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Steinmann, Victor
Learning Python
Commits
bfb60671
Commit
bfb60671
authored
5 months ago
by
Steinmann
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constraints working no or trivial solution found
parent
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optimierung_pymoo.ipynb
+32
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View file @
bfb60671
...
...
@@ -202,24 +202,19 @@
"source": [
"Durchfluss aus Incidenzmatrix beerechnen\n",
"\n",
"Zeilen = knoten \n",
"Spalten = kanten\n",
"Summe pro knoten = 0\n",
"\n",
".Q_valve <= .Q\n",
".Q['pumps']==.Q**2['successors']"
"$-l Q^2 = \\alpha_1 Q^2+\\alpha_2 Q n+\\alpha_3 n^2$"
]
},
{
"cell_type": "code",
"execution_count":
6
,
"execution_count":
25
,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q_valve': {'valveA':
4
.0, 'valveB':
4
.0, 'valveC':
4
.0}}}\n"
"{None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q_valve': {'valveA':
1
.0, 'valveB':
1
.0, 'valveC':
2
.0}}}\n"
]
}
],
...
...
@@ -244,24 +239,15 @@
"modell.n = pyo.Var(modell.pumps,bounds=(750/3600,1))\n",
"modell.Q = pyo.Var(modell.nodes)\n",
"\n",
"#Objective\n",
"def PumpPower(modell):\n",
" return sum(np.dot(\n",
" np.array(\n",
" [modell.Q[i]**3,(modell.Q[i]**2)*modell.n[i],modell.Q[i]*modell.n[i]**2,modell.n[i]**3]\n",
" ),LR_P.coef_\n",
" ) for i in modell.pumps)\n",
"modell.Power_Objective = pyo.Objective(rule=PumpPower,sense=pyo.minimize)\n",
"\n",
"#expressions for constraints:\n",
"def PumpFlow(modell,pump):\n",
" return np.dot(np.array([modell.Q[pump]**2,modell.n[pump]*modell.Q[pump],modell.n[pump]**2]),LR_H.coef_)\n",
"def Pump_delivery_req(modell,pump):\n",
" return PumpFlow(modell,pump)
+ (
pyo.summation(modell.Q,index=graph.successors(pump))
**2)==0.
\n",
" return PumpFlow(modell,pump)
==
pyo.summation(modell.Q,index=graph.successors(pump))\n",
"\n",
"\n",
"def valve_req_rule(modell,valve):\n",
" return
pyo.summation(modell.Q,index=graph.predecessors(
valve
))
>=modell.Q_valve[valve]\n",
" return
modell.Q[
valve
]
>=modell.Q_valve[valve]\n",
"\n",
"#modell.Flow_Objective = pyo.Objective(modell.pumps,rule=Flow_req,sense=pyo.minimize)\n",
"\n",
...
...
@@ -269,20 +255,22 @@
"def continuityRule(modell,node):\n",
" return pyo.summation(modell.Q, index=graph.predecessors(node))==pyo.summation(modell.Q, index=graph.successors(node))\n",
"\n",
"#Objective\n",
"def PumpPower(modell):\n",
" return sum(np.dot(\n",
" np.array(\n",
" [modell.Q[i]**3,(modell.Q[i]**2)*modell.n[i],modell.Q[i]*modell.n[i]**2,modell.n[i]**3]\n",
" ),LR_P.coef_\n",
" ) for i in modell.pumps)\n",
"modell.Power_Objective = pyo.Objective(rule=PumpPower,sense=pyo.minimize)\n",
"\n",
"\n",
"#alternative\n",
"def continuityRule2(modell,node):\n",
" return 0.==sum(graph[node][i][0]['weight'] for i in graph[node])\n",
"#continuity adjustment for change in hight needed\n",
"#construction of test Data dictionairy missing\n",
"TestData={\n",
" None:{\n",
" \n",
" 'nodes':[key for key in graph.nodes.keys()],\n",
" 'pumps':[key for key in graph.nodes.keys() if 'pump' in key],\n",
" 'valves':[key for key in graph.nodes.keys() if 'valve' in key],\n",
" 'Q_valve':{'valveA':
4
.,'valveB':
4
.,'valveC':
4
.},\n",
" 'Q_valve':{'valveA':
1
.,'valveB':
1
.,'valveC':
2
.},\n",
" }\n",
"}\n",
"print(TestData)"
...
