基于机器学习的多孔碳材料吸附CO<sub>2</sub>的关键因素

许大伟; 杨榛

doi:10.7524/j.issn.0254-6108.2023022301

[1]

CAO L, LEI S, GUAN Y X, et al. CCUS industry under target of carbon-peak and carbon-neutrality: Progress and challenges[J]. Frontiers in Energy Research, 2022, 10: 860665. doi: 10.3389/fenrg.2022.860665

[2]

CHAPMAN A, ERTEKIN E, KUBOTA M, et al. Achieving a carbon neutral future through advanced functional materials and technologies[J]. Bulletin of the Chemical Society of Japan, 2022, 95(1): 73-103. doi: 10.1246/bcsj.20210323

[3]

RAHMAN F A, AZIZ M M A, SAIDUR R, et al. Pollution to solution: Capture and sequestration of carbon dioxide (CO₂) and its utilization as a renewable energy source for a sustainable future[J]. Renewable and Sustainable Energy Reviews, 2017, 71: 112-126. doi: 10.1016/j.rser.2017.01.011

[4]

王燕. 烟气中二氧化碳的化学处理技术及应用研究[J]. 科技信息, 2011(4): 114. WANG Y. Chemical treatment technology and application research of carbon dioxide in flue gas[J]. Science & Technology Information, 2011(4): 114 (in Chinese).

[5]

CREAMER A E, GAO B. Carbon-based adsorbents for postcombustion CO₂ capture: A critical review[J]. Environmental Science & Technology, 2016, 50(14): 7276-7289.

[6]

ZHANG Z H, SCHOTT J A, LIU M M, et al. Prediction of carbon dioxide adsorption via deep learning[J]. Angewandte Chemie International Edition, 2019, 58(1): 259-263. doi: 10.1002/anie.201812363

[7]

杨榛, 顾幸生, 梁晓怿, 等. 基于PCA-GA神经网络模式识别的炭纤维复合材料导电综合性能优化及预测的研究[J]. 计算机与应用化学, 2008, 25(12): 1543-1548. YANG Z, GU X S, LIANG X Y, et al. Optimization and prediction research on carbon fiber composite integrated conductive performance based on PCA-GANN pattern recognition[J]. Computers and Applied Chemistry, 2008, 25(12): 1543-1548(in Chinese).

[8]

MAULANA KUSDHANY M I, LYTH S M. New insights into hydrogen uptake on porous carbon materials via explainable machine learning[J]. Carbon, 2021, 179: 190-201. doi: 10.1016/j.carbon.2021.04.036

[9]

CAI J Z, CHU X, XU K, et al. Machine learning-driven new material discovery[J]. Nanoscale Advances, 2020, 2(8): 3115-3130. doi: 10.1039/D0NA00388C

[10]

YUAN X Z, SUVARNA M, LOW S, et al. Applied machine learning for prediction of CO₂ adsorption on biomass waste-derived porous carbons[J]. Environmental Science & Technology, 2021, 55(17): 11925-11936.

[11]

WANG S, LI Y, DAI S, et al. Prediction by convolutional neural networks of CO₂/N₂ selectivity in porous carbons from N₂ adsorption isotherm at 77 K[J]. Angewandte Chemie International Edition, 2020, 59(44): 19645-19648. doi: 10.1002/anie.202005931

[12]

ZHU X Z, TSANG D C W, WANG L, et al. Machine learning exploration of the critical factors for CO₂ adsorption capacity on porous carbon materials at different pressures[J]. Journal of Cleaner Production, 2020, 273: 122915. doi: 10.1016/j.jclepro.2020.122915

[13]

HAGHIGHATLARI M, HACHMANN J. Advances of machine learning in molecular modeling and simulation[J]. Current Opinion in Chemical Engineering, 2019, 23: 51-57. doi: 10.1016/j.coche.2019.02.009

[14]

刘楠. 煤基多孔碳的制备、改性及其对CO₂/CH₄吸附分离研究[D]. 焦作: 河南理工大学, 2020. LIU N. Study on preparation and modification of coal-based porous carbon and its adsorption and separation for CO₂/CH₄[D]. Jiaozuo: Henan Polytechnic University, 2020(in Chinese).

