Molecular dynamics simulations are performed to observe the evolutions of 512 and 51262 cage-like water clusters filled with or without a methane molecule immersed in bulk liquid water at 250 K and 230 K. The lifetime...Molecular dynamics simulations are performed to observe the evolutions of 512 and 51262 cage-like water clusters filled with or without a methane molecule immersed in bulk liquid water at 250 K and 230 K. The lifetimes of these clusters are calculated according to their Lindemann index δ (t) using the criteria of δ≥0.07. For both the filled and empty clusters, we find the dynamics of bulk water determines the lifetimes of cage-like water clusters, and that the lifetime of 512 62 cage-like cluster is the same as that of 512 cage-like cluster. Although the methane molecule indeed makes the filled cage-like cluster more stable than the empty one, the empty cage-like cluster still has chance to be long-lived compared with the filled clusters. These observations support the labile cluster hypothesis on the formation mechanisms of gas hydrates.展开更多
The occurrence of liquid condensation in natural gas accounts for new challenges during the interoperability between transmission networks,where condensation would lead to higher pressure drops,lower line capacity and...The occurrence of liquid condensation in natural gas accounts for new challenges during the interoperability between transmission networks,where condensation would lead to higher pressure drops,lower line capacity and may cause safety problem.A successful case of hydrocarbon dew point(HCDP)analysis is demonstrated during the mixing of natural gases in the transmission pipeline.Methods used to predict the HCDP are combined with equations of state(EOS)and characterization of C6+heavy components.Predictions are compared with measured HCDP with different concentrations of mixed gases at a wide range of pressure and temperature scopes.Software named"PipeGasAnalysis"is developed and helps to systematic analyze the condensation problem,which will provide the guidance for the design and operation of the network.展开更多
The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow...The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.展开更多
As an emerging zero-dimensional nano crystalline porous material,porous organic cages(POCs)with soluble properties in organic solvents,are promising candidates as molecular fillers in mixed matrix membranes(MMMs).The ...As an emerging zero-dimensional nano crystalline porous material,porous organic cages(POCs)with soluble properties in organic solvents,are promising candidates as molecular fillers in mixed matrix membranes(MMMs).The pore structure of POCs should be adjusted to trigger efficient gas separation performance,and the interaction between filler and matrix should be optimized.In this work,ionic liquid(IL)was introduced into the molecular fillers of CC3,to construct the IL@CC3/PIM-1 membrane to effectively separate CO_(2) from CH_(4).The advantages of doping IL include:(1)narrowing the cavity size of POCs from 4.4 to 3.9Åto enhance the diffusion selectivity,(2)strengthening the CO_(2) solubility to heighten the gas permeability,and(3)improving the compatibility between filler and matrix to upgrade membrane stability.After the optimization of the membrane composite,the IL@CC3/PIM-1-10%membrane possesses the CO_(2) permeability of 7868 Barrer and the CO_(2)/CH_(4) selectivity of 73.4,which compared to the CC3/PIM-1-10%membrane,improved by 15.9%and 106.2%,respectively.Furthermore,the membrane has maintained a stable separation performance at varied temperatures and pressures during the long-term test.The proposed method offers an efficient way to improve the performance of POCs-based MMMs in gas separation.展开更多
The mixing rule for a new group-contribution equation of state was proposed by combining the excess Gibbs energy model with the modified Hard-Sphere Three-Parameter Equation of State designated as the MCSPT equation. ...The mixing rule for a new group-contribution equation of state was proposed by combining the excess Gibbs energy model with the modified Hard-Sphere Three-Parameter Equation of State designated as the MCSPT equation. Low-and high-pressure vapor-liquid equilibria of 28 binary and 9 ternary systems containing strongly polar substances were predicted by using the interaction parameters of the original and modified UNIFAC model. Predicted results have shown that the proposed GO-MCSPT equation has an extensive applicability with satisfactory accuracy.展开更多
研究了用离子液体(ILs)萃取分离混合C_4烃(C_4)中微量甲醇的过程.考察了不同组成的离子液体的萃取性能,发现1-丁基-3-甲基咪唑磷酸二丁酯([Bmim][DBP])具有最佳的萃取性能.采用量子化学方法,研究了[Bmim][DBP]与甲醇的作用机理.结果表明...研究了用离子液体(ILs)萃取分离混合C_4烃(C_4)中微量甲醇的过程.考察了不同组成的离子液体的萃取性能,发现1-丁基-3-甲基咪唑磷酸二丁酯([Bmim][DBP])具有最佳的萃取性能.采用量子化学方法,研究了[Bmim][DBP]与甲醇的作用机理.结果表明,在离子液体阴、阳离子以及离子液体与甲醇之间均存在稳定的氢键,并且氢键加强了分子间的相互作用.[Bmim][DBP]的阴离子[DBP]^-与甲醇中的—OH形成了键长为0.171 nm的氢键,其相互作用能为-62.08 k J/mol,强于其它阴离子与甲醇的相互作用能.还探讨了[Bmim][DBP]离子液体与混合C_4烃的比例、萃取时间及离子液体循环次数等因素对萃取效果的影响,结果表明,当m(ILs)∶m(C_4)=1∶2,于25℃萃取60 min时,萃取率为99.65%,离子液体循环使用5次后萃取率仍保持稳定.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.40102005 and No.49725205).
