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CLCO_Data.py
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CLCO_Data.py
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from collections import defaultdict
# noinspection PyTypeChecker
class CLCO_Data:
## put scenario specific data here in the init method
def __init__(self, scenario):
if 1000 < scenario <2999 or scenario in [4501, 4502, 4503, 4511, 4512, 4513, 4521, 4522, 4523]:
if scenario % 10 == 1:
self.FEEDSTOCK_SUPPLY = [5767 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [111.5]
if scenario % 10 == 2:
self.FEEDSTOCK_SUPPLY = [1356.1 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [16.5]
if scenario % 10 == 3:
self.FEEDSTOCK_SUPPLY = [76.1 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [26.2]
elif scenario > 2999:
if scenario % 10 == 0:
self.FEEDSTOCK_SUPPLY = [4867 * 3 + 2501.4 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [120]
elif scenario % 10 == 1:
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [111.5, 122.2]
self.FEEDSTOCK_SUPPLY = [5767 * 3, 1338 * 3]
else:
self.FEEDSTOCK_SUPPLY = [16.4, 85.7, 0.0, 14.5, 18.3, 158.2, 34.8, 6.1, 27.4, 2722.3, 49.0, 7.8, 29.9,
37.7, 2721.6, 33.4, 382.5, 23.8, 455.0, 91.4, 0.0, 48.2, 228.4, 0.1, 13.1,
12.5, 16.5, 9.9, 24.9, 0.3, 0.2, 19.9, 28.0, 4068.4, 186.4, 626.8, 13.1, 13.4,
38.1, 4.2, 2.5, 34.5, 0.0, 1.6, 818.1, 3.0, 33.0, 69.7, 335.9, 35.8, 1784.3,
70.8, 2276.5, 12.0, 12.8, 25.8, 3.5, 37.6, 3856.4, 32.3, 13.4, 377.7]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [13.8, 20.4, 4.6, 17.8, 23.1, 21.2, 24.7, 12.6, 17.8, 21.2, 16.6,
14.2, 23.7, 16.7, 22.5, 27.2, 26.5, 14.5, 14.1, 16.1, 23.6,
29.6, 26.2, 5.2, 22.8, 16.4, 16.9, 22.9, 12.8, 12.4, 4.4, 21.2,
19.9, 16.5, 16.3, 17.4, 17.7, 23.9, 19.8, 10.2, 8.6, 16.4, 6.2,
8.8, 17.3, 9.4, 15.4, 12.1, 14.1, 32.5, 21.3, 30.3, 19.9, 14.4,
14.0, 20.7, 18.8, 20.7, 22.4, 12.7, 15.3, 11.9]
else:
if scenario == 51:
self.FEEDSTOCK_SUPPLY = [1356.1 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [16.5]
elif scenario == 52:
self.FEEDSTOCK_SUPPLY = [76.1 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [26.2]
elif 420 < scenario < 430:
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [111.5, 122.2]
self.FEEDSTOCK_SUPPLY = [5767 * 3, 1338 * 3]
elif 430 < scenario < 440:
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [90.9, 84.7, 19.2]
self.FEEDSTOCK_SUPPLY = [5008 * 3, 1062 * 3, 1035 * 3]
elif 440 < scenario < 450:
self.FEEDSTOCK_SUPPLY = [1059 * 3, 4043.7, 967.7 * 3, 1035 * 3]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [15.2, 70.12, 23.2, 19.2]
else:
self.FEEDSTOCK_SUPPLY = [16.4, 85.7, 0.0, 14.5, 18.3, 158.2, 34.8, 6.1, 27.4, 2722.3, 49.0, 7.8, 29.9,
37.7, 2721.6, 33.4, 382.5, 23.8, 455.0, 91.4, 0.0, 48.2, 228.4, 0.1, 13.1,
12.5, 16.5, 9.9, 24.9, 0.3, 0.2, 19.9, 28.0, 4068.4, 186.4, 626.8, 13.1, 13.4,
38.1, 4.2, 2.5, 34.5, 0.0, 1.6, 818.1, 3.0, 33.0, 69.7, 335.9, 35.8, 1784.3,
70.8, 2276.5, 12.0, 12.8, 25.8, 3.5, 37.6, 3856.4, 32.3, 13.4, 377.7]
self.INTRA_COUNTY_TRANSPORT_DISTANCE = [13.8, 20.4, 4.6, 17.8, 23.1, 21.2, 24.7, 12.6, 17.8, 21.2, 16.6,
14.2, 23.7, 16.7, 22.5, 27.2, 26.5, 14.5, 14.1, 16.1, 23.6,
29.6, 26.2, 5.2, 22.8, 16.4, 16.9, 22.9, 12.8, 12.4, 4.4, 21.2,
19.9, 16.5, 16.3, 17.4, 17.7, 23.9, 19.8, 10.2, 8.6, 16.4, 6.2,
8.8, 17.3, 9.4, 15.4, 12.1, 14.1, 32.5, 21.3, 30.3, 19.9, 14.4,
14.0, 20.7, 18.8, 20.7, 22.4, 12.7, 15.3, 11.9]
# X,Y coordinates of each centroid
XCOORD = [584, 250, 596, 432, 196, 371, 136, 354, 449, 604, 612, 412, 502, 604, 190, 598, 555, 547, 239, 572, 540,
503, 417, 588, 463, 272, 445, 282, 545, 619, 586, 194, 464, 402, 312, 558, 238, 400, 497, 604, 598, 622,
572, 581, 592, 576, 545, 345, 350, 494, 303, 696, 519, 392, 378, 561, 593, 627, 334, 604, 235, 327]
YCOORD = [4716, 4682, 4522, 4667, 4683, 4764, 4692, 4666, 4704, 4955, 4678, 4716, 4671, 4624, 4740, 4885, 4937,
4773, 4765, 4680, 4834, 4807, 4874, 4498, 4848, 4734, 4751, 4798, 4750, 4510, 4514, 4805, 4787, 4762,
4747, 4583, 4808, 4815, 4720, 4586, 4501, 4729, 4490, 4556, 4773, 4741, 4715, 4695, 4738, 4926, 4682,
4535, 4618, 4669, 4700, 4637, 4823, 4796, 4799, 4556, 4732, 4722]
ORIGINAL_FEEDSTOCK_SUPPLY = [16.4, 85.7, 0.0, 14.5, 18.3, 158.2, 34.8, 6.1, 27.4, 2722.3, 49.0, 7.8, 29.9,
37.7, 2721.6, 33.4, 382.5, 23.8, 455.0, 91.4, 0.0, 48.2, 228.4, 0.1, 13.1,
12.5, 16.5, 9.9, 24.9, 0.3, 0.2, 19.9, 28.0, 4068.4, 186.4, 626.8, 13.1, 13.4,
38.1, 4.2, 2.5, 34.5, 0.0, 1.6, 818.1, 3.0, 33.0, 69.7, 335.9, 35.8, 1784.3,
70.8, 2276.5, 12.0, 12.8, 25.8, 3.5, 37.6, 3856.4, 32.3, 13.4, 377.7]
### PARAMETERS TO VARY IN SENSITIVITY ANALYSIS
ENERGY_CONTENT = 1
P_CONTENT = 1
N_CONTENT = 1
K_CONTENT = 1
FERT_PRICE = 1
### TEO ECONOMICS PARAMETERS
TIME_PERIODS = 40
YEARS = 10
ANNUAL_DISCOUNT_RATE = .07
MONTHLY_DISCOUNT_RATE = (1 + ANNUAL_DISCOUNT_RATE) ** (1 / 4) - 1
LAND_APPLICATION_MONTH = 1
OPEX_TPC = .09 / TIME_PERIODS * YEARS
CAPEX_RATIO = 1
### MODEL PARAMETERS
## STAGE 1 PARAMETERS
## STAGE 2 PARAMETERS
# YIELDS ARE IN TONS
PYRO_YIELD = defaultdict(dict) # indexed on temperature and yield product
PYRO_YIELD['feedstock', 'Biochar', 400] = .53
PYRO_YIELD['feedstock', 'Biochar', 450] = .48
PYRO_YIELD['feedstock', 'Biochar', 500] = .47
PYRO_YIELD['feedstock', 'Biochar', 550] = .45
PYRO_YIELD['feedstock', 'Biochar', 600] = .44
PYRO_YIELD['feedstock', 'Biochar', 700] = .39
PYRO_YIELD['feedstock', 'Biochar', 800] = .37
PYRO_YIELD['feedstock', 'Syngas', 400] = .19
PYRO_YIELD['feedstock', 'Syngas', 450] = .18
PYRO_YIELD['feedstock', 'Syngas', 500] = .218
PYRO_YIELD['feedstock', 'Syngas', 550] = .19
PYRO_YIELD['feedstock', 'Syngas', 600] = .124
PYRO_YIELD['feedstock', 'Syngas', 700] = .33
PYRO_YIELD['feedstock', 'Syngas', 800] = .38
PYRO_YIELD['feedstock', 'Biooil', 400] = .16
PYRO_YIELD['feedstock', 'Biooil', 450] = .25
PYRO_YIELD['feedstock', 'Biooil', 500] = .26
PYRO_YIELD['feedstock', 'Biooil', 550] = .25
PYRO_YIELD['feedstock', 'Biooil', 600] = .22
PYRO_YIELD['feedstock', 'Biooil', 700] = .18
PYRO_YIELD['feedstock', 'Biooil', 800] = .14
PYRO_YIELD['feedstock', 'AP', 400] = .12
PYRO_YIELD['feedstock', 'AP', 450] = .09
PYRO_YIELD['feedstock', 'AP', 500] = .09
PYRO_YIELD['feedstock', 'AP', 550] = .11
PYRO_YIELD['feedstock', 'AP', 600] = .10
PYRO_YIELD['feedstock', 'AP', 800] = .10
PYRO_YIELD['feedstock', 'AP', 700] = .11
HTC_WATER = 5 # ton/ton feedstock
HTL_WATER = 9 # ton/ton feedstock
# HTL Yield Data. Yield at each temperature must sum to 1
HTL_YIELD = defaultdict(dict) # indexed on temperature and yield product
HTL_YIELD['feedstock', 'Hydrochar', 350] = .171
HTL_YIELD['feedstock', 'GP', 350] = .114
HTL_YIELD['feedstock', 'AP', 350] = .545 + HTL_WATER
HTL_YIELD['feedstock', 'Biooil', 350] = .17
# HTC Yield Data. Yield at each temperature must sum to 1
HTC_YIELD = defaultdict(dict) # indexed on temperature and yield product
HTC_YIELD['feedstock', 'Hydrochar', 180] = .597
HTC_YIELD['feedstock', 'Hydrochar', 200] = .549
HTC_YIELD['feedstock', 'Hydrochar', 220] = .501
