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Chapter 6. Design and Environmental Impact Analysis of a Hybrid PSA-Membrane Separation System value at the end and start of a cycle. In the present study, the following variables defined in Eq. 6.1 and 6.2 are used to determine CSS. Cavgi = ∫1 0 Ci(z∗)dz∗; ∫1 Tavg = z∗ = z/L; T(z∗)dz∗; i = 1, 2..NCOMP z∗ = z/L (6.1) (6.2) 0 Since, there are five components in the system, there are six such indicators for detecting CSS. The simulation stops when the maximum of the difference between the indicators at the start and end of a cycle becomes less than 10−4. From the simulations it is found that increasing cycletime also increases the hydrogen recovery but reduces the hydrogen purity. The best operating point corresponds to the cycletime for which the system achieves the maximum recovery with hydrogen purity being more than 99.99 %. For the present system, this comes at the value of 480 seconds, with hydrogen recovery at 56.7 %, and hydrogen product purity at 99.995 % . 6.4 Hybrid PSA-Membrane Simulation Study The best method to design PSA membrane hybrid is by performing a full-scale dynamic optimization, with the objective of maximizing the PSA hydrogen prod- uct recovery. The key decision variables such as PSA cycletime, PSA bed length, number of beds, bed diameter and membrane area can then be estimated, with PSA hydrogen product purity and membrane CO2 purity acting as constraint for the optimization problem. However, such a detailed dynamic optimization prob- lem involving both PSA and membrane is quite time consuming since it involves repeated CSS evaluation for each new iteration, as different decision variables 147PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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