1

Modelling, Operation and Control of an LNG Plant

Jens Strandberg

&

Sigurd Skogestad

Department of Chemical Engineering,

Norwegian University of Science and Technology

Trondheim, Norway

2

Outline

• Statoil's Snøhvit LNG Project

• Optimal Operation of LNG Plant

• Contollability Analysis

• Identifying the Model

• Results

• Conclusions and Further Work

3

Statoil's SnøhvitLNG Project

• Natural gas liquefaction plant situated at Melkøya island outside Hammerfest, northern Norway.

• Receiving natural gas from the Snøhvit, Albatross and Askeladd fields in the Barents Sea

• Liquefied Natural Gas (LNG) to be shipped by carriers to markets in Europe and the USA.

• Plant to go on-line in 2006

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The Mixed Fluid Cascade (MFC) Process

• Developed by Linde-Statoil Technology Alliance.

• Consists of– Precooling section

– Liquefaction section

– Subcooling section

• Heat exchangers are plate-fin types and spiral wound heat exchangers.

• Refrigerants are mixtures of methane, ethane, propane, nitrogen and others

• LNG product is cooled to -160ºC

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Optimal Operation of LNG plant

• Different from Optimal Design

• What to control?

• Optimization criteria (Economics)

• Degrees of Freedom:– 4 compressors

– 4 expansion valves

– NG flow

– refrigerant compositions (not considered here)

– Total: 9

• Controllability

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Optimal Operation of LNG plant

Case 1. Given feed rate. Keep final NG temperature at setpoint.

• Remaining DOF's = 7.

• Objective function for optimization:

• However, optimization studies indicate that keeping all the intermediate NG outlet temperatures constant is the best self-optimizing control structure.

• In this case, the remaining DOF's are 4.

J min u iW i ,comp W turbine

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Optimal Operation of LNG plant

Case 2. Keep final NG temperature at setpoint & Maximize LNG production.

• NG feed now “free”

• Compressors optimally at max, remaining DOF's = 4

• Keeping intermediate temperatures constant. DOF = 1

• So far: only steady-state considerations...

J max um LNG

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Controllability

• What is the controllability of the plant?

• Check:– Speed of response to reject disturbances

– Speed of response to track reference changes

– Input constraints arising from disturbances

– Effective time delay

• Consider one heat exchanger NG Shell

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Linear Models

• Controllability analysis -Need linear model of the plant.

• Starting point: tried to linearize a dynamic model of a spiral wound heat exchanger.

• Model developed by Sintef Trondheim for Statoil and is a pure simulation model.

• To derive a linear model by doing perturbations directly on the model equations proved infeasible.

• Instead, black-box model identification techniques were applied.

• Matlab's System Identification Toolbox has been used to create low order SISO models for the heat exchanger.

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Model identification

• Method applied to the liquefaction heat exchanger.

• 4 streams– 4 input temperatures

– 4 input pressures

– 4 input mass flows

• Simulations made with PRBS-type perturbations. One input at a time.

• Matlab routines used:– n4sid, pem, bj, oe.

NG Shell

y u

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Comparing model outputs

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Resulting models and Controllability

• Controllability– speed of responses OK

– input constraints OK

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Conclusions and Further Work

• Illustrated systematic approach for control system design:

– what to control

– economics

– controllability

• Usually large difference between optimal design and optimal operation

• Illustrated use of model identification techniques to derive linear models

• Detailed economic optimization

• Linearization of model equations directly

• MIMO identification

• Further contollability studies

• Controller designs

• Startup optimizations

Conclusions Further Work

14

Acknowledgements

• Thanks to Sintef and Statoil for use of Dcoil simulation model

• Supporters:– Norwegian Research Fund

(NFR)

– Natural Gas Research Center, NTNU