HTC_YIELD['feedstock', 'Hydrochar', 250] = .43
HTC_YIELD['feedstock', 'GP', 180] = .01
HTC_YIELD['feedstock', 'GP', 200] = .056
HTC_YIELD['feedstock', 'GP', 220] = .188
HTC_YIELD['feedstock', 'GP', 250] = .2785
HTC_YIELD['feedstock', 'AP', 180] = .403 + HTC_WATER
HTC_YIELD['feedstock', 'AP', 200] = .394 + HTC_WATER
HTC_YIELD['feedstock', 'AP', 220] = .310 + HTC_WATER
HTC_YIELD['feedstock', 'AP', 250] = .291 + HTC_WATER
DRY_BIOMASS_REF = 3025 * 3 # 3 exists to adjust from months to quarters
# AD Yield Data. Yield at each temperature must sum to 1
AD_YIELD = defaultdict(dict) # indexed on temperature and yield product
AD_YIELD['feedstock', 'digestate', 1.5] = .7535 # ton digestate/ton feedstock
AD_YIELD['feedstock', 'digestate', 3] = .8108
AD_YIELD['feedstock', 'digestate', 4.5] = .831
AD_YIELD['feedstock', 'biogas', 1.5] = 152 # Nm^3/ton feedstock
AD_YIELD['feedstock', 'biogas', 3] = 116.6
AD_YIELD['feedstock', 'biogas', 4.5] = 104.1
AD_YIELD['COD', 'digestate', 1.5] = 0 # ton digestate/ton COD
AD_YIELD['COD', 'digestate', 3] = 0 # low confidence for 'COD', 'digestate'
AD_YIELD['COD', 'digestate', 4.5] = 0
AD_YIELD['COD', 'biogas', 1.5] = 405.9 # Nm^3/ton COD
AD_YIELD['COD', 'biogas', 3] = 379.6
AD_YIELD['COD', 'biogas', 4.5] = 353.4
# for the operational costs for each technology
OPEX = defaultdict(dict) # indexed on technology
OPEX['Labor Cost'] = 164.8
OPEX['Labor Exponent'] = .475
HEAT_EXCHANGER = (1 - .76)
OPEX['Pyrolysis', 'Heat', 400] = 853 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 450] = 896 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 500] = 939 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 550] = 983 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 600] = 1026 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 700] = 1112 # MJ/ton feedstock
OPEX['Pyrolysis', 'Heat', 800] = 1198 # MJ/ton feedstock
OPEX['Pyrolysis', 'Electricity'] = 0
OPEX['HTL', 'Heat'] = 1345 * HEAT_EXCHANGER # MJ/ton sludge
OPEX['HTL', 'Electricity'] = 9.9 # KWh/ton sludge
OPEX['HTC', 'Heat', 180] = 585 * HEAT_EXCHANGER # MJ/ton sludge
OPEX['HTC', 'Heat', 200] = 664 * HEAT_EXCHANGER # MJ/ton sludge
OPEX['HTC', 'Heat', 220] = 744 * HEAT_EXCHANGER # MJ/ton sludge
OPEX['HTC', 'Heat', 250] = 870 * HEAT_EXCHANGER # MJ/ton sludge
OPEX['HTC', 'Electricity'] = 15.48 # KWh/ton sludge
OPEX['AD', 'Heat'] = 208.07 # MJ/m^3
OPEX['AD', 'Electricity'] = 26.97 # kWh/m^3
OPEX['CHP', 'Heat'] = 0
OPEX['CHP', 'Electricity'] = 0
OPEX['Electricity'] = .07 # $/kWh
OPEX['Fuel'] = .0082 # $/MJ
OPEX['Wastewater'] = 2.3 # $/ton
OPEX['Freshwater'] = 1.45 # $/ton
OPEX['Landfill'] = 69.64 # $/ton
OPEX['Atmosphere'] = 0 # $11.82/ton of emitted carbon dioxide under RGGI
OPEX['transit'] = .1 # $dollars/km
#
CAPEX = defaultdict(dict)
CAPEX['Pyrolysis', 'process', 'coefficient'] = 77606 * CAPEX_RATIO
CAPEX['Pyrolysis', 'process', 'exponent'] = .6194
CAPEX['Pyrolysis', 'storage', 'coefficient'] = 226351 * CAPEX_RATIO
CAPEX['Pyrolysis', 'storage', 'exponent'] = .4045
CAPEX['HTL', 'process', 'coefficient'] = 16017643 * CAPEX_RATIO
CAPEX['HTL', 'process', 'exponent'] = .6
CAPEX['HTL', 'storage', 'coefficient'] = 226351* CAPEX_RATIO
CAPEX['HTL', 'storage', 'exponent'] = .4045
CAPEX['HTC', 'process', 'coefficient'] = 16017643* CAPEX_RATIO
CAPEX['HTC', 'process', 'exponent'] = .6
CAPEX['HTC', 'storage', 'coefficient'] = 226351* CAPEX_RATIO
CAPEX['HTC', 'storage', 'exponent'] = .4045
CAPEX['AD', 'process', 'coefficient'] = 4773* CAPEX_RATIO
CAPEX['AD', 'process', 'exponent'] = .699
CAPEX['AD', 'storage', 'coefficient'] = 191.2* CAPEX_RATIO
CAPEX['AD', 'storage', 'exponent'] = .1
CAPEX['CHP', 'process', 'coefficient'] = 1546* CAPEX_RATIO
CAPEX['CHP', 'process', 'exponent'] = .5291
CAPEX['CHP', 'storage', 'coefficient'] = 226351* CAPEX_RATIO
CAPEX['CHP', 'storage', 'exponent'] = .4045
CAPEX['Feedstock', 'process', 'coefficient'] = 9.648* CAPEX_RATIO
CAPEX['Feedstock', 'process', 'exponent'] = 1
CAPEX['Feedstock', 'storage', 'coefficient'] = 191.2* CAPEX_RATIO
CAPEX['Feedstock', 'storage', 'exponent'] = 1
CAPEX['Solid', 'storage', 'coefficient'] = 191.2* CAPEX_RATIO
CAPEX['Solid', 'storage', 'exponent'] = .1
ON_FARM_TRANSPORT_DISTANCE = 1
DIESEL_USE = .0071 # units gallons/km*ton (cost of this diesel is subsumed into transportation costs, but is counted in LCAs)
DIESEL_PRICE = 4.23
DIESEL_BIOOIL_RATIO = 3
TON_DIESEL_TO_GAL = 306.1 # http://www.cbi.dk/conversion.html
CHP_HEAT_EFFICIENCY = .5 # MJ out /MJ in
CHP_ELECTRICITY_EFFICIENCY = .10 # kWh/MJ in
MJ_TO_KW = .277
HOURS_PER_PERIOD = 660 * 3
LOAD_TRANSIT_COST = 5.17
# Pyrolysis Yield Data. Yield at each temperature must sum to 1
HHV = defaultdict(dict) # indexed on temperature and yield product
HHV['Pyrolysis', 'feedstock', 'Biochar', 400] = 14100 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 450] = 12600 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 500] = 11900 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 550] = 11600 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 600] = 1200 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 700] = 10700 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biochar', 800] = 10300 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 400] = 4180 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 450] = 8080 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 500] = 10000 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 550] = 10600 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 600] = 12300 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 700] = 13200 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Syngas', 800] = 13200 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 400] = 28000 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 450] = 18800 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 500] = 15000 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 550] = 19100 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 600] = 18100 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 700] = 18000 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'Biooil', 800] = 26400 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 400] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 450] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 500] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 550] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 600] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 800] = .0 * ENERGY_CONTENT
HHV['Pyrolysis', 'feedstock', 'AP', 700] = .0 * ENERGY_CONTENT
HHV['HTL', 'feedstock', 'Hydrochar', 350] = 25400 * ENERGY_CONTENT
HHV['HTL', 'feedstock', 'GP', 350] = .0 * ENERGY_CONTENT
HHV['HTL', 'feedstock', 'Biooil', 350] = 38200 * ENERGY_CONTENT
