Is it possible to improve CO_(2)solubility in potassium carbonate(K_(2)CO_(3))-based transition temperature mixtures(TTMs)?To assess this possibility,a ternary transition-temperature mixture(TTTM)was prepared by using...Is it possible to improve CO_(2)solubility in potassium carbonate(K_(2)CO_(3))-based transition temperature mixtures(TTMs)?To assess this possibility,a ternary transition-temperature mixture(TTTM)was prepared by using a hindered amine,2-amino-2-methyl-1,3-propanediol(AMPD).Fourier transform infrared spectroscopy(FT-IR)was employed to detect the functional groups including hydroxyl,amine,carbonate ion,and aliphatic functional groups in the prepared solvents.From thermogravimetric analysis(TGA),it was found that the addition of AMPD to the binary mixture can increase the thermal stability of TTTM.The viscosity findings showed that TTTM has a higher viscosity than TTM while their difference was decreased by increasing temperature.In addition,Eyring’s absolute rate theory was used to compute the activation parameters(∆G^(*),∆H^(*),and ∆S^(*)).The CO_(2)solubility in liquids was measured at a temperature of 303.15 K and pressures up to 1.8 MPa.The results disclosed that the CO_(2)solubility of TTTM was improved by the addition of AMPD.At the pressure of about 1.8 MPa,the CO_(2)mole fractions of TTM and TTTM were 0.1697 and 0.2022,respectively.To confirm the experimental data,density functional theory(DFT)was employed.From the DFT analysis,it was found that the TTTM+CO_(2)system has higher interaction energy(|∆E|)than the TTM+CO_(2)system indicating the higher CO_(2)affinity of the former system.This study might help scientists to better understand and to improve CO_(2)solubility in these types of solvents by choosing a suitable amine as HBD and finding the best combination of HBA and HBD.展开更多
Carbon dioxide-abated hydrogen can be synthesised via various processes,one of which is sorption enhanced steam methane reforming(SE-SMR),which produces separated streams of high purity H_(2) and CO_(2).Properties of ...Carbon dioxide-abated hydrogen can be synthesised via various processes,one of which is sorption enhanced steam methane reforming(SE-SMR),which produces separated streams of high purity H_(2) and CO_(2).Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR,therefore the use of artificial intelligence models is useful in order to assist scale up.Advantages of a data driven soft-sensor model over ther-modynamic simulations,is the ability to obtain real time information dependent on actual process conditions.In this study,two soft sensor models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.Both artificial neural networks and the random forest models were devel-oped as soft sensor prediction models.They were shown to provide good predictions for gas concentrations in the reformer and regenerator reactors of the SE-SMR process using temperature,pressure,steam to carbon ratio and sorbent to carbon ratio as input process features.Both models were very accurate with high R^(2) values,all above 98%.However,the random forest model was more precise in the predictions,with consistently higher R^(2) values and lower mean absolute error(0.002-0.014)compared to the neural network model(0.005-0.024).展开更多
基金support of National Key Research and Development Program of China(Grant No.2019YFC1904602)the Key SCI-Tech Innovation 2025 Program of Ningbo,China(Grant No.2018B10025).
文摘Is it possible to improve CO_(2)solubility in potassium carbonate(K_(2)CO_(3))-based transition temperature mixtures(TTMs)?To assess this possibility,a ternary transition-temperature mixture(TTTM)was prepared by using a hindered amine,2-amino-2-methyl-1,3-propanediol(AMPD).Fourier transform infrared spectroscopy(FT-IR)was employed to detect the functional groups including hydroxyl,amine,carbonate ion,and aliphatic functional groups in the prepared solvents.From thermogravimetric analysis(TGA),it was found that the addition of AMPD to the binary mixture can increase the thermal stability of TTTM.The viscosity findings showed that TTTM has a higher viscosity than TTM while their difference was decreased by increasing temperature.In addition,Eyring’s absolute rate theory was used to compute the activation parameters(∆G^(*),∆H^(*),and ∆S^(*)).The CO_(2)solubility in liquids was measured at a temperature of 303.15 K and pressures up to 1.8 MPa.The results disclosed that the CO_(2)solubility of TTTM was improved by the addition of AMPD.At the pressure of about 1.8 MPa,the CO_(2)mole fractions of TTM and TTTM were 0.1697 and 0.2022,respectively.To confirm the experimental data,density functional theory(DFT)was employed.From the DFT analysis,it was found that the TTTM+CO_(2)system has higher interaction energy(|∆E|)than the TTM+CO_(2)system indicating the higher CO_(2)affinity of the former system.This study might help scientists to better understand and to improve CO_(2)solubility in these types of solvents by choosing a suitable amine as HBD and finding the best combination of HBA and HBD.
文摘Carbon dioxide-abated hydrogen can be synthesised via various processes,one of which is sorption enhanced steam methane reforming(SE-SMR),which produces separated streams of high purity H_(2) and CO_(2).Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR,therefore the use of artificial intelligence models is useful in order to assist scale up.Advantages of a data driven soft-sensor model over ther-modynamic simulations,is the ability to obtain real time information dependent on actual process conditions.In this study,two soft sensor models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.Both artificial neural networks and the random forest models were devel-oped as soft sensor prediction models.They were shown to provide good predictions for gas concentrations in the reformer and regenerator reactors of the SE-SMR process using temperature,pressure,steam to carbon ratio and sorbent to carbon ratio as input process features.Both models were very accurate with high R^(2) values,all above 98%.However,the random forest model was more precise in the predictions,with consistently higher R^(2) values and lower mean absolute error(0.002-0.014)compared to the neural network model(0.005-0.024).
