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    A Data-Driven Model for Energy Consumption in the Sintering Process

    Source: Journal of Manufacturing Science and Engineering:;2016:;volume( 138 ):;issue: 010::page 101001
    Author:
    Wang, Junkai
    ,
    Qiao, Fei
    ,
    Zhao, Fu
    ,
    Sutherland, John W.
    DOI: 10.1115/1.4033661
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: As environmental performance becomes increasingly important, the sintering process is receiving more attention since it consumes large amounts of energy. This paper proposes a data-driven model for sintering energy consumption, which considers both model accuracy and time efficiency. The proposed model begins with removing data anomalies using a local outlier factor (LOF) algorithm and an attribute selection module using the RReliefF method. Then, to accurately predict sintering energy consumption, an integrated predictive model is employed that uses bagging-enhanced extreme learning machine (ELM) and support vector regression (SVR) machine, combined with an entropy weight method. A case study is used to demonstrate the effectiveness of the proposed model using actual production data for a year. Results show that the proposed model outperforms other models and is computationally efficient. Optimal parameters of the LOF (1.3) and number of attributes (30) were identified. It was found that coke powder has the most significant impact on the solid energy consumption (SEC), while cooling water flow rate provides the most significant impact on the gas energy consumption (GEC) within each recorded attribute variation. Parametric analysis further revealed the relationships between energy consumption and the significant attributes mentioned above. It is suggested that the proposed model could effectively reduce the energy consumption by attaining more efficient attribute settings.
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      A Data-Driven Model for Energy Consumption in the Sintering Process

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4234600
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    contributor authorWang, Junkai
    contributor authorQiao, Fei
    contributor authorZhao, Fu
    contributor authorSutherland, John W.
    date accessioned2017-11-25T07:17:29Z
    date available2017-11-25T07:17:29Z
    date copyright2016/22/6
    date issued2016
    identifier issn1087-1357
    identifier othermanu_138_10_101001.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234600
    description abstractAs environmental performance becomes increasingly important, the sintering process is receiving more attention since it consumes large amounts of energy. This paper proposes a data-driven model for sintering energy consumption, which considers both model accuracy and time efficiency. The proposed model begins with removing data anomalies using a local outlier factor (LOF) algorithm and an attribute selection module using the RReliefF method. Then, to accurately predict sintering energy consumption, an integrated predictive model is employed that uses bagging-enhanced extreme learning machine (ELM) and support vector regression (SVR) machine, combined with an entropy weight method. A case study is used to demonstrate the effectiveness of the proposed model using actual production data for a year. Results show that the proposed model outperforms other models and is computationally efficient. Optimal parameters of the LOF (1.3) and number of attributes (30) were identified. It was found that coke powder has the most significant impact on the solid energy consumption (SEC), while cooling water flow rate provides the most significant impact on the gas energy consumption (GEC) within each recorded attribute variation. Parametric analysis further revealed the relationships between energy consumption and the significant attributes mentioned above. It is suggested that the proposed model could effectively reduce the energy consumption by attaining more efficient attribute settings.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Data-Driven Model for Energy Consumption in the Sintering Process
    typeJournal Paper
    journal volume138
    journal issue10
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4033661
    journal fristpage101001
    journal lastpage101001-12
    treeJournal of Manufacturing Science and Engineering:;2016:;volume( 138 ):;issue: 010
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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