def simulate(
variables, # planted corn stover in hectares
LC = land_costs, # land costs per county
C_Y = bu_per_acre_C_yield, # corn yield per acre
LL = land_limits, # land limits
DM = dist_map, # distance mapping
R_idx = refinery_idx # location of refinery in simple case
):
# Empty variables
CS_farm_kg = 0 # corn stover production in kg
CS_cultivation_capex = 0 # total costs
CS_cultivation_opex = 0
CS_travel_opex = 0
CS_flow_matrix = np.zeros((len(counties),len(counties)))
CS_flow = 0
CS_refinery_kg = 0
CS_ethanol = np.zeros((len(counties),1))
CS_refinery_opex = 0
CS_refinery_capex = 0
Constraints = [] # constraints
##############################
# Cultivation and Harvesting
for i in range(0,len(counties)):
# Per ha values (need to expand)
(CS_per_ha, seeds_per_ha, fertilization_per_ha, lime_per_ha) = CS_cultivation.sim(C_Y[i])
# kg CS produced in given county 'i'
CS_farm_kg += CS_per_ha*variables[i]
# Capital costs (need to expand)
CS_cultivation_capex += variables[i]*LC[i]
# Operating costs (need to expand)
harvesting = 38.31*ha_to_acre # $ per ha
CS_cultivation_opex += variables[i]*(seeds_per_ha*.00185 + fertilization_per_ha[0]*0.55 + fertilization_per_ha[1]*0.46 + fertilization_per_ha[2]*0.50 + lime_per_ha*0.01 + harvesting) #herbicide in per ha??
################################
# Flow to refinery
for j in range(0,len(counties)):
# County to county mass transfer (kg of CS) (from county 'i' to county 'j')
CS_flow_matrix[i,j] += variables[(i+1)*len(counties) + j]
# Travel costs in bale-miles
CS_travel_opex += DM[i,j]*CS_flow_matrix[i,j]*(lb_to_kg)*(1/1500)*0.50
# Transportation constraints (flow from county 'i' must be <= mass produced)
CS_flow = sum(CS_flow_matrix[i,:])
Constraints.append(CS_flow - CS_farm_kg)
# Cultivation constraints (land limits)
Constraints.append(variables[i] - LL[i])
################################
# Refinery
for j in range(0,len(counties)):
# Find total mass sent to refinery in county 'j'
CS_refinery_kg = sum(CS_flow_matrix[:,j])
# Ethanol produced at refinery in county 'j'
CS_ethanol[j] = CS_processing.sim(CS_refinery_kg)
# Refinery opex
#ADD THIS
# Refinery capex
if CS_refinery_kg > 0:
scale = CS_refinery_kg/(5563*142) # Based on kg per ha and ha scaling in Jack's TEA file
CS_refinery_capex+= 400000000*(scale)**.6
Constraints.append(1990000-sum(CS_ethanol))
Constraints.append(sum(CS_ethanol) - 2010000)
Constraints = list(Constraints)
return [CS_refinery_capex, CS_travel_opex], Constraints
def simulate(
vars, # cultivation hectares per county, hub-to-hub biomass flows
LC=reduced_land_costs, # land costs per county
C_Y=reduced_C_yield, # corn yield per acre
LL=reduced_land_limits, # land limits
DM=dist_map, # hub to hub distances
C2H_map=map_C2H, # binary matrix mapping counties (rows) to hubs (columns)
C2H=dist_C2H, # county to hub distances
locations=locations, #possible location of biorefineries,
hubs=hubs,
Q=quota):
# Empty parameters
CS_cultivation_capex = 0
CS_cultivation_opex = 0
CS_travel_opex = 0
CS_flow_matrix = np.zeros((len(hubs), len(locations)))
CS_C2H_prod = np.zeros((len(LC), len(hubs)))
CS_flow = 0
CS_refinery_kg = 0
CS_ethanol = np.zeros((len(locations), 1))
CS_refinery_capex = 0
Constraints = [] # constraints
# Per ha values (need to expand)
(CS_per_ha, seeds_per_ha, fertilization_per_ha,
lime_per_ha) = CS_cultivation.sim(C_Y)
# Capital costs (need to expand)
L = np.array(LC)
v = np.array(vars)
CS_cultivation_capex = np.dot(v[0:len(LC)], L)
##############################
# Cultivation and Harvesting
# Operating costs (need to expand)
harvesting = 38.31 * ha_to_acre # $ per ha
CS_cultivation_opex = np.dot(
v[0:len(LC)],
(seeds_per_ha * .00185 + fertilization_per_ha[0] * 0.55 +
fertilization_per_ha[1] * 0.46 + fertilization_per_ha[2] * 0.50 +
lime_per_ha * 0.01 + harvesting)) #herbicide in per ha??
