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Optimal Applications and Combinations of Renewable Fuel Production from Biomass and Electricity Andrea König, Kirsten Ulonska, Alexander Mitsos, and Jörn Viell Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b03790 • Publication Date (Web): 22 Jan 2019 Downloaded from http://pubs.acs.org on January 24, 2019
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Energy & Fuels
Optimal Applications and Combinations of Renewable Fuel Production from Biomass and Electricity Andrea König,† Kirsten Ulonska,†,‡ Alexander Mitsos,† and Jörn Viell∗,† †Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Forckenbeckstr. 51, 52074 Aachen, Germany ‡Current Address: Dept. of Biochemical and Chemical Engineering - Laboratory of Fluid Separations, Technical University Dortmund, Emil-Figge-Str. 70, 44227 Dortmund, Germany E-mail:
[email protected] Abstract
1
2
As renewable electricity sources emerge, the conversion of electricity and CO2 to
3
carbon-based fuels (e-fuels) arises as a complementary or competing option to bio-fuels.
4
This work provides a systematic performance comparison of both bio- and e-fuel path-
5
ways to identify characteristic differences and optimal applications of both production
6
types. We construct a reaction network that features biochemical and thermochemical
7
conversion of lignocellulosic biomass, transesterification of waste vegetable oil, and
8
e-based routes (E-routes) using renewable H2 . The network is optimized for economic
9
and environmental criteria using two pathway screening tools, i.e., Reaction Network
10
Flux Analysis and Process Network Flux Analysis. Furthermore, we apply a linear
11
combination metric to analyze the advantages of bio-e-hybrid designs on a global fleet
12
level.
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The results show that lignocellulosic-based fuels are relatively inexpensive but
14
typically incur energy-intensive separations and high carbon losses. E-routes, on the
15
contrary, result in only small carbon losses and global warming potentials as low as 5
16
gCO2 ,eq. MJfuel .
17
When combinations are considered, biomass can be utilized by upgrading with e-based
18
H2 . In case of bio-e-hybrid ethanol plants, co-fermentation of sugars and utilization of
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CO2 emitted during fermentation are identified as viable low-cost options for carbon
20
loss reduction. These hybrid pathway designs outperform combinations of purely bio-
21
and purely e-based pathways at the fleet level.
However, they come at high cost due to the use of expensive renewable H2 .
22
Introduction
23
Climate change as well as the increasingly limited supply of fossil resources urge society to
24
develop sustainable processes that convert renewable resources into fuels and chemicals. In
25
particular, the energy-related CO2 emissions of transportation make up approximately 20%
26
of worldwide CO2 emissions. 1 Future transportation scenarios assume that (hybrid) internal
27
combustion engines (ICE) will keep a significant share at least until 2050. 2 In order to reduce
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the CO2 emissions of ICEs, sustainable production processes for renewable fuels have to be
29
developed.
30
In the past, research as well as industry have largely focused on biomass as raw material for
31
renewable fuel production. 3,4 However, as renewable electricity production technologies become
32
more established, new opportunities arise. In particular, the conversion of electricity, CO2 ,
33
and water to carbon-based fuels called electro-fuels (e-fuels) has recently been discussed as an
34
option for simultaneously storing renewable electricity and de-fossilizing the transportation
35
sector. 5–7
36
To this end, several e-fuel production routes have been assessed. 5,6,8–13 Furthermore, for
37
individual process concepts, a combination of biomass and electricity feedstocks, e.g., by
38
hydrogen-enhanced gasification, has been analyzed. 14–23 Most of these studies are based on 2
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Energy & Fuels
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flowsheet simulations that cover the process level. However, they do not typically perform
40
subsequent optimization nor do they extend the analysis to a global fleet of plants.
41
At the process level, the high number of possible pathways requires optimization-based
42
methodologies to investigate optimal applications of the different production types and
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discover synergies of pathway combinations. Here, all considered components and pathways
44
are aggregated in a superstructure-like network representation. Optimization is then used
45
to systematically identify which pathways are favorable with respect to a given objective.
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Furthermore, in case of multi-objective optimization, trade-offs between several conflicting
47
objectives, such as economic and sustainability criteria, can be quantified. 24
48
Optimization-based performance evaluation methodologies have already been applied in
49
the context of bio-based fuels and chemicals. 25–37 However, many of these methods require
50
a lot of process information, which raises the problem of insufficient data availability for
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immature processes and long data preparation times for large networks. 38 Rapid screening
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methodologies like the work of Bao et al. 39 or Reaction Network Flux Analysis (RNFA)
53
presented by Voll and Marquardt, 40 are capable of circumventing this problem by evaluating
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pathway performance based on stoichiometry and yield data only. This comes at the cost of
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several assumptions, e.g., ideal and instantaneous separation. 40
56
When a more detailed analysis is envisaged in early process development phases, separation
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steps have to be considered. 41 To this end, Kong and Shah 42 propose a framework which
58
includes simple distillation models. Similarly, Process Network Flux Analysis (PNFA) 38
59
extends RNFA by including the energy demands of separation by means of reduced-order
60
models that consider non-ideal thermodynamics. 43,44
61
While optimization-based performance evaluation is widely used for bio-based routes,
62
the same is not the case for E-routes and other feedstocks, yet. An existing superstructure
63
approach 45,46 aggregates conversion pathways from biomass, coal, and natural gas. H2
64
production from electrolysis using renewable electricity is also included but is merely utilized
65
to meet emission constraints. 45 A comparison of different feedstocks is considered by Schack et 3
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al. 47 They develop a linear optimization approach to determine optimal conversion pathways
67
from different feedstocks including fossils and renewables. However, they consider only one
68
product, methanol, thus not addressing more general, characteristic performance differences
69
and bottlenecks of e-fuel and bio-fuel production routes.
70
To compare hybrid plants with a combination of non-hybrid plants on a global fleet level,
71
Mancini and Mitsos 48 as well as Sheu et al. 49,50 propose a linear combination metric. Here,
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the total production volume can be either covered by several identical hybrid plants or by
73
a combination of non-hybrid plants (the so-called "linear combination"). So far, the linear
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combination metric has been applied in the context of power generation only, 48–50 but not
75
for bio-, e- and bio-e-hybrid plants.
76
Herein, we present a performance screening of various bio-, e-, and bio-e-based pathways
77
on a process level and a fleet-wide level. To determine characteristic bottlenecks and optimal
78
applications of each production type, we apply RNFA and PNFA in a complementary manner.
79
Multi-objective optimization methods evaluate what production concepts are best applied
80
with respect to economic as well as environmental objectives. In order to determine whether
81
possible performance advantages of a hybrid plant also hold on a fleet-wide level, we analyze
82
the optimization results using the aforementioned linear combination metric.
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The remaining article is structured as follows. First, the basic concepts of RNFA and
84
PNFA are briefly explained followed by a description of methodological modifications. Then,
85
the considered reaction network is presented. After that, the screening results are discussed.
