Larestani Aydin, Amiri-Ramsheh Behnam, Atashrouz Saeid, Abedi Ali, Mohaddespour Ahmad, Hemmati-Sarapardeh Abdolhossein
Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Oil and Gas Division, Garnault Consulting Engineers Co, Kerman, Iran.
Sci Rep. 2025 Aug 29;15(1):31832. doi: 10.1038/s41598-025-17028-8.
Energy and environmental policy agencies have been looking for suitable adsorbent materials to promote the use of adsorbed natural gas (ANG). Various candidate adsorbent materials have been developed and tested for methane adsorption. Metal-Organic Frameworks (MOFs) have shown a promising performance in methane adsorption and are of particular interest due to their power in adsorption and separation of gases, chemical tunability, ease of synthesis, and high surface area. Accurate calculation of the theoretical adsorption potential of methane in MOFs and its validation through experiments brings about significant challenges. A growing number of researchers are adopting soft-computing approaches, particularly machine-learning (ML) algorithms, to tackle these challenges. Although ML algorithms have been applied in assessing methane uptake capacity of MOFs, the majority of these efforts have primarily focused on feature selection or the criteria for MOF screening. This communication, however, mainly focuses on the implementation of ensemble-based ML paradigms, including gradient boosting (GBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost) in accurate estimation of methane uptake capacity of experimentally synthesized MOFs based on some readily available features including temperature, pressure, and MOF's pore volume and surface area, for the first time. To this end, a database containing almost 2600 datapoints was attained. The results indicated the high performance of the XGBoost algorithm in estimating the methane uptake capacity of MOFs with a correlation coefficient (R) of 0.9955. Moreover, further analyses revealed that the developed predictive model can reliably estimate the physical trend of CH capacity variations with changing pressure. Also, further analysis indicated the large impact of pressure value on the predicted values. The employed outlier detection technique showed that almost 95% of the collected data points were valid.
能源与环境政策机构一直在寻找合适的吸附材料，以推动吸附天然气（ANG）的应用。人们已经开发并测试了多种用于甲烷吸附的候选吸附材料。金属有机框架（MOF）在甲烷吸附方面展现出了良好的性能，因其在气体吸附与分离方面的能力、化学可调性、易于合成以及高比表面积而备受关注。准确计算MOF中甲烷的理论吸附潜力并通过实验进行验证带来了重大挑战。越来越多的研究人员采用软计算方法，特别是机器学习（ML）算法来应对这些挑战。尽管ML算法已应用于评估MOF的甲烷吸附能力，但这些工作大多主要集中在特征选择或MOF筛选标准上。然而，本通讯主要关注基于集成的ML范式的实施，包括梯度提升（GBoost）、极端梯度提升（XGBoost）、轻量级梯度提升机（LightGBM）以及具有分类特征支持的梯度提升（CatBoost），首次基于一些易于获取的特征（包括温度、压力以及MOF的孔体积和表面积）准确估计实验合成MOF的甲烷吸附能力。为此，获得了一个包含近2600个数据点的数据库。结果表明，XGBoost算法在估计MOF的甲烷吸附能力方面具有高性能，相关系数（R）为0.9955。此外，进一步分析表明，所开发的预测模型能够可靠地估计CH容量随压力变化的物理趋势。而且，进一步分析表明压力值对预测值有很大影响。所采用的异常值检测技术表明，收集到的数据点中几乎95%是有效的。