...
@@ -301,7 +289,7 @@
},
{
"cell_type": "code",
"execution_count":
7
,
"execution_count":
26
,
"metadata": {},
"outputs": [
{
...
...
@@ -324,7 +312,7 @@
" Ipopt 3.14.16 Interior Point Optimizer developed by A. Waechter et.al. (github.com/coin-or/Ipopt)\n",
"\n",
"user parameter file <scip.set> not found - using default parameters\n",
"read problem <C:\\Users\\STEINM~1\\AppData\\Local\\Temp\\tmp
n32p48nz
.pyomo.nl>\n",
"read problem <C:\\Users\\STEINM~1\\AppData\\Local\\Temp\\tmp
rv0ikbwh
.pyomo.nl>\n",
"============\n",
"\n",
"original problem has 9 variables (0 bin, 0 int, 0 impl, 9 cont) and 12 constraints\n",
...
...
@@ -333,9 +321,9 @@
"=============\n",
"\n",
"presolving:\n",
"(round 1, fast) 2 del vars, 6 del conss, 0 add conss,
5
chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs\n",
"(round 1, fast) 2 del vars, 6 del conss, 0 add conss,
12
chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs\n",
"presolving (2 rounds: 2 fast, 0 medium, 0 exhaustive):\n",
" 2 deleted vars, 6 deleted constraints, 0 added constraints,
5
tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients\n",
" 2 deleted vars, 6 deleted constraints, 0 added constraints,
16
tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients\n",
" 0 implications, 0 cliques\n",
"presolving detected infeasibility\n",
"Presolving Time: 0.00\n",
...
...
@@ -350,25 +338,18 @@
"model.name=\"unknown\";\n",
" - termination condition: infeasible\n",
" - message from solver: infeasible\n",
"\n",
"Problem: \n",
"- Lower bound: -inf\n",
" Upper bound: inf\n",
" Number of objectives: 1\n",
" Number of constraints: 0\n",
" Number of variables: 0\n",
" Sense: unknown\n",
"Solver: \n",
"- Status: warning\n",
" Message: infeasible\n",
" Termination condition: infeasible\n",
" Id: 200\n",
" Error rc: 0\n",
" Time: 0.047808170318603516\n",
"Solution: \n",
"- number of solutions: 0\n",
" number of solutions displayed: 0\n",
"\n"
"n : Size=2, Index=pumps\n",
" Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
" pump1 : 0.20833333333333334 : None : 1 : False : True : Reals\n",
" pump2 : 0.20833333333333334 : None : 1 : False : True : Reals\n",
"Q : Size=6, Index=nodes\n",
" Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
" pump1 : None : None : None : False : True : Reals\n",
" pump2 : None : None : None : False : True : Reals\n",
" source : None : None : None : False : True : Reals\n",
" valveA : None : None : None : False : True : Reals\n",
" valveB : None : None : None : False : True : Reals\n",
" valveC : None : None : None : False : True : Reals\n"
]
}
],
...
...
@@ -381,8 +362,8 @@
"instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule)\n",
"instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req)\n",
"result=opt.solve(instance, tee=True)\n",
"\n",
"
print(result)
"
"
instance.n.pprint()
\n",
"
instance.Q.pprint()\n
"
]
}
],
...
...