86
Finally, the most important findings are summarized and a brief outlook is given.
87
Methods: RNFA and PNFA
88
Reaction Network Flux Analysis (RNFA) 40 as well as Process Network Flux Analysis
89
(PNFA) 38 are optimization-based screening tools capable of early-stage process performance
90
evaluations. The main foundation of both RNFA and PNFA is a reaction network consisting
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of components, i, and conversion steps, j. Using stoichiometry and yield input data, station-
92
ary mole balances are formulated for each component i in the network. This mass-based
93
evaluation is complemented by a simple cost estimate in RNFA. 40
94
In PNFA, mixing and separation steps are additionally considered. For this, further data
95
of reaction conditions, e.g., the required amount of solvent and inerts, is needed. Moreover,
96
for every separation task, feasible separation technologies are identified and their energy
97
demands as well as that of each reaction step are calculated. These are then used to estimate
98
sustainability criteria like the global warming potential (GWP). Furthermore, utility costs
99
are determined for a more detailed cost estimate. 38,51
100
In the present work, we alter RNFA and PNFA, such that their field of application can
101
now be extended to e-fuel production pathways. Methodological changes concern recycle
102
streams and reactant-product separation, selectivity-limited yield constraints, investment
103
cost function, and upstream-chain emissions of feedstocks. These modifications are further
104
explained in the following.
105
Recycle Streams and Reactant-Product Separation
106
Some reactions that potentially use e-based feedstocks are subject to low per-pass conversions,
107
e.g., CO2 -based methanol synthesis with approximately 40% per-pass conversion at 100 bar. 8
108
To avoid that low per-pass conversions lead to high material losses especially of H2 , recycling
109
of unreacted components is necessary. Hence, in this study, both RNFA and PNFA assume a
110
full recycle of unconverted reactants which means that the overall conversion is set to 100%
111
in every reaction step. This means that yield, Yj , is equal to selectivity, Sj , in this study.
112
The implementation of the recycle streams is straight-forward in case of RNFA but more
113
complicated in PNFA. In RNFA, recycles do not have to be modeled as additional conversion
114
steps since RNFA assumes ideal separation and hence the product flux vector b, that contains
115
all product components, is already a net balance of all substances i. In PNFA, however, the
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assumption of recycles also brings up the question of subsequent separation of products and 5
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117
unconverted reactants. In previous works, 38,51 reactant-product separation is neglected. In
118
this study, however, we model the separation of reactants and products for per-pass conversion
119
below a threshold value, Xlimit . Although this procedure requires a somewhat arbitrary value
120
for Xlimit , it avoids unnecessarily large networks where recycle streams are explicitly modeled
121
even for reactions with almost full conversion. Here, we pick a value of Xlimit = 0.75 as none
122
of the considered reactions of this case study have a yield closer than ±5% to this threshold.
123
The single-pass conversions of HR8 (XHR8 = 70%) and HR15 (XHR15 = 80%) are closest
124
to Xlimit , however, the separation of reactants and products are in both cases performed
125
using a flash unit which is assumed to have no energy demand in PNFA. Hence, even though
126
the threshold value Xlimit is subject to uncertainty, pathway evaluation is not significantly
127
affected by it even for the pathways which have a conversion close to the threshold, i.e., HR8
128
and HR15.
129
Selectivity-Limited Yield Constraints
130
The selectivity of a reaction, Sj , states to what extent the consumed (limiting) reactant
131
is turned to a specific product. The remainder is converted to undesired side products.
132
Unlike unconverted reactants, which are, in this study, recycled, side products cannot be
133
simply recycled and reacted to the final product. Thus, they constitute the true losses of the
134
production pathway.
135
To account for losses due to side product formation, instead of assuming the previously
136
used conversion limitation, we now implement selectivity-limited yield constraints in both
137
RNFA and PNFA. A discussion of the differences of conversion-limited and selectivity-limited
138
yield constraints is found in Dahmen and Marquardt. 52 In the present contribution, we
139
implement the selectivity limitation by means of a molar side product flux vector, w, that
140
contains all side products wi , which cannot be used for further conversion. However, since
141
quantitative side product data is generally not given in literature, we assume that side
142
products, wi , have the same physico-chemical properties as the products, bi , contained in the 6
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143
Energy & Fuels
product flux vector, b.
144
Yield data, Yj , and side product fluxes, wi , are directly integrated into the mole balance.
145
The mole balance formulation is visualized based on a generic network representation in Fig.
146
1 that contains a side product flux, wi , and a product flux, bi . The product flux, bi , can
147
be quantified by a balance of outgoing reaction fluxes, fout,j and incoming reaction fluxes,
148
fin,j . Here, the yield of each reaction acts as a split fraction which determines how much of
149
the reactant is converted to product and how much is reacted to side products. While the
150
product flux bi is reduced by the reactions out, j, the corresponding side product, wi , cannot
151
be further utilized. Figure 1: Generic network representation with a product flux bi , its side product flux wi , and reaction fluxes fj showing how yield Yj and side product fluxes wi are incorporated in the molar flux balances. Eq. (2)
𝑤𝑖 1 − 𝑌in,𝑗
… 𝑓in,𝑗
152
153
𝑏𝑖
𝑌in,𝑗
Eq. (1)
𝑌out,𝑗
…
𝑓out,𝑗
When transferring the simple example to a more generalized expression, the balances for each product bi can be written as
nout X
(υout,i,j · fout,j ) +
j=1
154
1 − 𝑌out,𝑗
nin X
(Yin,j · υin,i,j · fin,j ) = bi
∀i.
(1)
j=1
Here, nout and nin describe the number of reactions that consume or produce bi , respectively.
155
Similarly, the stoichiometric coefficients υin,i,j and υout,i,j correspond to reactions where bi is
156
produced or consumed, respectively.
157
158
The mole balance of the corresponding side product, wi , is formulated in a similar manner but omits the term of outgoing fluxes, 7
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nin X
((1 − Yin,j ) · υin,i,j · fin,j ) = wi
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∀i.
(2)
j=1
159
A detailed description how the flux balances are applied in network modeling examples of
160
RNFA and PNFA is given in the first section of the Supporting Information.
161
Investment Cost Function
162
We changed the investment cost function of RNFA and PNFA to account for the generally
163
higher investment costs of gas-phase processing steps in comparison to liquid-phase processing.
164
To this end, we use an adapted version of an empirical step-counting function. 53 The investment
165
costs (IC) are calculated as
nproduct X CEPCI2016 IC = · (Inv1g · ( bproduct,i Mproduct,i )Inv2g · N Ug CEPCI2010 i=1 nproduct
+ Inv1l · (
X
bproduct,i Mproduct,i )Inv2l · N Ul ).
(3)
i=1
166
167
Here, N Ug and N Ul denote the number of active gas-phase and liquid-phase conversion Pnproduct bproduct,i Mproduct,i represents the annual mass-based steps, respectively. The term i=1
168
production volume. The empirical parameters for liquid- and gas-phase conversion Inv1l ,
169
Inv2l , Inv1g , and Inv2g are provided by El-Halwagi 53 and are updated from the reference
170
year 2010 to the year 2016 by means of the Chemical Engineering Plant Cost Index (CEPCI).