HHV['HTL', 'feedstock', 'AP', 350] = 0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'Hydrochar', 180] = 17300 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'Hydrochar', 200] = 20300 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'Hydrochar', 220] = 20000 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'Hydrochar', 250] = 20400 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'GP', 180] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'GP', 200] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'GP', 220] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'GP', 250] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'AP', 180] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'AP', 200] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'AP', 220] = .0 * ENERGY_CONTENT
HHV['HTC', 'feedstock', 'AP', 250] = 0 * ENERGY_CONTENT
HHV['methane'] = 37.669
NUTRIENTS = defaultdict(dict) # indexed on temperature and yield product
NUTRIENTS['Feedstock', 'N'] = 38.6 * N_CONTENT # kg avoided/ton feedstock
NUTRIENTS['Feedstock', 'P'] = 21.5 * P_CONTENT
NUTRIENTS['Feedstock', 'K'] = 17.5 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 400, 'N'] = 73 * N_CONTENT # kg fertlizer avoided/ton biochar
NUTRIENTS['Pyrolysis', 'feedstock', 400, 'P'] = 004.4 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 400, 'K'] = 013.1 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 450, 'N'] = 69 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 450, 'P'] = 004.3 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 450, 'K'] = 012.8 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 500, 'N'] = 64 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 500, 'P'] = 007.5 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 500, 'K'] = 022.7 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 550, 'N'] = 59 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 550, 'P'] = 05.0 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 550, 'K'] = 015.0 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 600, 'N'] = 53 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 600, 'P'] = 009.0 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 600, 'K'] = 011.0 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 700, 'N'] = 49 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 700, 'P'] = 006.3 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 700, 'K'] = 018.1 * K_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 800, 'N'] = 58 * N_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 800, 'P'] = 006.6 * P_CONTENT
NUTRIENTS['Pyrolysis', 'feedstock', 800, 'K'] = 019.0 * K_CONTENT
NUTRIENTS['HTL', 'feedstock', 350, 'N'] = 118.18 * N_CONTENT
NUTRIENTS['HTL', 'feedstock', 350, 'P'] = 91.43 * P_CONTENT
NUTRIENTS['HTL', 'feedstock', 350, 'K'] = .89 * K_CONTENT
NUTRIENTS['HTC', 'feedstock', 180, 'N'] = 58.606 * N_CONTENT
NUTRIENTS['HTC', 'feedstock', 180, 'P'] = 66.613 * P_CONTENT
NUTRIENTS['HTC', 'feedstock', 180, 'K'] = 1.566 * K_CONTENT
NUTRIENTS['HTC', 'feedstock', 200, 'N'] = 70.808 * N_CONTENT
NUTRIENTS['HTC', 'feedstock', 200, 'P'] = 73.219 * P_CONTENT
NUTRIENTS['HTC', 'feedstock', 200, 'K'] = 1.566 * K_CONTENT
NUTRIENTS['HTC', 'feedstock', 220, 'N'] = 83.010 * N_CONTENT
NUTRIENTS['HTC', 'feedstock', 220, 'P'] = 79.825 * P_CONTENT
NUTRIENTS['HTC', 'feedstock', 220, 'K'] = 1.566 * K_CONTENT
NUTRIENTS['HTC', 'feedstock', 250, 'N'] = 101.313 * N_CONTENT
NUTRIENTS['HTC', 'feedstock', 250, 'P'] = 89.734 * P_CONTENT
NUTRIENTS['HTC', 'feedstock', 250, 'K'] = 1.566 * K_CONTENT
NUTRIENTS['AD', 'N'] = 10.30 * N_CONTENT # kg avoided per ton digestate
NUTRIENTS['AD', 'P'] = 2.72 * P_CONTENT
NUTRIENTS['AD', 'K'] = 2.19 * K_CONTENT
LOADING = defaultdict(dict)
LOADING["feedstock", 1.5] = 2.68 # m^3 / ton feedstock
LOADING["feedstock", 3] = 1.34
LOADING["feedstock", 4.5] = .89
LOADING["COD", 1.5] = 2.832 # m^3 / ton COD
LOADING["COD", 3] = 2.832
LOADING["COD", 4.5] = 2.832
COD = defaultdict(dict)
COD['Pyrolysis', 'feedstock', 'Biochar', 400] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 450] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 500] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 550] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 600] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 700] = .0
COD['Pyrolysis', 'feedstock', 'Biochar', 800] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 400] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 450] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 500] = 0
COD['Pyrolysis', 'feedstock', 'Syngas', 550] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 600] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 700] = .0
COD['Pyrolysis', 'feedstock', 'Syngas', 800] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 400] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 450] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 500] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 550] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 600] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 700] = .0
COD['Pyrolysis', 'feedstock', 'Biooil', 800] = .0
COD['Pyrolysis', 'feedstock', 'AP', 400] = .36
COD['Pyrolysis', 'feedstock', 'AP', 450] = .2
COD['Pyrolysis', 'feedstock', 'AP', 500] = .29
COD['Pyrolysis', 'feedstock', 'AP', 550] = .33
COD['Pyrolysis', 'feedstock', 'AP', 600] = .32
COD['Pyrolysis', 'feedstock', 'AP', 700] = .3
COD['Pyrolysis', 'feedstock', 'AP', 800] = .4
REVENUE = defaultdict(dict)
REVENUE['N'] = .837 * FERT_PRICE # $/kg avoided fertilizer
REVENUE['P'] = 1.136 * FERT_PRICE
REVENUE['K'] = 1.061 * FERT_PRICE
REVENUE['Biooil'] = .0153 # $/MJ
REVENUE['electricity'] = .06 # units $/kWh
REVENUE['hydrochar'] = .0030 # units $/MJ
IMPACT = defaultdict(dict)
IMPACT['CLCA', 'natural gas', 'acidification'] = .000081729 # per MJ
IMPACT['CLCA', 'natural gas', 'climate change'] = .16083
IMPACT['CLCA', 'natural gas', 'ecotoxicity: freshwater'] = .00099
IMPACT['CLCA', 'natural gas', 'ecotoxicity: marine'] = .00131
IMPACT['CLCA', 'natural gas', 'ecotoxicity: terrestrial'] = .07985
IMPACT['CLCA', 'natural gas', 'energy resources'] = .0538
IMPACT['CLCA', 'natural gas', 'eutrophication: freshwater'] = 3.88341e-6
IMPACT['CLCA', 'natural gas', 'eutrophication: marine'] = 3.10865e-7
IMPACT['CLCA', 'natural gas', 'human toxicity: carcinogenic'] = .00045
IMPACT['CLCA', 'natural gas', 'human toxicity: non -carcinogenic'] = .01513
IMPACT['CLCA', 'natural gas', 'ionising radiation'] = .00015
IMPACT['CLCA', 'natural gas', 'land use'] = .00028
IMPACT['CLCA', 'natural gas', 'material resources'] = .00021
IMPACT['CLCA', 'natural gas', 'ozone depletion'] = 3.09979e-8
IMPACT['CLCA', 'natural gas', 'particulate matter formation'] = 3.51976e-5
IMPACT['CLCA', 'natural gas', 'photochemical oxidant formation: human health'] = .00016
IMPACT['CLCA', 'natural gas', 'photochemical oxidant formation: terrestrial ecosystems'] = .00018
IMPACT['CLCA', 'natural gas', 'water use'] = 7.38057e-5
IMPACT['CLCA', 'grid electricity', 'acidification'] = .00015 # per kWh
IMPACT['CLCA', 'grid electricity', 'climate change'] = .14709
