Estimation of CO2 adsorption in high capacity metal−organic frameworks: Applications to greenhouse gas control

Dashti, Amir; Bahrololoomi, Arash; Amirkhani, Farid; Mohammadi, Amir H.

doi:10.1016/j.jcou.2020.101256

Cited by 34 publications

(12 citation statements)

References 74 publications

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“…Four additional data analysis parameters including the correlation factor ( R 2 ), average absolute relative deviation (AARD), standard deviation (STD), and root mean squared error (RMSE) are shown in Table 4 to demonstrate the models’ performances. In the following equations, x stands for the CO 2 absorption 56 …”

Section: Resultsmentioning

confidence: 99%

“…The ANFIS combines the principles of the ANN and fuzzy logic to overcome the shortcomings of each model individually . The ANFIS consists of anodes in a five-layer network to build an inference system for predicting/estimating parameters in non-linear systems. , The fuzzy logic helps us to construct this inference through the training step . The ANFIS utilizes membership functions (MFs) to define each node output O i j , in which i is the number of nodes in layer j .…”

Section: Model Developmentmentioning

confidence: 99%

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Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

et al. 2021

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Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO 2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO 2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l -asparaginate, potassium l -glutaminate, sodium l -phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization–adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO 2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO 2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models’ results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO 2 loading capacities of AAs aqueous solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Model Developmentmentioning

confidence: 99%

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

et al. 2021

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“…Furthermore, CO 2 gas in the atmosphere reduces the oxygen concentration in the air and causes many diseases. Accordingly, the capture, adsorption, and conversion of CO 2 gas to the fine materials including cyclic carbonates, carbon monoxide, formic acid, and others is significant and has attracted considerable attention from scientists. − Within the last two decades, metal–organic frameworks (MOFs), due to some properties such as their high surface area and porosity and chemical and thermal stability, have received tremendous attention in many applications such as adsorption, separation, catalysis, gas storage, sensing, etc. − The other advantages of MOFs over other traditional porous solids are greater compositional and structural diversity and more accessible tailoring of properties and applications by judicious selection of the inorganic nodes and organic linkers, both presynthesis and postsynthesis. ,, However, high energy and time-consumption are the greatest disadvantages of the mentioned approaches, which leads to high costs in production and their application. Furthermore, different morphologies and particle sizes are crucial issues in metal–organic frameworks.…”

Section: Introductionmentioning

confidence: 99%

Modulating Carbon Dioxide Storage by Facile Synthesis of Nanoporous Pillared-Layered Metal–Organic Framework with Different Synthetic Routes

2022

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A Zn(II)-based paddle wheel pillared-layered metal−organic framework, [Zn 2 (DBrTPA) 2 (DABCO)].(DMF) 2 (MUT-4), containing 1,4-diazabicyclo[2.2.2]octane (DABCO) and 2,5-dibromoterephthalic acid (DBrTPA) has been successfully synthesized with different synthetic methods, including solvothermal, sonochemical, and their mixing methods, some of which are energy-efficient, rapid, and room-temperature synthetic procedures. Structural characterization of MUT-4 with single-crystal Xray crystallography showed that it crystallizes in the tetragonal I4 1 / acd space group. MUT-4 has shown higher performance than known MOFs in the CO 2 adsorption such as UiO-66, UiO-66-

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“…Reviewing literature, the authors gathered 1191 data points related to the adsorption capacity of different MOFs at various temperatures and pressures. The investigated MOFs are ZIF-8, Zn-MOF-74, Mg-MOF-74, PCN-16, MOF-5, PCN-11, BeBTB, Co-BDP, Mg 2 (dobdc), Cu-BTTri, MOF-177, IRMOF-1, IRMOF-6, IRMOF-3, IRMOF-11, Cu-BTC, MOF-505, MOF-74, and MOF-2 50 – 53 .…”

Section: Introductionmentioning

confidence: 99%

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

Abdi

Hadavimoghaddam

Hadipoor

et al. 2021

Sci Rep

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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.

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Estimation of CO2 adsorption in high capacity metal−organic frameworks: Applications to greenhouse gas control

Cited by 34 publications

References 74 publications

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Modulating Carbon Dioxide Storage by Facile Synthesis of Nanoporous Pillared-Layered Metal–Organic Framework with Different Synthetic Routes

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

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Estimation of CO2 adsorption in high capacity metal−organic frameworks: Applications to greenhouse gas control

Cited by 34 publications

References 74 publications

Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Modulating Carbon Dioxide Storage by Facile Synthesis of Nanoporous Pillared-Layered Metal–Organic Framework with Different Synthetic Routes

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

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Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods