1

SUPPORTING INFORMATION

Enthalpy data and toxicity score for all reactants and products

The enthalpies of combustion and formation are determined based on Joback’s group

contribution method [1,2] and the method by Marrero & Gani [2]. The toxicity score TS is

determined based on the classification of R-phrases. The upper bound of the TS is set to 1000

representing the maximal value in the TS classification [3,4].

∆HCombustion [kJ/mol] ∆HFormation [kJ/mol] TS [-]

Biomass 2800 -770

Cellulose 2800 -770

Hemicellulose 2800 -770

Lignin 2800 -770

Hydrogen 242 0

Oxygen 0 0

Water 0 -242

Carbon monoxide 283 -111

Carbon dioxide 0 -394

Methane 803 -75 300

Ethanol 1277 -237 300

Furan 1998 -62 1000

Butanol 2507 -278 400

2-Methylfuran 2601 -94 750

2-methyltetrahydrofuran 2961 -218 400

3-methyltetrahydrofuran 2961 -218 400

2-5-Dimethylfurane 3205 -126 400

Ethyllevulinate 3591 -618 300*

Butyllevulinate 4821 -659 300*

Tetrahydrofurfurallevulinate 5123 -751 400

2

Tetrahydrofuran 2356 -188 400

Ethylfuran 3247 -84 300

Ethyltetrahydrofuran 3568 -247 400*

2-5-Dimethyltetrahydrofuran 3558 -257 300

Butylfuran 4477 -126 300

Butyltetrahydrofuran 4798 -288 400*

Cyclohexanol 3550 -265 400

Benzylalcohol 3634 -90 400

6-Butylundecane 9421 -358 300*

6-Pentylundecane 10036 -379 300*

ethylfurfurylether 3699 -267 300*

ethyltetrahydrofurfurylether 4059 -392 300

Octanol 4968 -360 300

Acetone 1690 -217 300

Table A1: Enthalpy data and toxicity scores. * The score is based on a molecule with a similar molecular structure.

3

Environmental impact

The simplified version of the EI calculation relies on energy consumption (EC), resource

consumption (RC), emission impact (Em) and toxicity potential (TP). Equations (A1) to (A4)

show the formulae for the individual factors:

�� = ∆������∙ ����

(A1) �� = ∑ �������������

�����∙ ���� (A2)

�� = ����∙ ��������∙ ����

(A3) �� = � !"#$ % !"#$ &'����())) (A4)

Em not only accounts for CO2 emissions, but also for the CO2-equivalent of other gas

emissions with global warming potential. For the determination of the toxicity potential,

components with a high molecular similarity are classified similarly (same toxicity score), when

no risk and safety sentences are available. This avoids a misinterpretation of the toxicity

potential due to a non-existing toxicity classification, which is often the case for newly

developed molecules. As outlined in [3,4], the toxicity score TS is classified into harmless (TS: 0-

300) and injuring effects (TS > 300). Ethanol has a TS of 0, which causes problems since the

toxicity potential would be omitted due to a resulting weighting factor of 0. Hence all fuel

candidates exhibiting a toxicity score of 0 or 100 are set to TS of 300. Due to the classification

into harmless and injuring effects, this approach does not change the general statement, whether

a molecule is harmless or not.

Standardization of the environmental impact

To avoid a dependency of the normalization on the case study, the four single impacts are

weighted based on the reference production of ethanol (cf. Eq. (3)), which exhibits an EI of 1.

All impacts contribute equally (25%) to the EI in case of ethanol. The normalization is based on

4

the production of 100 000 tons of cellulosic ethanol per year, which is equivalent to a heating

value of 2.77·1012 kJ/year. Equations (A5) to (A8) show the calculation of the weighting factors:

*�+ = ).-.�+/��0

= ).-.∙�/��0∙ /��0∆�/��0

(A5) *1+ = ).-.1+/��0

= ).-.∙�/��0∙ /��0∑ �������������

(A6)

*�2 = ).-.�2/��0

= ).-.∙�/��0∙ /��0����∙ ���

(A7) *&3 = ).-.&3/��0

= ).-.∙&'4�5�/��0∙ /��0∙&'/��0

(A9)

The calculation of the energy loss ∆�� 67 requires the enthalpy of combustion of all educts

and of the product ethanol; this data is compiled in Table A1. For each mole of ethanol one mole

of CO2 is released during fermentation. With this information the individual factors for ethanol

and the weighting factors can be calculated as follows:

��� 67[:;<=] = 20.17 → *�+ = 0.013 [<=:;]

��� 67[−] = 2.77 → *1+ = 0.090 [−]

��� 67 F <=GH-IJ.<=KLMNOPQR = 0.96 → *�2 = 0.261 [<=KLMNOPQ<=GH-IJ.]

��� 67[<=KLMNOPQTIUL ] = 0.0299 → *&3 = 8.35 ∙ 10XY [ TIUL<=KLMNOPQ]

5

Reaction yields and references

Reaction yields have been collected from literature and are compiled in Table A2. Unknown

reaction yields are marked and set to 0.97. The split of lignin into its constituents is assumed to

be ideal and marked with “Assumption Lignin”. The split between the lignin alcohols is

implemented using optimization constraints. The upper bound of the reaction yields is 1 in case

of chemical conversions representing the theoretical yield. The determination of the theoretical

yield differs for the fermentations. The upper bound in case of ethanol is 0.51 g/g (R8), for

itaconic acid 0.72 g/g (R9), for butanol 0.5 g/g (R10) and in case of succinic acid 1.1 g/g (R50)

respectively.

reaction yield reference reaction yield reference

R1 0.97 Assumption R51 0.94 [5]

R2 0.97 Assumption R52 0.97 [6]

R3 0.97 Assumption R53 0.94 [6]

R4 0.54 [7] R54 1 [6]

R5 0.97 [8] R55 0.4 [9]

R6 0.97 [10] R56 0.9 [11]

R7 0.9 [12] R57 0.51 [13]

R8 0.47* [14] R58 0.81 [15]

R9 0.62* [16] R59 0.83 [17,18]

R10 0.39* [19] R60 0.95 [18]

R11 0.7 [20] R62 1 [21]

R12 0.7 [20] R63 0.925 [22]

R13 1 [20] R64 0.95 [15]

R14 0.99 [23] R65 1 Assumption Lignin

R15 0.95 [23] R66 0.97 Assumption

R16 0.99 [23] R67 0.97 Assumption

R17 1 [23] R68 0.97 Assumption

R18 1 [23] R69 0.97 Assumption

R19 1 [23] R70 0.83 [24]

6

R20 0.96 [23] R71 1 [25]

R21 1 [23] R72 1 [26]

R22 1 [23] R73 0.97 Assumption

R23 0.97 [23] R74 0.97 Assumption

R24 0.8 [20] R75 0.9 [27]

R25 0.9 [6] R76 0.92 [28]

R26 0.97 [6] R77 0.95 [29]

R27 1 [30] R78 0.968 [31]

R28 0.8 [32] R79 1 [33]

R29 0.97 [34] R80 1 [35]

R30 0.29 [36] R81 0.95 [37]

R31 0.79 [36] R82 0.91 [38]

R32 0.99 [39] R83 1 Assumption Lignin

R33 0.83 [40] R84 1 Assumption Lignin

R34 1 [41] R85 0.94 [42]

R35 0.87 [43] R86 1 [42]

R36 0.66 [44] R87 0.96 [42]

R37 0.95 [45] R88 0.86 [42]

R38 0.95 [45] R89 1 [42]

R39 0.95 [45] R90 0.973 [42]

R40 0.95 [45] R91 0.93 [42]

R41 0.94 [45] R92 1 [42]

R42 0.94 [45] R93 0.942 [42]

R43 0.67 [45] R94 0.97 Assumption

R44 0.93 [46] R95 0.97 Assumption

R45 0.8 [47] R96 0.63 [48]

R46 0.8 [47] R97 0.97 Assumption

R47 0.8 [47]

R48 0.8 [47]

R49 0.95 [49]

R50 0.91* [50]

