Simple rules for PID tuning Sigurd Skogestad NTNU, Trondheim, Norway.
1 Active constraint regions for economically optimal operation of distillation columns Sigurd...
-
Upload
evangeline-patrick -
Category
Documents
-
view
232 -
download
0
Transcript of 1 Active constraint regions for economically optimal operation of distillation columns Sigurd...
1
Active constraint regions for economically optimal operation of distillation columns
Sigurd Skogestad and Magnus G. Jacobsen
Department of Chemical EngineeringNorwegian University of Science and Tecnology (NTNU)Trondheim, Norway
AIChE Annual Meeting, Minneapolis18 Oct. 2011
2
Question: What should we control (c)? (primary controlled variables y1=c)
• Introductory example: Runner
What should we control?
3
– Cost to be minimized, J=T
– One degree of freedom (u=power)
– What should we control?
Optimal operation - Runner
Optimal operation of runner
4
Sprinter (100m)
• 1. Optimal operation of Sprinter, J=T– Active constraint control:
• Maximum speed (”no thinking required”)
Optimal operation - Runner
5
• 2. Optimal operation of Marathon runner, J=T• Unconstrained optimum!• Any ”self-optimizing” variable c (to control at
constant setpoint)?• c1 = distance to leader of race
• c2 = speed
• c3 = heart rate
• c4 = level of lactate in muscles
Optimal operation - Runner
Marathon (40 km)
6
Conclusion Marathon runner
c = heart rate
select one measurement
• Simple and robust implementation• Disturbances are indirectly handled by keeping a constant heart rate• May have infrequent adjustment of setpoint (heart rate)
Optimal operation - Runner
7
Conclusion: What should we control (c)? (primary controlled variables)
1. Control active constraints!
2. Unconstrained variables: Control self-optimizing variables!
– The ideal self-optimizing variable c is the gradient (c = J/ u = Ju)
– In practice, control individual measurements or combinations, c = H y– We have developed a lot of theory for this
8
Distillation columns: What should we control?
• Always product compositions at spec? NO
• This presentation: Change in active constraints
9
Optimal operation distillation column
• Distillation at steady state with given p and F: N=2 DOFs, e.g. L and V
• Cost to be minimized (economics)
J = - P where P= pD D + pB B – pF F – pV V
• ConstraintsPurity D: For example xD, impurity · max
Purity B: For example, xB, impurity · max
Flow constraints: min · D, B, L etc. · max
Column capacity (flooding): V · Vmax, etc.
Pressure: 1) p given (d) 2) p free: pmin · p · pmax
Feed: 1) F given (d) 2) F free: F · Fmax
• Optimal operation: Minimize J with respect to steady-state DOFs (u)
value products
cost energy (heating+ cooling)
cost feed
10
Example column with 41 stages
u = [L V]
for expected disturbances d = (F, pV)
11
Possible constraint combinations (= 2n = 23 = 8)
1. 0*
2. xD
3. xB*
4. V*
5. xD, V
6. xB, V*
7. xD, xB
8. xD, xB, V (infeasible, only 2 DOFs)
*Not for this case because xB always optimally active (”Avoid product give away”)
12
Constraint regions as function of d1=F and d2=pV
3 regions
13
5 regions
Only get paid for main component (”gold”)
14
I: L – xD=0.95, V – xB? Self-optimizing?! xBs = f(pV)II: L – xD=0.95, V = VmaxIII: As in I
Control, pD independent of purity
15
I: L – xD?, V – xB? Self-optimizing? II: L – xD?, V = VmaxIII: L – xB=0.99, V = Vmax ”active constraints”
No simple decentralized structure. OK with MPC
16
2 Distillation columns in seriesWith given F (disturbance): 4 steady-state DOFs (e.g., L and V in each column)
DOF = Degree Of FreedomRef.: M.G. Jacobsen and S. Skogestad (2011)
Energy price: pV=0-0.2 $/mol (varies)Cost (J) = - Profit = pF F + pV(V1+V2) – pD1D1 – pD2D2 – pB2B2
> 95% BpD2=2 $/mol
F ~ 1.2mol/spF=1 $/mol < 4 mol/s < 2.4 mol/s
> 95% CpB2=1 $/mol
N=41αAB=1.33
N=41αBC=1.5
> 95% ApD1=1 $/mol
25 = 32 possible combinations of the 5 constraints
17 DOF = Degree Of FreedomRef.: M.G. Jacobsen and S. Skogestad (2011)
Energy price: pV=0-0.2 $/mol (varies)Cost (J) = - Profit = pF F + pV(V1+V2) – pD1D1 – pD2D2 – pB2B2
> 95% BpD2=2 $/mol
F ~ 1.2mol/spF=1 $/mol < 4 mol/s < 2.4 mol/s
> 95% CpB2=1 $/mol
1. xB = 95% BSpec. valuable product (B): Always active!Why? “Avoid product give-away”
N=41αAB=1.33
N=41αBC=1.5
> 95% ApD1=1 $/mol
2. Cheap energy: V1=4 mol/s, V2=2.4 mol/sMax. column capacity constraints active!Why? Overpurify A & C to recover more B
2 Distillation columns in series. Active constraints?
18
Active constraint regions for two distillation columns in series
[mol/s]
[$/mol]
CV = Controlled Variable
Energyprice
BOTTLENECKHigher F infeasible because all 5 constraints reached
8 regions
19
Active constraint regions for two distillation columns in series
[mol/s]
[$/mol]
CV = Controlled Variable
Assume low energy prices (pV=0.01 $/mol).How should we control the columns?
Energyprice
20
Control of Distillation columns in series
Given
LC LC
LC LC
PCPC
Assume low energy prices (pV=0.01 $/mol).How should we control the columns? Red: Basic regulatory loops
22
Control of Distillation columns in series
Given
LC LC
LC LC
PCPC
Red: Basic regulatory loops
CC
xB
xBS=95%
MAX V1 MAX V2
CONTROL ACTIVE CONSTRAINTS!
23
Control of Distillation columns in series
Given
LC LC
LC LC
PCPC
Red: Basic regulatory loops
CC
xB
xBS=95%
MAX V1 MAX V2
Remains: 1 unconstrained DOF (L1):Use for what? CV=xA? •No!! Optimal xA varies with F •Maybe: constant L1? (CV=L1)•Better: CV= xA in B1? Self-optimizing?
CONTROL ACTIVE CONSTRAINTS!
24
Active constraint regions for two distillation columns in series
CV = Controlled Variable
3 2
01
1
0
2
[mol/s]
[$/mol]
1
Cheap energy: 1 remaining unconstrained DOF (L1) -> Need to find 1 additional CVs (“self-optimizing”)
More expensive energy: 3 remaining unconstrained DOFs -> Need to find 3 additional CVs (“self-optimizing”)
Energyprice
25
Conclusion
• Generate constraint regions by offline simulation for expected important disturbances– Time consuming - so focus on important disturbance
range
• Implementation / control– Control active constraints!
– Switching between these usually easy
– Less obvious what to select as ”self-optimizing” CVs for remaining unconstrained degrees of freedom