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Merge pull request #16 from LLNL/sys_dev_lohc
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Update s2a_systems_functions.py
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@yuan-mengyao
yuan-mengyao authored Oct 16, 2024
2 parents c77000f + b9a3243 commit 503202e
Showing 1 changed file with 68 additions and 82 deletions.
150 changes: 68 additions & 82 deletions systems2atoms/systems/s2a_systems_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
import os
import math
import pandas as pd
from scipy.interpolate import LinearNDInterpolator

import pathlib
this_file = pathlib.Path(__file__).parent.resolve()
Expand Down Expand Up @@ -1025,10 +1026,12 @@ def heat_exchanger_fixed_costs(
)

# calculate heat exchanger uninstalled cost ($, output dollar year)
hx_uninst_cost_usd = \
1.25 * 11092.0 * (
100.0 * num_hx * hx_capacity_ton_per_unit / out_temp_K
)**0.8579 * dollar_year_multiplier
hx_uninst_cost_usd = 0.0
if num_hx > 0:
hx_uninst_cost_usd = \
1.25 * 11092.0 * (
100.0 * num_hx * hx_capacity_ton_per_unit / out_temp_K
)**0.8579 * dollar_year_multiplier

# calculate heat exchanger installed cost ($, output dollar year)
hx_inst_cost_usd = hx_uninst_cost_usd * inst_factor
Expand Down Expand Up @@ -1870,8 +1873,6 @@ def truck_labor_cost(
# ----------------------------------------------------------------------------
# function: CO2 all-in transport cost (includes conditioning)

# TODO: use scipy.interpolate (fix DLL load error)

def CO2_transport_all_in_cost(
CO2_flow_kt_per_yr,
deliv_dist_mi,
Expand Down Expand Up @@ -1909,68 +1910,44 @@ def CO2_transport_all_in_cost(
input_dollar_year = input_dollar_year,
output_dollar_year = output_dollar_year
)

# find nearest low and high values to given CO2 flowrate
x = CO2_flow_kt_per_yr
xs = df_co2['Size (kt-CO2/y)'].unique()
if x < xs.min() or x > xs.max():
raise ValueError(
'CO2 flowrate needs to be between {} and {} ktonne/year.'.format(
xs.min(), xs.max())
)
x1 = xs[xs <= x].max()
x2 = xs[xs >= x].min()

# find nearest low and high values to given transport distance
y = deliv_dist_mi
ys = df_co2['Distance (mi)'].unique()
if y < ys.min() or y > ys.max():
raise ValueError(
'Transport distance needs to be between {} and {} miles.'.format(
ys.min(), ys.max())
)
y1 = ys[ys <= y].max()
y2 = ys[ys >= y].min()

# find transport costs corresponding to nearest CO2 flowrates and distances
z11 = df_co2['Total ($/t-CO2 gross)'].loc[(
df_co2['Size (kt-CO2/y)'] == x1
) & (
df_co2['Distance (mi)'] == y1
)].values[0]
z12 = df_co2['Total ($/t-CO2 gross)'].loc[(
df_co2['Size (kt-CO2/y)'] == x1
) & (
df_co2['Distance (mi)'] == y2
)].values[0]
z21 = df_co2['Total ($/t-CO2 gross)'].loc[(
df_co2['Size (kt-CO2/y)'] == x2
) & (
df_co2['Distance (mi)'] == y1
)].values[0]
z22 = df_co2['Total ($/t-CO2 gross)'].loc[(
df_co2['Size (kt-CO2/y)'] == x2
) & (
df_co2['Distance (mi)'] == y2
)].values[0]

# interpolate transport cost ($/tCO2 gross, input dollar year)
if (x1 == x2) and (y1 == y2):
z = z11
elif x1 == x2:
z = (z11 * (y2 - y) + z22 * (y - y1)) / (y2 - y1)
elif y1 == y2:
z = (z11 * (x2 - x) + z22 * (x - x1)) / (x2 - x1)

# define points for interpolation (CO2 flowrate and distance)
x = df_co2['Size (kt-CO2/y)'].values
y = df_co2['Distance (mi)'].values

# define values for interpolation (CO2 transport cost)
z = df_co2['Total ($/t-CO2 gross)'].values

# define interpolater
interp = LinearNDInterpolator(list(zip(x, y)), z)

# interpolate transport cost (input dollar year)
# for given CO2 flowrate and distance
X = CO2_flow_kt_per_yr
Y = deliv_dist_mi
Z = interp(list((X, Y)))