%% Cell type:markdown id: tags:
Formulieren der Optimierungsgleichung in pymoo
%% Cell type:markdown id: tags:
Es gilt die Kontinuitätsgleichung:
$
\S
igma
\d
ot{V}_k(t) = O$
und die aus der Topologie resultierende Inzidenzmatrix $A_i$
sowie die aus dem Pumpenkennfeld folgende Beziehung:
$
\D
elta p=
\a
lpha_1 Q^2+
\a
lpha_2 Q n+
\a
lpha_3 n^2 : n
\i
n
\{
0
\}
\c
up [n_{
\m
athrm{min}},n_{
\m
athrm{max}}] $
$P=
\b
eta_1 Q^3+
\b
eta_2 Q^2 n+
\b
eta_3 Q n^2+
\b
eta_4n^3+
\b
eta_5$
und die beziehung für den Druckverlust an den Ventilen:
$
\D
elta p_{
\m
athrm{loss}} = -
\f
rac{1}{2}
\v
arrho
\z
eta
\l
eft(
\f
rac{Q}{A}
\r
ight)^2 = -l Q^2 :l
\i
n [l_{
\m
athrm{min}}:
\i
nfty )$
nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert.
$$
\b
egin{align
*
}
\m
athrm{min}
\s
um_{p
\i
n
\m
athcal{P}} Po_{p}
\\
Q_{p,i}
\g
eq
\s
um_{strang} Q_v +
\s
um_{strang} Q_p
\\
Q_p , n
\e
psilon [n_{min},n_{max}]
\\
\o
verrightarrow{n} = (1,n,n^2,n^3)^T
\\
min P = A
\o
verrightarrow{n}
\\
-n
\l
eq n_{min}
\\
n
\l
eq n_{max}
\e
nd{align
*
}
$$
Förderhöhe als constraint continuität fomulieren pro strang
%% Cell type:code id: tags:
```
python
!
pip
install
pyomo
```
%% Output
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: pyomo in c:\users\steinmann\appdata\roaming\python\python312\site-packages (6.8.2)
Requirement already satisfied: ply in c:\users\steinmann\appdata\roaming\python\python312\site-packages (from pyomo) (3.11)
[notice] A new release of pip is available: 24.2 -> 25.0
[notice] To update, run: C:\Program Files\Python312\python.exe -m pip install --upgrade pip
%% Cell type:code id: tags:
```
python
#Pump-Powercurve and Pump-Hightcurve
import
regression_own
(
LR_H
,
LR_P
)
=
regression_own
.
regress_pump
()
```
%% Output
R^20.9998289611292903
R^20.9994449560888792
%% Cell type:code id: tags:
```
python
#Graph constroctor
#Alle Ventile sind direkt mit der Quelle/Senke Verbunden
import
multiDiGraph
as
gr
nodes
=
[
'
source
'
,
'
pump1
'
,
'
pump2
'
,
'
valveA
'
,
'
valveB
'
,
'
valveC
'
]
graph
=
gr
.
construct_graph
(
'
source
'
,(
'
source
'
,
'
pump1
'
,
0.
),(
'
pump1
'
,
'
pump2
'
,
0.
),(
'
pump2
'
,
'
valveA
'
,
0.
),(
'
pump2
'
,
'
valveB
'
,
0.
),
(
'
pump1
'
,
'
valveC
'
,
0.
),(
'
valveA
'
,
'
source
'
,
4.
),(
'
valveB
'
,
'
source
'
,
4.
),(
'
valveC
'
,
'
source
'
,
4.
))
#ist das notwendig?!?
for
node
in
graph
.
nodes
:
#definieren der Drehzahl für jede Pumpe im graphen
#inizieren des Durchflusses für jedes Ventil im Graphen
if
'
pump
'
in
node
:
graph
.
nodes
[
node
][
'
n
'
]
=
750
/
3600
else
:
graph
.
nodes
[
node
][
'
n
'
]
=
None
graph
.
nodes
[
node
][
'
flow
'
]
=
0.
if
'
valve
'
in
node
:
graph
.
nodes
[
node
][
'
flow
'
]
=
graph
[
node
][
'
source
'
][
0
][
'
weight
'
]
for
node
in
graph
.