# Automatic flow to pre-processing hub
CS_C2H_prod = C2H_map * CS_per_ha * v[0:len(LC)]
CS_cultivation_opex = sum(CS_per_ha * vars[0:len(LC)] * (lb_to_kg) *
(1 / 1500) * 0.50 * C2H)
# Cultivation constraints (land limits)
for i in range(0, len(LC)):
Constraints.append(vars[i] - LL[i] - 5) #allow some slack in DVs
################################
# Flow to refinery
for j in range(0, len(hubs)):
for k in range(0, len(locations)):
# Mass transfer (1Ms kg of CS) from hub 'j' to hub 'k'
CS_flow_matrix[j,
k] = CS_flow_matrix[j, k] + vars[i + 1 + j *
len(locations) + k]
# Travel costs in bale-miles
CS_travel_opex += DM[j, k] * CS_flow_matrix[j, k] * (lb_to_kg) * (
1 / 1500) * 0.50
for j in range(0, len(hubs)):
# Hubs collect biomass from counties
CS_hub_kg = sum(CS_C2H_prod[:, j])
# Transportation constraints (all delivery from hub 'j' must be <= mass produced)
CS_flow = sum(CS_flow_matrix[j, :])
Constraints.append(CS_flow - CS_hub_kg - 5) #allow some slack in DVs
###############################
# Refinery
for k in range(0, len(locations)):
# Find total mass received at hub 'l'
CS_refinery_kg = sum(CS_flow_matrix[:, k])
# Ethanol produced at refinery at hub 'l'
CS_ethanol[k] = CS_processing.sim(CS_refinery_kg)
# Refinery capex
if CS_refinery_kg > 5:
scale = CS_refinery_kg / (
5563 * 142
) # Based on kg per ha and ha scaling in Jack's TEA file
CS_refinery_capex += 400000000 * (scale)**.6
# Sets ethanol production quota (L)
Constraints.append(Q * 0.95 - sum(CS_ethanol)[0])
Constraints.append(sum(CS_ethanol)[0] - Q * 1.05)
Constraints = list(Constraints)
# Returns list of objectives, Constraints
return [CS_refinery_capex, CS_travel_opex], Constraints
def test(DV, LC, C_Y, LL, DM, C2H_map, C2H, locations, hubs, Q):
# Empty parameters
CS_cultivation_capex = 0
CS_cultivation_opex = 0
CS_travel_opex = 0
CS_flow_matrix = np.zeros((len(hubs), len(locations)))
CS_C2H_prod = np.zeros((len(LC), len(hubs)))
CS_flow = 0
CS_refinery_kg = 0
CS_ethanol = np.zeros((len(locations), 1))
CS_refinery_capex = 0
Constraints = [] # constraints
##############################
# Cultivation and Harvesting
for i in range(0, len(LC)):
# Per ha values (need to expand)
(CS_per_ha, seeds_per_ha, fertilization_per_ha,
lime_per_ha) = CS_cultivation.sim(C_Y[i])
# Capital costs (need to expand)
CS_cultivation_capex += DV[i] * LC[i]
# Operating costs (need to expand)
harvesting = 38.31 * ha_to_acre # $ per ha
CS_cultivation_opex += DV[i] * (
seeds_per_ha * .00185 + fertilization_per_ha[0] * 0.55 +
fertilization_per_ha[1] * 0.46 + fertilization_per_ha[2] * 0.50 +
lime_per_ha * 0.01 + harvesting) #herbicide in per ha??
