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mentioned properties. For this reason, this model is being considered valid and is going to be used further ahead in this work, assuming that the LDF factor, Ω𝑐, is equal 15 for both adsorbents. 4.4 Parameter estimation A detailed gPROMS® process model is constructed from equations describing the physical and chemical phenomena that take place in the system. These equations usually involve parameters that can be adjusted to make the model predictions match observed reality. gPROMS® contains parameter estimation capabilities that are going to be employed in the present work with the purpose of evaluating the capacities of this tool. With this tool, multiple parameters occurring in dynamic or steady-state models may be estimated. Parameter estimation in gPROMS® is based on the Maximum Likelihood formulation. In addition to the model parameters, there is also the option to additionally estimate the variance of the measuring accuracy. This accuracy can be parameterised by a constant variance model, a constant relative variance model or heteroscedastic variance model, which combines the constant variance and the constant relative variance model. Usually, the experimental data available in a PSA process is the breakthrough curve. This data can be introduced in gPROMS® with the purpose of estimating parameters as LDF coefficients, diffusivities, isotherm parameters, or any other parameter pretended by the user. It is important to take into consideration that, in order to obtain good estimations the model being used has to be working properly and ideally, should be robust. Also, the relation between the control variables and the parameter being estimated is extremely important. The control variables must be very sensitive to a change in the considered parameter. If a small change in the value of the parameter has no impact in the control variable, then the control variable is not suitable for the desired parameter estimation. The parameter estimation tool minimizes the residuals between the prediction and the measured data used for the estimation. All the errors associated with the gPROMS® models employed for the prediction and, all the errors related with the measured data are all introduced into the parameter estimation. The simulations being performed in this section take a considerable amount of time until an optimal result is provided. However, the possibility of fitting some parameters of a process in order to obtain a model that offers a good prediction of the phenomena taking place in real processes assumes an important role in simulation procedures. Pressure Swing Adsorption for Hydrogen Purification Modelling and Simulation 38PDF Image | PRESSURE SWING ADSORPTION FOR THE PURIFICATION OF HYDROGEN
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