Improved Distillation Column Recovery and Reduced Energy Usage

by | Oct 28, 2009 | Services, Consulting & Training | 0 comments

Here’s another great presentation from the recent Emerson Exchange from refining & chemicals industry solutions director, Pete Sharpe. You may recall Pete from earlier process optimization-related posts.

His presentation, SmartProcess Distillation Application Improves Recovery and Saves Energy–A Case Study, describes a project to improve operations on a high-purity distillation process at a Goodyear facility. These distillation columns, 11 in total, were part of a purification unit with multiple trains. The ultra-high purity product specifications required very tight quality controls.

From a control strategy standpoint, the process had multiple large, 200+ tray columns with extremely long time constants. Also, different feedstock suppliers provided feed with different qualities. The operators had to have large safety margins to compensate for disturbances caused by the feed variability. These conservative margins reduced the overall recovery rate and increased the energy required per unit output. Overall, the purification unit was a large energy consumer within the plant.

Pete and the APC consultants worked with the engineers to scope a project that included a functional specification that included the design for all 11 columns in the purification unit. The initial implementation phase covered only the first column in the series to:

  • Have the Goodyear team gain experience with the technology
  • Develop acceptance by the plant operators
  • Demonstrate the value of the distillation optimization to the management staff.

The plant engineering staff with support from the Emerson APC consultants would implement subsequent columns.

During the functional design specification phase that included an on-site audit of the installed instrumentation and control strategies, the team identified regulatory control issues including sticking control valves. As I’ve mentioned in an earlier post, process variability can often be traced back to valve performance problems and these should be addressed first.

After these issues had been addressed, the team installed a SmartProcess Distillation Optimizer that embeds DeltaV model predictive control and neural network function blocks on the first column. The control strategies were based on a “what comes in must go out” approach that included material (overhead to feed ratio) and energy (reflux to feed ratio) balances. The objective of the optimization was to minimize overhead product loss while controlling bottoms impurities to target.

The manipulated variables (MV) for the MPC controller included the distillate rate and reboiler steam. The control variables (CV) included the lower tray pressure-compensated temperature (PCT) and the overhead PCT. The disturbance variables (DV) included the feed flows and reflux temperature. The column constraint variables included measurements from on-line analyzers for overhead product loss and light impurities in the bottoms as well as internal flux rate, column delta pressure and the reflux/distillate ratio.

Pete and the team had to work through reboiler steam temperature instability caused by a process design issue. Also, the overhead pressure controller range was insufficient to handle the difference in temperature between day and night. Two valves controlled the pressure in a condenser system, one large and one small. The pressure swings caused changes in the temperature and quality of the column output. The team developed a control strategy to adjust the large valve to keep the small valve within the correct control range.

A two-week period on-site was required to address these issues, commission the optimizer, and train the operating and engineering staff. The results from this project were impressive. The optimizer immediately started reducing distillate rate and overhead product losses. The average overhead product loss was reduced 22% while the impurities in the bottoms were maintained within specification. Steam usage dropped 7%.

For this one column, this optimization project delivered an estimated annual value of $700,000 (USD) through increased recovery rates and decreased energy usage.


Update: I’ve uploaded a copy of Pete’s presentation to Slideshare and embedded it within this post. It’s also available for download from Slideshare.

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