Table A2: Reaction yields and references for all network reactions. * Fermentation yield given in [g/g]

7

Parameter variation

For the sensitivity analysis all parameters are varied for the determination of their individual

influence. The weighting factors of the EI, the property data and toxicity score as well as the

reaction yields are presented in Tables 1, A1 and A2. In addition, the parameters determining the

cost calculations as well as lower and upper bound of biomass composition are presented in

Table A3.

parameter description unit value reference

RC_B Raw material costs biomass $/kg 0.05 [47]

RC_H2 Raw material costs hydrogen $/kg 2.7 [20]

RC_H2O Raw material costs water $/kg 0.0005 [43]

RC_FA Raw material costs formic acid $/kg 1.05 [43]

RC_MeOH Raw material costs methanol $/kg 1 [43]

RC_A Raw material costs acetone $/kg 0.81 [44]

i Interest rate % 8 Assumption

n Run time years 10 Assumption

Invest1 Coefficient for IC calculation - 3 [51]

Invest2 Coefficient for IC calculation - 0.84 [51]

lb_C Lower bound cellulose fraction - 0.4 [20]

ub_C Upper bound cellulose fraction - 0.8 [20]

lb_HC Lower bound hemicellulose fraction - 0.15 [20]

ub_HC Upper bound hemicellulose fraction - 0.3 [20]

lb_lignin Lower bound lignin fraction - 0.1 [20]

ub_lignin Upper bound lignin fraction - 0.25 [20]

Table A3: Additional parameters required in the RNFA

8

Influence of the parameter Invest2 on the sensitivity analysis

The OAT as well as the MC analysis identified the parameter Invest2 as the most influential

parameter. Therefore a detailed discussion and quantification of this parameter’s influence is

conducted in the following. Figure 1 shows the comparison of the results of OAT analysis with

and without considering the parameter Invest2 in the sensitivity analysis. In addition, Table A4

quantifies the maximum relative deviations from the results of the RNFA for nominal parameters

with and without considering the parameter Invest2 for the case of minimal TAC.

fuel OAT incl. all parameter [%] OAT w/o Invest2 [%]

Ethanol 46 28

2-MF 57 24

2-MTHF 38 33

PUD 45 29

EFE 61 22

EL 42 31

Cyclohexanol 83 17

Methane 57 57

Table A41: Maximal relative deviations for the point of minimal TAC, with and without considering

Invest2 coefficient

Both, the graphical comparison as well as the quantification of the maximal relative deviations

underline the statement of the high influence of the cost coefficient on the process analysis. But

there exist also distinct differences for the different fuel candidates and their respective

production processes. While the cost coefficient clearly has a high influence for cylohexanol,

there is no influence on the process performance for methane. This might be due to the very low

TAC of the methane process compared to the cyclohexanol process. The most stable behaviour

considering all parameter variations are shown for the fuel candidates 2-MTHF, EL, PUD and

ethanol, which exhibit an uncertainty range of ±46% in the TAC. Without the consideration of

9

the parameter Invest2, for all fuel candidates except methane, the TAC are within a range of

±33% compared to the nominal cases. Hence the high influence of the parameter Invest2 can be

proven by the results of OAT analysis. Figure 2 presents the analogue comparison for the MC

analysis. Both analyses show the strong influence of the cost coefficient Invest2 for each top-

scorer.

10

1. Results of OAT analysis for top-scorers with and without parameter Invest2

11

Figure 1: Monte Carlo analysis- left: 15 %deviation including all parameters; right 15% deviation excluding

parameter Invest2

12

2. Results of MC analysis for top-scorers with and without parameter Invest2

13

Figure 2: Monte Carlo analysis- left: 15 %deviation including all parameters; right 15% deviation excluding

parameter Invest2

14

Results of Monte Carlo analysis for a parameter variation of ± 30%

Figure 3: Monte Carlo results for a 30% parameter deviation

15

Results of Monte Carlo analysis for a parameter variation of ± 30%

Figure 4: MC results for all top-scorer. Left: Minimization of TAC, Right: Minimization of EI

16

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