دانلود مقاله انگلیسی رایگان:ویژگی های جذب CO2 / CH4 فوق بحرانی در انواع مختلف ذغال سنگ و رویکرد یادگیری ماشین - 2019
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  • Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach
    Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach


    ترجمه فارسی عنوان مقاله:

    ویژگی های جذب CO2 / CH4 فوق بحرانی در انواع مختلف ذغال سنگ و رویکرد یادگیری ماشین


    منبع:

    Sciencedirect - Elsevier - Chemical Engineering Journal, 368 (2019) 847-864: doi:10:1016/j:cej:2019:03:008


    نویسنده:

    Meng Menga, Zhengsong Qiub,⁎, Ruizhi Zhongc, Zhenguang Liud, Yunfeng Liub, Pengju Chena


    چکیده انگلیسی:

    The injection of CO2 into deep coal beds can not only improve the recovery of CH4, but also contribute to the geological sequestration of CO2. The adsorption characteristics of coal determine the amount of the greenhouse gas that deep coal seams can store in place. Using self-developed adsorption facility of supercritical fluids, this paper studied the adsorption behavior of supercritical CO2 and CH4 on three types of coal (anthracite, bituminous coal A, bituminous coal B) under different temperatures of 35 °C, 45 °C and 55 °C. The influence of temperature, pressure, and coal rank on the Gibbs excess and absolute/real adsorption amount of supercritical CO2/CH4 on coal samples has been analyzed. Several traditional isotherm models are applied to interpret the experimental data and Langmuir related models are verified to provide good performances. However, these models are limited to isothermal conditions and are highly depended on extensive experiments. To overcome these deficiencies, one innovative adsorption model is proposed based on machine learning methods. This model is applied to the adsorption data of both this paper and four early publications. It was proved to be highly effective in predicting adsorption behavior of a certain type of coal. To further break the limit of coal type, the second optimization model is provided based on published data. Using the second model, one can predict the adsorption behavior of coal based on the fundamental physicochemical parameters of coal. Overall, working directly with the real data, the machine learning technique makes the unified adsorption model become possible, avoiding tedious theoretical assumptions, derivations and strong limitations of the traditional model.
    Keywords: Supercritical CO2 | Supercritical CH4 | Coal | Adsorption model | Machine learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 5325 کیلوبایت

    قیمت: رایگان


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