IMPACT['CLCA', 'grid electricity', 'ecotoxicity: freshwater'] = .0403
IMPACT['CLCA', 'grid electricity', 'ecotoxicity: marine'] = .05004
IMPACT['CLCA', 'grid electricity', 'ecotoxicity: terrestrial'] = 2.68815
IMPACT['CLCA', 'grid electricity', 'energy resources'] = .04878
IMPACT['CLCA', 'grid electricity', 'eutrophication: freshwater'] = 2.77439e-5
IMPACT['CLCA', 'grid electricity', 'eutrophication: marine'] = 2.75437e-6
IMPACT['CLCA', 'grid electricity', 'human toxicity: carcinogenic'] = .00761
IMPACT['CLCA', 'grid electricity', 'human toxicity: non -carcinogenic'] = .24485
IMPACT['CLCA', 'grid electricity', 'ionising radiation'] = .0012
IMPACT['CLCA', 'grid electricity', 'land use'] = .0322
IMPACT['CLCA', 'grid electricity', 'material resources'] = 0 - .00016
IMPACT['CLCA', 'grid electricity', 'ozone depletion'] = 7.5452e-8
IMPACT['CLCA', 'grid electricity', 'particulate matter formation'] = 9.17148e-5
IMPACT['CLCA', 'grid electricity', 'photochemical oxidant formation: human health'] = .00025
IMPACT['CLCA', 'grid electricity', 'photochemical oxidant formation: terrestrial ecosystems'] = .00027
IMPACT['CLCA', 'grid electricity', 'water use'] = .00153
IMPACT['CLCA', 'diesel', 'acidification'] = .084564 # per gal
IMPACT['CLCA', 'diesel', 'climate change'] = 20.643822
IMPACT['CLCA', 'diesel', 'ecotoxicity: freshwater'] = .618192
IMPACT['CLCA', 'diesel', 'ecotoxicity: marine'] = .828144
IMPACT['CLCA', 'diesel', 'ecotoxicity: terrestrial'] = 85.938894
IMPACT['CLCA', 'diesel', 'energy resources'] = 5.834916
IMPACT['CLCA', 'diesel', 'eutrophication: freshwater'] = .0031446
IMPACT['CLCA', 'diesel', 'eutrophication: marine'] = .000523174
IMPACT['CLCA', 'diesel', 'human toxicity: carcinogenic'] = .414072
IMPACT['CLCA', 'diesel', 'human toxicity: non -carcinogenic'] = 72.6084
IMPACT['CLCA', 'diesel', 'ionising radiation'] = .100602
IMPACT['CLCA', 'diesel', 'land use'] = 1.602342
IMPACT['CLCA', 'diesel', 'material resources'] = .139968
IMPACT['CLCA', 'diesel', 'ozone depletion'] = 6.95029e-6
IMPACT['CLCA', 'diesel', 'particulate matter formation'] = .048114
IMPACT['CLCA', 'diesel', 'photochemical oxidant formation: human health'] = .157464
IMPACT['CLCA', 'diesel', 'photochemical oxidant formation: terrestrial ecosystems'] = .161838
IMPACT['CLCA', 'diesel', 'water use'] = .042282
IMPACT['CLCA', 'water', 'acidification'] = 0.000064348 # per kg
IMPACT['CLCA', 'water', 'climate change'] = 0.035842
IMPACT['CLCA', 'water', 'ecotoxicity: freshwater'] = 0.00497887
IMPACT['CLCA', 'water', 'ecotoxicity: marine'] = 0.0063379
IMPACT['CLCA', 'water', 'ecotoxicity: terrestrial'] = 0.22
IMPACT['CLCA', 'water', 'energy resources'] = 0.0144505
IMPACT['CLCA', 'water', 'eutrophication: freshwater'] = 0.0001722
IMPACT['CLCA', 'water', 'eutrophication: marine'] = 0.000202928
IMPACT['CLCA', 'water', 'human toxicity: carcinogenic'] = 0.03356
IMPACT['CLCA', 'water', 'human toxicity: non -carcinogenic'] = 0.33
IMPACT['CLCA', 'water', 'ionising radiation'] = 0.00025847
IMPACT['CLCA', 'water', 'land use'] = 0.00414242
IMPACT['CLCA', 'water', 'material resources'] = 0.000173772
IMPACT['CLCA', 'water', 'ozone depletion'] = 1.5916E-08
IMPACT['CLCA', 'water', 'particulate matter formation'] =3.42606E-05
IMPACT['CLCA', 'water', 'photochemical oxidant formation: human health'] = 0.000103887
IMPACT['CLCA', 'water', 'photochemical oxidant formation: terrestrial ecosystems'] = 0.000111422
IMPACT['CLCA', 'water', 'water use'] = 0.00464745
IMPACT['CLCA', 'biochar-chp', 400, 'acidification'] = 6.346948 # per ton
IMPACT['CLCA', 'biochar-chp', 400, 'climate change'] = 1626.836
IMPACT['CLCA', 'biochar-chp', 400, 'ecotoxicity: freshwater'] = 31.0905
IMPACT['CLCA', 'biochar-chp', 400, 'ecotoxicity: marine'] = 42.50954167
IMPACT['CLCA', 'biochar-chp', 400, 'ecotoxicity: terrestrial'] = 894.2768
IMPACT['CLCA', 'biochar-chp', 400, 'energy resources'] = 336.210
IMPACT['CLCA', 'biochar-chp', 400, 'eutrophication: freshwater'] = .75395
IMPACT['CLCA', 'biochar-chp', 400, 'eutrophication: marine'] = .048402
IMPACT['CLCA', 'biochar-chp', 400, 'human toxicity: carcinogenic'] = 54.38095
IMPACT['CLCA', 'biochar-chp', 400, 'human toxicity: non -carcinogenic'] = 1153.446
IMPACT['CLCA', 'biochar-chp', 400, 'ionising radiation'] = .75395
IMPACT['CLCA', 'biochar-chp', 400, 'land use'] = 16.9709
IMPACT['CLCA', 'biochar-chp', 400, 'material resources'] = 1.54904
IMPACT['CLCA', 'biochar-chp', 400, 'ozone depletion'] = .0004433
IMPACT['CLCA', 'biochar-chp', 400, 'particulate matter formation'] = 1.905458
IMPACT['CLCA', 'biochar-chp', 400, 'photochemical oxidant formation: human health'] = 2.152208
IMPACT['CLCA', 'biochar-chp', 400, 'photochemical oxidant formation: terrestrial ecosystems'] = 2.179625
IMPACT['CLCA', 'biochar-chp', 400, 'water use'] = 1.603875
IMPACT['CLCA', 'biochar-chp', 450, 'acidification'] = 5.67175
IMPACT['CLCA', 'biochar-chp', 450, 'climate change'] = 1453.768
IMPACT['CLCA', 'biochar-chp', 450, 'ecotoxicity: freshwater'] = 27.783
IMPACT['CLCA', 'biochar-chp', 450, 'ecotoxicity: marine'] = 37.98725
IMPACT['CLCA', 'biochar-chp', 450, 'ecotoxicity: terrestrial'] = 799.141
IMPACT['CLCA', 'biochar-chp', 450, 'energy resources'] = 300.4435
IMPACT['CLCA', 'biochar-chp', 450, 'eutrophication: freshwater'] = .67375
IMPACT['CLCA', 'biochar-chp', 450, 'eutrophication: marine'] = .04325
IMPACT['CLCA', 'biochar-chp', 450, 'human toxicity: carcinogenic'] = 48.59575
IMPACT['CLCA', 'biochar-chp', 450, 'human toxicity: non -carcinogenic'] = 1030.7395
IMPACT['CLCA', 'biochar-chp', 450, 'ionising radiation'] = .67375
IMPACT['CLCA', 'biochar-chp', 450, 'land use'] = 15.165
IMPACT['CLCA', 'biochar-chp', 450, 'material resources'] = 1.3842
IMPACT['CLCA', 'biochar-chp', 450, 'ozone depletion'] = .00039621
IMPACT['CLCA', 'biochar-chp', 450, 'particulate matter formation'] = 1.715
IMPACT['CLCA', 'biochar-chp', 450, 'photochemical oxidant formation: human health'] = 1.92325
IMPACT['CLCA', 'biochar-chp', 450, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.94775
IMPACT['CLCA', 'biochar-chp', 450, 'water use'] = 1.43325
IMPACT['CLCA', 'biochar-chp', 500, 'acidification'] = 5.35665
IMPACT['CLCA', 'biochar-chp', 500, 'climate change'] = 1373.0
IMPACT['CLCA', 'biochar-chp', 500, 'ecotoxicity: freshwater'] = 26.239
IMPACT['CLCA', 'biochar-chp', 500, 'ecotoxicity: marine'] = 35.876
IMPACT['CLCA', 'biochar-chp', 500, 'ecotoxicity: terrestrial'] = 754.74
IMPACT['CLCA', 'biochar-chp', 500, 'energy resources'] = 283.75
IMPACT['CLCA', 'biochar-chp', 500, 'eutrophication: freshwater'] = .63631
IMPACT['CLCA', 'biochar-chp', 500, 'eutrophication: marine'] = .04085
IMPACT['CLCA', 'biochar-chp', 500, 'human toxicity: carcinogenic'] = 45.895
IMPACT['CLCA', 'biochar-chp', 500, 'human toxicity: non -carcinogenic'] = 973.47
IMPACT['CLCA', 'biochar-chp', 500, 'ionising radiation'] = .63631
IMPACT['CLCA', 'biochar-chp', 500, 'land use'] = 14.322
IMPACT['CLCA', 'biochar-chp', 500, 'material resources'] = 1.3073
