Abstract
Refinery and chemical plant operations depend heavily on distillation and separation towers. Tower gamma scanning is well established in the process industries as a qualitative tool to help troubleshoot towers. Advancements in data analysis have led to a quantitative approach in expressing gamma scan data in numerical terms easily understood by process and operations engineers.
For packed towers, a grid-scan of 3 or 4 equal-distant scans crossing through the beds of packing would typically be used to investigate the quality of liquid distribution. The conventional approach to “analyzing” a gamma scan has been to visualize how well the scan data from the individual scans matched each other or how well they “overlaid” with each other. This is a totally subjective analysis lacking consistency, open to varying interpretation and does not translate well from tower-to-tower. Therefore, the resulting conclusions from this approach can be very ambiguous regarding the magnitude of any detected liquid mal-distribution.
An alternative analytical approach, termed PackView™, has been developed whereby a relative density scale is calculated from data that the grid-scan provides. The density scale begins at the density of the dry or non-operating packing. The density scale displays the calculated density of liquid retained in the bed of packing based on the scan data results.
Another calculation by which to put the liquid distribution into perspective and to get a measure on the useful capacity of the packing is to calculate the liquid holdup fraction or liquid volume fraction. If the measured liquid retention density is divided by the process liquid density at bed conditions (the liquid density at the actual operating temperature and pressure), liquid holdup or liquid volume fraction can be established. A comparison of the liquid holdup fraction to the packing operating capacity curves provides an objective appraisal of current operating capacity.
It is always easier to understand and discuss technical issues when quantitative information can be used to compare operational parameters with engineering design. This advanced analysis provides a new method of extracting quantitative information from gamma scan data to diagnose and characterize the operation of distillation and separation towers. It is our goal that using the advanced analytics presented will improve the value of gamma scan data and facilitate improvements in the operation of mass transfer equipment.
