Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Ali A. AlHammadi, Milad Kamkar, Ali Elkamel
{"title":"利用机器学习算法开发多孔吸附材料的二氧化碳吸附模型","authors":"Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Ali A. AlHammadi, Milad Kamkar, Ali Elkamel","doi":"10.1021/acsaem.4c01465","DOIUrl":null,"url":null,"abstract":"Porous adsorbents have common characteristics, such as high porosity and a large specific surface area. These characteristics, attributed to the internal structure of the material, significantly affect their adsorption performance. In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO<sub>2</sub> adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO<sub>2</sub> adsorption performance as a function of characteristics and adsorption isotherm parameters. XGBoost was selected as the best ML algorithm due to its highest accuracy (<i>R</i><sup>2</sup> = 0.9980; MSE = 0.0001). The predicted results revealed that the adsorption pressure parameter is the most effective in all of the mentioned porous adsorbents. With regard to materials type, while carbon-based materials require higher pressures for a more effective CO<sub>2</sub> adsorption, MOFs exhibit a higher potential for adsorbing CO<sub>2</sub> under lower pressure conditions. The study also revealed that carbon-based adsorbents, zeolites, and porous polymers with smaller pore diameters demonstrate a high level of CO<sub>2</sub> uptake. In contrast, the adsorption performance of MOFs does not show a consistent trend with respect to pore sizes. Also, in all adsorbents, the effect of a pore size smaller than 1 nm on more CO<sub>2</sub> adsorption was evident.","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of the CO2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms\",\"authors\":\"Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Ali A. AlHammadi, Milad Kamkar, Ali Elkamel\",\"doi\":\"10.1021/acsaem.4c01465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Porous adsorbents have common characteristics, such as high porosity and a large specific surface area. These characteristics, attributed to the internal structure of the material, significantly affect their adsorption performance. In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO<sub>2</sub> adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO<sub>2</sub> adsorption performance as a function of characteristics and adsorption isotherm parameters. XGBoost was selected as the best ML algorithm due to its highest accuracy (<i>R</i><sup>2</sup> = 0.9980; MSE = 0.0001). The predicted results revealed that the adsorption pressure parameter is the most effective in all of the mentioned porous adsorbents. With regard to materials type, while carbon-based materials require higher pressures for a more effective CO<sub>2</sub> adsorption, MOFs exhibit a higher potential for adsorbing CO<sub>2</sub> under lower pressure conditions. The study also revealed that carbon-based adsorbents, zeolites, and porous polymers with smaller pore diameters demonstrate a high level of CO<sub>2</sub> uptake. In contrast, the adsorption performance of MOFs does not show a consistent trend with respect to pore sizes. Also, in all adsorbents, the effect of a pore size smaller than 1 nm on more CO<sub>2</sub> adsorption was evident.\",\"PeriodicalId\":4,\"journal\":{\"name\":\"ACS Applied Energy Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Energy Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1021/acsaem.4c01465\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acsaem.4c01465","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Development of the CO2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms
Porous adsorbents have common characteristics, such as high porosity and a large specific surface area. These characteristics, attributed to the internal structure of the material, significantly affect their adsorption performance. In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO2 adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO2 adsorption performance as a function of characteristics and adsorption isotherm parameters. XGBoost was selected as the best ML algorithm due to its highest accuracy (R2 = 0.9980; MSE = 0.0001). The predicted results revealed that the adsorption pressure parameter is the most effective in all of the mentioned porous adsorbents. With regard to materials type, while carbon-based materials require higher pressures for a more effective CO2 adsorption, MOFs exhibit a higher potential for adsorbing CO2 under lower pressure conditions. The study also revealed that carbon-based adsorbents, zeolites, and porous polymers with smaller pore diameters demonstrate a high level of CO2 uptake. In contrast, the adsorption performance of MOFs does not show a consistent trend with respect to pore sizes. Also, in all adsorbents, the effect of a pore size smaller than 1 nm on more CO2 adsorption was evident.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.