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Updated: Sep 13, 2025

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基于机器学习的预测煤炭颗粒大小对CO2吸附的影响₂

Zeyu Wang¹, Xijian Li¹, Shoukun Chen¹

¹Guizhou University Mining College, Guiyang, Guizhou 550025, China.

Langmuir : the ACS journal of surfaces and colloids

|July 29, 2025

概括

随机化数据显著改善了用于预测煤炭中二氧化碳 (CO2) 吸附的机器学习模型. 这种方法通过避免序列依赖性来提高准确性,这对于碳捕获战略至关重要.

科学领域:

环境科学与工程环境科学与工程
地质化学和材料科学材料科学

背景情况:

全球碳排放量不断上升,需要有效的碳捕获,利用和储存 (CCUS) 技术来缓解气候变化并实现碳中和.
煤炭是潜在的二氧化碳吸附剂,但其吸附特性受到颗粒大小等因素的影响,需要优化预测模型.

研究的目的:

为了研究不同颗粒大小的不同煤样品的二氧化碳吸附特性.
开发和评估用于预测煤炭二氧化碳吸附能力的机器学习模型,重点关注数据预处理影响.

主要方法:

在不同颗粒大小的三个煤样本上进行了二氧化碳异热吸附实验.
采用四个机器学习模型 (XGBoost,SVM,LSTM,CNN) 训练有序和随机数据排序.
使用泰勒图,TOPSIS方法和SHAP价值分析来评估模型和因素的重要性.

主要成果:

与顺序排序相比,随机数据排序显著提高了预测准确性 (平均R2改进值为~0.1),减轻了过拟合.
SVM和CNN模型在泰勒图和TOPSIS分析中分别显示出优异的性能.
60-80网格之间的煤颗粒大小被确定为影响较大颗粒 (>200网格) CO2吸附能力的关键因素.

结论:

随机数据预处理对于开发可靠的机器学习模型来预测煤炭中的二氧化碳吸附至关重要.
机器学习有效模拟二氧化碳吸附过程,并识别关键影响因素,如煤孔结构和吸附动力学.
优化的预测模型提高了对基于煤炭的碳捕获技术的理解和效率.

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