IMPACT['CLCA', 'biochar-chp', 500, 'ozone depletion'] = .00037420
IMPACT['CLCA', 'biochar-chp', 500, 'particulate matter formation'] = 1.61972
IMPACT['CLCA', 'biochar-chp', 500, 'photochemical oxidant formation: human health'] = 1.8164
IMPACT['CLCA', 'biochar-chp', 500, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.8395
IMPACT['CLCA', 'biochar-chp', 500, 'water use'] = 1.3536
IMPACT['CLCA', 'biochar-chp', 550, 'acidification'] = 5.2216
IMPACT['CLCA', 'biochar-chp', 550, 'climate change'] = 1338.4
IMPACT['CLCA', 'biochar-chp', 550, 'ecotoxicity: freshwater'] = 25.578
IMPACT['CLCA', 'biochar-chp', 550, 'ecotoxicity: marine'] = 34.972
IMPACT['CLCA', 'biochar-chp', 550, 'ecotoxicity: terrestrial'] = 735.71
IMPACT['CLCA', 'biochar-chp', 550, 'energy resources'] = 276.59
IMPACT['CLCA', 'biochar-chp', 550, 'eutrophication: freshwater'] = .62027
IMPACT['CLCA', 'biochar-chp', 550, 'eutrophication: marine'] = .039820
IMPACT['CLCA', 'biochar-chp', 550, 'human toxicity: carcinogenic'] = 44.738
IMPACT['CLCA', 'biochar-chp', 550, 'human toxicity: non -carcinogenic'] = 948.93
IMPACT['CLCA', 'biochar-chp', 550, 'ionising radiation'] = .62027
IMPACT['CLCA', 'biochar-chp', 550, 'land use'] = 13.961
IMPACT['CLCA', 'biochar-chp', 550, 'material resources'] = 1.2743
IMPACT['CLCA', 'biochar-chp', 550, 'ozone depletion'] = .00036477
IMPACT['CLCA', 'biochar-chp', 550, 'particulate matter formation'] = 1.57888
IMPACT['CLCA', 'biochar-chp', 550, 'photochemical oxidant formation: human health'] = 1.7706
IMPACT['CLCA', 'biochar-chp', 550, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.7931
IMPACT['CLCA', 'biochar-chp', 550, 'water use'] = 1.3195
IMPACT['CLCA', 'biochar-chp', 600, 'acidification'] = 5.40166
IMPACT['CLCA', 'biochar-chp', 600, 'climate change'] = 1384.5
IMPACT['CLCA', 'biochar-chp', 600, 'ecotoxicity: freshwater'] = 26.46
IMPACT['CLCA', 'biochar-chp', 600, 'ecotoxicity: marine'] = 36.178
IMPACT['CLCA', 'biochar-chp', 600, 'ecotoxicity: terrestrial'] = 761.08
IMPACT['CLCA', 'biochar-chp', 600, 'energy resources'] = 286.13
IMPACT['CLCA', 'biochar-chp', 600, 'eutrophication: freshwater'] = .64166
IMPACT['CLCA', 'biochar-chp', 600, 'eutrophication: marine'] = .041193
IMPACT['CLCA', 'biochar-chp', 600, 'human toxicity: carcinogenic'] = 46.281
IMPACT['CLCA', 'biochar-chp', 600, 'human toxicity: non -carcinogenic'] = 981.65
IMPACT['CLCA', 'biochar-chp', 600, 'ionising radiation'] = .64166
IMPACT['CLCA', 'biochar-chp', 600, 'land use'] = 14.443
IMPACT['CLCA', 'biochar-chp', 600, 'material resources'] = 1.3183
IMPACT['CLCA', 'biochar-chp', 600, 'ozone depletion'] = .00037734
IMPACT['CLCA', 'biochar-chp', 600, 'particulate matter formation'] = 1.6333
IMPACT['CLCA', 'biochar-chp', 600, 'photochemical oxidant formation: human health'] = 1.8316
IMPACT['CLCA', 'biochar-chp', 600, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.855
IMPACT['CLCA', 'biochar-chp', 600, 'water use'] = 1.365
IMPACT['CLCA', 'biochar-chp', 700, 'acidification'] = 4.8164
IMPACT['CLCA', 'biochar-chp', 700, 'climate change'] = 1234.5
IMPACT['CLCA', 'biochar-chp', 700, 'ecotoxicity: freshwater'] = 23.593
IMPACT['CLCA', 'biochar-chp', 700, 'ecotoxicity: marine'] = 32.259
IMPACT['CLCA', 'biochar-chp', 700, 'ecotoxicity: terrestrial'] = 678.63
IMPACT['CLCA', 'biochar-chp', 700, 'energy resources'] = 255.13
IMPACT['CLCA', 'biochar-chp', 700, 'eutrophication: freshwater'] = .57215
IMPACT['CLCA', 'biochar-chp', 700, 'eutrophication: marine'] = .03673
IMPACT['CLCA', 'biochar-chp', 700, 'human toxicity: carcinogenic'] = 41.267
IMPACT['CLCA', 'biochar-chp', 700, 'human toxicity: non -carcinogenic'] = 875.31
IMPACT['CLCA', 'biochar-chp', 700, 'ionising radiation'] = .57215
IMPACT['CLCA', 'biochar-chp', 700, 'land use'] = 12.878
IMPACT['CLCA', 'biochar-chp', 700, 'material resources'] = 1.1755
IMPACT['CLCA', 'biochar-chp', 700, 'ozone depletion'] = .0033647
IMPACT['CLCA', 'biochar-chp', 700, 'particulate matter formation'] = 1.4667
IMPACT['CLCA', 'biochar-chp', 700, 'photochemical oxidant formation: human health'] = 1.6332
IMPACT['CLCA', 'biochar-chp', 700, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.6540
IMPACT['CLCA', 'biochar-chp', 700, 'water use'] = 1.2171
IMPACT['CLCA', 'biochar-chp', 800, 'acidification'] = 4.6364
IMPACT['CLCA', 'biochar-chp', 800, 'climate change'] = 1188.3
IMPACT['CLCA', 'biochar-chp', 800, 'ecotoxicity: freshwater'] = 22.711
IMPACT['CLCA', 'biochar-chp', 800, 'ecotoxicity: marine'] = 31.053
IMPACT['CLCA', 'biochar-chp', 800, 'ecotoxicity: terrestrial'] = 653.26
IMPACT['CLCA', 'biochar-chp', 800, 'energy resources'] = 245.60
IMPACT['CLCA', 'biochar-chp', 800, 'eutrophication: freshwater'] = .55076
IMPACT['CLCA', 'biochar-chp', 800, 'eutrophication: marine'] = .035357
IMPACT['CLCA', 'biochar-chp', 800, 'human toxicity: carcinogenic'] = 39.725
IMPACT['CLCA', 'biochar-chp', 800, 'human toxicity: non -carcinogenic'] = 842.58
IMPACT['CLCA', 'biochar-chp', 800, 'ionising radiation'] = .55076
IMPACT['CLCA', 'biochar-chp', 800, 'land use'] = 12.397
IMPACT['CLCA', 'biochar-chp', 800, 'material resources'] = 1.1315
IMPACT['CLCA', 'biochar-chp', 800, 'ozone depletion'] = .00032389
IMPACT['CLCA', 'biochar-chp', 800, 'particulate matter formation'] = 1.4219
IMPACT['CLCA', 'biochar-chp', 800, 'photochemical oxidant formation: human health'] = 1.5721
IMPACT['CLCA', 'biochar-chp', 800, 'photochemical oxidant formation: terrestrial ecosystems'] = 1.5922
IMPACT['CLCA', 'biochar-chp', 800, 'water use'] = 1.1716
IMPACT['CLCA', 'biochar-land', 400, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 400, 'climate change'] = 0 - 2067.67
IMPACT['CLCA', 'biochar-land', 400, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 400, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 400, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 400, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 400, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 400, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 400, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 400, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 400, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 400, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 400, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 400, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 400, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 400, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 400, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 400, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 450, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 450, 'climate change'] = 0 - 2314.67
