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

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

September 26, 2003

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

September 26 2003 3

The Basic Refrigeration Cycle

(Dossat, 1991)

A

Condensation BC

Vaporization

D

Specific enthalpy

Log pressure

p1

p2

Expansion Compression

GasGas andLiquid

Liquid

Compressor

Evaporator

Motor

Expansion valve

Condenser

Receiver

Cooledstream out

Q in

Q out

C

DA

B

September 26 2003 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

Compressor

Evaporator

Motor

Expansion valve

Condenser

Receiver

Cooledstream out

Q in

Q out

TT

Power

Compressor

Evaporator

Motor

Expansion valve

Condenser

Receiver

Cooledstream out

Q in

Q out

TT

Power

LT

PT

September 26 2003 5

A Typical Control Structure

Compressor

Evaporator

Motor

Expansion valve

Condenser

Receiver

Cooledstream out

Q in

Q out

TT

Power

LT

PTLC

TC

SIC

September 26 2003 6

Other Refrigeration Processes

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liquefied gas is withdrawn as product, replaced by dry gas (e.g. air)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)

(Wilson and Jones, 1994)

Condenser

Receiver

Evaporators

September 26 2003 7

Where Is Refrigeration Applied?

Refrigerators and freezers in homes, warehouses, hospitalsProcessing and transport of foodAir conditioningHeat pumps (efficient heating by cooling the environment)Process industry whenever cooling water temperature is not

sufficientLiquefaction and separation of air: oxygen, nitrogen, argonLiquefaction 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

September 26 2003 8

Energy Saving by Improved Control or Operation

EU, 1990: the total electricity consumption 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)

Process control 30%

Computer controlledspeed fans 30-44%

Computer aided operation: 20%

Energy savings in demonstration projects:

September 26 2003 9

Optimal Operation and Control

In the industry: optimal means improvedA solution that maximizes (or minimizes) a criterionCriterion?

– In the end: Maximize profit– Maximize throughput– Minimize cost, i.e. total power consumption or power

consumption per produced unitFree variables?Constraints?Process modelTypical disturbances:

– Varying cooling demand– Compressor upsets– Varying heat-transfer in condenser

0

min

pF

Pp

pp

September 26 2003 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)


Skogestad and Postletwaite (1996)

Control

Operation

Optimal

Control Hierarchy


What Can Be Gained With Optimal Operation…

less compressor recycling less suction temperature overheating higher suction pressure increased cooled stream temperature more effective cooling cyclewith more than one compressor: improved power distributionconnected to other process units (e.g. pumps and fans): improved

power distribution between the units

A

Condensation BC

Vaporization

D

Specific enthalpy

Log pressure

p1

p2

Expansion Compression

GasGas andLiquid

Liquid

Compressor

Evaporator

Motor

Expansion valve

Condenser

Receiver

Cooledstream outQ in

Q out

C

DA

B


… 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

y

y

y

yref

yref

yref


Air Separation Units

Produce oxygen, nitrogen and argon from airAir is liquefied with a nitrogen refrigeration cycleSeparation of the components with distillation columnsHigh purity requirements

Main control and operational challenges: the distillation columnsSchenk 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


LNG Plants

Natural gas cooled to below -163°C– Liquefied at 1atm

Volume reduction with a factor of 600Possible to transport gas with ships

– Alternative to pipe transport


Optimal Operation of LNG Plants

Main objectives:Maximize LNG production

or Minimize storage

Minimize energy consumption


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, …)


Snøhvit LNG Plant (Norway)

Gas produced at the gas fields Snøhvit, Albatross and AskeladdSubsea production160 km of piping into the LNG plantProduction: 5.7 billion Sm3 LNG/year 2006-2035Operated by Statoil ASA


LNG, Mixed Fluid Cascade Process (simplified)

Precooling

Liquefaction

Subcooling

NG

LNG

Sea water

Sea water

Sea water

-160°C

-50°C

-80°C


Basic Control strategy

Precooling

Liquefaction

Subcooling

NG

FIC

TIC

TIC

TIC

LNG

PIC

PIC

PIC


Operation

Adjust to obtaindesired production rate

Subcooling

NG

FIC

TIC

TIC

TIC

LNG

PIC

PIC

PICT1

T2

Precooling

Liquefaction

P1

P2

P3

Specified


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


Optimization

Optimizationserver

(SQP)

Adjust freevariables

Results

User interface(Excel)

Model

(Hysys)

Optimizationproblem definition

Objective functionand constraint

values

When available:Measurements


Results: Optimal Operation

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)


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


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)


GL2Z LNG Plant in Arzew, Algeria (Zaïm, 2002)

6 identical liquefaction trains Product delivered to ships Optimization in two levels

1. Plantwide optimization: Minimize storage and thereby– storage loss– production cost (produce as

little as possible)

2. Maximize process efficiency of each train


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


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


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


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 ASATerje Herzberg, Dept. of Chemical Engineering, NTNUMorten Hovd, Dept. of Engineering Cybernetics, NTNUStaff at the NTNU Library


References (1)

Refrigeration TextbooksDossat, 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 EfficiencyEU: 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/


References (2)

Refrigeration Process ControlBalchen, 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.


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


References (4)

Refrigeration Operation and Control ApplicationsAlvarez, 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.


References (5)

LNG and Control of LNG plantsMandler, 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 PresentationCERN: 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


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)

Documents

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