Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models

Jafar Abdi,Masoud Hadipoor,Fahimeh Hadavimoghaddam,Abdolhossein Hemmati-Sarapardeh

doi:10.1038/s41598-021-04168-w

Abstract

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m2/g) and pore volume (cm3/g) of the MOFs and the output was CO2 uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO2 adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO2 adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.

Highlights

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects
For predicting the amount of CO2 absorbed on the surface of various MOFs, we developed various models using XGboost, LightGBM, Catboost and Random Forest (RF) methods
It has been discovered that the amount of CO2 adsorption on the MOF structure is directly related to the surface area and the polarity of the surface

Summary

19 Zn-MOF-74 885

CO2 uptake (mmol/g) 0–3.20 0−10.4 0–10.4 0−10.7 0–14.8 0–18.9 0–19.7 0–22.0 0–33.9 1.16–16.99 0.09–15.15 0.28–16.56 1.79–30.17 0 -22.84 0–30.34 0–21.51 0–19.36 0–13.55 0–10.65 0–11.83 0–33.9. To conceptualize and perceive the effect of various parameters on the MOF’s C O2 uptake capacity, the authors have incorporated temperature (K), pressure (bar), surface area (m2/g) and pore volume (cm3/g). To provide a precise and reliable set of models, deliberately about 80% of data points were devoted to model establishment and training phase and just about 20% were considered for testing phase. Having a range of uncertainties and being associated with outliers, experimental data can introduce errors in the modeling process. To prevent any undesirable and not reliable outcome, the experimental models must undergo data evaluation and outlier detection. For the case of C O2 adsorption on various MOFs, the faulty data points will enormously impact the preciseness of the predicting models. This study seeks benefits from the well-known leverage value procedure for spotting an outlier[68]

25 XGBoost

Results and discussion

Conclusions