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Optimization of LNG plants:Challenges and strategies

Magnus G. JacobsenSigurd Skogestad

ESCAPE-21, May 31, 2011Porto Carras, Chalkidiki, Greece

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Outline

• Design vs Operation

• Challenges in simulation and optimization

• Example process: C3-MR

• Reliability and accuracy study

• Conclusions

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Design vs operation

• Design: Select – Process equipment

– Nominal operating conditions

• Operation: – Process equipment is given

– Run the process at an economical optimum

– Satisfy quality and safety constraints

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Optimization objectivemin

s.t. ( , , ) 0

( , , ) 0

energy productuJ energy price product value

f x u d

g x u d

• Minimize the cost J– x : internal process variables, – u : inputs (degrees of freedom) – d: are disturbances

• Low energy price: Maximize production rate, given the available energy

• High energy price: Minimize energy consumption, producing the contracted amount

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Potential challenges

• Precise optimization requires good models. • Commercial modelling software (Aspen, Unisim etc)

does not have good steady-state models for LNG heat exchangers.

• Strong correlation between process variables• Discontinuities in constraint functions

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Example process: C3-MR

• Most widely used process for liquefaction of natural gas

• Uses propane (C3) for precooling down to -40°C• Uses a mixed refrigerant for liquefaction in a spiral-

wound heat exchanger• Three or four pressure levels in precooling

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

Precooling Liquefaction

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Unisim model, precooling part

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Degrees of freedom, liquefaction part• 6 degrees of freedom in liquefaction part

– Mixed refrigerant flowrate (FMR)

– Refrigerant pressures (Ph,MR, Pl,MR)

– Refrigerant composition (xMR), 3 mole fractions

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Unisim model, liquefaction part

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

LNG

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Solving of heat exchanger model

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Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Model formulations

1. Specify temperatures in the MCHE submodel• We must add heat exchanger area specifications as equality

constraints in optimization

2. Specify heat exchanger UA values, include recycle convergence in optimization problem (infeasible-path optimization) [1]

3. Specify heat exchanger UA values, and let the simulator solve recycles

[1] See t.e. Biegler, L., Hughes, R., 1982. Infeasible path optimization with sequential modular simulators. AIChE journal 28 (6), 994–1002.

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Model solution reliability study

• Focus on liquefaction part• Unisim used for flowsheet calculations • Matlab for optimization & equation solving• Reliability of Unisim model itself

– How frequently will the simulation program fail to return aconverged flowsheet?

• Overall accuracy– How accurately can we solve the liquefaction model with the three

different formulations mentioned earlier?

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Results: Unisim reliability

• For each model formulation, Unisim model called 300 times– Temperatures specified: No failures (the Unisim model converged at

every call)

– Areas specified:• Recycles inactive: 11 failures (4%)• Recycles active: 177 failures (59%)

• Shows that the Unisim solver – easily solves for temperature,

– less easily for HX area

– recycle convergence is not robust

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Results: Heat exchangers solved by MATLAB

• Solve UA(T) = UAspecified to a relative error of 10-5 using different MATLAB solvers

• 150 runs with different values for process inputs u– fsolve.m: 103 failures (69%)

– fmincon, active-set algorithm: 128 failures (85%)

– fmincon, interior-point algorithm: 86 failures (57%)

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Results: Recycles solved in MATLAB• Converge recycle stream temperatures to a tolerance

of 0.1°C (relative error 10-4)• 150 runs varying the values of u

– fsolve.m: 29 failures (19%)

– fmincon, active-set algorithm: 138 failures (92%)

– fmincon, interior-point algorithm: 132 failures (88%)

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

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Conclusions

• Model Formulation II most reliable for simulation– For optimization, formulation I works best

• Matlab’s standard equation solver fsolve is more likely to converge recycles than the internal recycle solver in Unisim

• Further work on the formulation of the optimization problem is needed

Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies