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Commit 5ddbad81 authored by Steinmann's avatar Steinmann
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rewritten flow into parameter not variable!!

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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Formulieren der Optimierungsgleichung in pymoo Formulieren der Optimierungsgleichung in pymoo
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Es gilt die Kontinuitätsgleichung: Es gilt die Kontinuitätsgleichung:
$ \Sigma \dot{V}_k(t) = O$ $ \Sigma \dot{V}_k(t) = O$
und die aus der Topologie resultierende Inzidenzmatrix $A_i$ und die aus der Topologie resultierende Inzidenzmatrix $A_i$
sowie die aus dem Pumpenkennfeld folgende Beziehung: sowie die aus dem Pumpenkennfeld folgende Beziehung:
$\Delta p=\alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2 : n \in \{0\} \cup [n_{\mathrm{min}},n_{\mathrm{max}}] $ $\Delta p=\alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2 : n \in \{0\} \cup [n_{\mathrm{min}},n_{\mathrm{max}}] $
$P=\beta_1 Q^3+\beta_2 Q^2 n+\beta_3 Q n^2+\beta_4n^3+\beta_5$ $P=\beta_1 Q^3+\beta_2 Q^2 n+\beta_3 Q n^2+\beta_4n^3+\beta_5$
und die beziehung für den Druckverlust an den Ventilen: und die beziehung für den Druckverlust an den Ventilen:
$\Delta p_{\mathrm{loss}} = - \frac{1}{2} \varrho \zeta \left(\frac{Q}{A}\right)^2 = -l Q^2 :l\in [l_{\mathrm{min}}:\infty )$ $\Delta p_{\mathrm{loss}} = - \frac{1}{2} \varrho \zeta \left(\frac{Q}{A}\right)^2 = -l Q^2 :l\in [l_{\mathrm{min}}:\infty )$
nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert. nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert.
$$ $$
\begin{align*} \begin{align*}
\mathrm{min} \sum_{p \in \mathcal{P}} Po_{p} \\ \mathrm{min} \sum_{p \in \mathcal{P}} Po_{p} \\
Q_{p,i} \geq \sum_{strang} Q_v + \sum_{strang} Q_p \\ Q_{p,i} \geq \sum_{strang} Q_v + \sum_{strang} Q_p \\
Q_p , n\epsilon [n_{min},n_{max}] \\ Q_p , n\epsilon [n_{min},n_{max}] \\
\overrightarrow{n} = (1,n,n^2,n^3)^T \\ \overrightarrow{n} = (1,n,n^2,n^3)^T \\
min P = A \overrightarrow{n} \\ min P = A \overrightarrow{n} \\
-n\leq n_{min} \\ -n\leq n_{min} \\
n\leq n_{max} n\leq n_{max}
\end{align*} \end{align*}
$$ $$
Förderhöhe als constraint continuität fomulieren pro strang Förderhöhe als constraint continuität fomulieren pro strang
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
!pip install pyomo !pip install pyomo
``` ```
%% Output %% Output
Defaulting to user installation because normal site-packages is not writeable Defaulting to user installation because normal site-packages is not writeable
Collecting pyomo Collecting pyomo
Downloading Pyomo-6.8.2-py3-none-any.whl.metadata (8.0 kB) Downloading Pyomo-6.8.2-py3-none-any.whl.metadata (8.0 kB)
Collecting ply (from pyomo) Collecting ply (from pyomo)
Downloading ply-3.11-py2.py3-none-any.whl.metadata (844 bytes) Downloading ply-3.11-py2.py3-none-any.whl.metadata (844 bytes)
Downloading Pyomo-6.8.2-py3-none-any.whl (3.7 MB) Downloading Pyomo-6.8.2-py3-none-any.whl (3.7 MB)
---------------------------------------- 0.0/3.7 MB ? eta -:--:-- ---------------------------------------- 0.0/3.7 MB ? eta -:--:--
---------------------------- ----------- 2.6/3.7 MB 12.6 MB/s eta 0:00:01 ---------------------------- ----------- 2.6/3.7 MB 12.6 MB/s eta 0:00:01
---------------------------------------- 3.7/3.7 MB 11.6 MB/s eta 0:00:00 ---------------------------------------- 3.7/3.7 MB 11.6 MB/s eta 0:00:00
Downloading ply-3.11-py2.py3-none-any.whl (49 kB) Downloading ply-3.11-py2.py3-none-any.whl (49 kB)
Installing collected packages: ply, pyomo Installing collected packages: ply, pyomo
Successfully installed ply-3.11 pyomo-6.8.2 Successfully installed ply-3.11 pyomo-6.8.2
[notice] A new release of pip is available: 24.3.1 -> 25.0 [notice] A new release of pip is available: 24.3.1 -> 25.0
