Optimal Operation and Control of Refrigeration Processes (including LNG Plants)

1. Optimal Operation and Control of Refrigeration Processes(including LNG Plants) September 26, 2003

2. Outline • The basic refrigeration cycle • Other refrigeration processes • Where is refrigeration applied? • Energy saving by improved operation or control • Optimal operation and control • LNG plants • Summary Acknowledgments References

3. The Basic Refrigeration Cycle (Dossat, 1991)

4. Operation and Control of Refrigeration Processes • Main output: cooled stream outlet temperature • Main input: compressor effect Several internal variables that must/may be be controlled: • Pressure (and thereby temperature) before compressor • Evaporator level Possible control inputs • Expansion valve opening • Heat transfer in condenser • Cooled stream flow rate • Refrigerant composition

5. A Typical Control Structure

6. Other Refrigeration Processes (Wilson and Jones, 1994) • Multiple stages refrigeration • Open liquefaction cycle: liquefied gas is withdrawn as product, replaced by dry gas (e.g. air) • Absorption refrigeration – no compressor needed (e.g. gas refrigerators) Condenser Evaporators Receiver

7. Where Is Refrigeration Applied? • Refrigerators and freezers in homes, warehouses, hospitals • Processing and transport of food • Air conditioning • Heat pumps (efficient heating by cooling the environment) • Process industry whenever cooling water temperature is not sufficient • Liquefaction and separation of air: oxygen, nitrogen, argon • Liquefaction of gases: LNG, hydrogen, helium, chlorine, … • Re-liquefaction (ship gas transport) • Conventional superconductors • Particle accelerator (e.g. CERN), 1.9K • Rocket fuel: liquid hydrogen and oxygen

8. Energy Saving by Improved Control or Operation • EU, 1990: the total electricityconsumption for refrigeration in the food industry was estimated at 8TWh/year (Norway’s total electrical energy production 2002: 122TWh/year) • Centre for Analysis and Dissemination of Demonstrated Energy Technologies (CADDET). Improved control examples: • Gilde, Norway: run the “correct” compressors (5% savings) • Inghams Enterprises, Somerville (Australia): avoid compressor cycling (966MWh/year) • Rainier Cold Storage, Port of Seattle: compressors adjusted after load and environmental changes (367MWh/year) Energy savings in demonstration projects: Computer controlled speed fans 30-44% Process control 30% Computer aided operation: 20%

9. Optimal Operation and Control • In the industry: optimal means improved • A solution that maximizes (or minimizes) a criterion • Criterion? • In the end: Maximize profit • Maximize throughput • Minimize cost, i.e. total power consumption or power consumption per produced unit • Free variables? • Constraints? • Process model • Typical disturbances: • Varying cooling demand • Compressor upsets • Varying heat-transfer in condenser

10. Operation? Control? • Optimal operation = optimal steady state working point Operation may also involve • maintenance of equipment • manual interventions • turnarounds but these are not covered here • Optimal control = optimal way to reach this working point and handle disturbances • Linear Quadratic Gaussian Control (LQG) • Model Predictive Control (MPC)

11. Control Hierarchy Operation Optimal Control Skogestad and Postletwaite (1996)

12. What Can Be Gained With Optimal Operation… • less compressor recycling • less suction temperature overheating  higher suction pressure • increased cooled stream temperature  more effective cooling cycle • with more than one compressor: improved power distribution • connected to other process units (e.g. pumps and fans): improved power distribution between the units

13. … and with Optimal Control? • the process is kept at optimum (despite disturbances) • transients are optimal • the margins can be reduced  the optimum can be improved yref y yref y yref y

14. Air Separation Units • Produce oxygen, nitrogen and argon from air • Air is liquefied with a nitrogen refrigeration cycle • Separation of the components with distillation columns • High purity requirements • Main control and operational challenges: the distillation columns • Schenk et al. (2002): Simultaneous optimal design of • process (number of trays and diameter) • control structure (pairing of outputs and inputs) • controller tuning 1.5 days of CPU time

15. LNG Plants • Natural gas cooled to below -163°C • Liquefied at 1atm • Volume reduction with a factor of 600 • Possible to transport gas with ships • Alternative to pipe transport

16. Optimal Operation of LNG Plants Main objectives: • Maximize LNG production or • Minimize storage • Minimize energy consumption