171
For further information on the modeling of the integer variables N Ul (N Ug ), the reader is
172
referred to Ulonska et al. 38
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Energy & Fuels
173
Upstream-Chain Emissions of Feedstocks
174
We consider fuel production from different feedstocks, i.e., e-based H2 , CO2 , and biomass.
175
Some of these feedstocks are themselves products of processes, e.g., electrolysis of water, which
176
are not explicitly modeled herein. To ensure unified system boundaries, the GWP estimated
177
in PNFA now also includes upstream emissions from different feedstocks, GWPspec,raw in
178
addition to previously considered emissions from process utilities, GWPspec,process .
179
The process-related GWP, GWPspec,process , is calculated based on utilities only without
180
taking into account emissions related to process equipment. 38 Thus, to ensure a consistent
181
calculation, the GWP factor for feedstocks, gwpraw , is calculated based on utility expenses of
182
upstream processing steps taken from literature 54–57 while disregarding emissions associated
183
to apparatus production or supply aspects. GWPspec,raw is then determined as
Pnraw j=1
GWPspec,raw = Pnproduct i=1
184
185
186
187
fraw,j Mraw,j gwpraw,j bproduct,i ∆Hcomb,product,i
(4)
with Mraw,j and fraw,j referring to the molar masses and supply fluxes of raw material Pnproduct raw, respectively, and i=1 bproduct,i ∆Hcomb,product,i denoting the total amount of energy produced. No credit is given for removing CO2 from the atmosphere as we assume that the end use
188
in this study is always fuel combustion which releases the carbon back into the atmosphere.
189
Thus, we take a cradle-to-grave approach.
190
Complete Problem Formulations
191
The complete RNFA optimization problem is formulated as follows,
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min
f ,b,w ,y
CostRNFA =
Pnproduct i=1
CL =
TACRNFA bproduct,i ·∆Hcomb,product,i
Pni =product #Ci bi Pi=1,i6 nraw i=1 #Craw,j fraw,j
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s.t. Mass balances for products and side products incl. yields (Eqs. 1, 2), Total costs: TACRNFA = Craw + Cwaste + Raw material costs: Craw =
nraw X
ir · IC 1 − (1 + ir)−t
fraw,j Mraw,j Praw,j ,
j=1
Waste costs: Cwaste =
nX waste
((wwaste,i + bwaste,i )Mwaste,i Pwaste ),
(5)
i=1
Investment costs (Eq. 3), Number of conversion steps extended by gas-phase steps (cf. Ulonska et al. 38 ), Composition of lignocellulosic biomass (cf. Voll 58 ), nproduct
Fixed production:
X
bproduct,i · ∆Hcomb,product,i = α,
i=1
f , b, w ≥ 0, y ∈ {0, 1}. 192
For each considered target fuel, RNFA is optimized based on an environmental criterion,
193
i.e., carbon loss (CL), and an economic criterion, i.e., specific production cost (CostRNFA in
194
USD ). MJ
195
Specific production costs are calculated based on the total annual costs, TACRNFA , which
196
include raw material costs, Craw , waste costs, Cwaste , and investment costs, IC, annualized by
197
means of the interest rate, ir, and the project run-time, t. As IC is determined from the
198
empirical step-counting function (cf. Eq. 3), a set of binary variables y has to be introduced
199
to indicate which reaction steps j are active. We do not consider credits for co-products in
200
the cost function as the focus is kept strictly on a comparison of fuels.
The results are benchmarked against the performance of an established bio-fuel, ethanol.
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Energy & Fuels
201
The second objective, CL, is especially important when a full replacement of all fossil
202
fuels is envisaged as, in that case, carbon could become a limiting factor in the production
203
capacity of renewable fuels. CL is defined as the molar amount of carbon lost to components
204
other than the target product, divided by the amount of carbon in the raw materials. Here,
205
#Ci and #Craw,j denote the number of carbon atoms in each component i and raw material
206
of supply flux j, respectively.
207
Similar to previous bio-fuel screenings conducted with RNFA 59,60 and PNFA, 38,51 we
208
consider a fixed fuel production of α = 2.77·1012 kJ , which corresponds to the energy equivalent yr
209
of 100,000 tons of ethanol per year. Furthermore, the cellulose, hemicellulose, and lignin
210
fraction of the lignocellulosic biomass can be varied within given bounds specified in Tab.
211
S11 of the Supporting Information.
212
The complete PNFA problem is formulated as
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CostPNFA =
min
f ,b,w ,y
Pnproduct i=1
TACPNFA bproduct,i ∆Hcomb,product,i
GWP = GWPspec,raw + GWPspec,process
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s.t. Mass balances for products and side products incl. yields (Eqs. 1-2), Energy demand calculation for separation and reaction steps (cf. Ulonska et al. 38 ), Total costs: TACPNFA = Craw + Cwaste + Cutility + Raw material cost: Craw =
nraw X
ir · IC, 1 − (1 + ir)−t
fraw,j Mraw,j Praw,j ,
j=1
Waste costs: Cwaste =
nw X
((wwaste,i + bwaste,i )Mwaste,i Pwaste ),
i=1 nutility
Utility costs: Cutility =
X
(Eutility,k Putility,k ),
k=1
Investment costs (Eq. 3), Number of conversion steps extended by gas-phase steps (cf. Ulonska et al. 38 ), Process-related GWP: GWPspec,process = GWPspec,heat + GWPspec,elec + GWPspec,refrig , Feedstock-related GWP (Eq. 4), Composition of lignocellulosic biomass (cf. Voll 58 ), nproduct
Fixed production:
X
bproduct,i · ∆Hcomb,product,i = α,
i=1
f , b, w ≥ 0, y ∈ {0, 1}. (6) 213
Here, CostPNFA denotes the economic objective while global warming potential, GWP, in
214
gCO2 ,eq , MJfuel
215
CostPNFA , includes utility costs, Cutility , determined from the energy demand of each utility,
is considered as an environmental objective. In PNFA, the specific production cost,
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216
217
Energy & Fuels
k, and the corresponding price Putility,k . The present PNFA model only considers heat integration in the context of vapor re-
218
compression distillation units. Unlike previous works, 38,51 we do not perform pinch analysis
219
nor do we allow waste products to be used for internal heat supply. This way, more transparent
220
process insights are obtained which allow for easier detection of bottlenecks as high energy
221
requirements are not offset by internal combustion pathways.
222
Both RNFA and PNFA are mixed-integer nonlinear programming (MINLP) problems
223
which are solved with the deterministic global solver BARON V18.5.8 61 in GAMS V25.1.1. 62
224
Reaction Network
225
To determine characteristic bottlenecks and optimal applications of bio- and e-fuels, a
226
comprehensive reaction network needs to be constructed first. In principle, this can be
227
done automatically using network generators, e.g., RING 63,64 or ReNeGen. 65 However,
228
typically, these do not give specific yield and other reaction data needed for RNFA and
229
PNFA evaluation. Instead, the Reaxys database 66 offers thousands of peer-reviewed reactions
230
including quantitative data on yield and reaction conditions. Thus, herein, the suitable
231
pathways are extracted from Reaxys. Fig. 2 gives an overview of the considered main
232
feedstocks, i.e., lignocellulosic biomass, waste vegetable oil, H2 , and CO2 (upper part) as well
233
as the specific conversion pathways and fuel products (lower part).
234
This study incorporates several upstream processing steps of the feedstocks, i.e., vegetable
235
oil purification, electrolysis, and carbon capture. We assume that CO2 is captured from
236
the exhaust gases of steel plants and that H2 is gained through electrolysis of water using
237
electricity from wind power. E-fuel synthesis is considered to be operating at steady state
238
with constant supply of renewable H2 . Similar to other e-fuel studies, 6,9,12 this is achieved by
239
placing a cavern storage between the electrolyzer and the synthesis plant to store excess H2
240
and thus compensate for fluctuations of renewable electricity.
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241
We include the feedstock upstream chains by assigning raw material prices and, in case of
242
PNFA, a GWP value to the four main feedstocks. With respect to GWP, we assume that
243
all continuous processes operate with grid electricity, whereas the dynamic H2 production
244
utilizes renewable electricity only. We do not consider supply chain aspects like storage and
245
transport. Thus, the cavern storage is not attributed to any costs or emissions. All feedstock
246
prices, GWP-related parameters, as well as several other economic parameters are found in
247
the Supporting Information in Tab. S8, S9, and S10, respectively. In addition, all yield data
248
and reaction conditions are given in the Supporting Information in Tab. S1 and S2 whereas
249
the respective energy demand of the separation steps needed as input for PNFA is found in
250
Tab. S3-S7 in the Supporting Information.
251
To analyze the characteristic performances of the different feedstock and conversion
252
options, we divide the reaction network (lower part of Fig. 2) roughly into four production
253
types, i.e., biochemical conversion of lignocellulosic biomass (BC), thermochemical conversion
254
of lignocellulosic biomass (TC), e-based H2 conversion (E), and oil-based conversion (V).
255
The BC-routes leading to the products ethanol, iso-butanol, 2-butanone, ethyl levulinate,
256
and γ-valerolactone are taken from a former case study by Ulonska et al. 38 These conversion
257
routes are characterized by a pretreatment of lignocellulosic biomass (BR1), subsequent
258
enzymatic hydrolysis (BR4, BR5) 38,67 as well as several possible fermentation steps (e.g.,
259
BR6, BR7, BR35, BR38) 68–70 or catalytic conversions (e.g., BR8, BR27, BR41). 71–73 Lignin
260
is not utilized in BC-routes since a prior RNFA screening found such conversion concepts not
261
to be promising. 59 Thus, in this study, lignin can only be further converted in TC-routes.
262
TC-routes, i.e., steam gasification reactions (BR43-BR46), 74 convert lignocellulosic
263
biomass into syngas consisting of CO2 , CO, and H2 . Fuels, such as methane, methanol,
264
ethanol, FT-fuels, as well as ethers can be produced from syngas.
265
In E-routes, i.e., pathways utilizing renewable, e-based H2 , syngas is produced via reverse
266
water-gas shift reaction (HR2). 75 Thus, the products of TC-routes can alternatively be
267
produced from E-routes. In addition, direct conversion of CO2 and H2 (e.g., HR4 for 14
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Energy & Fuels
Figure 2: Overview of main feedstocks, their production sources, and the focus of the study. The electrolyzer is assumed to produce H2 dynamically using electricity from wind power. RNFA and PNFA assume stationary processes with a fixed production volume of 100,000 tons of ethanol energy equivalent per year. The process-related GWP in PNFA is calculated assuming the use of grid electricity. The reaction network is shown in the lower part of the figure: gray boxes indicate raw materials, bold type, gray components depict target fuels, gray arrows indicate RNFA analysis only. For simplicity, the network only shows the main reactants and products of each reaction. This means, auxiliaries are not depicted and CO2 and CO requirements are only shown when considered main reactant or main product. Purified waste vegetable oil
Purification Waste vegetable oil
Focus of this study: optimal fuel synthesis with RNFA and PNFA
Residual lignocellulosic biomass Residual lignocell. biomass load
eElectrolyzer
Wind
time
Steel plant
water
Exhaust gases
H2
H2 Cavern storage (fluctuating) (steady state)
Carbon capture
Feedstock supply
CO2
15
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time
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268
methanol synthesis 76 and HR16 for dimethyl ether synthesis 77 ) is incorporated as reactions
269
especially suitable for E-based designs.
270
A second major raw material source for bio-fuel production is vegetable oil. Thus,
271
we consider it as a second type of biomass in the network. These V-routes feature a
272
transesterification reaction, BHR2b, 78 to produce fatty acid methyl esters (FAME). As this
273
conversion requires methanol which can either be produced via E-routes or TC-routes, V-
274
routes are always combined with a second route. FAME can also be converted to upgraded
275
FAME (uFAME) using bio- or e-based ethylene as a co-reactant (BHR3). 79
276
Further combinations of different routes can occur with respect to furan production, i.e.,
277
2,5-dimethylfuran and 2-methylfuran. Here, hydrogenation steps (BR57, BR58, BR16, BR52,
278
BR53, BR70, BR72) 80–86 are common pathways encountered after converting lignocellulosic
279
biomass to sugars. In these cases, renewable H2 , which can be either E- or TC-based, is
280
combined with BC-routes.
281
A summary of all considered fuel products is given in Tab. 1. In case the product is a
282
mixture, we model it as only one or a few representative molecules that have a molecular
283
structure equal or similar to the average chain length of the product mixture. These
284
representative molecules, also listed in Tab. 1, are then used for calculation of stoichiometry,
285
physico-chemical properties, and energy requirements.
