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Implementation of Coordinator MPC on a Large-Scale Gas Plant

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1. Implementation of Coordinator MPC on a Large-Scale Gas Plant ... Naphtha. Ethane. Condensate. Sleipner. condensate. Tampen rich gas. Halten/ Nordland rich gas ... – PowerPoint PPT presentation

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Title: Implementation of Coordinator MPC on a Large-Scale Gas Plant


1
Implementation of Coordinator MPC on a
Large-Scale Gas Plant
  • Elvira Marie B. Aske, Stig Strand and
    Sigurd Skogestad
  • Department of Chemical Engineering
  • Norwegian University of Science and Technology
    (NTNU)
  • Trondheim, Norway
  • StatoilHydro RD, Process Control, Trondheim,
    Norway

AIChE Annual Meeting, Philadelphia,
USA November, 2008
skoge_at_ntnu.no
2
Outline
  • Introduction and motivation
  • The Kårstø gas plant
  • Maximum throughput as optimal operation
  • Approach Coordinator MPC
  • Maximize flow through linear network
  • Estimate feasible remaining capacity (R) in units
    using local MPCs
  • Application to Kårstø Gas Plant
  • Previous work Works well on simulations
  • Here Actual implementation
  • Design
  • Tuning (plant runs)
  • Experiences
  • Conclusion

Aske, E.M.B., S. Strand and S. Skogestad (2008).
Coordinator MPC for maximizing plant throughput.
Comput. Chem. Eng. 32(1-2), 195204.
3
Kårstø plant
Gas processing area
Control room
4
North Sea gas network
  • Kårstø plant Receives gas from more than 30
    offshore fields
  • Limited capacity at Kårstø may limit offshore
    production (both oil and gas)

Norwegian continental shelf
TRONDHEIM
Oslo
UK
GERMANY
5
Kårstø plant 20 years of development
Europipe IIsales gas
Halten/Nordland rich gas
Tampen rich gas
Statpipesales gas
Sleipnercondensate
PropaneN-butaneI-butaneNaphtha
How manipulate feeds and crossovers?

Condensate
1985
2000
1993
2005
2003
Ethane
6
Maximum throughput
  • Often Economic optimal operation maximum
    throughput
  • Operate with max feasible flow through
    bottlenecks
  • No remaining unconstrained DOFs (RTO not needed)
  • Coordinator MPC
  • Manipulate TPMs (feed valves and crossovers)
    presently used by operators
  • Throughput determined at plant-wide level (not by
    one single unit) ? coordination required
  • Frequent changes ? dynamic model for optimization

TPM Throughput Manipulator
7
Approach
?
  • Objective Max throughput, subject to feasible
    operation
  • Remaining capacity (R) Rs 0 in bottleneck
    units
  • Throughput manipulators (TPMs) Feeds and
    crossovers
  • Approach Use Coordinator MPC to optimally adjust
    TPMs
  • Coordinates the network flows to the local MPC
    applications
  • Decompose the problem (decentralized).
  • Assume Local MPCs closed when running Coordinator
    MPC
  • Need flow network model (No need for a detailed
    model of the entire plant)
  • Decoupling Treat TPMs as DVs in Local MPCs
  • Use local MPCs to estimate feasible remaining
    capacity (R) in each unit

8
Coordinator MPC Coordinates network flows, not
MPCs
9
Remaining capacity (using local MPCs)
  • Feasible remaining feed capacity for unit k
  • Obtained by solving extra steady-state LP
    problem in each local MPC
  • subject to already given present state, model
    equations and constraints
  • Very little extra effort!

current feed to unit k
max feed to unit k within feasible operation
10
Local MPC applications
  • Kårstø Most local MPC applications are on
    two-product distillation columns
  • CVs Distillate- and bottom products quality
    (estimated)
  • differential pressure and other
    constraints
  • MVs Temperature setpoint (boilup) and reflux
    flow
  • DV (disturbance) Feed flow
  • New Local MPCs estimate their feasible remaining
    capacity (R)

