Optimising the economics and the carbon and water footprints of
bioethanol supply chains
Supporting Information
Andrea Bernardi, Sara Giarola, and Fabrizio Bezzo
CAPE-Lab − Computer-Aided Process Engineering Laboratory
DII - Dipartimento di Ingegneria Industriale, Università di Padova,
via Marzolo 9, 35131, Padova
Part A
Here the economic part of the model is proposed, according to the paper by Giarola et al. (2012). For
more details see the original reference. The modelling framework is summarised as follows.
- Annual cash flow
CFk ,t  PBTk ,t  Dk ,t  TAX k ,t ,  k ,t
(A.1)
PBTk ,t  Inc k ,t  VarC k ,t  FixCk ,t  Dk ,t ,  k , t
(A.2)
Inc k ,t   PjT,k ,t  MPj ,  j , k ,t
(A.3)
- Gross profits
j
- Fixed costs
Dk ,t  TCI k  dk t ,  k ,t
(A.4)
FixCk ,t    Inc k ,t ,  k ,t
(A.5)
VarCk ,t  BPC k ,t  TCk ,t  EPCk ,t ,  k ,t
(A.6)
BPC k ,t   CapTi ,k ,t  UPCi ,  k ,t
(A.7)
- Variable costs
i

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TC k ,t   CapTi ,k ,t  UTCi ,  k ,t
(A.8)
i
T
EPCk ,t  coef 1,k  P' ethanol
' ,k ,t  coef 2 ,k  Yk ,
k ,t
(A.9)
- Taxation
TAX k ,t  Tr  PBTk ,t  Vk ,t  M ,  k ,t
(A.10)
PBTk ,t  M  1  Vk ,t  ,  k ,t
(A.11)
PBTk ,t  M  Vk ,t ,  k ,t
(A.12)
TAX k ,t  0 ,  k ,t
(A.13)
- Capacity planning constraints
CapMini ,k  Yk  Capi ,k ,t  CapMaxi ,k  Yk ,  i,k ,t
(A.14)
CapTi ,k ,t  Capi ,k ,t  1  burni ,k ,
(A.15)
i , k ,t
Cap' stover' ,k ,t  Cap' corn' ,k ,t   k ,  t , k  fratio ( k )
(A.16)
- Sustainability constraints
CapTi ,k ,t  BAi ,  i , k ,t
(A.17)
BAi  LA  BYi  quotai ,  i
(A.18)
- Production constraints
Pf i ,k ,t  Capi ,k ,t   i ,  i,k ,t
(A.19)
T
P' ethanol
' ,k ,t   Pf i ,k ,t ,  k ,t
(A.20)
i
T
P' DDGS
' ,k ,t  Pf' corn' ,k ,t   ,  k ,t
T
P' Tpower' ,k ,t  P' ethanol
' ,k ,t 
k
,  k ,t

(A.21)
(A.22)
- Capital costs linearisation constraints
BFk ,t   Capi ,k ,t ,  k ,t
i
2
(A.23)
BFk ,t   k , p  BN k , p ,  k ,t
(A.24)
p
TCI k   k , p  CI k , p  10 6 ,  k
(A.25)
p
k , p  0 ,  k , p
(A.26)
k , p  y k , p 1  y k , p  0 ,  k , p
(A.27)
y k ,' 6'  0 ,  k
(A.28)
P 1
y
k ,p
1,  k
(A.29)

k ,p
 Yk ,  k
(A.30)
p 1
p
- Technology allocation constraints (i.e., a conversion facility is assigned only one technology; when
a processing facility using technology k is established for ethanol production, the binary variable, Yk,
is set to 1, otherwise is 0).
Y
k
1
(A.31)
k
- The allocation factor of the product j obtained from technology k, falj,k, can be calculated on energy
basis, on economic basis or on mass basis. In this work only energy and economic allocation has been
considered. The following equation reports the allocation factor on economic basis:
fal j ,k 
MPj  w j ,k
 MPv  wv,k
(A.32)
v
where MPj is the market price of the product j and wj,k is the mass flow of the product j for technology
k. The energy allocation factor is obtained using the energy content of the product j (expressed in
terms of Lower Heating Value, LHV) instead of market price.
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Part B
The carbon footprint-related impact factor per each LCA node of the upstream biofuels SC are here
collected, according to the work by Giarola et al. (2012).
Table S.1 gathers the total impact factor of the SC operations on global warming for the biomass
production (bp), biomass pretreatment (bpt), biomass transport (bt) and fuel production (fp).
Table S.2 collects the credits for avoided emissions on global warming eckCF due to SC operations.
Finally, CF- and WF-related breakdown per each life cycle phase are here illustrated (Figure S.1).
Table S.1. Global impact factors for the LCA phase of the biofuels SC.
technology corn-to-ethanol pathway
f
CF
i ,bp
f i CF
,bpt
stover-to-ethanol pathway
393.75 kg CO2-eq/t of biomass 34.24 kg CO2-eq/t of biomass
63.34 kg CO2-eq/t of biomass
0
f
CF
i ,bt
5.38 kg CO2-eq/t of biomass
5.38 kg CO2-eq/t of biomass
f
CF
i , fp
1052.2 kg CO2-eq/t of ethanol
257.55 kg CO2-eq/t of ethanol
CF
Table S.2. Parameter ec k [kg CO2-eq/t of ethanol] representing the credits for
avoided emissions.
4
k
eckCF
1
342.22
2
1427.38
3
1383.47
4
357.40
5
628.41
6
648.02
7
658.19
8
286.19
9
305.80
10
315.97
a)
b)
Figure S.1. Total impact breakdown for a) carbon and b) water footprint (bp - biomass production
phase; bpt – biomass pretreatment; bt – biomass transport; fp – fuel production; CRD – credits for
avoided emissions).
List of symbols
Acronyms
CF
Carbon Footprint
LCA
Life Cycle Assessment
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LHV
Lower Heating value
SC
Supply Chain
WF
Water Footprint
Sets
cC
set of production costs regression coefficients
C = {slope,intercept}
iI
set of biomass typology, I = {corn, stover}
jJ
set of product, J = {ethanol, DDGS, power}
kK
set of conversion technologies, K = {1,…,10}
lL
environmental objective functions, L = {CF,WF}
pP
set of plant scale index, P = {1,…,6}
sS
set of life cycle stages, S = {bp, bpt, bt, fp}
tT
set of time intervals (years), T = {1,…,20}
tech(k)  K
subset of conversion technologies producing DDGS to be sold,
tech(k) = 1,3,5,6,7
fratio(k)  K
subset of conversion technologies using both biomass typology for ethanol
production,
fratio(k) = 5,6,7,8,9,10
Scalars
δ
DDGS conversion factor [tDDGS/tethanol]
LA
land surface availability [ha]
M
maximum profit value [€], s.t. M>>PBT

ethanol density [kg/L]
Tr
taxation rate
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
fixed costs over incomes
Parameters
k
stover to corn ratio fed to the plant of technology k [tstover/tcorn]
BAi
biomass i available for ethanol production [t/y]
BAi = LA∙BYi∙quotai
BNk,p
biomass needs for technology k at each linearisation interval p [t/y]
burni,k
fraction of biomass i fed to the CHP station in technology k
BYi
cultivation yields for each biomass i [t/ha]
coefc,k
coefficients (slope [€/tethanol], intercept [€]) for linear regression of production
costs for technology k
CapMaxi,k
maximum capacity in terms of biomass i for conversion technology k [t/y]
CapMini,k
minimum capacity in terms of biomass i for conversion technology k [t/y]
CIk,p
capital investment at each linearisation interval p for the conversion technology
k [M€]
dkt
depreciation charge at time t
emission factors for biomass i and life cycle stage s on climate change [kg CO2-
f i l,s
eq/t] (l = CF) or on water resources [ m3H2O /t] (l = WF)]
credits for avoided emissions of conversion technology k on climate change [kg
ec kl
CO2-eq/t] (l = CF) or on water resources [ m3H2O /t] (l = WF)]
fali
allocation factor parameter for each biomass i
falj,k
allocation factor parameter for each product j and technology k
i
conversion of biomass i to ethanol [tethanol/tbiomass]
MPj
7
market price of product j [€/t] or [€/MWh]
quotai
maximum quota of collectable biomass i for ethanol production
UPCi
unit purchase cost for biomass i [€/t]
UTCi
unit transport cost for biomass i [€/t]
ωk
electricity sold potential of technology k (kWh/Lethanol)
wj,k
mass flow of the product j for technology k (t/h)
Continuous variables
BFk,t
total biomass feedstock for biofuel production to conversion k at time t [t/y]
BPCk,t
biomass purchase cost for conversion technology k at time t [€/y]
Capi,k,t
inlet of biomass i exclusively for ethanol production of conversion facility k at
time t [t/y]
CapTi,k,t
total inlet of biomass i to the conversion facility k at time t (for ethanol
production and CHP station) [t/y]
CFk,t
cash flow for conversion technology k at time t [€/y]
CRD kl ,t
credits from avoided impacts related to conversion technology k at time t on
climate change [kg CO2-eq/y] (l = CF) or on water resources [ m3H2O /y] (l = WF)
Dk,t
depreciation charge for technology k at time t [€/y]
EPCk,t
ethanol production cost for conversion technology k at time t [€/y]
FixCk,t
fixed costs for conversion technology k at time t [€/y]
Inck,t
gross earnings related to conversion technology k at time t [€/y]
k,p
linearisation variables for TCI for technology k at interval p
PBTk,t
profit before taxes for conversion technology k at time t [€/y]
Pfi,k,t
ethanol production rate from biomass i through facility k at time t [t/y]
PjT,k ,t
total production rate for product j through technology k at time t [t/y]
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TAXk,t
tax amount for conversion technology k at time t [€/y]
TCk,t
transport cost for conversion technology k at time t [€/y]
TCIk
total capital investment for conversion technology k [€]
VarCk,t
variable costs for conversion technology k at time t [€/y]
Binary variables
Vk,t
1 if taxation has not to be applied for production facility k at time t, 0 otherwise
Yk
1 if a production facility k is to be established, 0 otherwise
yk,p
supporting variable for linearisation of plant scale
References
Giarola S, Zamboni A, Bezzo F, Environmentally conscious capacity planning and technology
selection for bioethanol supply chains. Renewable Energy 43:61-72 (2012).
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