[notice] To update, run: C:\Users\Victor\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\python.exe -m pip install --upgrade pip [notice] To update, run: C:\Users\Victor\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\python.exe -m pip install --upgrade pip
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#Pump-Powercurve and Pump-Hightcurve #Pump-Powercurve and Pump-Hightcurve
import regression_own import regression_own
(LR_H,LR_P)=regression_own.regress_pump() (LR_H,LR_P)=regression_own.regress_pump()
``` ```
%% Output %% Output
R^20.9998289611292903 R^20.9998289611292903
R^20.9994449560888792 R^20.9994449560888792
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#Graph constroctor #Graph constroctor
#Alle Ventile sind direkt mit der Quelle/Senke Verbunden #Alle Ventile sind direkt mit der Quelle/Senke Verbunden
import multiDiGraph as gr import multiDiGraph as gr
nodes =['source','pump1','pump2','valveA','valveB','valveC'] nodes =['source','pump1','pump2','valveA','valveB','valveC']
graph = gr.construct_graph('source',('source','pump1',0.),('pump1','pump2',0.),('pump2','valveA',0.),('pump2','valveB',0.), 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.)) ('pump1','valveC',0.),('valveA','source',4.),('valveB','source',4.),('valveC','source',4.))
#ist das notwendig?!? #ist das notwendig?!?
for node in graph.nodes: for node in graph.nodes:
#definieren der Drehzahl für jede Pumpe im graphen #definieren der Drehzahl für jede Pumpe im graphen
#inizieren des Durchflusses für jedes Ventil im Graphen #inizieren des Durchflusses für jedes Ventil im Graphen
if 'pump' in node: if 'pump' in node:
graph.nodes[node]['n']=750/3600 graph.nodes[node]['n']=750/3600
else: else:
graph.nodes[node]['n']=None graph.nodes[node]['n']=None
graph.nodes[node]['flow']=0. graph.nodes[node]['flow']=0.
if 'valve' in node: if 'valve' in node:
graph.nodes[node]['flow']= graph[node]['source'][0]['weight'] graph.nodes[node]['flow']= graph[node]['source'][0]['weight']
for node in graph.nodes: for node in graph.nodes:
#Berechnen des Durchflusses im Knoten #Berechnen des Durchflusses im Knoten
if 'valve' in node: if 'valve' in node:
continue continue
for inF in graph.predecessors(node): for inF in graph.predecessors(node):
graph.nodes[node]['flow'] += graph[inF][node][0]['weight'] graph.nodes[node]['flow'] += graph[inF][node][0]['weight']
#Berechnen des Durchflusses der abgehenden Kanten #Berechnen des Durchflusses der abgehenden Kanten
tempF=graph.nodes[node]['flow'] tempF=graph.nodes[node]['flow']
SC=0 SC=0
for outF in graph.successors(node): for outF in graph.successors(node):
if 'valve' in outF: if 'valve' in outF:
graph[node][outF][0]['weight']=graph.nodes[outF]['flow'] graph[node][outF][0]['weight']=graph.nodes[outF]['flow']
tempF=tempF - graph.nodes[outF]['flow'] tempF=tempF - graph.nodes[outF]['flow']
else: else:
SC+=1 SC+=1
for outF in graph.successors(node): for outF in graph.successors(node):
if SC!=0. and not'valve' in outF: if SC!=0. and not'valve' in outF:
graph[node][outF][0]['weight']=tempF/SC graph[node][outF][0]['weight']=tempF/SC
else:continue else:continue
``` ```
%% Output %% Output
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import networkx as nx import networkx as nx
Mtrx= nx.incidence_matrix(graph,nodes,oriented=True) Mtrx= nx.incidence_matrix(graph,nodes,oriented=True)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import networkx as nx import networkx as nx
def create_dict(GR:nx.multidigraph): def create_dict(GR:nx.multidigraph):
data={None:{'nodes':{}, data={None:{'nodes':{},
'pumps':{}, 'pumps':{},
'valves':{}, 'valves':{},
} }
} }
for node in GR.nodes: for node in GR.nodes:
data[None]['nodes'][node]=None data[None]['nodes'][node]=None
data[None]['Q'][node]=GR.nodes[node]['flow'] data[None]['Q'][node]=GR.nodes[node]['flow']
if 'pump' in node: if 'pump' in node:
data[None]['pumps'][node]=None data[None]['pumps'][node]=None
data[None]['n'][node]=0. data[None]['n'][node]=0.