文摘Molecular dynamics simulations are performed to observe the evolutions of 512 and 51262 cage-like water clusters filled with or without a methane molecule immersed in bulk liquid water at 250 K and 230 K. The lifetimes of these clusters are calculated according to their Lindemann index δ (t) using the criteria of δ≥0.07. For both the filled and empty clusters, we find the dynamics of bulk water determines the lifetimes of cage-like water clusters, and that the lifetime of 512 62 cage-like cluster is the same as that of 512 cage-like cluster. Although the methane molecule indeed makes the filled cage-like cluster more stable than the empty one, the empty cage-like cluster still has chance to be long-lived compared with the filled clusters. These observations support the labile cluster hypothesis on the formation mechanisms of gas hydrates.
基金Project(2011ZX05026-004-03)supported by the Key National Science and Technology Specific Program,ChinaProject(NCET-12-0969)supported by the Program for New Century Excellent Talents in University,China+1 种基金Project(51104167)supported by the National Natural Science Foundation of ChinaProject(BJ-2011-02)supported by the Research Funds of China University of Petroleum-Beijing
文摘The occurrence of liquid condensation in natural gas accounts for new challenges during the interoperability between transmission networks,where condensation would lead to higher pressure drops,lower line capacity and may cause safety problem.A successful case of hydrocarbon dew point(HCDP)analysis is demonstrated during the mixing of natural gases in the transmission pipeline.Methods used to predict the HCDP are combined with equations of state(EOS)and characterization of C6+heavy components.Predictions are compared with measured HCDP with different concentrations of mixed gases at a wide range of pressure and temperature scopes.Software named"PipeGasAnalysis"is developed and helps to systematic analyze the condensation problem,which will provide the guidance for the design and operation of the network.
文摘The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.
基金supported by the National Natural Science Foundation of China(Nos.21875285,22171288,22005340)the Key Research and Development Projects of Shandong Province(No.2019JZZY010331)+2 种基金the Natural Science Foundation of Shandong Province(Nos.ZR2020MB017,ZR2022MB009)the Fundamental Research Funds for the Central Universities(No.23CX07004A)the Outstanding Youth Science Fund Projects of Shandong Province(Nos.2022HWYQ-070,ZR2022YQ15).
文摘As an emerging zero-dimensional nano crystalline porous material,porous organic cages(POCs)with soluble properties in organic solvents,are promising candidates as molecular fillers in mixed matrix membranes(MMMs).The pore structure of POCs should be adjusted to trigger efficient gas separation performance,and the interaction between filler and matrix should be optimized.In this work,ionic liquid(IL)was introduced into the molecular fillers of CC3,to construct the IL@CC3/PIM-1 membrane to effectively separate CO_(2) from CH_(4).The advantages of doping IL include:(1)narrowing the cavity size of POCs from 4.4 to 3.9Åto enhance the diffusion selectivity,(2)strengthening the CO_(2) solubility to heighten the gas permeability,and(3)improving the compatibility between filler and matrix to upgrade membrane stability.After the optimization of the membrane composite,the IL@CC3/PIM-1-10%membrane possesses the CO_(2) permeability of 7868 Barrer and the CO_(2)/CH_(4) selectivity of 73.4,which compared to the CC3/PIM-1-10%membrane,improved by 15.9%and 106.2%,respectively.Furthermore,the membrane has maintained a stable separation performance at varied temperatures and pressures during the long-term test.The proposed method offers an efficient way to improve the performance of POCs-based MMMs in gas separation.
基金Supported by the National Natural Science Foundation of China
文摘The mixing rule for a new group-contribution equation of state was proposed by combining the excess Gibbs energy model with the modified Hard-Sphere Three-Parameter Equation of State designated as the MCSPT equation. Low-and high-pressure vapor-liquid equilibria of 28 binary and 9 ternary systems containing strongly polar substances were predicted by using the interaction parameters of the original and modified UNIFAC model. Predicted results have shown that the proposed GO-MCSPT equation has an extensive applicability with satisfactory accuracy.
文摘研究了用离子液体(ILs)萃取分离混合C_4烃(C_4)中微量甲醇的过程.考察了不同组成的离子液体的萃取性能,发现1-丁基-3-甲基咪唑磷酸二丁酯([Bmim][DBP])具有最佳的萃取性能.采用量子化学方法,研究了[Bmim][DBP]与甲醇的作用机理.结果表明,在离子液体阴、阳离子以及离子液体与甲醇之间均存在稳定的氢键,并且氢键加强了分子间的相互作用.[Bmim][DBP]的阴离子[DBP]^-与甲醇中的—OH形成了键长为0.171 nm的氢键,其相互作用能为-62.08 k J/mol,强于其它阴离子与甲醇的相互作用能.还探讨了[Bmim][DBP]离子液体与混合C_4烃的比例、萃取时间及离子液体循环次数等因素对萃取效果的影响,结果表明,当m(ILs)∶m(C_4)=1∶2,于25℃萃取60 min时,萃取率为99.65%,离子液体循环使用5次后萃取率仍保持稳定.
