低渗煤中氮气与二氧化碳气体吸附的表征模型

杨靖浩 ,  刘会虎 ,  张琨 ,  刘峻麟 ,  薛生 ,  徐宏杰

科学技术与工程 ›› 2024, Vol. 24 ›› Issue (3) :980-985

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科学技术与工程 ›› 2024, Vol. 24 ›› Issue (3) :980-985 DOI: 10.12404/j.issn.1671-1815.2303105
矿冶工程

低渗煤中氮气与二氧化碳气体吸附的表征模型

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Characterization Model of N2 and CO2 Gas Adsorption in Low Permeability Coal

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摘要

地质领域常以等温吸附试验为研究方法,探究煤中气体之间的吸附行为。以刘庄矿13煤为研究对象,开展了N2、CO2气体吸附实验,在综合分析拟合回归系数平方R2、模型计算值与实验值的残差平方和v与相对误差s的基础上,选出低渗煤中气体吸附的最佳吸附模型。结果表明:对于低渗煤中N2吸附的模型拟合精度由高到低为:优化L模型>优化D-A模型>优化D-R模型>D-A模型>L模型>D-R模型>F模型;对于低渗煤中CO2吸附的模型拟合精度由高到低为:优化L模型>优化D-A模型=优化D-R模型>D-A模型>L模型>D-R模型>F模型。实验结果可作为低渗煤层中气体吸附及CO2封存提供地质依据。

Abstract

Isothermal adsorption experiments are widely used in the field of geology as a research method to investigate the adsorption behavior between gases in coal seams. Taking the coal reservoirs of No. 13 Coal from Liuzhuang Mine as the research object, N2 and CO2 gas adsorption experiments were carried, and the optimal adsorption model for gas adsorption in low permeability coal was selected on the basis of a comprehensive analysis of the squared regression coefficients of the fit R2, the residual sum of squares of the calculated and experimental values of the model v, and the relative errors s. The results show that the models fitting degree of N2 adsorption in low permeability coal from high to low: optimized L model > optimized D-A model > optimized D-R model> D-A model > L model > D-R model > F model. The models fitting degree of CO2 adsorption in low permeability coal from high to low: optimized L model > optimized D-A model = optimized D-R model > D-A model > L model > D-R model > F model. The experimental results can provide geological basis for gas adsorption and CO2 sequestration in low-permeability coal seams.

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杨靖浩, 刘会虎, 张琨, 刘峻麟, 薛生, 徐宏杰. 低渗煤中氮气与二氧化碳气体吸附的表征模型[J]. 科学技术与工程, 2024, 24(3): 980-985 DOI:10.12404/j.issn.1671-1815.2303105
YANG Jing-hao, LIU Hui-hu, ZHANG Kun, LIU Jun-lin, XUE Sheng, XU Hong-jie. Characterization Model of N2 and CO2 Gas Adsorption in Low Permeability Coal[J]. Science Technology and Engineering, 2024, 24(3): 980-985 DOI:10.12404/j.issn.1671-1815.2303105

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基金资助

国家自然科学基金(42277483)

能源研究院合肥综合性国家科学中心项目(21KZS218)

2023年安徽省重大基础研究项目(2023z04020001)

安徽省自然科学基金(2008085MD121)

安徽理工大学2022研究生创新基金(2022CX2004)

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