nodes
:
#Berechnen des Durchflusses im Knoten
if
'
valve
'
in
node
:
continue
for
inF
in
graph
.
predecessors
(
node
):
graph
.
nodes
[
node
][
'
flow
'
]
+=
graph
[
inF
][
node
][
0
][
'
weight
'
]
#Berechnen des Durchflusses der abgehenden Kanten
tempF
=
graph
.
nodes
[
node
][
'
flow
'
]
SC
=
0
for
outF
in
graph
.
successors
(
node
):
if
'
valve
'
in
outF
:
graph
[
node
][
outF
][
0
][
'
weight
'
]
=
graph
.
nodes
[
outF
][
'
flow
'
]
tempF
=
tempF
-
graph
.
nodes
[
outF
][
'
flow
'
]
else
:
SC
+=
1
for
outF
in
graph
.
successors
(
node
):
if
SC
!=
0.
and
not
'
valve
'
in
outF
:
graph
[
node
][
outF
][
0
][
'
weight
'
]
=
tempF
/
SC
else
:
continue
```
%% Output
%% Cell type:code id: tags:
```
python
import
networkx
as
nx
Mtrx
=
nx
.
incidence_matrix
(
graph
,
nodes
,
oriented
=
True
)
```
%% Cell type:code id: tags:
```
python
import
networkx
as
nx
def
create_dict
(
GR
:
nx
.
multidigraph
):
data
=
{
None
:{
'
nodes
'
:{},
'
pumps
'
:{},
'
valves
'
:{},
}
}
for
node
in
GR
.
nodes
:
data
[
None
][
'
nodes
'
][
node
]
=
None
data
[
None
][
'
Q
'
][
node
]
=
GR
.
nodes
[
node
][
'
flow
'
]
if
'
pump
'
in
node
:
data
[
None
][
'
pumps
'
][
node
]
=
None
data
[
None
][
'
n
'
][
node
]
=
0.
if
'
valve
'
in
node
:
data
[
None
][
'
valves
'
][
node
]
=
None
return
data
```
%% Cell type:markdown id: tags:
Durchfluss aus Incidenzmatrix beerechnen
Zeilen = knoten
Spalten = kanten
Summe pro knoten = 0
.Q_valve <= .Q
.Q['pumps']==.Q
**
2['successors']
$-l Q^2 =
\a
lpha_1 Q^2+
\a
lpha_2 Q n+
\a
lpha_3 n^2$
%% Cell type:code id: tags:
```
python
#defining abstract modell for given Network
import
pyomo.environ
as
pyo
from
pyomo.dataportal
import
DataPortal
import
numpy
as
np
from
sklearn.linear_model
import
LinearRegression
modell
=
pyo
.
AbstractModel
()
#notwendige Mengen zur Berechnung der Constraints
modell
.
nodes
=
pyo
.
Set
()
modell
.
pumps
=
pyo
.
Set
()
modell
.
valves
=
pyo
.
Set
()
modell
.
Q_valve
=
pyo
.
Param
(
modell
.
valves
)
#Optimierungsvariable
modell
.
n
=
pyo
.
Var
(
modell
.
pumps
,
bounds
=
(
750
/
3600
,
1
))
modell
.
Q
=
pyo
.
Var
(
modell
.
nodes
)
#Objective
def
PumpPower
(
modell
):
return
sum
(
np
.
dot
(
np
.
array
(
[
modell
.
Q
[
i
]
**
3
,(
modell
.
Q
[
i
]
**
2
)
*
modell
.
n
[
i
],
modell
.
Q
[
i
]
*
modell
.
n
[
i
]
**
2
,
modell
.
n
[
i
]
**
3
]
),
LR_P
.
coef_
)
for
i
in
modell
.
pumps
)
modell
.
Power_Objective
=
pyo
.
Objective
(
rule
=
PumpPower
,
sense
=
pyo
.
minimize
)
#expressions for constraints:
def
PumpFlow
(
modell
,
pump
):
return
np
.
dot
(
np
.
array
([
modell
.
