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# The Institute for the Design of Advanced Energy Systems Integrated Platform
# Framework (IDAES IP) was produced under the DOE Institute for the
# Design of Advanced Energy Systems (IDAES).
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# Turbine Unit Model with IAPWS Property Package#

Author: Anuja Deshpande
Maintainer: Brandon Paul
Updated: 2023-06-01

## Learning Outcomes#

• Demonstrate use of the turbine unit model in IDAES

• Demonstrate different simulation options available

## Problem Statement#

In this example, we will expand steam in a turbine using the turbine unit model and the IAPWS property package for water/steam. It is assumed that the turbine operates at steady state.

The inlet specifications are as follows:

• Flow Rate = 100 mol/s

• Mole fraction (H2O) = 1

• Pressure = 150000 Pa

• Temperature = 390 K

We will simulate 2 different cases, depending on the operating specifications by the user:

Case 1: In this case, we will specify the turbine isentropic efficiency and the pressure decrease variable.

• Pressure Decrease = 25000 Pa

• Isentropic Efficiency = 0.9

Case 2: In this case, we will specify the turbine isentropic efficiency and the pressure ratio variable.

• Pressure Ratio = 0.90131

• Isentropic Efficiency = 0.9

IDAES documentation reference for turbine model:https://idaes-pse.readthedocs.io/en/stable/

## Setting up the problem in IDAES#

In the following cell, we will be importing the necessary components from Pyomo and IDAES.

# Import objects from pyomo package
from pyomo.environ import ConcreteModel, SolverFactory, value, units

# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model
from idaes.core import FlowsheetBlock

# Import idaes logger to set output levels
import idaes.logger as idaeslog

# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.
m = ConcreteModel()

m.fs = FlowsheetBlock(
dynamic=False
)  # dynamic or ss flowsheet needs to be specified here

# Import the IAPWS property package to create a properties block for the flowsheet
from idaes.models.properties import iapws95
from idaes.models.properties.helmholtz.helmholtz import PhaseType

# Add properties parameter block to the flowsheet with specifications
m.fs.properties = iapws95.Iapws95ParameterBlock()
2024-03-18 23:18:56 [ERROR] idaes.core.base.process_block: Failure in build: fs.properties
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/idaes/core/base/process_block.py", line 41, in _rule_default
b.build()
File "/home/docs/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/idaes/models/properties/general_helmholtz/helmholtz_functions.py", line 1441, in build
raise RuntimeError("Helmholtz EoS external functions not available")
RuntimeError: Helmholtz EoS external functions not available
ERROR: Constructing component 'fs.properties' from data=None failed:
RuntimeError: Helmholtz EoS external functions not available
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[2], line 23
20 from idaes.models.properties.helmholtz.helmholtz import PhaseType
22 # Add properties parameter block to the flowsheet with specifications
---> 23 m.fs.properties = iapws95.Iapws95ParameterBlock()

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/pyomo/core/base/block.py:568, in _BlockData.__setattr__(self, name, val)
563 if name not in self.__dict__:
564     if isinstance(val, Component):
565         #
566         # Pyomo components are added with the add_component method.
567         #
--> 568         self.add_component(name, val)
569     else:
570         #
571         # Other Python objects are added with the standard __setattr__
572         # method.
573         #
574         super(_BlockData, self).__setattr__(name, val)

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/pyomo/core/base/block.py:1126, in _BlockData.add_component(self, name, val)
1118     logger.debug(
1119         "Constructing %s '%s' on %s from data=%s",
1120         val.__class__.__name__,
(...)
1123         str(data),
1124     )
1125 try:
-> 1126     val.construct(data)
1127 except:
1128     err = sys.exc_info()[1]

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/pyomo/core/base/block.py:2167, in Block.construct(self, data)
2165                     obj.construct(data.get(name, None))
2166         # Trigger the (normal) initialization of the block
-> 2167         self._getitem_when_not_present(_idx)
2168 finally:
2169     # We must allow that id(self) may no longer be in
2170     # _BlockConstruction.data, as _getitem_when_not_present will
2171     # have already removed the entry for scalar blocks (as the
2172     # BlockData and the Block component are the same object)
2173     if data is not None:

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/pyomo/core/base/block.py:2082, in Block._getitem_when_not_present(self, idx)
2079     data = None
2081 try:
-> 2082     obj = self._rule(_block, idx)
2083     # If the user returns a block, transfer over everything
2084     # they defined into the empty one we created.  We do
2085     # this inside the try block so that any abstract
2086     # components declared by the rule have the opportunity
2087     # to be initialized with data from
2088     # _BlockConstruction.data as they are transferred over.
2089     if obj is not _block and isinstance(obj, _BlockData):

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/pyomo/core/base/initializer.py:316, in IndexedCallInitializer.__call__(self, parent, idx)
314     return self._fcn(parent, *idx)
315 else:
--> 316     return self._fcn(parent, idx)

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/idaes/core/base/process_block.py:41, in _rule_default(b, *args)
35 """
36 Default rule for ProcessBlock, which calls build(). A different rule can
37 be specified to add additional build steps, or to not call build at all
38 using the normal rule argument to ProcessBlock init.
39 """
40 try:
---> 41     b.build()
42 except Exception:
43     logging.getLogger(__name__).exception(f"Failure in build: {b}")

File ~/checkouts/readthedocs.org/user_builds/idaes-examples/envs/latest/lib/python3.8/site-packages/idaes/models/properties/general_helmholtz/helmholtz_functions.py:1441, in HelmholtzParameterBlockData.build(self)
1439 """Populate the parameter block"""
1440 if not self.available():
-> 1441     raise RuntimeError("Helmholtz EoS external functions not available")
1442 super().build()
1443 # Check if the specified component is supported

RuntimeError: Helmholtz EoS external functions not available

## Case 1: Fix pressure change and turbine efficiency#

### Add Turbine Unit#

# Import turbine unit model from the model library
from idaes.models.unit_models.pressure_changer import Turbine

# Create an instance of the turbine unit, attaching it to the flowsheet
# Specify that the property package to be used with the turbine is the one we created earlier.
m.fs.turbine_case_1 = Turbine(property_package=m.fs.properties)
# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package
# DOF = Number of Model Variables - Number of Model Constraints
from idaes.core.util.model_statistics import degrees_of_freedom

# Call the degrees_of_freedom function, get intitial DOF
DOF_initial = degrees_of_freedom(m)
print("The initial DOF is {0}".format(DOF_initial))
The initial DOF is 5

### Fix Inlet Stream Conditions#

# Fix the stream inlet conditions
m.fs.turbine_case_1.inlet.flow_mol[0].fix(
100
)  # converting to mol/s as unit basis is mol/s

# Use htpx method to obtain the molar enthalpy of inlet stream at the given temperature and pressure conditions
m.fs.turbine_case_1.inlet.enth_mol[0].fix(
value(iapws95.htpx(T=390 * units.K, P=150000 * units.Pa))
)
m.fs.turbine_case_1.inlet.pressure[0].fix(150000)

### Fix Pressure Change and Turbine Efficiency#

# Fix turbine conditions
m.fs.turbine_case_1.deltaP.fix(-10000)
m.fs.turbine_case_1.efficiency_isentropic.fix(0.9)

# Call the degrees_of_freedom function, get final DOF
DOF_final = degrees_of_freedom(m)
print("The final DOF is {0}".format(DOF_final))
The final DOF is 0

### Initialization#

# Initialize the flowsheet, and set the logger level to INFO
m.fs.turbine_case_1.initialize(outlvl=idaeslog.INFO)
2023-11-02 10:24:37 [INFO] idaes.init.fs.turbine_case_1: Initialization Complete: optimal - Optimal Solution Found

### Solve Model#

# Solve the simulation using ipopt
# Note: If the degrees of freedom = 0, we have a square problem
opt = SolverFactory("ipopt")
solve_status = opt.solve(m, tee=True)
Ipopt 3.13.2:

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt

This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.

