PDF Publication Title:
Text from PDF Page: 062
conditions and initial guesses, however, with different lower and upper bounds for each component. The new lower and upper bounds employed for the following simulation are gathered in Appendix 5 (section 5.1.1). The parameters for these conditions were obtained after two major iterations and the results and respective confidence intervals are presented in Appendix 5 (Table 5.14). As can be seen in the analysis of these results, gPROMS® didn’t provide confidence intervals for methane in the activated carbon layer. This situation occurs when the parameter being estimated assumes the value of the upper or lower bound, preventing gPROMS® from being able to perform the iterations needed and, therefore, preventing it from providing the results. The comparison of the parameters estimated when considering a smaller interval where these values can change with the results obtained in the estimation with a higher interval of variation shows that the final values obtained differ more from the ones suggested in the literature [31]. However, better confidence intervals were obtained for hydrogen, carbon monoxide and nitrogen in both adsorbents, although the parameters for hydrogen are still estimated with a high level of uncertainty when compared with the remaining components. A problem associated with small intervals, between the lower and upper bounds, relies in the fact that several simulations are stopped due to the value being estimated “hitting” one of the bounds, like happened for methane in the present parameter estimation. For this reason, and also due to the fact that the values obtained with this method provided parameters considerably different from the ones in the literature, in the following simulations concerning the estimation of the LDF coefficients the lower and upper bounds equal to zero and one, respectively, are going to be assumed for all the components in both adsorbents. According to the component, the magnitude of the molar fraction used as measured data varies. For example, the molar fractions of hydrogen are always high considering that it is the component that is intended to be purified and the molar fractions of carbon dioxide are small due to its molar fraction in the feed mixture. For this reason, an attempt of using a relative constant variance model in the parameter estimation was made, taking into consideration that with this variance model errors are directly proportional to the magnitude of the measured value. The results for this simulation show that, while the initial iteration resembles with the measured data used as input for the parameter estimation procedure, the major iterations produced by this tool are quite different (see Appendix 5, Figure 5.13) which could explain the lack of sense in the final values obtained from this simulation (Appendix 5, Table 5.14). The analysis of the measured data provided for this estimation shows that the number of observations with lower molar fraction is higher than the number of observations with a high molar fraction. For this reason, the greater effort of gPROMS® is made for the lower molar Pressure Swing Adsorption for Hydrogen Purification Modelling and Simulation 42PDF Image | PRESSURE SWING ADSORPTION FOR THE PURIFICATION OF HYDROGEN
PDF Search Title:
PRESSURE SWING ADSORPTION FOR THE PURIFICATION OF HYDROGENOriginal File Name Searched:
32541.pdfDIY PDF Search: Google It | Yahoo | Bing
CO2 Organic Rankine Cycle Experimenter Platform The supercritical CO2 phase change system is both a heat pump and organic rankine cycle which can be used for those purposes and as a supercritical extractor for advanced subcritical and supercritical extraction technology. Uses include producing nanoparticles, precious metal CO2 extraction, lithium battery recycling, and other applications... More Info
Heat Pumps CO2 ORC Heat Pump System Platform More Info
CONTACT TEL: 608-238-6001 Email: greg@infinityturbine.com (Standard Web Page)