# Automatic flow to pre-processing hub
CS_C2H_prod[i, :] = C2H_map[i, :] * CS_per_ha * DV[i]
CS_cultivation_opex += CS_per_ha * DV[i] * (lb_to_kg) * (
1 / 1500) * 0.50 * C2H[i]
# Cultivation constraints (land limits)
Constraints.append(DV[i] - LL[i] - 5) #allow some slack in DVs
################################
# Flow to refinery
for j in range(0, len(hubs)):
for k in range(0, len(locations)):
# Mass transfer (1Ms kg of CS) from hub 'j' to hub 'k'
CS_flow_matrix[
j,
k] = CS_flow_matrix[j, k] + DV[i + 1 + j * len(locations) + k]
# Travel costs in bale-miles
CS_travel_opex += DM[j, k] * CS_flow_matrix[j, k] * (lb_to_kg) * (
1 / 1500) * 0.50
for j in range(0, len(hubs)):
# Hubs collect biomass from counties
CS_hub_kg = sum(CS_C2H_prod[:, j])
# Transportation constraints (all delivery from hub 'j' must be <= mass produced)
CS_flow = sum(CS_flow_matrix[j, :])
Constraints.append(CS_flow - CS_hub_kg - 5) #allow some slack in DVs
###############################
# Refinery
for k in range(0, len(locations)):
# Find total mass received at hub 'l'
CS_refinery_kg = sum(CS_flow_matrix[:, k])
# Ethanol produced at refinery at hub 'l'
CS_ethanol[k] = CS_processing.sim(CS_refinery_kg)
# Refinery capex
if CS_refinery_kg > 5:
scale = CS_refinery_kg / (
5563 * 142
) # Based on kg per ha and ha scaling in Jack's TEA file
CS_refinery_capex += 400000000 * (scale)**.6
# Sets ethanol production quota (L)
Constraints.append(Q * 0.95 - sum(CS_ethanol)[0])
Constraints.append(sum(CS_ethanol)[0] - Q * 1.05)
Constraints = list(Constraints)
# Returns list of objectives, Constraints
return [CS_refinery_capex, CS_travel_opex], Constraints
def QD(groups, reduced_counties, locations):
#####################################################################
########## IMPORT DATA ##########################
#####################################################################
# import county level data
df_geo = pd.read_excel('geodata_total.xlsx', header=0)
counties = list(df_geo['co_state'])
#county-to-hub data
filename = 'C2H_' + str(groups) + '.xlsx'
df_C2H = pd.read_excel(filename, header=0)
c = list(df_C2H['co_state'])
#eliminate and counties that don't appear in both lists
for i in counties:
idx = counties.index(i)
if i in c:
pass
else:
df_geo = df_geo.drop(index=idx)
df_geo = df_geo.reset_index(drop=True)
counties = list(df_geo['co_state'])
land_costs = df_geo.loc[:, 'land_cost_dpa'].values # $ per acre
land_limits = df_geo[
'land_limits_acre'].values # county ag production area in acres
# Corn Stover
bu_per_acre_C_yield = df_geo[
'yield_bpa'].values #yield in bushels per acre
################################
# Convert to function inputs
reduced_land_costs = []
reduced_land_limits = []
reduced_C_yield = []
# Hub grouping information, distance look-up tables
dist_C2H = []
county_hubs = []
for county in reduced_counties:
idx = counties.index(county)
reduced_land_costs.append(land_costs[idx])
reduced_land_limits.append(land_limits[idx])
reduced_C_yield.append(bu_per_acre_C_yield[idx])
# distance from each county to pre-defined hub
dist_C2H.append(df_C2H.loc[df_C2H['co_state'] == county,
'travel_dist_km'].values[0])
# list of hubs assigned to each county
county_hubs.append(df_C2H.loc[df_C2H['co_state'] == county,
