Amine-functionalized low-cost industrial grade multi-walled carbon nanotubes for the capture of carbon dioxide

Industrial grade multi-walled carbon nanotubes(IG-MWCNTs) are a low-cost substitute for commercially purified multi-walled carbon nanotubes(P-MWCNTs). In this work, IG-MWCNTs were functionalized with tetraethylenepentamine(TEPA) for CO2capture. The TEPA impregnated IG-MWCNTs were characterized with various experimental methods including N2adsorption/desorption isotherms, elemental analysis, X-ray diffraction, Fourier transform infrared spectroscopy and thermogravimetric analysis. Both the adsorption isotherms of IGMWCNTs-n and the isosteric heats of different adsorption capacities were obtained from experiments. TEPA impregnated IG-MWCNTs were also shown to have high CO2adsorption capacity comparable to that of TEPA impregnated P-MWCNTs. The adsorption capacity of IG-MWCNTs based adsorbents was in the range of 2.145 to 3.088 mmol/g, depending on adsorption temperatures. Having the advantages of low-cost and high adsorption capacity, TEPA impregnated IG-MWCNTs seem to be a promising adsorbent for CO2capture from flue gas.

Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.