# set transport cost to zero if either CO2 flowrate or distance is zero
if X * Y == 0.0:
Z = 0.0
else:
z = (
z11 * (x2 - x) * (y2 - y) + \
z21 * (x - x1) * (y2 - y) + \
z12 * (x2 - x) * (y - y1) + \
z22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1))
# print message if CO2 flowrate is out of range (transport cost = nan)
if X < x.min() or X > x.max():
print(
'CO2 flowrate needs to be '
'between {} and {} ktonne/year.'.format(
x.min(), x.max())
)
# print message if distance is out of range (transport cost = nan)
if Y < y.min() or Y > y.max():
print(
'Transport distance needs to be '
'between {} and {} miles.'.format(
y.min(), y.max())
)

# calculate transport cost ($/tCO2 gross, output dollar year)
liq_CO2_trucking_cost_usd_per_tCO2 = z * dollar_year_multiplier
liq_CO2_trucking_cost_usd_per_tCO2 = Z * dollar_year_multiplier

# calculate annual transport cost ($/yr, output dollar year)
liq_CO2_trucking_cost_usd_per_yr = \
Expand Down Expand Up @@ -3526,10 +3503,12 @@ def calcs(

# calculate inlet molar flowrate (mol/hr) to refueling station separator
# TODO: make this more general (H2 vs. other gases?)
LOHC_STN_psa_in_flow_mol_per_hr = \
LOHC_STN_LOHC_flow_mol_per_hr * LOHC_dehydr_yield * (
stoic_mol_H2_per_mol_LOHC + stoic_mol_CO2_per_mol_LOHC
)
LOHC_STN_psa_in_flow_mol_per_hr = 0.0
if stoic_mol_CO2_per_mol_LOHC > 0.0:
LOHC_STN_psa_in_flow_mol_per_hr = \
LOHC_STN_LOHC_flow_mol_per_hr * LOHC_dehydr_yield * (
stoic_mol_H2_per_mol_LOHC + stoic_mol_CO2_per_mol_LOHC
)

# calculate inlet volumetric flowrate (Nm^3/hr) to refueling station
# separator
Expand Down Expand Up @@ -10407,15 +10386,19 @@ def calcs(
# reconditioning - LOHC:
# refueling station PSA *refrigerator* (precooling) energy consumption

# initialize PSA *refrigerator* (precooling) energy consumption
# (zero by default)
LOHC_STN_psa_refrig_elec_kWh_per_kg = 0.0

# calculate refueling station PSA *refrigerator* energy (kWh/kg H2)
# if PSA inlet flowrate > 0
# and if inlet temperature > outlet temperature
# inlet temperature = dehydrogenation reaction temperature
# outlet temperature = PSA operating temperature
# set refrigerator energy to zero if
# inlet temperature <= outlet temperature
if LOHC_dehydr_temp_K <= LOHC_STN_psa_temp_K:
LOHC_STN_psa_refrig_elec_kWh_per_kg = 0.0
else:
LOHC_STN_psa_refrig_elec_kWh_per_kg = \
# TODO: add molar mass of inlet gas mixture
if (LOHC_STN_psa_in_flow_mol_per_hr > 0.0) and \
(LOHC_dehydr_temp_K > LOHC_STN_psa_temp_K):
LOHC_STN_psa_refrig_elec_kWh_per_kg = \
heat_exchanger_energy(
out_temp_K = LOHC_STN_psa_temp_K,
in_temp_K = LOHC_dehydr_temp_K
Expand Down Expand Up @@ -10530,16 +10513,19 @@ def calcs(
# reconditioning - LOHC:
# refueling station PSA *refrigerator* (precooling) installed cost and
# annual O&M cost

# calculate number of PSA refrigerators needed at refueling station

# initialize number of PSA refrigerators (zero by default)
LOHC_STN_num_psa_refrigs = 0.0

# calculate number of PSA refrigerators needed at refueling station
# (= number of hoses)
# if PSA inlet flowrate > 0
# and if inlet temperature > outlet temperature
# assume linear relative to station capacity
# (HDSAM V3.1: 1000 kg H2/day --> 4 hoses, 4 refrigerators)
# set number of refrigerators to zero if
# inlet temperature <= outlet temperature
if LOHC_dehydr_temp_K <= LOHC_STN_psa_temp_K:
LOHC_STN_num_psa_refrigs = 0
else:
# TODO: revisit - this assumption applies to hydrogen refrigerators only?
if (LOHC_STN_psa_in_flow_mol_per_hr > 0.0) and \
(LOHC_dehydr_temp_K > LOHC_STN_psa_temp_K):
LOHC_STN_num_psa_refrigs = \
target_stn_capacity_kg_per_day / (1000.0 / 4)

Expand Down

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