Optimization of LNG plants: Challenges and strategies

1. Optimization of LNG plants:Challenges and strategies Magnus G. Jacobsen Sigurd Skogestad ESCAPE-21, May 31, 2011 Porto Carras, Chalkidiki, Greece

2. 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

3. 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

4. Optimization objective • 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

5. 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

6. 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

7. Precooling Liquefaction Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

8. Unisim model, precooling part Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

9. 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

10. Unisim model, liquefaction part LNG Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

11. Solving of heat exchanger model

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

13. Model formulations • Specify temperatures in the MCHE submodel • We must add heat exchanger area specifications as equality constraints in optimization • Specify heat exchanger UA values, include recycle convergence in optimization problem (infeasible-path optimization) [1] • 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

14. 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

15. 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

16. 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

17. 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

18. 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