if 'valve' in node: if 'valve' in node:
data[None]['valves'][node]=None data[None]['valves'][node]=None
return data return data
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Durchfluss aus Incidenzmatrix beerechnen Durchfluss aus Incidenzmatrix beerechnen
$-l Q^2 = \alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2$ $-l Q^2 = \alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2$
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#defining abstract modell for given Network #defining abstract modell for given Network
import pyomo.environ as pyo import pyomo.environ as pyo
from pyomo.dataportal import DataPortal
import numpy as np import numpy as np
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
modell = pyo.AbstractModel() modell = pyo.AbstractModel()
#notwendige Mengen zur Berechnung der Constraints #notwendige Mengen zur Berechnung der Constraints
modell.nodes = pyo.Set() modell.nodes = pyo.Set()
modell.pumps = pyo.Set() modell.pumps = pyo.Set()
modell.valves = pyo.Set() modell.valves = pyo.Set()
modell.Q_valve=pyo.Param(modell.valves) modell.Q_valve=pyo.Param(modell.valves)
modell.Q = pyo.Param(modell.nodes)
#Optimierungsvariable #Optimierungsvariable
modell.n = pyo.Var(modell.pumps,bounds=(750/3600,1)) modell.n = pyo.Var(modell.pumps,bounds=(750/3600,1))
modell.Q = pyo.Var(modell.nodes,bounds=(0.,10.))
#expressions for constraints: #expressions for constraints:
def PumpFlow(modell,pump): def PumpFlow(modell,pump):
return pyo.summation(np.array([modell.Q[pump]**2, modell.n[pump]*modell.Q[pump], modell.n[pump]**2]),LR_H.coef_,index=[0,1,2]) return pyo.summation(np.array([modell.Q[pump]**2, modell.n[pump]*modell.Q[pump], modell.n[pump]**2]),LR_H.coef_,index=[0,1,2])
def Pump_delivery_req(modell,pump): def Pump_delivery_req(modell,pump):
return PumpFlow(modell,pump) ==pyo.summation(modell.Q,index=graph.successors(pump)) return PumpFlow(modell,pump) ==modell.Q[pump]
def valve_req_rule(modell,valve): def valve_req_rule(modell,valve):
return modell.Q[valve]>=modell.Q_valve[valve] return modell.Q[valve]>=modell.Q_valve[valve]
def continuity_inflow(modell,node): def continuity_inflow(modell,node):
return modell.Q[node]==pyo.summation(modell.Q, index=graph.successors(node)) return modell.Q[node]==pyo.summation(modell.Q, index=graph.successors(node))
def continuity_outflow(modell,node): def continuity_outflow(modell,node):
return modell.Q[node]==pyo.summation(modell.Q,index=graph.predecessors(node)) return modell.Q[node]==pyo.summation(modell.Q,index=graph.predecessors(node))
#Objective #Objective
def PumpPower(modell): def PumpPower(modell,pump):
return sum(np.dot( return pyo.summation(
np.array( np.array([modell.Q[pump]**3, (modell.Q[pump]**2)*modell.n[pump], modell.Q[pump]*modell.n[pump]**2, modell.n[pump]**3, 1.]),
[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_, index=[0,1,2,3,4])
),LR_P.coef_ modell.Power_Objective = pyo.Objective(modell.pumps,rule=PumpPower,sense=pyo.minimize)
) for i in modell.pumps)
modell.Power_Objective = pyo.Objective(rule=PumpPower,sense=pyo.minimize)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Frage: gibt es nur eine Lösung für Drehzahl? Frage: gibt es nur eine Lösung für Drehzahl?
Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from pyomo.opt import SolverFactory from pyomo.opt import SolverFactory
TestData={ TestData={
None:{ None:{
'nodes':[key for key in graph.nodes.keys()], 'nodes':[key for key in graph.nodes.keys()],
'pumps':[key for key in graph.nodes.keys() if 'pump' in key], 'pumps':[key for key in graph.nodes.keys() if 'pump' in key],
'valves':[key for key in graph.nodes.keys() if 'valve' in key], 'valves':[key for key in graph.nodes.keys() if 'valve' in key],
'Q_valve':{'valveA':1.,'valveB':1.,'valveC':1.}, 'Q':[graph.nodes[key]['flow']for key in graph.nodes.keys()]
} }
} }
print(TestData)
opt = pyo.SolverFactory('scipampl', executable=r'C:\Program Files\SCIPOptSuite 9.2.1\bin\scip.exe') opt = pyo.SolverFactory('scipampl', executable=r'C:\Program Files\SCIPOptSuite 9.2.1\bin\scip.exe')
instance = modell.create_instance(TestData) instance = modell.create_instance(TestData)
instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule) #instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule)
instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req) instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req)
#instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_inflow) #instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_inflow)
#instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_outflow) #instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_outflow)
result=opt.solve(instance, tee=True) result=opt.solve(instance, tee=True)
print(result) print(result)
instance.n.pprint() instance.n.pprint()
instance.Q.pprint() instance.Q.pprint()
``` ```
%% Output %% Output
SCIP version 9.2.1 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: Soplex 7.1.3] [GitHash: 0d2d3c7c2d] {None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q': [12.0, 12.0, 8.0, 4.0, 4.0, 4.0]}}
Copyright (c) 2002-2025 Zuse Institute Berlin (ZIB) WARNING: Failed to create solver with name 'scipampl': Failed to set
executable for solver asl. File with name=C:\Program Files\SCIPOptSuite
External libraries: 9.2.1\bin\scip.exe either does not exist or it is not executable. To skip this
Soplex 7.1.3 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de) [GitHash: 60fd96f2] validation, call set_executable with validate=False.
CppAD 20180000.0 Algorithmic Differentiation of C++ algorithms developed by B. Bell (github.com/coin-or/CppAD) Traceback (most recent call last):
TinyCThread 1.2 small portable implementation of the C11 threads API (tinycthread.github.io) File "C:\Users\Steinmann\AppData\Roaming\Python\Python312\site-packages\pyomo\opt\base\solvers.py", line 162, in __call__
MPIR 3.0.0 Multiple Precision Integers and Rationals Library developed by W. Hart (mpir.org) opt = self._cls[_implicit_solvers[mode]](**kwds)
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) File "C:\Users\Steinmann\AppData\Roaming\Python\Python312\site-packages\pyomo\solvers\plugins\solvers\ASL.py", line 46, in __init__
PaPILO 2.4.1 parallel presolve for integer and linear optimization (github.com/scipopt/papilo) (built with TBB) [GitHash: 11974394] SystemCallSolver.__init__(self, **kwds)
Nauty 2.8.8 Computing Graph Automorphism Groups by Brendan D. McKay (users.cecs.anu.edu.au/~bdm/nauty) File "C:\Users\Steinmann\AppData\Roaming\Python\Python312\site-packages\pyomo\opt\solver\shellcmd.py", line 66, in __init__
sassy 1.1 Symmetry preprocessor by Markus Anders (github.com/markusa4/sassy) self.set_executable(name=executable, validate=validate)
Ipopt 3.14.16 Interior Point Optimizer developed by A. Waechter et.al. (github.com/coin-or/Ipopt) File "C:\Users\Steinmann\AppData\Roaming\Python\Python312\site-packages\pyomo\opt\solver\shellcmd.py", line 115, in set_executable
raise ValueError(
user parameter file <scip.set> not found - using default parameters ValueError: Failed to set executable for solver asl. File with name=C:\Program Files\SCIPOptSuite 9.2.1\bin\scip.exe either does not exist or it is not executable. To skip this validation, call set_executable with validate=False.