This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation.  All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************

This is Ipopt version 3.13.2, running with linear solver ma27.

Number of nonzeros in equality constraint Jacobian...:       18
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:        8

Total number of variables............................:        9
variables with only lower bounds:        0
variables with lower and upper bounds:        4
variables with only upper bounds:        0
Total number of equality constraints.................:        9
Total number of inequality constraints...............:        0
inequality constraints with only lower bounds:        0
inequality constraints with lower and upper bounds:        0
inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
0  0.0000000e+00 2.36e-07 0.00e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
1  0.0000000e+00 2.84e-14 1.05e-08  -1.0 9.07e-03    -  9.90e-01 1.00e+00h  1

Number of Iterations....: 1

(scaled)                 (unscaled)
Objective...............:   0.0000000000000000e+00    0.0000000000000000e+00
Dual infeasibility......:   0.0000000000000000e+00    0.0000000000000000e+00
Constraint violation....:   2.8421709430404007e-14    2.8421709430404007e-14
Complementarity.........:   0.0000000000000000e+00    0.0000000000000000e+00
Overall NLP error.......:   2.8421709430404007e-14    2.8421709430404007e-14

Number of objective function evaluations             = 2
Number of objective gradient evaluations             = 2
Number of equality constraint evaluations            = 2
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 2
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 1
Total CPU secs in IPOPT (w/o function evaluations)   =      0.008
Total CPU secs in NLP function evaluations           =      0.002

EXIT: Optimal Solution Found.

from pyomo.opt import TerminationCondition, SolverStatus

# Check if termination condition is optimal
assert solve_status.solver.termination_condition == TerminationCondition.optimal
assert solve_status.solver.status == SolverStatus.ok

### View Results#

# Display Outlet pressure
m.fs.turbine_case_1.outlet.pressure.display()
_pressure_outlet_ref : Size=1, Index=fs._time, ReferenceTo=fs.turbine_case_1.control_volume.properties_out[...].component('pressure')
Key : Lower                  : Value    : Upper        : Fixed : Stale : Domain
0.0 : 1.0000000000000002e-06 : 140000.0 : 1100000000.0 : False : False : PositiveReals
# Display a readable report
m.fs.turbine_case_1.report()
====================================================================================
Unit : fs.turbine_case_1                                                   Time: 0.0
------------------------------------------------------------------------------------
Unit Performance

Variables:

Key                   : Value   : Units         : Fixed : Bounds
Isentropic Efficiency : 0.90000 : dimensionless :  True : (None, None)
Mechanical Work : -19597. :          watt : False : (None, None)
Pressure Change : -10000. :        pascal :  True : (None, None)
Pressure Ratio : 0.93333 : dimensionless : False : (None, None)

------------------------------------------------------------------------------------
Stream Table
Units           Inlet     Outlet
Molar Flow          mole / second     100.00     100.00
Mass Flow       kilogram / second     1.8015     1.8015
T                          kelvin     390.00     384.28
P                          pascal 1.5000e+05 1.4000e+05
Vapor Fraction      dimensionless     1.0000     1.0000
Molar Enthalpy       joule / mole     48727.     48531.
====================================================================================

## Case 2: Fix Pressure Ratio and Turbine Efficiency#

### Add Turbine Unit#

# Create an instance of another turbine unit, attaching it to the flowsheet
# Specify that the property package to be used with the turbine is the one we created earlier.
m.fs.turbine_case_2 = Turbine(property_package=m.fs.properties)

# Call the degrees_of_freedom function, get intitial DOF
DOF_initial = degrees_of_freedom(m.fs.turbine_case_2)
print("The initial DOF is {0}".format(DOF_initial))
The initial DOF is 5

### Fix Inlet Stream Conditions#

# Fix the stream inlet conditions
m.fs.turbine_case_2.inlet.flow_mol[0].fix(
100
)  # converting to mol/s as unit basis is mol/s

# Use htpx method to obtain the molar enthalpy of inlet stream at the given temperature and pressure conditions
m.fs.turbine_case_2.inlet.enth_mol[0].fix(
value(iapws95.htpx(T=390 * units.K, P=150000 * units.Pa))
)
m.fs.turbine_case_2.inlet.pressure[0].fix(150000)