16
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Energy & Fuels
Table 1: Considered fuel products sorted by the possibly involved production types: BC: biochemical conversion of lignocellulosic biomass, TC: thermochemical conversion of lignocellulosic biomass, E: e-based H2 conversion, V: oil-based conversion. Additional information is given, i.e., the molecular formula, CAS number or PubChem ID, suitable engine type and chemical group. CI: Compression-ignition engine, SI: spark-ignition engine, FT: FischerTropsch. †: only considered in RNFA. ‡: representative molecule. *: LPG, i.e., liquid petroleum gas, and gasoline are summarized as one fuel fraction for spark-ignition engines. Production Types BC, E, TC
Fuel product
Molecular formula C5 H12 O2 C 2 H 6 O1 C7 H12 O3 C8 H18 O5 ‡
5729-59-9 ‡
CI
ether
C 6 H 8 O1 C 5 H 6 O1 C1 H4 O1 C1 H4 C3 H8 ‡ C15 H32 ‡ C3 H8 O2 C2 H6 O1
625-86-5 534-22-5 67-56-1 74-82-8 74-98-6‡ 629-62-9‡ 109-87-5 115-10-6
SI SI SI SI SI CI CI CI
furan furan alcohol alkane alkane alkane ether ether
C6 H14 O5 ‡
13352-75-5 ‡
CI
ether
V, E, TC
diethoxymethane (DEM) ethanol ethyl levulinate oxymethylene diethyl ether 2-4 (OMDEE)† 2,5-dimethylfuran (DMF) 2-methylfuran (2-MF) methanol methane LPG/gasoline (FT-gasoline)*,† diesel (FT-diesel)† dimethoxymethane (DMM)† dimethyl ether (DME) oxymethylene dimethyl ether 3-5 (OMDME)† fatty acid methyl esters (FAME)
CAS / PubChem ID 462-95-3 64-17-5 539-88-8
ester
upgraded FAME (uFAME)
CI
ester
BC
γ-valerolactone iso-butanol 2-butanone
112-62-9‡ 91713328‡, 112-38-9‡, 124-11-8‡ 108-29-2 78-83-1 78-93-3
CI
V, BC, E, TC
C19 H36 O2 ‡ C15 H26 O4 ‡, C12 H22 O2 ‡, C9 H18 ‡ C19 H36 O2 C4 H10 O1 C5 H8 O2
SI SI SI
ester alcohol ketone
E, TC
CI or SI CI SI SI
Chem. group ether alcohol ester
286
By aggregating this broad but still not exhaustive range of products and corresponding
287
pathways, a complex network is formed featuring in total 70 reactions and 18 fuel products
288
all of which are based on either non-edible biomass, i.e., lignocellulosics, waste biomass,
289
i.e., waste vegetable oil, or renewable electricity, i.e., e-based H2 . Well-known bio-fuels, e.g.,
290
bio-ethanol and FAME, and e-fuels, e.g., methane, are included as well as novel oxygenates,
291
e.g., OMDMEs. In order to identify optimal pathway designs, we apply RNFA and PNFA as
292
discussed in the next sections.
293
Results: Cost and Carbon Loss Optimization Using RNFA
294
To gain general insights on characteristic bottlenecks and optimal applications of the different
295
feedstock and conversion options, we conduct a broad but simple performance screening
17
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296
with RNFA. Here, cost is the economic criterion while carbon loss (CL) represents the
297
environmental objective. For each considered fuel product (cf. Tab. 1), the reaction network
298
is optimized by solving the RNFA problem. Fig. 3 visualizes the results in a graph where the
299
two objectives, cost and CL, are presented on each of the axes. To give a better overview, the
300
results are sorted by their production types with an established renewable fuel, ethanol, being
301
the benchmark shown in every graph. For each fuel, a separate Pareto front (dotted curves)
302
is formed which comprises all efficient, non-dominated production pathway designs. These
303
optimal pathway designs are visualized as points on the Pareto fronts. When comparing the
304
Pareto fronts of the different fuels, performance differences can be analyzed.
305
Fig. 3 shows that all Pareto fronts lie in a relatively close range to each other which is
306
primarily due to the similar yields as overall conversion is set to 100%. Furthermore, most of
307
the Pareto fronts cover a broad range of cost and CL, indicating a large trade-off between the
308
two objectives. Specific production costs lie between 0.9 and 8.3
309
from 0 to 70%. Only few fuels, like those produced from vegetable oil (cf. Fig. 3(f)), obtain
310
favorable optimization results which are comparable or better than the benchmark, ethanol.
USD-ct , MJfuel
whereas CL varies
311
In the following, we analyze the different production types (BC, TC, E, V), as well as
312
their combinations. At the point of minimal cost (minCost), bio-routes are always preferred
313
(cf. Tab. S12 and S13 of the Supporting Information). This is because lignocellulosic
314
biomass (0.05
315
the much higher energy content of H2 , i.e., 120
316
minCL, however, E-routes are favored for all fuels (cf. Tab. S12 and S13 of the Supporting
317
Information) since the supply of H2 and CO2 can be set to the exact required ratio without
318
any losses. In contrast, lignocellulosic biomass has a predefined composition range with
319
complex structures that do not allow for such a flexible utilization. While TC-routes suffer
320
from selectivity losses due to tar formation, 74 BC-routes are not able to efficiently convert
321
the lignin fraction. 71 Hence, it is concluded that lignocellulosic biomass is best used when
322
cost is the main target while e-based feedstocks are preferable when high carbon utilization
USD ) kg
is much cheaper than e-based H2 (5.8
18
MJ , kg
USD ), kg
even when taking into account
compared to biomass, i.e., 17
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At
Page 19 of 42
323
is envisaged. Figure 3: RNFA results with Pareto fronts for each considered fuel sorted by the active production types. Every point represents a Pareto-optimal pathway design with ethanol as a benchmark in every graph ( ). The data points are connected by curves to guide the eye. At the point of minimal cost (minCost), bio-based routes are always preferred. At the point of minimal carbon loss (minCL), E-routes are always optimal. (a) BC/E/TC-routes - (intermediate) ethanol
(b) BC/E/TC-routes - furans 0.75
ethanol 0.50
DEM ethyl levulinate
0.25
OMDEE
Carbon loss [-]
Carbon loss [-]
0.75
0.50
DMF 0.25
0.00 0.02 0.04 0.06 0.08 0.10
0.00 0.02 0.04 0.06 0.08 0.10
CostRNFA [USD/MJ]
CostRNFA [USD/MJ]
(c) E/TC-routes - alcohols and alkanes
(d) E/TC-routes - ethers 0.75
methanol 0.50
Carbon loss [-]
Carbon loss [-]
0.75
methane FT-gasoline
0.25
FT-diesel 0.00
DME 0.50
DMM 0.25
OMDME 0.00
0.00 0.02 0.04 0.06 0.08 0.10
0.00 0.02 0.04 0.06 0.08 0.10
CostRNFA [USD/MJ]
CostRNFA [USD/MJ]
(e) BC-routes
(f) V-routes with BC (uFAME only), E, TC
0.75
0.50
2-butanone 0.25
γ-valerolactone
Carbon loss [-]
0.75
iso-butanol
0.50
FAME uFAME
0.25
0.00
0.00
0.00 0.02 0.04 0.06 0.08 0.10
0.00 0.02 0.04 0.06 0.08 0.10
CostRNFA [USD/MJ]
324
2-MF
0.00
0.00
Carbon loss [-]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
CostRNFA [USD/MJ]
V-based fuels, i.e., FAME and uFAME, show the same trends. Here, the intermediates are
325
produced from lignocellulosic biomass at minCost but utilize e-based feedstocks at minCL.