'destinationID'].values[0])
# Pre-define location of refineries
# put a number > 0 and < number of hubs if desired; if not, problem defaults to full list of hubs
#hub-to-hub data
filename = 'H2H_' + str(groups) + '.xlsx'
df_H2H = pd.read_excel(filename, header=0)
hubs = list(df_H2H['OriginID'].unique())
################################
# Convert to function inputs
dist_map = np.zeros((len(hubs), len(locations)))
# convert look-up table to distance matrix
for i in range(0, len(hubs)):
c1 = hubs[i]
for j in range(0, len(locations)):
c2 = hubs[locations[j] - 1]
dist_map[i, j] = df_H2H.loc[(df_H2H['OriginID'] == c1) &
(df_H2H['DestinationID'] == c2),
'Total_Kilometers']
map_C2H = np.zeros((len(reduced_counties), len(hubs)))
# convert look-up table to distance matrix
for i in range(0, len(reduced_counties)):
h = int(county_hubs[i]) - 1
map_C2H[i, h] = 1
#####################################################################
########## FUNCTION DEFINITION ########################
#####################################################################
###################################
#pre-load test decision variables
V = []
for i in range(0, len(reduced_counties)):
V.append(reduced_land_limits[i] * 0.50) #50% of theoretical capacity
LC = reduced_land_costs # land costs per county
C_Y = reduced_C_yield # corn yield per acre
C2H_map = map_C2H # binary matrix mapping counties (rows) to hubs (columns)
locations = locations #possible location of biorefineries,
hubs = hubs
# Empty parameters
CS_flow_matrix = np.zeros((len(hubs), len(locations)))
CS_C2H_prod = np.zeros((len(LC), len(hubs)))
CS_refinery_kg = 0
CS_ethanol = np.zeros((len(locations), 1))
##############################
# Cultivation and Harvesting
for i in range(0, len(LC)):
# Per ha values (need to expand)
(CS_per_ha, seeds_per_ha, fertilization_per_ha,
lime_per_ha) = CS_cultivation.sim(C_Y[i])
# Automatic flow to pre-processing hub
CS_C2H_prod[i, :] = C2H_map[i, :] * CS_per_ha * V[i]
################################
# Flow to refinery
for j in range(0, len(hubs)):
for k in range(0, len(locations)):
V.append(sum(CS_C2H_prod[:, j]) * (1 / len(locations)))
# Mass transfer (kg of CS) from hub 'j' to hub 'k'
CS_flow_matrix[
j,
k] = CS_flow_matrix[j, k] + V[i + 1 + j * len(locations) + k]
###############################
# Refinery
for k in range(0, len(locations)):
# Find total mass received at hub 'l'
CS_refinery_kg = sum(CS_flow_matrix[:, k])
# Ethanol produced at refinery at hub 'l'
CS_ethanol[k] = CS_processing.sim(CS_refinery_kg)
###############################
# Upper bound hub 1Ms kgs
UB = 0
for j in range(0, len(hubs)):
UB = max(UB, sum(CS_C2H_prod[:, j]))
return [sum(CS_ethanol[:]), UB]
# Hubs collect biomass from counties
CS_hub_kg = sum(CS_C2H_prod[:, j])
# Transportation constraints (all delivery from hub 'j' must be <= mass produced)
CS_flow = sum(CS_flow_matrix[j, :])
Constraints.append(CS_flow - CS_hub_kg)
###############################
# Refinery
for k in range(0, len(locations)):
# Find total mass received at hub 'l'
CS_refinery_kg = sum(CS_flow_matrix[:, k])
# Ethanol produced at refinery at hub 'l'
CS_ethanol[k] = CS_processing.sim(CS_refinery_kg)
# Refinery opex
# ADD THIS
# Refinery capex
if CS_refinery_kg > 0:
scale = CS_refinery_kg / (