[15]

张涛, 王彬彬, 李瑶. 基于玉米秸秆的氮掺杂多孔碳制备及其对CO₂吸附和CO₂/N₂分离性能研究[J]. 河南理工大学学报(自然科学版), 2022, 41(6): 174-180. ZHANG T, WANG B B, LI Y. Preparation ofstalk-based nitrogen-doped porouscarbon and itsCO₂ adsorption and CO₂/N₂ separation properties[J]. Journal of Henan Polytechnic University (Natural Science), 2022, 41(6): 174-180(in Chinese).

[16]

王娅鸽, 王彬彬, 杨德威, 等. 氮掺杂柔性块体多孔碳的制备及其对CO₂/CH₄吸附分离研究[J]. 材料导报, 2023, 37(22): 22050326. WANG Y G, WANG B B, YANG D W, et al. Preparation of nitrogen-doped flexible bulk porous carbon and its study on CO₂/CH₄ adsorption and separation[J]. Materials Reports, 2023, 37(22): 22050326(in Chinese).

[17]

熊龙. 氮掺杂多孔碳纤维的制备及其二氧化碳吸附性能研究[D]. 昆明: 昆明理工大学, 2020. XIONG L. Preparation of nitrogen doped porous carbon fiber and its carbon dioxide adsorption performance[D]. Kunming: Kunming University of Science and Technology, 2020 (in Chinese).

[18]

刘康恺, 韩云龙, 孟龙月, 等. Zeolites模板法制备氮掺杂多孔碳材料及其CO₂吸附性能[J]. 实验室研究与探索, 2018, 37(9): 29-32. LIU K K, HAN Y L, MENG L Y, et al. Preparation and performance of CO₂ adsorption of N-doped porous carbons by zeolites-template method[J]. Research and Exploration in Laboratory, 2018, 37(9): 29-32(in Chinese).

[19]

SONG C C, LIU M H, YE W Y, et al. Nitrogen-containing porous carbon for highly selective and efficient CO₂ capture[J]. Energy & Fuels, 2019, 33(12): 12601-12609.

[20]

QIN F F, GUO Z Y, WANG J S, et al. Nitrogen-doped asphaltene-based porous carbon nanosheet for carbon dioxide capture[J]. Applied Surface Science, 2019, 491: 607-615. doi: 10.1016/j.apsusc.2019.06.194

[21]

KIM Y K, KIM G M, LEE J W. Highly porous N-doped carbons impregnated with sodium for efficient CO₂ capture[J]. Journal of Materials Chemistry A, 2015, 3(20): 10919-10927. doi: 10.1039/C5TA01776A

[22]

WANG J C, SENKOVSKA I, OSCHATZ M, et al. Highly porous nitrogen-doped polyimine-based carbons with adjustable microstructures for CO₂ capture[J]. Journal of Materials Chemistry A, 2013, 1(36): 10951-10961. doi: 10.1039/c3ta11995e

[23]

FU N, YANG B, WANG Y, et al. Micromeso hierarchical porous ultrathin nitrogen-doped carbon nanosheets with rough surfaces for efficient gas adsorption and separation[J]. Energy & Fuels, 2022, 36(22): 13705-13712.

[24]

SHARMA M, SNYDER M A. Facile synthesis of flower-like carbon microspheres for carbon dioxide capture[J]. Microporous and Mesoporous Materials, 2022, 335: 111801. doi: 10.1016/j.micromeso.2022.111801

[25]

KOU J H, SUN L B. Fabrication of nitrogen-doped porous carbons for highly efficient CO₂ capture: Rational choice of a polymer precursor[J]. Journal of Materials Chemistry A, 2016, 4(44): 17299-17307. doi: 10.1039/C6TA07305K

[26]