read problem <C:\Users\Victor\AppData\Local\Temp\tmpfcunk5gi.pyomo.nl> ERROR: Rule failed when initializing variable for Var Q with index None:
============ AssertionError:
ERROR: Constructing component 'Q' from data=[12.0, 12.0, 8.0, 4.0, 4.0, 4.0]
original problem has 8 variables (0 bin, 0 int, 0 impl, 8 cont) and 6 constraints failed:
AssertionError:
solve problem
============= ---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
presolving: Cell In[21], line 14
(round 1, fast) 0 del vars, 3 del conss, 0 add conss, 11 chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs 12 print(TestData)
(0.0s) symmetry computation started: requiring (bin +, int +, cont +), (fixed: bin -, int -, cont -) 13 opt = pyo.SolverFactory('scipampl', executable=r'C:\Program Files\SCIPOptSuite 9.2.1\bin\scip.exe')
(0.0s) symmetry computation finished: 1 generators found (max: 1500, log10 of symmetry group size: 0.0) (symcode time: 0.00) ---> 14 instance = modell.create_instance(TestData)
dynamic symmetry handling statistics: 15 instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule)
orbitopal reduction: no components 16 instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req)
orbital reduction: no components File ~\AppData\Roaming\Python\Python312\site-packages\pyomo\core\base\PyomoModel.py:734, in Model.create_instance(self, filename, data, name, namespace, namespaces, profile_memory, report_timing, **kwds)
lexicographic reduction: no permutations 731 if None not in _namespaces:
handled 1 out of 1 symmetry components 732 _namespaces.append(None)
(round 2, exhaustive) 0 del vars, 3 del conss, 1 add conss, 11 chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs --> 734 instance.load(data, namespaces=_namespaces, profile_memory=profile_memory)
presolving (3 rounds: 3 fast, 2 medium, 2 exhaustive): 736 #
0 deleted vars, 3 deleted constraints, 0 added constraints, 11 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients 737 # Indicate that the model is concrete/constructed
0 implications, 0 cliques 738 #
presolved problem has 8 variables (0 bin, 0 int, 0 impl, 8 cont) and 4 constraints 739 instance._constructed = True
1 constraints of type <linear> File ~\AppData\Roaming\Python\Python312\site-packages\pyomo\core\base\PyomoModel.py:771, in Model.load(self, arg, namespaces, profile_memory)
3 constraints of type <nonlinear> 769 msg = "Cannot load model model data from with object of type '%s'"
Presolving Time: 0.00 770 raise ValueError(msg % str(type(arg)))
--> 771 self._load_model_data(dp, namespaces, profile_memory=profile_memory)
time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl. File ~\AppData\Roaming\Python\Python312\site-packages\pyomo\core\base\PyomoModel.py:823, in Model._load_model_data(self, modeldata, namespaces, **kwds)
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | -- | Inf | unknown 820 if component.ctype is Model:
L 0.0s| 1 | 0 | 22 | - | subnlp| 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown 821 continue
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown --> 823 self._initialize_component(
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown 824 modeldata, namespaces, component_name, profile_memory
0.0s| 1 | 0 | 26 | - | 920k | 0 | 25 | 4 | 39 | 3 | 1 | 0 | 0 |-1.963132e+01 | 1.151406e+01 | Inf | unknown 825 )
0.0s| 1 | 0 | 26 | - | 920k | 0 | 25 | 4 | 39 | 3 | 1 | 0 | 0 |-1.963132e+01 | 1.151406e+01 | Inf | unknown 827 # Note: As is, connectors are expanded when using command-line pyomo but not calling model.create(...) in a Python script.
0.0s| 1 | 0 | 30 | - | 935k | 0 | 25 | 4 | 42 | 6 | 2 | 0 | 0 |-1.885311e+01 | 1.151406e+01 | Inf | unknown 828 # John says this has to do with extension points which are called from commandline but not when writing scripts.