11
Coordinator MPC
  • Objective Maximize plant throughput, subject to
    achieving feasible operation
  • MVs TPMs (feeds and crossovers that affect
    several units)
  • CVs total plant feed constraints
  • Constraints (R gt backoff gt 0, etc.) at highest
    priority level
  • Objective function Total plant feed as CV with
    high, unreachable set point with lower priority
  • DVs feed composition changes, disturbance flows
  • Model step-response models obtained from
  • Calculated steady-state gains (from feed
    composition)
  • Plant tests (dynamic)

12
KÅRSTØ MPC COORDINATOR IMPLEMENTATION (2008)
Export gas
Rich gas
MV
CV
Export gas
CV
CV
CV
MV
CV
CV
CV
Rich gas
CV
CV
CV
MV
Half of the plant included 6 MVs 22 CVs 7 DVs
MV
Condensate
CV
MV
CV
CV
MV
CV
CV
CV
CV
CV
13
Step response models in coodinator MPC
Remaining capacity (R) goes down when feed
increases
more
14
Coordinator MPC in closed loop
  • Test runs January to April 2008

15
TEST 07 FEB 2008
Export gas
Rich gas
MV
CV
Export gas
CV
CV3
CV
MV1
CV
CV
CV2
Rich gas
CV
DV
CV
CV1
MV2
MV
Condensate
CV
MV
CV
CV
MV
CV
CV
CV
CV
CV
16
TEST 07 FEB 2008
MV1
CV3
CV1
DV
MV2
CV2
t 0 min Turn on t 250 - 320 min Change
model gains (tuning) t 500 min Adjust back-off
for R in demethanizer t 580 600 min Feed
composition change (DV)
17
Experiences
  • Using local MPCs to estimate feasible remaining
    capacity leads to a plant-wide application with
    reasonable size
  • The estimate remaining capacity relies on
  • accuracy of the steady-state models
  • correct and reasonable CV and MV constraints
  • use of gain scheduling to cope with larger
    nonlinearities
  • Crucial to inspect the models and tuning of the
    local applications in a systematic manner
  • Requires follow-up work and extensive training of
    operators and operator managers
  • New way of thinking
  • New operator handle instead of feedrate Rs
    (back-off)

18
Conclusions
  • Frequent changes in feed composition, pipeline
    pressures and other disturbances require a
    dynamic model for optimization
  • Coordinator MPC is promising tool for
    implementing maximum throughput at the Kårstø gas
    plant.
  • More focus among operator personnel on
  • capacity of each unit
  • Plant-wide perspective to decide the plant- and
    crossover flows

19
Acknowledgements
  • StatoilHydro and Gassco
  • Kjetil Meyer, Roar Sørensen
  • Operating managers and personnel at the Statpipe
    and Sleipner trains.

References
  • Aske, E.M.B., S. Strand and S. Skogestad (2008).
    Coordinator MPC for maximizing plant throughput.
    Comput. Chem. Eng. 32(1-2), 195204.
  • Full paper E. Aske, E. Ph.D. thesis, NTNU,
    Trondheim, Norway, 2009 (Chapter 6). Available
    from the home page of S. Skogestad http//www.nt.
    ntnu.no/users/skoge/publications/thesis/2009_aske/

20
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21
COORDINATOR IN CLOSED LOOP DATE?
22
DATE?
Export gas
Rich gas
MV
CV
Export gas
CV
CV
CV
MV
CV
CV
CV
Rich gas
CV
CV
CV
MV
MV
Condensate
CV
MV
CV
CV
MV
CV
CV
CV
CV
CV
23
DATE?
CV Pipeline pressure
MV Feed
New constraint from pipeline network operators
CV Remaining capacity
MV Crossover
Increase backoff
6 hrs
9 hrs
24
COORDINATOR IN CLOSED LOOP 07 FEB 2008
25
07 FEB 2008
CV Pipeline pressure
MV Feed
6 hrs
9 hrs
MV Crossover
CV Remaining capacity
DV Feed composition
Composition disturbance
Model adjustment
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