PARSHETTI G K, CHOWDHURY S, BALASUBRAMANIAN R. Biomass derived low-cost microporous adsorbents for efficient CO₂ capture[J]. Fuel, 2015, 148: 246-254. doi: 10.1016/j.fuel.2015.01.032

[27]

RAO L L, LIU S F, WANG L L, et al. N-doped porous carbons from low-temperature and single-step sodium amide activation of carbonized water chestnut shell with excellent CO₂ capture performance[J]. Chemical Engineering Journal, 2019, 359: 428-435. doi: 10.1016/j.cej.2018.11.065

[28]

THRUN M C, GEHLERT T, ULTSCH A. Analyzing the fine structure of distributions[J]. PLoS One, 2020, 15(10): e0238835. doi: 10.1371/journal.pone.0238835

[29]

戚兴怡, 胡耀峰, 王若愚, 等. 机器学习在新材料筛选方面的应用进展[J]. 化学学报, 2023, 81(2): 158-174. doi: 10.6023/A22110446 QI X Y, HU Y F, WANG R Y, et al. Recent advance of machine learning in selecting new materials[J]. Acta Chimica Sinica, 2023, 81(2): 158-174 (in Chinese). doi: 10.6023/A22110446

[30]

FERNÁNDEZ-DELGADO M, SIRSAT M S, CERNADAS E, et al. An extensive experimental survey of regression methods[J]. Neural Networks, 2019, 111: 11-34. doi: 10.1016/j.neunet.2018.12.010

[31]

李炜, 梁添贵, 林元创, 等. 机器学习辅助高通量筛选金属有机骨架材料[J]. 化学进展, 2022, 34(12): 2619-2637. LI W, LIANG T G, LIN Y C, et al. Machine learning accelerated high-throughput computational screening of metal-organic frameworks[J]. Progress in Chemistry, 2022, 34(12): 2619-2637 (in Chinese).

[32]

TANVEER M, RAJANI T, RASTOGI R, et al. Comprehensive review on twin support vector machines[J]. Annals of Operations Research, 2022: 1-46.

[33]

SITEK W, TRZASKA J. Practical aspects of the design and use of the artificial neural networks in materials engineering[J]. Metals, 2021, 11(11): 1832. doi: 10.3390/met11111832

[34]

PALLE K, VUNGUTURI S, GAYATRI S N, et al. The prediction of CO₂ adsorption on rice husk activated carbons via deep learning neural network[J]. MRS Communications, 2022, 12(4): 434-440. doi: 10.1557/s43579-022-00197-2

[35]

AURET L, ALDRICH C. Interpretation of nonlinear relationships between process variables by use of random forests[J]. Minerals Engineering, 2012, 35: 27-42. doi: 10.1016/j.mineng.2012.05.008

[36]

ARASHI M, LUKMAN A F, ALGAMAL Z Y. Liu regression after random forest for prediction and modeling in high dimension[J]. Journal of Chemometrics, 2022, 36(4): e3393. doi: 10.1002/cem.3393

[37]

MIZUMOTO A. Calculating the relative importance of multiple regression predictor variables using dominance analysis and random forests[J]. Language Learning, 2023, 73(1): 161-196. doi: 10.1111/lang.12518

[38]

ZHANG X, ZHANG S H, YANG H P, et al. Influence of NH₃/CO₂ modification on the characteristic of biochar and the CO₂ capture[J]. BioEnergy Research, 2013, 6(4): 1147-1153. doi: 10.1007/s12155-013-9304-9

[39]

OUYANG L K, XIAO J F, JIANG H S, et al. Nitrogen-doped porous carbon materials derived from graphene oxide/melamine resin composites for CO₂ adsorption[J]. Molecules, 2021, 26(17): 5293. doi: 10.3390/molecules26175293

[40]

QI S C, LIU Y, PENG A Z, et al. Fabrication of porous carbons from mesitylene for highly efficient CO₂ capture: A rational choice improving the carbon loop[J]. Chemical Engineering Journal, 2019, 361: 945-952. doi: 10.1016/j.cej.2018.12.167