Q
[
pump
]
**
2
,
modell
.
n
[
pump
]
*
modell
.
Q
[
pump
],
modell
.
n
[
pump
]
**
2
]),
LR_H
.
coef_
)
def
Pump_delivery_req
(
modell
,
pump
):
return
PumpFlow
(
modell
,
pump
)
+
(
pyo
.
summation
(
modell
.
Q
,
index
=
graph
.
successors
(
pump
))
**
2
)
==
0.
return
PumpFlow
(
modell
,
pump
)
==
pyo
.
summation
(
modell
.
Q
,
index
=
graph
.
successors
(
pump
))
def
valve_req_rule
(
modell
,
valve
):
return
pyo
.
summation
(
modell
.
Q
,
index
=
graph
.
predecessors
(
valve
))
>=
modell
.
Q_valve
[
valve
]
return
modell
.
Q
[
valve
]
>=
modell
.
Q_valve
[
valve
]
#modell.Flow_Objective = pyo.Objective(modell.pumps,rule=Flow_req,sense=pyo.minimize)
#Constaints
def
continuityRule
(
modell
,
node
):
return
pyo
.
summation
(
modell
.
Q
,
index
=
graph
.
predecessors
(
node
))
==
pyo
.
summation
(
modell
.
Q
,
index
=
graph
.
successors
(
node
))
#Objective
def
PumpPower
(
modell
):
return
sum
(
np
.
dot
(
np
.
array
(
[
modell
.
Q
[
i
]
**
3
,(
modell
.
Q
[
i
]
**
2
)
*
modell
.
n
[
i
],
modell
.
Q
[
i
]
*
modell
.
n
[
i
]
**
2
,
modell
.
n
[
i
]
**
3
]
),
LR_P
.
coef_
)
for
i
in
modell
.
pumps
)
modell
.
Power_Objective
=
pyo
.
Objective
(
rule
=
PumpPower
,
sense
=
pyo
.
minimize
)
#alternative
def
continuityRule2
(
modell
,
node
):
return
0.
==
sum
(
graph
[
node
][
i
][
0
][
'
weight
'
]
for
i
in
graph
[
node
])
#continuity adjustment for change in hight needed
#construction of test Data dictionairy missing
TestData
=
{
None
:{
'
nodes
'
:[
key
for
key
in
graph
.
nodes
.
keys
()],
'
pumps
'
:[
key
for
key
in
graph
.
nodes
.
keys
()
if
'
pump
'
in
key
],
'
valves
'
:[
key
for
key
in
graph
.
nodes
.
keys
()
if
'
valve
'
in
key
],
'
Q_valve
'
:{
'
valveA
'
:
4
.
,
'
valveB
'
:
4
.
,
'
valveC
'
:
4
.
},
'
Q_valve
'
:{
'
valveA
'
:
1
.
,
'
valveB
'
:
1
.
,
'
valveC
'
:
2
.
},
}
}
print
(
TestData
)
```
%% Output
{None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q_valve': {'valveA':
4
.0, 'valveB':
4
.0, 'valveC':
4
.0}}}
{None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q_valve': {'valveA':
1
.0, 'valveB':
1
.0, 'valveC':
2
.0}}}
%% Cell type:markdown id: tags:
Frage: gibt es nur eine Lösung für Drehzahl?
Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen
%% Cell type:code id: tags:
```
python
from
pyomo.opt
import
SolverFactory
opt
=
pyo
.
SolverFactory
(
'
scipampl
'
,
executable
=
r
'
C:\Program Files\SCIPOptSuite 9.2.0\bin\scip.exe
'
)
instance
=
modell
.
create_instance
(
TestData
)
instance
.
Continuity_constaint
=
pyo
.
Constraint
(
instance
.
nodes
,
rule
=
continuityRule
)
instance
.
Flow_constraint
=
pyo
.
