Hassan Raouf, Baghban Alireza
Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia.
Process Engineering Department, National Iranian South Oilfields Company (NISOC), Ahwaz, Iran.
Sci Rep. 2025 Jul 8;15(1):24410. doi: 10.1038/s41598-025-09248-9.
Accurately forecasting carbon dioxide (CO) adsorption in KOH-activated biochar is crucial for advancements in geoenergy engineering and environmental technology. This research aims to develop robust machine learning models to capture the intricate relationships influencing CO adsorption, driven by variables like pressure, temperature, and the biochar's chemical and physical properties. We employed a comprehensive suite of machine learning methods, like convolutional neural networks, random forests, artificial neural networks, linear regression, ridge and lasso regressions, elastic net, support vector machines, decision trees, gradient boosting machines, k-nearest neighbors, light gradient boosting machines, extreme gradient boosting, CatBoost, and Gaussian process, to build predictive models. These models were trained and validated on a dataset of 329 data points, assessed through performance metrics and visualizations. The dataset's suitability was confirmed by Monte Carlo outlier detection. Detailed analysis, utilizing the Taylor Diagram and performance metrics, confirmed that SVR and CatBoost models achieved the highest accuracy in predicting CO adsorption. Their superior performance is evidenced by high R values of 0.9235 (SVR) and 0.9327 (CatBoost), coupled with low mean squared error values of 0.2207 (SVR) and 0.1942 (CatBoost). Sensitivity analysis further indicated all input parameters' correlation with CO adsorption, while SHAP analysis identified pressure and temperature as critical factors. The results demonstrate the power of advanced machine learning methods, particularly CatBoost and SVR, in predicting CO adsorption and offer valuable insights for industrial applications and future research efforts aimed at enhancing adsorption efficiency.
准确预测氢氧化钾活化生物炭中二氧化碳（CO）的吸附情况对于地质能源工程和环境技术的进步至关重要。本研究旨在开发强大的机器学习模型，以捕捉影响CO吸附的复杂关系，这些关系由压力、温度以及生物炭的化学和物理性质等变量驱动。我们采用了一系列综合的机器学习方法，如卷积神经网络、随机森林、人工神经网络、线性回归、岭回归和套索回归、弹性网络、支持向量机、决策树、梯度提升机、k近邻、轻梯度提升机、极端梯度提升、CatBoost和高斯过程，来构建预测模型。这些模型在一个包含329个数据点的数据集上进行训练和验证，并通过性能指标和可视化进行评估。通过蒙特卡罗离群值检测确认了数据集的适用性。利用泰勒图和性能指标进行的详细分析证实，支持向量回归（SVR）和CatBoost模型在预测CO吸附方面具有最高的准确性。它们的卓越性能体现在高R值上，分别为0.9235（SVR）和0.9327（CatBoost），同时平均平方误差值较低，分别为0.2207（SVR）和0.1942（CatBoost）。敏感性分析进一步表明所有输入参数与CO吸附的相关性，而SHAP分析确定压力和温度为关键因素。结果证明了先进机器学习方法，特别是CatBoost和SVR，在预测CO吸附方面的强大能力，并为工业应用和旨在提高吸附效率的未来研究工作提供了有价值的见解。