326
Due to the fact that the main feedstock, purified waste vegetable oil (0.93
327
MJ ), kg
328
does not reach the low cost of routes based purely on lignocellulosic biomass. However, high
329
yields and only small amounts of carbon lost to the by-product glycerol lead to a low overall
330
CL. Thus, V-based production is associated to costs that are between those of uncombined
USD kg
containing 38
is more expensive than lignocellulosic biomass, the overall cost of these pathway designs
19
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331
TC-routes and E-routes while the CL can reach values in the range of uncombined E-routes.
332
After analyzing the optimal feedstock choices at minCost and minCL, we discuss the
333
pathway designs of the middle section of the Pareto fronts. Fig. 4 shows the Pareto front of
334
ethanol as an example. At minCost, ethanol is only produced from lignocellulosic biomass
335
whereas at minCL, only E-routes are active. In the middle section of the Pareto front, a
336
highly integrated, bio-e-hybrid pathway design is found. Here, not only the cellulose fraction
337
is utilized but also the hemicellulose fraction is converted biochemically thereby following
338
a co-fermentation approach already known in literature. 87 Furthermore, lignin is gasified.
339
The resulting syngas is converted to product along with additional e-based H2 and the CO2
340
emitted during sugar fermentation (cf. Fig. 4). The example of ethanol can be generalized.
341
When both objectives, i.e., CL and cost, are of interest, biomass and electricity feedstocks
342
are combined for all considered fuels (cf. Tab. S12 and S13 of the Supporting Information). Figure 4: Pareto front of ethanol determined with RNFA (dotted curve) and comparison to linear combination of plants (dashed curve). On the right, optimal pathway designs are shown for the point of minimal cost (minCost), the point of minimal carbon loss (minCL) as well as one exemplary hybrid design in the middle section of the Pareto front.
Pathway design: Minimal cost (minCost) hemicellulose
Lignocellulosic biomass
0.75
+
Carbon loss [-]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 20 of 42
0.50
cellulose
C6 sugars
CO2 ethanol
lignin
Pathway design: Pareto design (hybrid) C5 sugars hemicellulose H2 LignoCO2 cellulose cellulosic lignin biomass C6 sugars syngas
+ 0.25
+
ethanol
+
0.00 0.00
0.02
0.04
0.06
0.08
CostRNFA [USD/MJ]
0.10
Pathway design: Minimal carbon loss (minCL) H2 ethanol CO2
343
After discussing the results on a process level, we now take a fleet-wide perspective. At a
344
fleet level, the overall production volume can be either covered by several identical hybrid 20
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Energy & Fuels
345
plants or by a combination of plants that either represent the minCL (e-fuel) or the minCost
346
(bio-fuel) design. In case of RNFA this means that a total fleet production of nall plants · α
347
can either be provided by nall plants hybrid plants, each with a production volume of α, or
348
it can be covered by a combination of nminCost plants and nminCL plants, all with identical
349
production volumes, α. Economies of scale do not apply in this case, as each plant has the
350
same production output, α.
351
The performance of a fleet of hybrid plants is calculated differently than that of a linear
352
combination. The overall performance of a fleet of hybrid plants is the same as the performance
353
of a single hybrid plant, since CL as well as costs are identical for each plant. Thus, the
354
performance of the hybrid fleet is represented by the Pareto fronts of the screening. The
355
overall performance of the linear combination is determined from the performances of minCL
356
and minCost and their plant fraction λminCost =
357
though the fuel output of each plant type (minCost or minCL) varies linearly with λ, the
358
corresponding performance criteria do not necessarily show a linear relationship. While the
359
specific production costs of the fleet are determined by linear interpolation using the plant
360
fractions λ, the overall carbon loss follows an inverse relationship. The resulting performance
361
curve of the linear combination manifests as a nonlinear curve in the Pareto graph (cf. dashed
362
curve in Fig. 4). A detailed description of the underlying calculations is given in Section
363
"Performance Curve of the Linear Combination" of the Supporting Information.
nminCost nall plants
=1−
nminCL nall plants
= 1 − λminCL . Even
364
The comparison of the two performance curves in Fig. 4 shows that, at a given CL level,
365
the hybrid design achieves lower production costs than the linear combination. Similarly, at
366
a given cost level, the CL of the hybrid design is lower than that of the linear combination.
367
Thus, the results suggest that when both CL and cost are of interest, hybrid bio-e-designs for
368
ethanol production are preferable to a linear combination of minCost and minCL.
369
The hybrid design performs better than the linear combination due to the utilization of
370
hemicellulose (BR4, BR7), lignin (BR46) and CO2 emitted during fermentation. The benefit,
371
each of the three integration options can provide individually, is determined by selectively 21
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372
excluding fluxes and re-running the RNFA problem. In the region of high CL and low
373
costs, hemicellulose hydrolysis and fermentation (BR4 and BR7) as well as lignin gasification
374
(BR46) each lead to benefits over the linear combination. More specifically, the benefits of
375
hemicellulose utilization are higher than those of lignin gasification since hemicellulose can be
376
converted more efficiently and represents a larger fraction of the biomass. However, the lower
377
the CL, the more does the benefit of both hemicellulose and lignin utilization diminish, since
378
these very low CL levels can only be reached by utilizing e-based feedstocks. Utilization of
379
CO2 emitted during glucose fermentation is the only integration option, that can outperform
380
the linear combination even at carbon loss levels of 15%. As no additional investment costs
381
are necessary, the integration of bio-based CO2 in the e-fuel pathways is an efficient option for
382
carbon loss reduction which also leads to a small cost benefit as less external CO2 is needed.
383
These RNFA-based findings highlight that bio-fuels and e-fuels have two distinct areas of
384
application and that their combination can create synergies. Bio-fuels are generally lower
385
in cost, which is in agreement with previous findings by Brynolf et al. 5 and Tremel 8 who
386
estimate e-fuel cost to be as high or higher than bio-fuel costs. However, TC-routes suffer
387
from low selectivities and BC-routes cannot effectively convert the lignin fraction, thus,
388
bio-based routes are inflicted with high carbon losses. An exception are V-routes which
389
have high yields and only lose small amounts of carbon to the by-product glycerol. E-fuels,
390
however, offer the highest potential to lower carbon losses. This has also been shown for single
391
bio-e-hybrid concepts, e.g., in a study of Agrawal et al., 17 but not in a larger screening. The
392
results are also confirmed when optimizing a smaller network for cost and carbon loss using
393
PNFA as discussed in Section "Cost and CL Optimization Using PNFA" of the Supporting
394
Information.
22
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Energy & Fuels
395
Results: Cost and GWP Optimization Using PNFA
396
Both biomass and electricity feedstocks represent energy inputs to the system. For a more
397
comprehensive analysis in terms of GWP, additional energy inputs, i.e., process utilities, have
398
to be included as well. Thus, we apply PNFA. Since the consideration of energy requirements
399
necessitates more process information, in PNFA, we analyze a smaller selection of fuels. The
400
considered fuels are chosen such that at least one fuel of each chemical group and at least
401
two fuels of each production type are analyzed.
