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 Earlier work on LNG optimization Increasing capacity through equipment improvements Process design modifications Optimal design of a process –Both continuous methods (SQP), gradient-free and mixed-integer methods have been used Optimal operation of a given process –Few papers overall –Very few using continuous methods, only one using it on the world’s most used liquefaction process! Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

6 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

7 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

8 PrecoolingLiquefaction

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

10 Degrees of freedom, liquefaction part 6 degrees of freedom in liquefaction part –Mixed refrigerant flowrate (F MR ) –Refrigerant pressures (P h,MR, P l,MR ) –Refrigerant composition (x MR ), 3 mole fractions

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

12 Solving of heat exchanger model

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

14 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., Infeasible path optimization with sequential modular simulators. AIChE journal 28 (6), 994–1002. Jacobsen, Skogestad – Optimization of LNG plants: Challenges and strategies

15 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

16 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

17 Results: Heat exchangers solved by MATLAB Solve UA(T) = UA specified to a relative error of 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

18 Results: Recycles solved in MATLAB Converge recycle stream temperatures to a tolerance of 0.1°C (relative error ) 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

19 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