###############################################################################
# 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 pyomo.environ import (ConcreteModel, Objective, Expression, Constraint, Param,
TransformationFactory, value, units as pyunits)
from pyomo.network import Arc
from pyomo.util import infeasible
from idaes.core import FlowsheetBlock
from idaes.core.util.scaling import (calculate_scaling_factors,
unscaled_constraints_generator,
unscaled_variables_generator,
badly_scaled_var_generator,
constraint_autoscale_large_jac)
from idaes.core.util.initialization import propagate_state
from watertap.examples.flowsheets.full_treatment_train.flowsheet_components import (pretreatment_NF,
desalination,
gypsum_saturation_index,
translator_block,
costing,
financials)
from watertap.examples.flowsheets.full_treatment_train.model_components import property_models
from watertap.examples.flowsheets.full_treatment_train.util import solve_with_user_scaling, check_dof
def build_components(m, has_bypass=True):
# build flowsheet
property_models.build_prop(m, base='ion')
pretrt_port = pretreatment_NF.build_pretreatment_NF(m, NF_type='ZO', NF_base='ion', has_bypass=has_bypass)
property_models.build_prop(m, base='TDS')
translator_block.build_tb(m, base_inlet='ion',
base_outlet='TDS',
name_str='tb_pretrt_to_desal')
# Arc to translator block
m.fs.s_pretrt_tb = Arc(source=pretrt_port['out'], destination=m.fs.tb_pretrt_to_desal.inlet)
property_models.build_prop(m, base='eNRTL')
gypsum_saturation_index.build(m, section='pretreatment')
m.fs.NF.area.fix(175)
if has_bypass:
m.fs.splitter.split_fraction[0, 'bypass'].fix(0.50)
m.fs.removal_Ca = Expression(
expr=(m.fs.feed.properties[0].flow_mass_phase_comp['Liq', 'Ca']
- m.fs.mixer.mixed_state[0].flow_mass_phase_comp['Liq', 'Ca'])
/ m.fs.feed.properties[0].flow_mass_phase_comp['Liq', 'Ca'])
m.fs.removal_Mg = Expression(
expr=(m.fs.feed.properties[0].flow_mass_phase_comp['Liq', 'Mg']
- m.fs.mixer.mixed_state[0].flow_mass_phase_comp['Liq', 'Mg'])
/ m.fs.feed.properties[0].flow_mass_phase_comp['Liq', 'Mg'])
[docs]def build(m, has_bypass=True):
"""
Build a flowsheet with nanofiltration as the pretreatment process.
"""
build_components(m, has_bypass=has_bypass)
# 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, NF_type='ZO')
return m
def scale(m, has_bypass=True):
pretreatment_NF.scale_pretreatment_NF(m, NF_type='ZO', NF_base='ion', has_bypass=has_bypass)
calculate_scaling_factors(m.fs.tb_pretrt_to_desal)
def initialize(m, has_bypass=True):
optarg = {'nlp_scaling_method': 'user-scaling'}
pretreatment_NF.initialize_pretreatment_NF(m, NF_type='ZO', NF_base='ion', has_bypass=has_bypass)
m.fs.pretrt_saturation.properties.initialize(optarg=optarg)
propagate_state(m.fs.s_pretrt_tb)
m.fs.tb_pretrt_to_desal.initialize(optarg=optarg)
def report(m, has_bypass=True):
pretreatment_NF.display_pretreatment_NF(m, NF_type='ZO', NF_base='ion', has_bypass=has_bypass)
m.fs.tb_pretrt_to_desal.report()
def solve_flowsheet(has_bypass=True):
m = ConcreteModel()
m.fs = FlowsheetBlock(default={"dynamic": False})
build(m, has_bypass=has_bypass)
TransformationFactory("network.expand_arcs").apply_to(m)
# scale
scale(m, has_bypass=has_bypass)
calculate_scaling_factors(m)
# initialize
initialize(m, has_bypass=has_bypass)
check_dof(m)
solve_with_user_scaling(m, tee=True, fail_flag=True)
# report
report(m, has_bypass=has_bypass)
return m
def simulate(m):
solve_with_user_scaling(m, tee=False, fail_flag=True)
def set_optimization_components(m, system_recovery, **kwargs):
# unfix variables
m.fs.splitter.split_fraction[0, 'bypass'].unfix()
m.fs.splitter.split_fraction[0, 'bypass'].setlb(0.001)
m.fs.splitter.split_fraction[0, 'bypass'].setub(0.99)
m.fs.NF.area.unfix()
m.fs.NF.area.setlb(0.1)
m.fs.NF.area.setub(1000)
m.fs.max_conc_factor_target = Param(initialize=3.5, mutable=True)
m.fs.eq_max_conc_NF = Constraint(
expr=m.fs.NF.feed_side.properties_out[0].mass_frac_phase_comp['Liq', 'Ca']
<= m.fs.max_conc_factor_target * m.fs.feed.properties[0].mass_frac_phase_comp['Liq', 'Ca'])
def set_up_optimization(m, system_recovery=0.50, **kwargs):
set_optimization_components(m, system_recovery, **kwargs)
calculate_scaling_factors(m)
check_dof(m, 2)
def optimize(m):
solve_with_user_scaling(m, tee=False, fail_flag=True)
def optimize_flowsheet(system_recovery=0.50, **kwargs):
m = solve_flowsheet(**kwargs)
set_up_optimization(m, system_recovery=system_recovery, **kwargs)
optimize(m)
print('==================================='
'\n Optimization ')
report(m, **kwargs)
return m
if __name__ == "__main__":
m = solve_flowsheet(True)