402
Fig. 5 shows the resulting Pareto fronts of each considered fuel sorted by its respective
403
active production type. As in a previous PNFA study, 51 the benchmark, ethanol, which is
404
USD present in every graph, performs very well with costs around 0.01 MJ and a GWP of approx. fuel
405
CO2eq. 15 M . However, even lower GWP values can be reached by FAME, uFAME, and e-based Jf uel
406
methane. In case of methane, a GWP of 5
407
magnitude lower in comparison to fossil fuel emissions (90
408
of GHG emissions of e-fuels predicted by Tremel. 8 However, Hombach et al. showed that
409
emissions associated with e-fuel production strongly increase when hydrogen is not produced
410
from renewable electricity sources. 13
gCO2 ,eq MJfuel
can be obtained which is one order of gCO2 ,eq 88 ) MJfuel
and lies within the range
411
Two distinct optimal areas of application exist for TC- and E-routes with respect to
412
methane, methanol, and DME production (cf. Fig. 5(b)). Here, TC-routes are favored
413
in terms of cost whereas E-routes are optimal when low GWP values are envisaged. The
414
reason for this is that TC-routes are associated to low feedstock costs, however, the heating
415
requirements of the gasifier and subsequent gas cleaning steps incur high emissions.
416
If V- or BC-route are combined with H2 utilization (cf. Fig. 5(a) and (d)), e-based
417
H2 is preferred over TC-based H2 even at minCost. Here, the additional investment costs
418
of implementing a gasification step with subsequent gas cleaning is higher for the given
419
production capacity than the savings of using a cheaper raw material. To determine whether
420
e-based designs are actually preferable to TC-based designs even in a vertically integrated
421
process scheme, i.e., a process scheme that fully accounts for feedstock production and the 23
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Energy & Fuels
Figure 5: PNFA results with Pareto fronts with Pareto fronts for each considered fuel sorted by the active production types. Every point represents a pareto-optimal pathway design with ethanol as benchmark in every graph ( ). The data points are connected by curves to guide the eye. (b) E/TC-routes
(a) BC/E-routes
100
GWP [gCO2,eq. per MJ]
GWP [gCO2,eq. per MJ]
125 100 75
DEM
50
DMF
25 0 0.00
0.05
0.10
75
methanol
50
methane
25
DME
0 0.00
CostPNFA [USD/MJ]
0.05
0.10
CostPNFA [USD/MJ] (d) V-routes with BC (uFAME only), E
(c) BC-routes*
ethanol ethyl levulinate iso-butanol 2-butanone 2-MF γ-valerolactone
75 50 25 0 0.00
0.05
0.10
CostPNFA [USD/MJ]
GWP [gCO2,eq. per MJ]
100
100
GWP [gCO2,eq. per MJ]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 24 of 42
75
FAME
50
uFAME 25 0 0.00
0.05
0.10
CostPNFA [USD/MJ]
*: 2-MF is produced via BR90, BR91 without the use of H2 directly from cellulose. Hence, even though biomass is pretreated, no biochemical pathways are present. For simplicity, it is still considered to be a BC-based route.
422
associated supply chain, the feedstock cost structure (variable vs. fix costs) needs to be
423
considered in future studies.
424
When considering fuels that can be produced via E-routes but also purely via BC-routes
425
(cf. Fig. 5(c)), e.g., ethanol and ethyl levulinate, optimization results show that uncombined
426
BC-routes are now activated throughout the Pareto front (cf. active fluxes in Tab. S16
427
and S17 in the Supporting Information). E-routes, such as syngas fermentation steps (HR8,
428
HR9) are optimal when CL is considered as main objective (cf. previous section), but do
429
not offer any GWP-related advantages when compared to sugar fermentation (BR6). While
430
sugar fermentation and syngas fermentation both require the separation of the solvent water,
431
syngas production is associated to additional energy requirements that are higher than those
432
of sugar production. Thus, no feedstock synergies are found for these fuels and therefore a 24
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433
fleet-level comparison using a linear combination metric is neither necessary nor applicable.
434
In summary, PNFA shows that, in terms of cost, E-routes are only preferable to bio-based
435
routes when very small quantities are required that do not justify large investment costs,
436
e.g., V-routes. In terms of GWP, E-routes are only preferable when they can substitute
437
energy-intensive processing steps, e.g., gasification in TC-routes.
438
To further explore what factors lead to these screening results and what characteristic
439
bottlenecks need to be addressed to improve each production type, we characterize the four
440
production types (BC, TC, E, V) with respect to their most prominent emission sources. Fig.
441
6 shows the GWP share of each fuel at the point of minimal GWP (minGWP) and the point
442
of minimal cost (minCost) sorted by the respective active production types. Emissions can
443
arise from energy requirements of reactions and separations as well as from upstream burdens
444
of the feedstocks, i.e., CO2 , H2 , and waste vegetable oil while lignocellulosic biomass is not
445
associated with any upstream emissions.
446
When considering BC-based fuels, i.e., ethanol, iso-butanol, γ-valerolactone, ethyl le-
447
vulinate, 2-butanone, and 2-MF, it can be seen that nearly all emissions are caused by
448
separation steps. In these separation steps, the product is separated from large amounts of
449
solvents that are typically present in BC-reactions. The reaction steps themselves do not
450
considerably contribute to GWP, as they occur at moderate conditions in liquid phase. Thus,
451
solvent-product separation is identified as the common bottleneck of BC-routes which needs
452
to be addressed.
453
At minCost, methane, methanol, and DME are produced via TC-routes. These TCgCO2 ,eq MJfuel
454
based designs have GWP values of 39-59
455
The rest is attributed to reactions. The comparatively high impact of reactions originates
456
from gas compression for methanol and DME synthesis as well as the heating requirements
457
of endothermic gasification. Thus, to improve the performance of TC-routes, the energy
458
requirements of separations, i.e., gas cleaning, the energy efficiency of gasification, and the
459
determination of optimal reactor pressures need to be addressed. 25
with 38-58% stemming from separations.
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Energy & Fuels
Figure 6: GWP shares determined with PNFA categorized by fuel and respective production type. (a) GWP shares at minCost
(b) GWP shares at minGWP 100%
75%
75%
50%
50%
25%
25%
0%
0% methanol methane DME FAME uFAME ethanol ethyl levulinate γ-valerolactone 2-butanone iso-butanol 2-MF DMF DEM
100%
methanol methane DME FAME uFAME ethanol ethyl levulinate γ-valerolactone 2-butanone iso-butanol 2-MF DMF DEM
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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TC
V, E BC (uFAME only)
BC* CO2
E
BC, E H2
Veg. oil
separations
V, BC, E (uFAME only)
BC*
BC, E
reactions
*: 2-MF is produced via BR90, BR91 without the use of H2 directly from cellulose. Hence, even though biomass is pretreated, no biochemical pathways are present. For simplicity, it is still considered to be a BC-based route.
