###############################################################################
# WaterTAP Copyright (c) 2021, The Regents of the University of California,
# through Lawrence Berkeley National Laboratory, Oak Ridge National
# Laboratory, National Renewable Energy Laboratory, and National Energy
# Technology Laboratory (subject to receipt of any required approvals from
# the U.S. Dept. of Energy). All rights reserved.
#
# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
# information, respectively. These files are also available online at the URL
# "https://github.com/watertap-org/watertap/"
#
###############################################################################
from idaes.core.util.scaling import (calculate_scaling_factors)
from watertap.examples.flowsheets.full_treatment_train.flowsheet_components import (pretreatment_softening,
financials,
costing)
# Added import statements for testing.
# Need the pretreatment_stoich_softening_block functions to setup
# flowsheet to solve for lime dosage
from watertap.examples.flowsheets.full_treatment_train.flowsheet_components.chemistry.pretreatment_stoich_softening_block import *
def build_components(m):
# build flowsheet
pretrt_port = pretreatment_softening.build(m)
property_models.build_prop(m, base='TDS')
pretreatment_softening.build_tb(m)
# Arc to translator block
m.fs.s_pretrt_tb = Arc(source=pretrt_port['out'], destination=m.fs.tb_pretrt_to_desal.inlet)
[docs]def build(m):
"""
Build a flowsheet with lime softening as the pretreatment process.
"""
build_components(m)
# set up costing
financials.add_costing_param_block(m.fs)
# annual water production
m.fs.annual_water_production = Expression(
expr=pyunits.convert(m.fs.tb_pretrt_to_desal.properties_out[0].flow_vol, to_units=pyunits.m ** 3 / pyunits.year)
* m.fs.costing_param.load_factor)
costing.build_costing(m, module=financials)
m.fs.removal_Ca = Expression(
expr=(m.fs.stoich_softening_mixer_unit.inlet_stream_state[0.0].flow_mol_comp['Ca(HCO3)2']
- m.fs.stoich_softening_separator_unit.outlet_stream_state[0.0].flow_mol_comp['Ca(HCO3)2'])
/ m.fs.stoich_softening_mixer_unit.inlet_stream_state[0.0].flow_mol_comp['Ca(HCO3)2']
)
m.fs.removal_Mg = Expression(
expr=(m.fs.stoich_softening_mixer_unit.inlet_stream_state[0.0].flow_mol_comp['Mg(HCO3)2']
- m.fs.stoich_softening_separator_unit.outlet_stream_state[0.0].flow_mol_comp['Mg(HCO3)2'])
/ m.fs.stoich_softening_mixer_unit.inlet_stream_state[0.0].flow_mol_comp['Mg(HCO3)2']
)
return m
def scale(m):
pretreatment_softening.scale(m)
calculate_scaling_factors(m.fs.tb_pretrt_to_desal)
def initialize(m):
optarg = {'nlp_scaling_method': 'user-scaling', 'halt_on_ampl_error': 'yes'}
pretreatment_softening.initialize(m)
propagate_state(m.fs.s_pretrt_tb)
m.fs.tb_pretrt_to_desal.initialize(optarg=optarg)
def report(m):
pretreatment_softening.display(m)
m.fs.tb_pretrt_to_desal.report()
def solve_flowsheet():
m = ConcreteModel()
m.fs = FlowsheetBlock(default={"dynamic": False})
build(m)
TransformationFactory("network.expand_arcs").apply_to(m)
# scale
scale(m)
calculate_scaling_factors(m)
# initialize
initialize(m)
check_dof(m)
solve_with_user_scaling(m, tee=True, fail_flag=True)
# report
report(m)
return m
def simulate(m):
solve_with_user_scaling(m, tee=False, fail_flag=True)
if __name__ == "__main__":
m = solve_flowsheet()