IMPACT['CLCA', 'biochar-land', 450, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 450, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 450, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 450, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 450, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 450, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 450, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 450, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 450, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 450, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 450, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 450, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 450, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 450, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 450, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 450, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 500, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 500, 'climate change'] = 0 - 2498.67
IMPACT['CLCA', 'biochar-land', 500, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 500, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 500, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 500, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 500, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 500, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 500, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 500, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 500, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 500, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 500, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 500, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 500, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 500, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 500, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 500, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 550, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 550, 'climate change'] = 0 - 2499.67
IMPACT['CLCA', 'biochar-land', 550, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 550, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 550, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 550, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 550, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 550, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 550, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 550, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 550, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 550, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 550, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 550, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 550, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 550, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 550, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 550, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 600, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 600, 'climate change'] = 0 - 2473.67
IMPACT['CLCA', 'biochar-land', 600, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 600, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 600, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 600, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 600, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 600, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 600, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 600, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 600, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 600, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 600, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 600, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 600, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 600, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 600, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 600, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 700, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 700, 'climate change'] = 0 - 2847.67
IMPACT['CLCA', 'biochar-land', 700, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 700, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 700, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 700, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 700, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 700, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 700, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 700, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 700, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 700, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 700, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 700, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 700, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 700, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 700, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 700, 'water use'] = .00102
IMPACT['CLCA', 'biochar-land', 800, 'acidification'] = .00284
IMPACT['CLCA', 'biochar-land', 800, 'climate change'] = 0 - 3155.67
IMPACT['CLCA', 'biochar-land', 800, 'ecotoxicity: freshwater'] = .02067
IMPACT['CLCA', 'biochar-land', 800, 'ecotoxicity: marine'] = .02742
IMPACT['CLCA', 'biochar-land', 800, 'ecotoxicity: terrestrial'] = 3.3108
IMPACT['CLCA', 'biochar-land', 800, 'energy resources'] = .19113
IMPACT['CLCA', 'biochar-land', 800, 'eutrophication: freshwater'] = .000083268
IMPACT['CLCA', 'biochar-land', 800, 'eutrophication: marine'] = 1.7302e-5
IMPACT['CLCA', 'biochar-land', 800, 'human toxicity: carcinogenic'] = .00323
IMPACT['CLCA', 'biochar-land', 800, 'human toxicity: non -carcinogenic'] = 1.3517
IMPACT['CLCA', 'biochar-land', 800, 'ionising radiation'] = .00341
IMPACT['CLCA', 'biochar-land', 800, 'land use'] = .06315
IMPACT['CLCA', 'biochar-land', 800, 'material resources'] = .0379
IMPACT['CLCA', 'biochar-land', 800, 'ozone depletion'] = 0 - 0.00145
IMPACT['CLCA', 'biochar-land', 800, 'particulate matter formation'] = 0.003
IMPACT['CLCA', 'biochar-land', 800, 'photochemical oxidant formation: human health'] = .00591
IMPACT['CLCA', 'biochar-land', 800, 'photochemical oxidant formation: terrestrial ecosystems'] = .00608
IMPACT['CLCA', 'biochar-land', 800, 'water use'] = .00102
IMPACT['CLCA', 'biochar-disposal', 'acidification'] = 0
IMPACT['CLCA', 'biochar-disposal', 'climate change'] = 0
IMPACT['CLCA', 'biochar-disposal', 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'biochar-disposal', 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'biochar-disposal', 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'biochar-disposal', 'energy resources'] = 0
IMPACT['CLCA', 'biochar-disposal', 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'biochar-disposal', 'eutrophication: marine'] = 0
IMPACT['CLCA', 'biochar-disposal', 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'biochar-disposal', 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'biochar-disposal', 'ionising radiation'] = 0
IMPACT['CLCA', 'biochar-disposal', 'land use'] = 0
