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使用先进的机器学习技术预测KOH激活生物炭中的二氧化碳吸附

Raouf Hassan¹, Alireza Baghban²

¹Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia.

Scientific reports

|July 8, 2025

概括

机器学习准确地预测了生物炭中的二氧化碳 (CO2) 吸附. 支持向量回归 (SVR) 和CatBoost模型显示出高精度,这对地质能源和环境技术进步至关重要.

科学领域:

材料科学材料科学材料科学
环境科学环境科学
化学工程是化学工程的重要组成部分.

背景情况:

准确的二氧化碳 (CO2) 吸附预测在KOH激活生物炭对于地球工程和环境技术至关重要.
了解吸附是开发高效碳捕获解决方案的关键.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测KOH激活生物炭中的二氧化碳吸附.
用各种ML技术确定影响CO2吸附的关键因素.

主要方法:

使用了一套多样化的ML算法,包括支持向量回归 (SVR),CatBoost,随机森林和神经网络.
模型在329个数据点的数据集上进行了训练和验证,使用指标和可视化来评估性能.
蒙特卡洛异常值检测和泰勒图用于数据集验证和模型性能分析.

主要成果:

SVR和CatBoost模型显示了对二氧化碳吸附的最高预测准确度.
SVR实现了0.9235的R2和0.2207的MSE,而CatBoost则实现了0.9327的R2和0.1942.4的MSE.
灵敏度和SHAP分析确定压力和温度是影响二氧化碳吸附的关键参数.

结论:

先进的ML模型,特别是CatBoost和SVR,对于预测生物炭中二氧化碳吸附非常有效.
这些发现为优化工业碳捕获过程和未来研究提供了宝贵的见解.
该研究强调了ML在提高环境应用的吸附效率方面的潜力.

关键词:

生物炭是一种生物炭.二氧化碳吸附方式数据驱动的模型是基于数据的.能源技术能源技术能源技术机器学习机器学习

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