### Fix Pressure Ratio & Turbine Efficiency#

# Fix turbine pressure ratio
m.fs.turbine_case_2.ratioP.fix(14 / 15)

# Fix turbine efficiency
m.fs.turbine_case_2.efficiency_isentropic.fix(0.9)

# Call the degrees_of_freedom function, get final DOF
DOF_final = degrees_of_freedom(m.fs.turbine_case_2)
print("The final DOF is {0}".format(DOF_final))
The final DOF is 0

### Initialization#

# Initialize the flowsheet, and set the output at INFO
m.fs.turbine_case_2.initialize(outlvl=idaeslog.INFO)
2023-11-02 10:24:38 [INFO] idaes.init.fs.turbine_case_2: Initialization Complete: optimal - Optimal Solution Found

### Solve Model#

# Solve the simulation using ipopt
# Note: If the degrees of freedom = 0, we have a square problem
opt = SolverFactory("ipopt")
solve_status = opt.solve(m.fs.turbine_case_2, tee=True)
Ipopt 3.13.2:

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt

This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.

This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation.  All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************

This is Ipopt version 3.13.2, running with linear solver ma27.

Number of nonzeros in equality constraint Jacobian...:       18
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:        8

Total number of variables............................:        9
variables with only lower bounds:        0
variables with lower and upper bounds:        4
variables with only upper bounds:        0
Total number of equality constraints.................:        9
Total number of inequality constraints...............:        0
inequality constraints with only lower bounds:        0
inequality constraints with lower and upper bounds:        0
inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
0  0.0000000e+00 2.36e-07 0.00e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
1  0.0000000e+00 2.84e-14 1.05e-08  -1.0 9.07e-03    -  9.90e-01 1.00e+00h  1

Number of Iterations....: 1

(scaled)                 (unscaled)
Objective...............:   0.0000000000000000e+00    0.0000000000000000e+00
Dual infeasibility......:   0.0000000000000000e+00    0.0000000000000000e+00
Constraint violation....:   2.8421709430404007e-14    2.8421709430404007e-14
Complementarity.........:   0.0000000000000000e+00    0.0000000000000000e+00
Overall NLP error.......:   2.8421709430404007e-14    2.8421709430404007e-14

Number of objective function evaluations             = 2
Number of objective gradient evaluations             = 2
Number of equality constraint evaluations            = 2
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 2
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 1
Total CPU secs in IPOPT (w/o function evaluations)   =      0.009
Total CPU secs in NLP function evaluations           =      0.001

EXIT: Optimal Solution Found.


### View Results#

# Display turbine pressure decrease
m.fs.turbine_case_2.outlet.pressure[0].display()
pressure : Pressure
Size=1, Index=None, Units=Pa
Key  : Lower                  : Value    : Upper        : Fixed : Stale : Domain
None : 1.0000000000000002e-06 : 140000.0 : 1100000000.0 : False : False : PositiveReals
# Display a readable report
m.fs.turbine_case_2.report()
====================================================================================
Unit : fs.turbine_case_2                                                   Time: 0.0
------------------------------------------------------------------------------------
Unit Performance

Variables:

Key                   : Value   : Units         : Fixed : Bounds
Isentropic Efficiency : 0.90000 : dimensionless :  True : (None, None)
Mechanical Work : -19597. :          watt : False : (None, None)
Pressure Change : -10000. :        pascal : False : (None, None)
Pressure Ratio : 0.93333 : dimensionless :  True : (None, None)

------------------------------------------------------------------------------------
Stream Table
Units           Inlet     Outlet
Molar Flow          mole / second     100.00     100.00
Mass Flow       kilogram / second     1.8015     1.8015
T                          kelvin     390.00     384.28
P                          pascal 1.5000e+05 1.4000e+05
Vapor Fraction      dimensionless     1.0000     1.0000
Molar Enthalpy       joule / mole     48727.     48531.
====================================================================================