def convertGrassBiomass(size, biomass_IO_array):
return_array = UF.createEmptyFrame()
forestry_vals = forestry_cited_values_dict
forestry_out = forestry_out_vals
in_list = conv_in_list
out_list = conv_out_list
match_list = [[UF.input_or_output, D.tl_output],
[UF.substance_name, 'Woody Biomass']]
grass_biomass_out_qty = UF.returnPintQty(biomass_IO_array, match_list)
scaling_param = size.qty * (grass_biomass_out_qty/
(size.qty * D.forestry_woody_biomass_val.qty))
# special write of woody biomass
return_array.loc[0] = UF.getWriteRow('Woody Biomass', D.conv,
D.tl_input, grass_biomass_out_qty)
row_count = 1
for substance in in_list:
pint_qty = scaling_param*forestry_vals[substance].qty
return_array.loc[row_count] = UF.getWriteRow(substance, D.conv,
D.tl_input, pint_qty)
row_count += 1
for substance in out_list:
pint_qty = scaling_param*forestry_out[substance].qty
return_array.loc[row_count] = UF.getWriteRow(substance, D.conv,
D.tl_output, pint_qty)
row_count += 1
return return_array
def main():
land_area_val = D.TEA_LCA_Qty('Land Area', 100, 'hectare')
biomass_output = D.TEA_LCA_Qty('Woody Biomass', 8960, 'kg/yr/ha')
biomass_IO_array = UF.createEmptyFrame()
biomass_IO_array.loc[0] = UF.getWriteRow('Woody Biomass', D.biomass_production,
D.tl_output, biomass_output.qty*land_area_val.qty)
return convertGrassBiomass(land_area_val, biomass_IO_array)
def growGrassForOneYear(size, biomass_output):
return_array = UF.createEmptyFrame()
crop_inputs = grass_inputs_dict
crop_outputs = grass_outputs_dict
# Calculate Atmospheric CO2 based on biomass output
return_array.loc[0] = UF.getWriteRow(
'Atmospheric CO2', D.biomass_production, D.tl_input,
biomass_output.qty * size.qty * D.CO2_fixing_proportion_grass.qty)
# special write of woody biomass
return_array.loc[1] = UF.getWriteRow('Woody Biomass', D.biomass_production,
D.tl_output,
biomass_output.qty * size.qty)
row_count = 2
# Scale crop inputs
for key in crop_inputs:
pint_qty = size.qty * crop_inputs[key].qty
return_array.loc[row_count] = UF.getWriteRow(key, D.biomass_production,
D.tl_input, pint_qty)
row_count += 1
# Scale crop outputs
for key in crop_outputs:
pint_qty = size.qty * crop_outputs[key].qty
return_array.loc[row_count] = UF.getWriteRow(key, D.biomass_production,
D.tl_output, pint_qty)
row_count += 1
return return_array
def upgradeGrassProducts(size, conv_IO_array):
return_array = UF.createEmptyFrame()
forestry_vals = forestry_upgr_in
forestry_out = forestry_upgr_out
in_list = upgr_in_list
out_list = upgr_out_list
match_list = [[UF.input_or_output, D.tl_output],
[UF.substance_name, 'Woody Biomass']]
grass_biomass_out_qty = UF.returnPintQty(conv_IO_array, match_list)
scaling_param = size.qty * (grass_biomass_out_qty /
(size.qty * D.forestry_woody_biomass_val.qty))
row_count = 0
for substance in in_list:
pint_qty = scaling_param * forestry_vals[substance].qty
return_array.loc[row_count] = UF.getWriteRow(substance, D.upgrading,
D.tl_input, pint_qty)
row_count += 1
for substance in out_list:
pint_qty = scaling_param * forestry_out[substance].qty
return_array.loc[row_count] = UF.getWriteRow(substance, D.upgrading,
D.tl_output, pint_qty)
row_count += 1
return return_array
def calcGHGImpact(tl_array):
jet_a_out = UF.returnPintQty(tl_array,
[[UF.substance_name, 'Jet-A'],
[UF.input_or_output, D.tl_output]]).magnitude
diesel_out = UF.returnPintQty(
tl_array, [[UF.substance_name, 'Diesel'],
[UF.input_or_output, D.tl_output]]).magnitude
gasoline_out = UF.returnPintQty(
tl_array, [[UF.substance_name, 'Gasoline'],
[UF.input_or_output, D.tl_output]]).magnitude
transport_fuel_energy = 46.0 * (jet_a_out + diesel_out + gasoline_out)