17. Optimal Control of LNG Refrigeration Plants (Mandler et al.,1998) • Main control objectives • Maintain a set LNG production rate • Maintain the LNG temperature within a desired range • Other control objectives depend on the process configuration • Constraints • Input ranges (valve ranges, power limits, compressor limits and rate change limits) • Process output ranges (suction pressures, relief valve settings, distance to compressor surge, …)

18. Snøhvit LNG Plant (Norway) • Gas produced at the gas fields Snøhvit, Albatross and Askeladd • Subsea production • 160 km of piping into the LNG plant • Production: 5.7 billion Sm3 LNG/year 2006-2035 • Operated by Statoil ASA

19. LNG, Mixed Fluid Cascade Process (simplified) NG Precooling Sea water -50°C Liquefaction Sea water -80°C Subcooling Sea water -160°C LNG

20. Basic Control strategy NG Precooling PIC TIC Liquefaction PIC TIC Subcooling PIC TIC FIC LNG

21. Operation NG Precooling P1 T1 PIC TIC P2 Liquefaction PIC T2 TIC P3 Subcooling PIC Specified Adjust to obtain desired production rate TIC FIC LNG

22. Optimal Operation, an Exercise • Objective: Minimize energy consumption in the 3 compressors • Free variables: Compressor suction pressures, P1, P2, and P3 Other free variables: • Temperatures T1 and T2 • Refrigerant composition in each cycle (nitrogen, methane, ethane, propane, …) • Some constraints: • LNG production rate and temperature • Flow into compressor shall be gas • Compressor constraints

23. Optimization

24. Changing the suction temperature margin from 10 to 5°C: Increase in suction pressure P1 0.63 bar P2 0.61 bar P3 0.84 bar Compressor consumption: 103 -> 93 MW Savings: 10MW (=0.09TWh/year) Results: Optimal Operation

25. Optimal Control, Snøhvit • Potential for savings with optimal control are not fully determined: • the actual disturbances are unknown • recycle of vaporized NG during ship loading • steady gas production? • composition variations? • regular pre-treatment? • compressor shut-downs? • Preliminary dynamic study (with disturbances as expected) • Low potential for savings identified • Exceptions • during large production level changes • during start-up • Will try to start without optimal control • Regulatory control shall be sufficient for stable and safe operation

26. Optimal Control: Possible Solution • Optimization criterion • Maximize LNG flow rate • Minimize energy consumption in the compressors • Possible manipulated variables: • NG temperatures after 1st and 2nd heat exchanger (T1, T2 ) • Set-point for refrigerant flow in subcooler • Set-point for LNG temperature • Refrigerant compositions • Constraints as before • Additional measurements: • NG inlet flow rate • NG inlet composition • Statoil MPC, SEPTIC (planned to be used in to control columns in the pre-treatment processes)

27. GL2Z LNG Plant in Arzew, Algeria (Zaïm, 2002) • 6 identical liquefaction trains • Product delivered to ships • Optimization in two levels • Plantwide optimization: Minimize storage and thereby • storage loss • production cost (produce as little as possible) • Maximize process efficiency of each train

28. Arzew, Algeria: Plantwide Optimization (Zaïm, 2002) • Adapt the LNG production to the downstream demand (i.e. ships arrivals and capacities) • Inputs • Ship loading schedule • Plan for maintenance of trains • Product quality requirements • Feed gas composition • Method • Define time intervals with constant demand • Determine required production in each train for each interval • Feedback from measured production

29. Optimal Control of Each Train (Zaïm, 2002) • Obtain desired • production rate • product quality • Minimize energy consumption • Other outputs to be controlled • two refrigerant temperatures in the main heat exchanger • pressures after the two expansion valves • Control inputs • Natural gas composition and flow • Mixed refrigerant composition and flow • Model Predictive Control • No simulation results available

30. Summary • The cooling cycle: Compression, condensation, expansion, vaporization • Control challenges: • Avoid liquid in the compressor • Inverse response in the evaporator • Refrigeration: Many important applications • at home and the food industry • process industry (liquefaction) • Energy demanding • Optimal operation and control • Minimize energy consumption and fulfil constraints • Identified potentials for savings (e.g. reduce compressor cycling) • Up to 30-40% of the energy consumption can be reduced • LNG plants: Liquefaction of natural gas • Two examples of optimal operation

