Parameters Affecting Column Performance

The binary model, conveniently represented on the Y-X or H-X diagrams, demonstrates the effect on column performance of the reflux ratio, product rate, number of trays, feed location, and thermal conditions. In the cases that follow in this section, the feed is assumed of fixed flow rate and composition. The effect of column pressure, which influences column performance through its effect on the equilibrium curve, is not considered here and is held constant. 

---

In bubble columns, since the gas bubbles are dispersed in the continuous liquid phase, fractional gas holdup (Eg) is an important design parameter, affecting column performance. The most direct and obvious effect is on the column volume, since a significant fraction of the volume is occupied by the gas. The indirect influences are also important. For instance, the possible spatial variation of Eg gives rise to pressure variation, which results in intense liquid phase motion. These secondary motions govern the rates of mixing plus heat and mass transfer. 

For purification, scale-up considerations are important even in the earliest phases of development. It is important to avoid the use of purification techniques of limited scale-up potential even for early clinical production because thorough justification of process changes and demonstration of biochemical comparability are necessary prior to product licensure. For successful scale-up, it is important to understand the critical parameters affecting the performance of each purification step at each scale. Conversely, it is important to verify that the scaled-down process is an accurate representation of the scaled-up process, so that process validation studies, such as viral clearance and column lifetime studies, can be performed at the laboratory scale. 

Based on the nonlinear pushover test results, it is clear that a residual drift ratio of 1% does not correspond to a specific column drift ratio, since many parameters affect the performance of these columns. However, it is interesting to stress that there is a zone of the drift ratio between 2 and 3.5% within which the recoverability limit state could be checked. Hence, the endpoint of the recoverable state can be defined by evaluating the residual inclination value within this zone. Ultimately, Fahmy et al. (2009) recommend three limit states for FRP-RC bridge columns as shown in Fig. 14.8. The first state is the state of pure recoverability whose end corresponds to column drift ratio 2% as shown Fig. 14.8. Here, the residual deformation of all the represented columns is below the recoverability limit. The second state is the state of recoverability limit check which falls between the 2% and 3.5% column drift ratios. The third state is the irrecoverable one, where the residual deformations exceed the recoverability limit. 

The key operational parameters for packed columns are the same as those seen in tray columns — there are three gas and liquid loads and the pressure drop along the column. Together these three factors not only affect column performance but are the determining factors in the choice of packing size, packing type, and column diameter. They also have practical limits to their range, a feature we saw before in the case of tray columns. 

The configuration of the GC system also affects the result. Many factors, such as column type, gas flow control, and temperature programming, if not set up correctly, will affect the performance of the GC. In the authors opinion, the first thing to do in a GC analysis is to choose the right column. One can then elucidate the optimal conditions for other factors (see Critical Parameters). 

R. Snell, J. Danielson, et al., Parameters affecting the quantitation performance of cold on-column and splitless injection systems used in capillary gas chromatography, J. Chromatogr. Sci., 25 225-230 (1987). 

Thus, with all the considerations of plate theory, there are several parameters that affect the performance of an SPE column because of plate number. They are as follows ... 

Whether you use a standardized test or your assay, it is worthwhile to check column performance on a regular basis and keep a log of it With today s computerized HPLC instruments this is fairly easy to do, and you can ea y generate control charts of the important column characteristics. I recommend monitoring for at least one peak plate count, peak symmetry, and retention time, and relative retention for a critical pair of analytes. Resolution is not as instructive a parameter, since it is affected by both plate count and relative retention. Thus it does not tell you anything about which of the underlying parameters is changing. 

The US DOE had a major effort to understand the many variables affecting the performance of a bubble column reactor. Dudukovic and Toseland [75] outlined the cooperative study by Air Products and Chemicals (APC), Ohio State University (OSU), Sandia National Laboratory (SNL), and Washington University in St. Louis (WU). The efforts of this group have developed valuable unique experimental techniques for the measurement of gas holdup, velocity, and eddy diffusivities in bubble columns. They have obtained data that allows improved insight in churn-turbulent flow and have assessed the impact of various effects (internals, solid concentration, high gas velocity, pressure, etc.). General ideal flow pattern-based models do not reflect bubble column reality to date the models are based on a combination where some parameters are evaluated from first principles and some from the database. 

If a system comprises several modules, it is recommended that system tests be performed for parameters that are affected by multiple modules (holistic testing) rather than performing tests module by module (modular testing). Individual module tests should be performed if the parameter is affected by that module only (e.g., the wavelength accuracy of an HPLC variable-wavelength detector or the temperature accuracy of a column compartment). 

After completing the calculations in the second column, copy the formulas into the third and fourth columns and perform the calculations for these two sets of input parameter values. State how increasing the system pressure and feed temperature affect the fraction of the feed vaporized (nV) and the final system temperature (T), and briefly explain why your results make sense. 

Aeration system performance is affected primarily by column size and airflow. Increases in airflow and column height improve removal efficiencies. Typical design parameters are provided for 13 common VOCs in Table 8. Design considerations include ... 

---