# note that excel formula has a few others. zero for grass so omitting.
total_MJ = transport_fuel_energy + UF.returnPintQty(
tl_array, [[UF.substance_name, 'Electricity'],
[UF.input_or_output, D.tl_output]]).magnitude
GHG_impact = 0
for i in range(len(tl_array)):
row_vals = tl_array.loc[i]
subst_name = row_vals[UF.substance_name]
in_or_out = row_vals[UF.input_or_output]
mag = row_vals[UF.magnitude]
if in_or_out != D.zeroed:
match_list = [[D.LCA_key_str, subst_name], [D.LCA_IO, in_or_out]]
LCA_val = UF.returnLCANumber(D.LCA_inventory_df, match_list,
D.LCA_GHG_impact)
GHG_impact += (LCA_val * mag)
return 75 + GHG_impact / total_MJ
def calcOPEX(tl_array):
inputs_cost = 0
for i in range(len(tl_array)):
row_vals = tl_array.loc[i]
subst_name = row_vals[UF.substance_name]
in_or_out = row_vals[UF.input_or_output]
mag = row_vals[UF.magnitude]
if in_or_out != D.zeroed:
match_list = [[D.LCA_key_str, subst_name], [D.LCA_IO, in_or_out]]
LCA_val = UF.returnLCANumber(D.LCA_inventory_df, match_list,
D.LCA_cost)
if in_or_out == D.tl_input:
inputs_cost += (LCA_val * mag)
return inputs_cost
def calcNonFuelValue(tl_array):
outputs_value = 0
for i in range(len(tl_array)):
row_vals = tl_array.loc[i]
subst_name = row_vals[UF.substance_name]
in_or_out = row_vals[UF.input_or_output]
mag = row_vals[UF.magnitude]
if in_or_out != D.zeroed:
match_list = [[D.LCA_key_str, subst_name], [D.LCA_IO, in_or_out]]
LCA_val = UF.returnLCANumber(D.LCA_inventory_df, match_list,
D.LCA_cost)
if in_or_out == D.tl_output and (subst_name != 'Jet-A'
and subst_name != 'Diesel'
and subst_name != 'Gasoline'):
outputs_value += (LCA_val * mag)
return outputs_value
def calcEROI(tl_array):
energy_investment = 0
energy_return = 0
for i in range(len(tl_array)):
row_vals = tl_array.loc[i]
subst_name = row_vals[UF.substance_name]
in_or_out = row_vals[UF.input_or_output]
mag = row_vals[UF.magnitude]
if in_or_out != D.zeroed:
match_list = [[D.LCA_key_str, subst_name], [D.LCA_IO, in_or_out]]
LCA_val = UF.returnLCANumber(D.LCA_inventory_df, match_list,
D.LCA_energy_impact)
if in_or_out == D.tl_input:
energy_investment += (LCA_val * mag)
else:
energy_return += (LCA_val * mag)
eroi = 0
try:
eroi += energy_return / energy_investment
except:
print('Divide by zero error')
return eroi
def calc_MFSP(tl_array):
capex_qty = UF.returnPintQty(tl_array,
[[UF.substance_name, 'Capital Cost']])
land_cost_qty = UF.returnPintQty(
tl_array, [[UF.substance_name, 'Land Capital Cost']])
capex = capex_qty.magnitude + land_cost_qty.magnitude #inputs ['capex']
labor = UF.returnPintQty(tl_array,
[[UF.substance_name, 'Labor']]).magnitude
#calculating total costs for inputs to the pathway (opex)
opex = calcOPEX(tl_array)
#Economic analysis variables
ecovar = {
'disc rate': 0.1,
't': 0.2,
'equity': 0.4,
'interest': 0.08,
'loan term': 10,
'maint rate': 0.03,
'ins rate': 0.01,
'land lease': 0,
'dep capex': 0.85
}
macrs = [0.143, 0.245, 0.175, 0.125, 0.089, 0.089, 0.089, 0.045]
###yrs = input('What is the project lifespan? ')
landcapex = land_cost_qty.magnitude
#Total depreciable investment
depinv = capex_qty.magnitude * ecovar['dep capex']
#Investment-loan share
invloanshare = capex * (1 - ecovar['equity'])
#Loan annual payment
loanannpay = capex * (1 - ecovar['equity']) * ecovar['interest'] * (
1 + ecovar['interest'])**ecovar['loan term'] / (
(1 + ecovar['interest'])**ecovar['loan term'] - 1)
#Investment-equity share
invequityshare = capex * (ecovar['equity'])
#Salvage value end of life
salvage = landcapex
#Annual insurance
annins = depinv * ecovar['ins rate']
#Annual maintenance
annmaint = depinv * ecovar['maint rate']
#Annual material and energy costs = opex, Annual labor costs = labor
#Fixed operating costs in $/yr
fopex = annins + annmaint + opex + labor
#creating MACRS list with zeros at the end to match duration of project
depyrs = len(macrs)