0.0s| 1 | 0 | 33 | - | 939k | 0 | 25 | 4 | 45 | 9 | 3 | 0 | 0 |-1.766937e+01 | 1.151406e+01 | Inf | unknown 829 # Uncommenting the next two lines switches this (command-line fails because it tries to expand connectors twice)
0.0s| 1 | 0 | 33 | - | 939k | 0 | 25 | 4 | 45 | 9 | 3 | 0 | 0 |-1.766937e+01 | 1.151406e+01 | Inf | unknown 830 # connector_expander = ConnectorExpander()
0.0s| 1 | 0 | 36 | - | 939k | 0 | 25 | 4 | 47 | 11 | 4 | 0 | 0 |-1.728948e+01 | 1.151406e+01 | Inf | unknown 831 # connector_expander.apply(instance=self)
0.0s| 1 | 0 | 37 | - | 941k | 0 | 25 | 4 | 48 | 12 | 5 | 0 | 0 |-1.712829e+01 | 1.151406e+01 | Inf | unknown 833 if profile_memory >= 2 and pympler_available:
0.0s| 1 | 0 | 38 | - | 941k | 0 | 25 | 4 | 49 | 13 | 6 | 0 | 0 |-1.701866e+01 | 1.151406e+01 | Inf | unknown File ~\AppData\Roaming\Python\Python312\site-packages\pyomo\core\base\PyomoModel.py:871, in Model._initialize_component(self, modeldata, namespaces, component_name, profile_memory)
0.0s| 1 | 0 | 39 | - | 943k | 0 | 25 | 4 | 50 | 14 | 7 | 0 | 0 |-1.701746e+01 | 1.151406e+01 | Inf | unknown 863 logger.debug(
0.0s| 1 | 0 | 41 | - | 951k | 0 | 25 | 4 | 52 | 16 | 8 | 0 | 0 |-1.695801e+01 | 1.151406e+01 | Inf | unknown 864 "Constructing %s '%s' on %s from data=%s",
0.0s| 1 | 0 | 332 | - | 957k | 0 | 25 | 4 | 52 | 16 | 9 | 0 | 0 | 1.150982e+01 | 1.151406e+01 | 0.04%| unknown 865 declaration.__class__.__name__,
time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl. (...)
0.0s| 1 | 0 | 334 | - | 957k | 0 | 25 | 4 | 54 | 18 | 10 | 0 | 0 | 1.150982e+01 | 1.151406e+01 | 0.04%| unknown 868 str(data),
0.0s| 1 | 0 | 346 | - | 957k | 0 | 25 | 4 | 37 | 20 | 11 | 0 | 0 | 1.151405e+01 | 1.151406e+01 | 0.00%| unknown 869 )
0.0s| 1 | 0 | 346 | - | 957k | 0 | 25 | 4 | 20 | 20 | 11 | 0 | 0 | 1.151405e+01 | 1.151406e+01 | 0.00%| unknown 870 try:
0.0s| 1 | 0 | 347 | - | 957k | 0 | 25 | 4 | 21 | 21 | 12 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown --> 871 declaration.construct(data)
L 0.0s| 1 | 0 | 347 | - | subnlp| 0 | 25 | 4 | 21 | 21 | 13 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown 872 except:
0.0s| 1 | 0 | 347 | - | 958k | 0 | 25 | 4 | 21 | 21 | 13 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown 873 err = sys.exc_info()[1]
* 0.0s| 1 | 0 | 347 | - | LP | 0 | 25 | 4 | 21 | 21 | 14 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown File ~\AppData\Roaming\Python\Python312\site-packages\pyomo\core\base\var.py:735, in Var.construct(self, data)
732 index = None
SCIP Status : problem is solved [optimal solution found] 733 try:
Solving Time (sec) : 0.00 734 # We do not (currently) accept data for constructing Variables
Solving Nodes : 1 --> 735 assert data is None
Primal Bound : +1.15140564709997e+01 (3 solutions) 737 if not self.index_set().isfinite() and self._dense:
Dual Bound : +1.15140564709997e+01 738 # Note: if the index is not finite, then we cannot
Gap : 0.00 % 739 # iterate over it. This used to be fatal; now we
740 # just warn
Problem: 741 logger.warning(
- Lower bound: -inf 742 "Var '%s' indexed by a non-finite set, but declared "
Upper bound: inf 743 "with 'dense=True'. Reverting to 'dense=False' as "
Number of objectives: 1 (...)
Number of constraints: 0 746 "'dense=False'" % (self.name,)
Number of variables: 7 747 )
Sense: unknown AssertionError:
Solver:
- Status: ok
Message: optimal solution found
Termination condition: optimal
Id: 0
Error rc: 0
Time: 0.3383963108062744
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 : 0.34519801810114614 : 1 : False : False : Reals
pump2 : 0.20833333333333334 : 0.4881837415314729 : 1 : False : False : Reals
Q : Size=6, Index=nodes
Key : Lower : Value : Upper : Fixed : Stale : Domain
pump1 : 0.0 : 0.0 : 10.0 : False : False : Reals
pump2 : 0.0 : 0.0 : 10.0 : False : False : Reals
source : 0.0 : None : 10.0 : False : True : Reals
valveA : 0.0 : 1.0 : 10.0 : False : False : Reals
valveB : 0.0 : 1.0 : 10.0 : False : False : Reals
valveC : 0.0 : 1.0 : 10.0 : False : False : Reals
......
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