31. Acknowledgments • Colleagues at Statoil ASA • Pål Flatby, John-Morten Godhavn, Silja E. Gylseth, Oddvar Jørstad, Håvard Nordhus, Jørgen Opdal, Geir A. Owren, Jan Richard Sagli • Dag Eimer, former colleague at Norsk Hydro ASA • Terje Herzberg, Dept. of Chemical Engineering, NTNU • Morten Hovd, Dept. of Engineering Cybernetics, NTNU • Staff at the NTNU Library

32. References (1) Refrigeration Textbooks Dossat, R. J. (1991), Principles of refrigeration, 3rd ed., Prentice-Hall International Editions, London. Flynn, Th. (1997), Cryogenic Engineering, Marcel Dekker, Inc., New York. Haselden, G. G. (ed.), Cryogenic fundamentals, Academic Press, London. Energy Consumption and Efficiency EU: http://europa.eu.int/comm/energy_transport/atlas/htmlu/refrigeration.html Grandum, S. and Eriksen, K. (2000), Control system minimizes energy use in a meat-processing factory, CADDET Energy Efficiency News Bulletin, No.3, pp. 16-17 Inghams Enterprises (2002), Advanced Food Refrigeration Control, CADDET web page, http://www.caddet-ee.org Rainier Cold Storage, Inc. (2000), Improved Refrigeration Control System in A Food Cold Storage Facility, CADDET web page, http://www.caddet-ee.org The Norwegian Water Resources and Energy Directorate (NVE) The energy folder 2002, http://www.nve.no/

33. References (2) Refrigeration Process Control Balchen, J. G. and Mummé, K. I. (1988), Process control. Structures and applications., Van Nostrand Reinhold, New York. Balchen, J. G., Telnes, K. and Di Ruscio, D. (1989), Frequency response adaptive control of a refrigeration cycle, Modeling, Identification and Control (MIC), Vol.10, No.1, pp. 3-11. Esnoz, A. and Lopez, A. (2003), Fuzzy logic PI controller with on-line optimum intermediate pressure for double stage refrigeration system, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA. Goldfarb, S. and Oldham, J. (1996), Refrigeration loop dynamic analysis using PROTISS, ESCAPE-6, 26-29 May 1996, Rhodes, Greece; Supplement to Computers & Chemical Engineering, Vol. 20, pp. S811-S816 Langley, B. C. (2002), Fine tuning Air Conditioning & Refrigeration Systems, The Fairmont Press Inc., Lilburn, GA. Lensen, B. A. (1991), Improve control of cryogenic gas plants, Hydrocarbon Processing, May, 1991, pp. 109-111 Marshall, S.A. and James, R. W. (1975), Dynamic analysis of an industrial refrigeration system to investigate capacity control, Proc. Inst. Mech. Engrs., Vol. 189, No.44/75, pp. 437-444 Wilson, J.A. and Jones, W.E. (1994), The influence of plant design on refrigeration circuit control and operation, ESCAPE-4, Dublin March 28-30, '94, pp. 215-221.

34. References (3) Optimal Operation and Control (see also applications and LNG) Chen, J. (1997), Optimal Performance analysis of irreversible cycles used as heat pumps and refrigerators, J. Phys. D: Appl. Phys., Vol. 30, pp. 582-587 D’Accadia, M. D., Sasso, M. and Sibilio, S. (1997), Optimum performance of heat engine-driven heat pumps: A finite-time approach, Energy Convers. Vol. 38, No. 4, pp. 401-413 Diaz, S., Tonelli, S., Bandoni, A. and Biegler, L.T. (2003), Dynamic Optimization for Switching Between Operating Modes in Cryogenic Plants, FOCAPO 2003. 4th Int. Conf. of Computer-Aided Process Operations, Proceedings of the Conference held at Coral Springs, Florida, January 12-15, 2003, pp. 601-604 Leducq, D., Guilpart, J. and Trystram, G. (2003), Application of a reduced dynamic model to the control of a refrigeration cycle, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA. Mandler, J.A. (1998), Modeling for Control Analysis and Design in Complex Industrial Separation and Liquefaction Processes, DYCOPS-5, 5th IFAC Symposium on Dynamics and Control of Process Systems, Corfu, Greece, June 8-10, 1998, pp. 405-413. Schenk, M., Sakizlis, V., Perkins, J.D. and Pistikopoulos E.N. (2002), Optimization-Based Methodologies for Integrating Design and Control in Cryogenic Plants, European Symposium on Computer Aided Process Engineering - 12, 26-29 May 2002, The Hague, The Netherlands, pp.331-336. Svensson, Ch., M. (1994), Studies on on-line optimizing control, with application to a heat pump, Ph.D. thesis, Dept. of Refrigeration and Air Conditioning, Norwegian University of Science and Technology, Trondheim, Norway