460
In E-routes (cf. Fig. 6(b)), the upstream chains of the feedstocks, CO2 and H2 , are
461
responsible for a large part of the GWP (38-100%) while the rest can be attributed to the
462
reaction steps (cf. Fig. 6(b)). The emissions of reaction steps are caused by compressing
463
gaseous reactants to high pressure levels of up to 184 bar which are required for methanol and
464
DME synthesis. In contrast, separations do not considerably contribute to GWP since reactor
465
effluents, e.g., methane and water, can easily be separated in a flash. To de-bottleneck the
466
high-pressure E-routes, i.e., methanol and DME production, pressure levels in both reaction
467
and separation steps need to be optimized.
468
Regarding V-based fuels, i.e., FAME and uFAME, the upstream emissions of the vegetable
469
oil account for a GWP share of up to 57% (cf. Fig. 6). Further emissions arise from energy
470
demands of separation steps as well as the production of intermediates, i.e., e-based methanol
471
and, in case of uFAME, BC-based ethylene. However, the transesterification reaction itself
472
(BHR2b) occurs in liquid phase at moderate conditions with relatively low solvent demands.
473
Thus, both the reaction and the subsequent solvent separation are associated to low emissions 26
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Energy & Fuels
474
which leaves upstream feedstock emissions as well as the production of intermediates as
475
bottlenecks that need to be refined in future designs.
476
The insights gained by RNFA and PNFA give first implications of how biomass and
477
e-based feedstocks can be optimally utilized for fuel production. However, two kinds of
478
uncertainty limit their validity: uncertainty given by the methodology and uncertainty of
479
parameters. RNFA and PNFA evaluate pathways based on relatively little information.
480
While this ensures fast screening, the results can be subject to relatively high uncertainties.
481
However, the uncertainty is likely very similar in case of comparable processes because the
482
assessment of the effort for individual process steps is comparable. In fact, a comparison with
483
literature results shows that PNFA reliably estimates costs in the right order of magnitude. 51
484
The second limitation is given by the uncertainty of parameters. A previous RNFA study 59
485
conducted an extensive sensitivity analysis. A one-at-a-time parameter variation showed that
486
in a relative comparison of fuels, the main differences between pathway performances are still
487
visible. 59 Thus, we expect that conclusions that compare the performances of fuels relative to
488
each other are not affected by parameter uncertainty.
489
Conclusion and Outlook
490
In this study, we conducted a systematic analysis to determine optimal application areas
491
of bio- and e-fuels, both on the process level and on the fleet level. Based on the results,
492
we can conclude that e-fuels are not cost-competitive to bio-fuels as long as the price of
493
renewable H2 cannot be lowered. However, E-routes are able to reach high carbon exploitation
494
and a low GWP, thus presenting an opportunity for resource-efficient and environmentally
495
sustainable fuel production. On the contrary, bio-fuels are cheaper but always suffer from the
496
carbon-inefficient re-functionalization of the complex molecules.
497
The combination of biomass and e-based feedstocks can provide further advantages on
498
the fleet level. In case of bio-e-hybrid ethanol production, co-utilization of CO2 emitted
27
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499
during fermentation leads to especially high benefits as no additional reactions or downstream
500
processing are needed. Similarly, FAME production via transesterification of waste vegetable
501
oil and e-based methanol gives an example of how hybrid designs can yield better process
502
performances by selectively substituting energy-intensive bio-based pathways with e-based
503
pathways.
504
These findings give first implications of how to integrate electricity and biomass feedstocks
505
in order to develop sustainable and economically viable processes from renewable materials.
506
Biomass fractions that are facile to convert should be used as an inexpensive feedstock basis
507
whereas remaining waste streams need to be selectively upgraded to increase, e.g., overall
508
carbon exploitation. Here, simple, short pathway concepts are preferable to keep down
509
investment costs and intermediate separations.
510
Acknowledgement
511
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research
512
Foundation) under Germany’s Excellence Strategy - Exzellenzcluster 236 "Tailor-Made Fuels
513
from Biomass" and Exzellenzcluster 2186 "The Fuel Science Center". The authors kindly
514
thank Luis Monigatti and Dominik Bongartz for their help in researching possible conversion
515
pathways.
516
Supporting Information Available
517
The following files are available free of charge.
518
• Supporting Information (PDF) including
519
Network Modeling Examples in RNFA and PNFA,
520
Reaction Parameters,
521
Energy Requirements of Separation Steps, 28
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Energy & Fuels
522
Other Model Parameters,
523
Performance Curve of the Linear Combination,
524
Cost and CL Optimization Using PNFA,
525
Active Fluxes.
526
Nomenclature
527
Abbreviations 2-MF
2-methylfuran
BC
biochemical lignocellulosic bio-based route
CEPCI
Chemical Engineering Plant Cost Index
CI
compression-ignition engine
DEM
diethoxymethane
DME
dimethyl ether
DMF
2,5-dimethylfuran
DMM
dimethoxymethane
E
electricity-based routes
e
electricity
FAME
fatty acid methyl ester
FT
Fischer-Tropsch
ICE
internal combustion engine
LPG
liquid petroleum gas
minCost
point of minimal cost
minCL
point of minimal carbon loss
minGWP
point of minimal GWP
MINLP
mixed-integer nonlinear program 29
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528
529
OMDEE
oxymethylene diethyl ether 2-4
OMDME
oxymethylene dimethyl ether 3-5
PNFA
process network flux analysis
RNFA
reaction network flux analysis
SI
Spark-ignition engine
TC
thermochemical lignocellulosic bio-based route
uFAME
upgraded fatty acid methyl ester
V
oil-based transesterification route
Greek Letters α
] design target, [ kJ yr
υ
stoichiometric coefficient [-]
∆H
kJ ] enthalpy change [ kmol
λ
plant type fraction [-]
Symbols #C
number of carbon atoms [-]
b
product flux vector [ kmol ] yr
C
cost [ USD ] yr
Cost
USD specific production cost [ MJ ] fuel
CL
carbon loss [-]
E
Energy demand [ kJ ] yr
f
molar flux vector [ kmol ] yr
GWP
CO2 ,eq global warming potential [ MJ ] fuel
gwp
global warming factor [-]
IC
investment costs [USD]
g
30
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530
Energy & Fuels
Inv1
pre-factor investment cost function [-]
Inv2
exponential factor investment cost calculation [-]
ir
interest rate [-]
M
g molar mass [ mol ]
n
number of components or plants [-]
NU
Number of active units [-]
P
] price [ USD kg
S
selectivity
t
project run-time [yr]
TAC
total annual cost [ USD ] yr
w
side product flux vector [ kmol ] yr
X
conversion [-]
Y
yield [-]
y
integer variable [-]
Subscripts 2010, 2016
respective year
comb
combustion
elec
electricity
g
gas
heat
heating
i
component index
in
incoming
j
reaction index
k
utility index
l
liquid
limit
threshold 31
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531
out
outgoing
all plants
all plants in fleet
process
process-related
product
product component
raw
raw material
refrig
refrigeration
spec
specific
waste
waste residue
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