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Tower Scan

Identification of Packed Bed Damage

A distillation column on a large oil refinery in the United Kingdom was having a number of processing issues.

The efficiency of a distillation column had, over a period of time, started to deteriorate causing downstream processing issues and greater separation costs. The column contained a number of discrete, linked packed beds.

A TRU-SCAN®/Tru-Grid™ Scan confirmed that the packed beds, as well as the trays, were still in their correct location and the distributors were positioned correctly and holding liquid. The results also highlighted that one of the beds; Bed 3, appeared to have a large density profile from top to bottom (increasing by approximately 25% over the 2 metre length).

After reviewing the detailed drawings and the history of the column. It became clear that new packing had been added at a recent shutdown, however it had been of a similar density (to within 4%) of the original material.

Further investigation of Bed 3, using a TRU-SCAN®/Tru-Grid™ Scan with a grid formation confirmed that a “hole” existed at the top of the bed and proceeded downwards in a cone shape. The results indicated that some of the debris from the packing had collected in the bottom section.

The debris from the damaged packing explains the issue regarding the density changing by approximately 25% over the length of the bed. The “hole”, which was shaped like an upside-down cone, explains why there was poor mass transfer taking place in the bed. Initially it was unknown what had caused the packing to fail so dramatically and quickly (it was only installed 19 months before), but following further analysis, it was confirmed to be a construction material.

During the next shutdown, the whole of Bed 3 was replaced with fresh packing and the beds below it were inspected for debris and build up. Only very small traces of debris were found.

A TRU-SCAN®/Tru-Grid™ Scan is an excellent method to investigate the performance and mechanical integrity of packed beds whilst remaining online and when used in pre-shutdown studies can identify unknown problems.

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