IMPACT['CLCA', 'biochar-disposal', 'material resources'] = 0
IMPACT['CLCA', 'biochar-disposal', 'ozone depletion'] = 0
IMPACT['CLCA', 'biochar-disposal', 'particulate matter formation'] = 0
IMPACT['CLCA', 'biochar-disposal', 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'biochar-disposal', 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'biochar-disposal', 'water use'] = 0
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'acidification'] = 104.78
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'climate change'] = 16753
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'ecotoxicity: freshwater'] = 464.50
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'ecotoxicity: marine'] = 639.67
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'ecotoxicity: terrestrial'] = 149700
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'energy resources'] = 4788
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'eutrophication: freshwater'] = 1.0680
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'eutrophication: marine'] = .23148
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'human toxicity: carcinogenic'] = 398.82
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'human toxicity: non -carcinogenic'] = 9288.6
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'ionising radiation'] = -70.38
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'land use'] = 175.16
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'material resources'] = -139.19
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'ozone depletion'] = .01166
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'particulate matter formation'] = 39.1
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'photochemical oxidant formation: human health'] = 325.31
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'photochemical oxidant formation: terrestrial ecosystems'] = 330
IMPACT['CLCA', 'pyro-bio-oil-chp', 400, 'water use'] = -90.712
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'acidification'] = 98.624
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'climate change'] = 15768
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'ecotoxicity: freshwater'] = 437.18
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'ecotoxicity: marine'] = 602.04
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'ecotoxicity: terrestrial'] = 140890
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'energy resources'] = 4507
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'eutrophication: freshwater'] = 1.0052
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'eutrophication: marine'] = .21786
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'human toxicity: carcinogenic'] = 375.36
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'human toxicity: non -carcinogenic'] = 8742.2
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'ionising radiation'] = -66.24
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'land use'] = 164.864
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'material resources'] = -131
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'ozone depletion'] = .010974
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'particulate matter formation'] = 36.8
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'photochemical oxidant formation: human health'] = 306.17
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'photochemical oxidant formation: terrestrial ecosystems'] = 310.59
IMPACT['CLCA', 'pyro-bio-oil-chp', 450, 'water use'] = -85.376
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'acidification'] = 96.078
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'climate change'] = 15361
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'ecotoxicity: freshwater'] = 425.89
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'ecotoxicity: marine'] = 586.50
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'ecotoxicity: terrestrial'] = 137530
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'energy resources'] = 4390.9
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'eutrophication: freshwater'] = .97927
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'eutrophication: marine'] = .21224
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'human toxicity: carcinogenic'] = 365.67
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'human toxicity: non -carcinogenic'] = 8516.52
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'ionising radiation'] = -64.53
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'land use'] = 160.608
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'material resources'] = -127.62
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'ozone depletion'] = .01069
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'particulate matter formation'] = 35.85
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'photochemical oxidant formation: human health'] = 298.27
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'photochemical oxidant formation: terrestrial ecosystems'] = 302.57
IMPACT['CLCA', 'pyro-bio-oil-chp', 500, 'water use'] = -83.172
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'acidification'] = 98.825
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'climate change'] = 15800
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'ecotoxicity: freshwater'] = 438.07
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'ecotoxicity: marine'] = 603.27
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'ecotoxicity: terrestrial'] = 141184
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'energy resources'] = 4516.4
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'eutrophication: freshwater'] = 1.0072
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'eutrophication: marine'] = .21831
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'human toxicity: carcinogenic'] = 376.12
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'human toxicity: non -carcinogenic'] = 8760.0
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'ionising radiation'] = -66.375
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'land use'] = 165.2
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'material resources'] = -131.275
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'ozone depletion'] = .01099
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'particulate matter formation'] = 36.875
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'photochemical oxidant formation: human health'] = 306.8
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'photochemical oxidant formation: terrestrial ecosystems'] = 311.22
IMPACT['CLCA', 'pyro-bio-oil-chp', 550, 'water use'] = -85.55
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'acidification'] = 98.155
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'climate change'] = 15693
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'ecotoxicity: freshwater'] = 43.105