5563 * 142
) # Based on kg per ha and ha scaling in Jack's TEA file
CS_refinery_capex += 400000000 * (scale)**.6
# Sets ethanol production quota (L)
Constraints.append(1100000 - sum(CS_ethanol))
# Constraints.append(sum(CS_ethanol) - 1300000)
# Constraints.append(11900000-sum(CS_farm_kg))
def simulate(
vars, # cultivation hectares per county, hub-to-hub biomass flows
LC = land_costs, # land costs per county
C_Y = bu_per_acre_C_yield, # corn yield per acre
LL = land_limits, # land limits
DM = dist_map, # hub to hub distances
C2H_map = map_C2H, # binary matrix mapping counties (rows) to hubs (columns)
C2H = dist_C2H # county to hub distances
):
# Empty parameters
CS_farm_kg = 0 # corn stover production in kg
CS_cultivation_capex = 0
CS_cultivation_opex = 0
CS_travel_opex = 0
CS_flow_matrix = np.zeros((len(hubs),len(hubs)))
CS_C2H_prod = np.zeros((len(counties),len(hubs)))
CS_flow = 0
CS_refinery_kg = 0
CS_ethanol = np.zeros((len(hubs),1))
CS_refinery_opex = 0
CS_refinery_capex = 0
Constraints = [] # constraints
##############################
# Cultivation and Harvesting
for i in range(0,len(counties)):
# Per ha values (need to expand)
(CS_per_ha, seeds_per_ha, fertilization_per_ha, lime_per_ha) = CS_cultivation.sim(C_Y[i])
# kg CS produced in given county 'i'
CS_farm_kg += CS_per_ha*vars[i]
# Capital costs (need to expand)
CS_cultivation_capex += vars[i]*LC[i]
# Operating costs (need to expand)
harvesting = 38.31*ha_to_acre # $ per ha
CS_cultivation_opex += vars[i]*(seeds_per_ha*.00185 + fertilization_per_ha[0]*0.55 + fertilization_per_ha[1]*0.46 + fertilization_per_ha[2]*0.50 + lime_per_ha*0.01 + harvesting) #herbicide in per ha??
# Automatic flow to pre-processing hub
CS_C2H_prod[i,:] = C2H_map[i,:]*CS_per_ha*vars[i]
CS_cultivation_opex += CS_per_ha*vars[i]*(lb_to_kg)*(1/1500)*0.50*C2H[i]
# Cultivation constraints (land limits)
Constraints.append(vars[i] - LL[i])
################################
# Flow to refinery
for j in range(0,len(hubs)):
for k in range(0,len(hubs)):
# Mass transfer (kg of CS) from hub 'j' to hub 'k'
CS_flow_matrix[j,k] = CS_flow_matrix[j,k] + vars[i + 1 + j*len(hubs) + k]
# Travel costs in bale-miles
CS_travel_opex += DM[j,k]*CS_flow_matrix[j,k]*(lb_to_kg)*(1/1500)*0.50
for j in range(0,len(hubs)):
# Hubs collect biomass from counties
CS_hub_kg = sum(CS_C2H_prod[:,j])
# Transportation constraints (all delivery from hub 'j' must be <= mass produced)
CS_flow = sum(CS_flow_matrix[j,:])
Constraints.append(CS_flow - CS_hub_kg)
DISTANCE VARIABLES
################################
# Refinery
for j in range(0,len(hubs)):
# Find total mass received at hub 'j'
CS_refinery_kg = sum(CS_flow_matrix[:,j])
# Ethanol produced at refinery at hub 'j'
CS_ethanol[j] = CS_processing.sim(CS_refinery_kg)
# Refinery opex
# ADD THIS
# Refinery capex
ADJUST TO MAKE NON-ZERO
if CS_refinery_kg > 0:
scale = CS_refinery_kg/(5563*142) # Based on kg per ha and ha scaling in Jack's TEA file
CS_refinery_capex+= 400000000*(scale)**.6
# Sets ethanol production quota (L)
Constraints.append(11900000-sum(CS_ethanol))
Constraints.append(sum(CS_ethanol) - 12100000)
Constraints = list(Constraints)
# Returns list of objectives, Constraints
return [CS_refinery_capex, CS_travel_opex], Constraints