1. Implementation of MPC in a deethanizer at the Kårstø Gas plant
Elvira Marie B. Aske*,**
Stig Strand**
Sigurd Skogestad*
*Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
** Statoil R&D, Process Control, Trondheim, Norway

2. Outline About the Kårstø gas plant Motivation for MPC implementation
Design
Estimator development
Model development
Tuning
Results
Conclusions and further work

3. The Kårstø Gas processing plant
Treats rich gas and unstabilized condensate recieved through pipelines from the Norwegian continental shelf
The products are sales gas, ethane, propane, i- and n-butane, naphta and condensate.
Sales gas is transported through pipe lines, while NGL and condensate are exported by ship
Some numbers (October 2005):
Rich gas processing capacity 88 MSm3/d
ethane production capacity t/year
About 720 shipping arrival a year
Kårstø is a part of the pipeline network in the North Sea
Biggest facility of its kind in Europe and third largest in the world
New extension prosjekct completed in October this year

4. Motivation for MPC implementation
Increased complexity at the plant due
to extension projects
Larger degree of system integration
Systems build with different design
Crossovers between process trains
Higher demands to regularity and through-put due to new fields developments that narrows the margins
MPC is a part of the ”control revolution” at the plant

5. The control hierarchy at the Kårstø plant
Regulatory control layer –
Tuning and reconfiguration
Local MPC implementation
Implementation
Coordinator MPC –
Coordinates the local MPCs, handles disturbances which effects several units - implementation in 2006
Plant production performance model for online optimization and planning to be implemented in 2007
Uses rigorous model to calculate the optimal operation point
Achieve full capacity utilization in integrating the systems

6. MPC implementation on a deethanizer
A typical implementation case for a distillation column at the Kårstø plant
Includes the stages in an MPC implementation:
design
estimator development
model development
tuning
Using the in-house developed MPC software SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control)
Implementation done by plant engineers together with engineers from Statoil Research Centre

7. MPC implementation on a deethanizer-Design
Control variables (CV):
Product qualities
Avoid flaring
Manipulating variables (MV):
Reflux
Tray 1 temp set point
Disturbance variables (DV):
Feed rate
Column pressure as an MV in future
Fuel gas to boilers limited capacity
Tray 1 temperature is in cascade with a doncdensate level controller
Historically reasons for the pressure is not an MV now. Have to prove that the MPC works well before manipulating the pressure. Planned to be implemented in 2006

8. Estimator development
Estimation of the product qualities are needed – reasons:
Long dead time in the GC measurements ( min) – bad for control
GC may be out of order in periods due to service and maintenance
Bad samples in the GC may occur
Steps in boil-up and reflux to obtain variations in the product qualities if necessary
Estimator based on column temperatures and pressure
Logarithm between the ratio of the key components to reduce nonlinearity and dependence of operation point
Logarithmic impurity of the key components
Skogestad: log(0.01*y/(1-0.01*y)) where y is impurity in mol%
Selecting temperatures and pressure to express the quality, and fitting by least squares
Here: two temperature needed both for top and bottom
Slow bias update of the estimator

9. Model development
Seeking the dynamic influence the MV’s and DV’s have on the CV’s
Executing steps in the process – recording the responses
Model identification: Tai-Ji ID software and built-in tools like last step response, FIR, ARX model fitting
Two days of step testing, one for each MV.
Could do both at the same time, but take one at the time gives us process knowledge and a good control of the process while doing step tests.
Feed rate quite stable during the test period, needs more data here.

10. Model development (2) Reflux Tray 1 temp Feed Model identification
obtained by the
Tai-Ji ID software
Resulting CVs from the steps

11. Tuning and operation CV MV Prioritizing Penalty on DeviationDV
Prioritizing
Penalty on
Deviation
Violation of constraints
MV moves
Model update parameters
Take base control constraints into consideration-consider operation range . Using the valve outlet as a controlled variable to avoid flaring.
In other columns, differential pressure is considered as a measure for flooding.
Earlier: very sensitive to feed changes -> the deetanizer might become a depropanizer in periods.
Here: feed increase from 183 t/h to 207 t/h, that is 13% increase
Common to switch between one and two stabilizers in operation. Results: feed changes from 110 t/h to 220 t/h.
Setpoint
6 hours history
2 hours prediction

12. MPC implementation - Results
C3 in C2
before MPC
C2 in C3
after MPC
20 days period with 20 minutes interval.
Standard deviation reduced with 46% (top) and 56% (bottom)
Flaring: 20-40% reduciton in flaring frequency and flaring episodes have most often a shorter duration.
With two much methane in the feed, flaring si unaviodable due to capacity litmits in the boiler system
20-40% reduction in flaring frequency and has often shorter duration
General results from MPC implementations:
Overfractionation before introducing MPC
10-12% reduction in steam consumption
Less variation in product quality  operate closer to the constraints
10-25% reduction in reflux flow rate  increase column capacity (simulations show at least 10% increase)

13. Further work – Coordinator MPC with focus on maximizing feed rate
Each local MPC reports back on its available capacity
Coordinator MPC has a simple linear model on how its manipulated variables affect the local capacities
Based on feedback, the coordinator MPC is then able to locate the bottleneck and, by adjusting the overall feed rate, maximizing the flow rate at the bottleneck
Scetch of the coordinator MPC idea

14. Conclusions
Better quality control – with the opportunity to specify the desired product quality
Reduced flaring frequency and shorter duration of the flaring
Operator uses less time to control the column, especially under feed disturbances
Acknowledgements
Personnel working at the Kårstø gas plant, Norway
Gassco for financial support