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'ecotoxicity: marine'] = 599.18
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'ecotoxicity: terrestrial'] = 140226
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'energy resources'] = 4485.8
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'eutrophication: freshwater'] = 1.0004
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'eutrophication: marine'] = .21683
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'human toxicity: carcinogenic'] = 373.57
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'human toxicity: non -carcinogenic'] = 8700.6
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'ionising radiation'] = -65.925
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'land use'] = 164.08
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'material resources'] = -130.385
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'ozone depletion'] = .010922
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'particulate matter formation'] = 36.625
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'photochemical oxidant formation: human health'] = 304.72
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'photochemical oxidant formation: terrestrial ecosystems'] = 309.115
IMPACT['CLCA', 'pyro-bio-oil-chp', 600, 'water use'] = -84.97
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'acidification'] = 98.088
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'climate change'] = 15682
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'ecotoxicity: freshwater'] = 434.80
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'ecotoxicity: marine'] = 598.77
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'ecotoxicity: terrestrial'] = 140131
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'energy resources'] = 4482.7
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'eutrophication: freshwater'] = .99976
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'eutrophication: marine'] = .21668
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'human toxicity: carcinogenic'] = 373.32
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'human toxicity: non -carcinogenic'] = 8694.7
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'ionising radiation'] = -65.88
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'land use'] = 163.96
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'material resources'] = -130.29
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'ozone depletion'] = .01091
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'particulate matter formation'] = 36.6
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'photochemical oxidant formation: human health'] = 304.512
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'photochemical oxidant formation: terrestrial ecosystems'] = 308.9
IMPACT['CLCA', 'pyro-bio-oil-chp', 700, 'water use'] = -84.912
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'acidification'] = 103.72
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'climate change'] = 16582
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'ecotoxicity: freshwater'] = 459.75
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'ecotoxicity: marine'] = 633.13
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'ecotoxicity: terrestrial'] = 148171
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'energy resources'] = 4739.9
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'eutrophication: freshwater'] = 1.05712
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'eutrophication: marine'] = .22911
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'human toxicity: carcinogenic'] = 394.74
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'human toxicity: non -carcinogenic'] = 9193.5
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'ionising radiation'] = -69.66
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'land use'] = 173.37
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'material resources'] = -137.77
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'ozone depletion'] = .01154
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'particulate matter formation'] = 38.7
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'photochemical oxidant formation: human health'] = 321.98
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'photochemical oxidant formation: terrestrial ecosystems'] = 326.63
IMPACT['CLCA', 'pyro-bio-oil-chp', 800, 'water use'] = -89.784
IMPACT['CLCA', 'syngas-chp', 400, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'climate change'] = 1099
IMPACT['CLCA', 'syngas-chp', 400, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 400, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'climate change'] = 1243.9
IMPACT['CLCA', 'syngas-chp', 450, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 450, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'climate change'] = 1084.9
IMPACT['CLCA', 'syngas-chp', 500, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 500, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'climate change'] = 1338
IMPACT['CLCA', 'syngas-chp', 550, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 550, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'climate change'] = 1416.4
IMPACT['CLCA', 'syngas-chp', 600, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 600, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'climate change'] = 1435.5
IMPACT['CLCA', 'syngas-chp', 700, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 700, 'water use'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'acidification'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'climate change'] = 1502.3
IMPACT['CLCA', 'syngas-chp', 800, 'ecotoxicity: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'ecotoxicity: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'ecotoxicity: terrestrial'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'energy resources'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'eutrophication: freshwater'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'eutrophication: marine'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'human toxicity: carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'human toxicity: non -carcinogenic'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'ionising radiation'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'land use'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'material resources'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'ozone depletion'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'particulate matter formation'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'photochemical oxidant formation: human health'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'photochemical oxidant formation: terrestrial ecosystems'] = 0
IMPACT['CLCA', 'syngas-chp', 800, 'water use'] = 0