35. References (4) Refrigeration Operation and Control Applications Alvarez, G. and Trystram, G. (1995), Design of a new strategy for the control of the refrigeration process: fruit and vegetables conditioned in a pallet, Food Control, Vol. 6, No. 6, pp. 347-355. Andersen, J. (2002), Temperature control in the large Hadron Collider at CERN, M.Sc. Thesis, Dept. of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway Cho, C. H. and Norden, N. (1982), Computer Optimization of Refrigeration Systems in a Textile Plant: A Case History, Automatica, Vol.18, No. 6, pp. 675-683. Flemsæter, B. (2000), Investigation, modelling and control of the 1.9K cooling loop for superconducting magnets for the large hadron collider, Ph.D. thesis, Dept. of Refrigeration and Air Conditioning, Norwegian University of Science and Technology, Trondheim, Norway Hokanson, D. A., Houk, B.G. and Johnston, Ch., R. (1989), DMC Control of a complex refrigerated fractionator, Adv. Instum. Control, pp. 541-552. Kaya, A. (1991), Improving efficiency in existing chillers with optimization technology, ASHRAE Journal, October 1991, pp. 30-38 Luong, T.T.H. and Pham, Q.T. (2003), Multi-objective optimization of food refrigeration processes, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA. Martin, M., Gannon, J. Rode, C. and McCarthy, J. (1981), Quasi-optimal algorithms for the control loops of the FERMILAB energy saver satellite refrigerator, IEEE Transactions of Nuclear Science, Vol. NS-28, No. 3, June, pp. 3251-3253 Olson, R.T. and Liebman, J.S.(1990), Optimization of a chilled water plant using sequential quadratic programming, Eng.Opt., Vol. 15, pp.171-191. Skimmeli, T. (1994), Control of Refrigeration Process at Dalgård (Indoor) Ice Rink, Master thesis, Department of Engineering Cybernetics, Norwegian University of Science and Technology Trelea, I.-C., Alvarez, G. and Trystram, G. (1997), Nonlinear predictive optimal control of a batch refrigeration process, J. Food Process Engn., Vol. 21, pp.1-32.

36. References (5) LNG and Control of LNG plants Mandler, J.A. and Brochu, P.A. (1997), Controllability Analysis of the LNG Process, Presented at 1997 AIChE Annual Meeting, Los Angeles, CA (Paper 197a) Mandler, J.A., Brochu, P.A., Fotopoulos, J. and Brochu, P.A. (1998), New Control Strategies for the LNG Process, Presented at LNG 12 Conference, Perth, Australia, May 1998 The Snøhvit project: www.statoil.com/snohvit Zaïm, A. (2002), Dynamic optimization of an LNG plant. Case study: GL2Z LNG plant in Arzew, Algeria, Ph.D. Thesis, Rheinisch-Westfälishen Technischen Hochschule (RWTH), Aachen, Shaker Verlag, Aachen. Other Sources for the Presentation CERN: http://public.web.cern.ch/public/ Gram Refrigerators: http://www.gram.dk/produkter.htm Skogestad, S. and Postletwaite, I. (1996), Multivariable feedback control, John Wiley & Sons, Chichester, UK

37. Refrigeration Operation and Control Applications • Process industry • NLG plant (Diaz, S. et al., 2003) • Multivariable control (DMC) of a fractionator with a refrigeration process (Hokanson et al.,1989) • Nylon plant: Steady state optimization of 8 cycles (Cho et al., 1982) • Food • Control for fruits and vegetables (Alvarez and Trystram, 1995) • Steady state optimization (Luong and Pham, 2003) • Air condition • Optimal operation (Olson and Liebman, 1990, Kaya, 1991) • Particle accelerators • FERMILAB (USA) (Martin, 1981) • CERN (Europe) (Flemsæter, 2000, Andersen, 2002) • Other Applications • New control structures for indoor ice rinks (Skimmeli, 1994)