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

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

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 950.000 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

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

The control hierarchy at the Kårstø plant Regulatory control layer – Tuning and reconfiguration 2002-2004 Local MPC implementation Implementation 2003-2006 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

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

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

Estimator development Estimation of the product qualities are needed – reasons: Long dead time in the GC measurements (10- 40 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

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.

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

Tuning and operation CV MV Prioritizing Penalty on Deviation DV 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

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)

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

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