Constraint
(
instance
.
valves
,
rule
=
valve_req_rule
)
instance
.
pump_Flow_constraint
=
pyo
.
Constraint
(
instance
.
pumps
,
rule
=
Pump_delivery_req
)
result
=
opt
.
solve
(
instance
,
tee
=
True
)
print
(
result
)
instance
.
n
.
pprint
()
instance
.
Q
.
p
print
()
```
%% Output
SCIP version 9.2.0 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: Soplex 7.1.2] [GitHash: 74cea9222e]
Copyright (c) 2002-2024 Zuse Institute Berlin (ZIB)
External libraries:
Soplex 7.1.2 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de) [GitHash: b040369c]
CppAD 20180000.0 Algorithmic Differentiation of C++ algorithms developed by B. Bell (github.com/coin-or/CppAD)
TinyCThread 1.2 small portable implementation of the C11 threads API (tinycthread.github.io)
MPIR 3.0.0 Multiple Precision Integers and Rationals Library developed by W. Hart (mpir.org)
ZIMPL 3.6.2 Zuse Institute Mathematical Programming Language developed by T. Koch (zimpl.zib.de)
AMPL/MP 690e9e7 AMPL .nl file reader library (github.com/ampl/mp)
PaPILO 2.4.0 parallel presolve for integer and linear optimization (github.com/scipopt/papilo) (built with TBB) [GitHash: 2d9fe29f]
Nauty 2.8.8 Computing Graph Automorphism Groups by Brendan D. McKay (users.cecs.anu.edu.au/~bdm/nauty)
sassy 1.1 Symmetry preprocessor by Markus Anders (github.com/markusa4/sassy)
Ipopt 3.14.16 Interior Point Optimizer developed by A. Waechter et.al. (github.com/coin-or/Ipopt)
user parameter file <scip.set> not found - using default parameters
read problem <C:\Users\STEINM~1\AppData\Local\Temp\tmp
n32p48nz
.pyomo.nl>
read problem <C:\Users\STEINM~1\AppData\Local\Temp\tmp
rv0ikbwh
.pyomo.nl>
============
original problem has 9 variables (0 bin, 0 int, 0 impl, 9 cont) and 12 constraints
solve problem
=============
presolving:
(round 1, fast) 2 del vars, 6 del conss, 0 add conss,
5
chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs
(round 1, fast) 2 del vars, 6 del conss, 0 add conss,
12
chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs
presolving (2 rounds: 2 fast, 0 medium, 0 exhaustive):
2 deleted vars, 6 deleted constraints, 0 added constraints,
5
tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients
2 deleted vars, 6 deleted constraints, 0 added constraints,
16
tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients
0 implications, 0 cliques
presolving detected infeasibility
Presolving Time: 0.00
SCIP Status : problem is solved [infeasible]
Solving Time (sec) : 0.00
Solving Nodes : 0
Primal Bound : +1.00000000000000e+20 (0 solutions)
Dual Bound : +1.00000000000000e+20
Gap : 0.00 %
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: infeasible
- message from solver: infeasible
Problem:
- Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 0
Number of variables: 0
Sense: unknown
Solver:
- Status: warning
Message: infeasible
Termination condition: infeasible
Id: 200
Error rc: 0
Time: 0.047808170318603516
Solution:
- number of solutions: 0
number of solutions displayed: 0
n : Size=2, Index=pumps
Key : Lower : Value : Upper : Fixed : Stale : Domain
pump1 : 0.20833333333333334 : None : 1 : False : True : Reals
pump2 : 0.20833333333333334 : None : 1 : False : True : Reals
Q : Size=6, Index=nodes
Key : Lower : Value : Upper : Fixed : Stale : Domain
pump1 : None : None : None : False : True : Reals
pump2 : None : None : None : False : True : Reals
source : None : None : None : False : True : Reals
valveA : None : None : None : False : True : Reals
valveB : None : None : None : False : True : Reals
valveC : None : None : None : False : True : Reals
...
...
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