Module 3 Exercise 1 Solution:

Create a Heater¶

In [1]:

import pyomo.environ as pe
from pyomo.common.config import ConfigBlock, ConfigValue, In
from idaes.core import (ControlVolume0DBlock,
                        declare_process_block_class,
                        EnergyBalanceType,
                        MomentumBalanceType,
                        MaterialBalanceType,
                        UnitModelBlockData,
                        useDefault,
                        FlowsheetBlock)
from idaes.core.util.config import is_physical_parameter_block
from methanol_param_VLE import PhysicalParameterBlock
from idaes.core.util.misc import add_object_reference

In [2]:

def make_control_volume(unit, name, config):
    if config.dynamic is not False:
        raise ValueError('IdealGasIsentropcCompressor does not support dynamics')
    if config.has_holdup is not False:
        raise ValueError('IdealGasIsentropcCompressor does not support holdup')

    control_volume = ControlVolume0DBlock(default={"property_package": config.property_package,
                                                   "property_package_args": config.property_package_args})

    setattr(unit, name, control_volume)

    control_volume.add_state_blocks(has_phase_equilibrium=config.has_phase_equilibrium)
    control_volume.add_material_balances(balance_type=config.material_balance_type,
                                         has_phase_equilibrium=config.has_phase_equilibrium)
    control_volume.add_total_enthalpy_balances(has_heat_of_reaction=False, 
                                               has_heat_transfer=True, 
                                               has_work_transfer=False)
    control_volume.add_total_pressure_balances(has_pressure_change=False)

In [3]:

def make_config_block(config):
    config.declare("material_balance_type",
        ConfigValue(default=MaterialBalanceType.componentPhase, domain=In(MaterialBalanceType)))
    config.declare("energy_balance_type",
        ConfigValue(default=EnergyBalanceType.enthalpyTotal, domain=In([EnergyBalanceType.enthalpyTotal])))
    config.declare("momentum_balance_type",
        ConfigValue(default=MomentumBalanceType.pressureTotal, domain=In([MomentumBalanceType.pressureTotal])))
    config.declare("has_phase_equilibrium",
        ConfigValue(default=False, domain=In([False])))
    config.declare("has_pressure_change",
        ConfigValue(default=False, domain=In([False])))
    config.declare("property_package",
        ConfigValue(default=useDefault, domain=is_physical_parameter_block))
    config.declare("property_package_args",
        ConfigBlock(implicit=True))

In [4]:

@declare_process_block_class("Heater")
class HeaterData(UnitModelBlockData):
    CONFIG = UnitModelBlockData.CONFIG()
    make_config_block(CONFIG)

    def build(self):
        super(HeaterData, self).build()

        make_control_volume(self, "control_volume", self.config)

        self.add_inlet_port()
        self.add_outlet_port()
        
        add_object_reference(self, 'heat', self.control_volume.heat[0.0])

In [7]:

m = pe.ConcreteModel()
m.fs = fs = FlowsheetBlock(default={"dynamic": False})
fs.properties = props = PhysicalParameterBlock(default={'Cp': 0.038056, 'valid_phase': 'Vap'})

fs.heater = Heater(default={"property_package": props, 'has_phase_equilibrium': False})
fs.heater.inlet.flow_mol.fix(1)
fs.heater.inlet.mole_frac[0, 'CH3OH'].fix(0.25)
fs.heater.inlet.mole_frac[0, 'CH4'].fix(0.25)
fs.heater.inlet.mole_frac[0, 'H2'].fix(0.25)
fs.heater.inlet.mole_frac[0, 'CO'].fix(0.25)
fs.heater.inlet.pressure.fix(0.1)
fs.heater.inlet.temperature.fix(3)
fs.heater.heat.fix(5)

opt = pe.SolverFactory('ipopt')
res = opt.solve(m, tee=True)
print(res.solver.termination_condition)
fs.heater.outlet.display()

Ipopt 3.12: 

******************************************************************************
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 is Ipopt version 3.12, running with linear solver ma27.

Number of nonzeros in equality constraint Jacobian...:       51
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:       13

Total number of variables............................:       17
                     variables with only lower bounds:        5
                variables with lower and upper bounds:       12
                     variables with only upper bounds:        0
Total number of equality constraints.................:       17
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 5.00e-01 1.00e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1  0.0000000e+00 5.00e-06 5.26e+00  -1.0 2.63e+00    -  5.37e-01 1.00e+00h  1
   2  0.0000000e+00 0.00e+00 2.22e+00  -1.7 1.31e+00    -  1.00e+00 1.00e+00h  1

Number of Iterations....: 2

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


Number of objective function evaluations             = 3
Number of objective gradient evaluations             = 3
Number of equality constraint evaluations            = 3
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 3
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 2
Total CPU secs in IPOPT (w/o function evaluations)   =      0.000
Total CPU secs in NLP function evaluations           =      0.000

EXIT: Optimal Solution Found.
optimal
outlet : Size=1
    Key  : Name        : Value
    None :    flow_mol : {0.0: 1.0}
         :   mole_frac : {(0.0, 'CH3OH'): 0.25, (0.0, 'CH4'): 0.25, (0.0, 'CO'): 0.25, (0.0, 'H2'): 0.25}
         :    pressure : {0.0: 0.1}
         : temperature : {0.0: 4.313853268866933}

In [ ]:

# For testing purposes
from pyomo.environ import TerminationCondition
assert res.solver.termination_condition == TerminationCondition.optimal