen-blown gasifier has a similar net efficiency to IGCC with the dry-feed entrained flow gasifier.
    publisherAmerican Society of Civil Engineers
    titlePerformance Prediction of a Transport Gasifier–Based IGCC System with CO2 Capture under Uncertainties
    typeJournal Paper
    journal volume142
    journal issue3
    journal titleJournal of Energy Engineering
    identifier doi10.1061/(ASCE)EY.1943-7897.0000302
    page04015035
    treeJournal of Energy Engineering:;2016:;Volume ( 142 ):;issue: 003
    contenttypeFulltext
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