dif = int(yrs) - depyrs
if dif > 0:
addzero = [0] * dif
macrs.extend(addzero)
depreciation = []
i = 0
#calculating depreciation of depreciable investment in each year
while i < len(macrs):
depreciation.append(depinv * macrs[i])
i += 1
i = 0
#calculating loan payment, interest, and principle for each time step
loanpay = []
loanint = []
loanprin = []
while i < int(ecovar['loan term']):
loanpay.append(loanannpay)
if i == 0:
loanint.append(invloanshare * ecovar['interest'])
loanprin.append(invloanshare - loanpay[i] + loanint[i])
else:
loanint.append(loanprin[i - 1] * ecovar['interest'])
loanprin.append(loanprin[i - 1] - loanpay[i] + loanint[i])
i += 1
#adding zeros to the end of loan payment, interest, and principle for duration of project
dif = int(yrs) - len(loanprin)
if dif > 0:
addzero = [0] * dif
loanpay.extend(addzero)
loanint.extend(addzero)
loanprin.extend(addzero)
result = s_opt.minimize_scalar(lambda price_per_MJ: NPV_goal(
price_per_MJ, fopex, depreciation, loanint, ecovar, invequityshare,
loanpay, tl_array))
return result.x * 152.79288
def NPV_goal(price_per_MJ, fopex, depreciation, loanint, ecovar,
invequityshare, loanpay, tl_array):
jet_a_out = UF.returnPintQty(tl_array,
[[UF.substance_name, 'Jet-A'],
[UF.input_or_output, D.tl_output]]).magnitude
diesel_out = UF.returnPintQty(
tl_array, [[UF.substance_name, 'Diesel'],
[UF.input_or_output, D.tl_output]]).magnitude
gasoline_out = UF.returnPintQty(
tl_array, [[UF.substance_name, 'Gasoline'],
[UF.input_or_output, D.tl_output]]).magnitude
transport_fuel_energy = 46.0 * (jet_a_out + diesel_out + gasoline_out)
nonfuel_value = calcNonFuelValue(tl_array)
annrevenue = nonfuel_value + price_per_MJ * transport_fuel_energy
#calculating total annual revenue from annual fuel revenue and co product revenue
# for key in prodcost:
# if key in prodcost and key in outputs:
# annrevenue += prodcost [key] * outputs[key]
#calculating net income
netincome = []
years = int(yrs)
i = 0
while i < years:
netincome.append(annrevenue - fopex - depreciation[i] - loanint[i])
i += 1
#calculating losses forward and taxable income
lossforward = []
taxincome = [0]
i = 0
while i < years:
if taxincome[i] < 0:
lossforward.append(taxincome[i])
else:
lossforward.append(0)
taxincome.append(lossforward[i] + netincome[i])
i += 1
taxincome.pop(0)
#calculating income tax
i = 0
incometax = []
while i < years:
if taxincome[i] < 0:
incometax.append(0)
else:
incometax.append(taxincome[i] * ecovar['t'])
i += 1
# calculating cash flow
cashflow = [-invequityshare]
i = 1
while i < years + 1:
cashflow.append(annrevenue - fopex - loanpay[i - 1] - incometax[i - 1])
i += 1
# calculating discounted cash flow
disccashflow = []
i = 0
while i < years + 1:
disccashflow.append(cashflow[i] / (1 + ecovar['disc rate'])**i)
i += 1
# calculating cumulative discounted cash flow
cumdisccashflow = [disccashflow[0]]
i = 1
while i < years + 1:
cumdisccashflow.append(cumdisccashflow[i - 1] + disccashflow[i])
i += 1
# NPV retrieval from cumulative discounted cash flow
npv = cumdisccashflow
return abs(npv[-1])
# Data and Universal Functions
import TEA_LCA_Data as D
import UnivFunc as UF
# Bolt on TEA and LCA
import TEA
import LCA
# Process steps
import Grow_Grass as GG
import GasFT as GFT
import Hydroprocessing as H
# Initialize empty Process Model output table
results_array = UF.createEmptyFrame()
# Scaling Value
land_area_val = D.TEA_LCA_Qty(D.substance_dict['Land Area'], 100, 'hectare')
biomass_output = D.TEA_LCA_Qty(D.substance_dict['Woody Biomass'], 8960,
'kg/yr/ha')
# Biomass Production
biomass_IO = GG.growGrassForOneYear(land_area_val, biomass_output)
results_array = results_array.append(biomass_IO, ignore_index=True)
# Extraction/Conversion
conversion_IO = GFT.convertGrassBiomass(land_area_val, results_array)
results_array = results_array.append(conversion_IO, ignore_index=True)
# Upgrading