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Screening pathways for the production of next generation biofuels Kirsten Ulonska, Anna Voll, and Wolfgang Marquardt Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.5b02460 • Publication Date (Web): 15 Dec 2015 Downloaded from http://pubs.acs.org on December 19, 2015
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Energy & Fuels
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Screening pathways for the production of next
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generation biofuels
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Kirsten Ulonska†, Anna Voll† and Wolfgang Marquardt*
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AUTHORS ADRESS Aachener Verfahrenstechnik – Process Systems Engineering, RWTH
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Aachen University, Turmstr. 46, 52064 Aachen, Germany
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† The first two authors contributed equally to this work.
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KEYWORDS Lignocellulosic Biofuels, Process Evaluation, Reaction Pathways, Reaction
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Network Flux Analysis, Sensitivity Analysis
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ABSTRACT
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A large number of alternative fuel molecules based on lignocellulosic biomass have been
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proposed recently, but a reliable evaluation of their economic potential is challenging due to the
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limited data available. A rapid screening methodology, Reaction Network Flux Analysis
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(RNFA), has been suggested to screen a large number of future reaction pathways. The RNFA is
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extended in this work by a comprehensive sensitivity analysis to account for inevitable
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uncertainty in the underlying data and hence in the ranking of biofuel candidates with respect to
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cost and environmental impact. The extended RNFA is then used to assess and rank candidate
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reaction pathways and associated processes for the production of a variety of proposed future
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pure-component
biofuels
from
lignocellulosic
biomass.
Ethyllevulinate
and
2-
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methyltetrahydrofuran have been identified as promising alternatives to bioethanol, while lignin-
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based biofuels can be excluded from further consideration. Methane is found to be attractive
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economically but shows significant environmental impact.
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1. Introduction and Motivation
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The ongoing discussion on the feedstock change from fossil to renewable carbon sources
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opens up new perspectives in terms of novel conversion processes and high performance
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products. Nevertheless, a potential transition from fossil- to bio-based products will only be
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realistic, if these substances not only exhibit promising properties but can be produced
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economically with low environmental impact.
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Due to the fact that the transportation sector was responsible for 27.6% of the energy
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consumption in the US in 2014 [1] and for 31.6% in Europe in 2013 [2], a lot of research groups
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have recently been focusing on the development of novel biofuel production processes [3–8].
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Most research activities aim at either thermochemical or biochemical conversion of
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lignocellulosic biomass. Thermochemical conversion of biomass starts with gasification to
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produce synthesis gas with a CO/H-ratio of 1 [9]. The synthesis gas can be upgraded to Fischer-
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Tropsch diesel or to methanol and then to methyl-tert-butylether or dimethyl ether. A
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comparative review on proposed thermochemical processes from various biomass feedstocks can
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be found in [9]. The major steps of thermochemical conversion processes, i.e., gasification,
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steam reforming and gas scrubbing, require a large amount of energy such that a significant
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fraction of the biomass’ energy content is needed to supply the required process heat.
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In contrast, biochemical conversion aims at low temperature refunctionalization of native
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molecular constituents of lignocellulosic biomass trying to reduce the energy demand of biofuel
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production by preserving the synthesis power of nature. Biomass is first fractionated into its
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main constituents cellulose, hemicellulose and lignin using various pretreatment techniques
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[10,11]. The fractions are then processed to so-called platform chemicals as building blocks of a
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variety of bio-based products. The platform chemicals can be transformed by means of multi-
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step molecular transformation to proposed biofuel molecules by a very large number of
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conceivable reaction pathways which comprise possible refunctionalization steps. The resulting
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set of pathways needs to be evaluated according to energetic, ecological and economic
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performance metrics to assess the feasibility of an associated production process [8,12–14].
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Conceptual design of processes relying on promising reaction pathways is often based on
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Douglas’ hierarchical approach [15]. It aims at detailed information on the reaction and the
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separation system. While this is already challenging for well-known processes, like biodiesel or
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bioethanol, it is even more ambitious for novel proposed pathways. Nevertheless a first ranking
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of promising and non-promising reaction pathways is inevitable to guide future research
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activities as well as for the detection of bottlenecks in the very first phase of the process design.
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This is the case for all recently proposed synthesis routes for the production of fuels via
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biochemical conversion. These difficulties can partially be overcome by the use of rapid
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screening techniques [12,16–18] which only need little input data for the pre-selection of
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reactions instead of detailed analysis methods. With the help of these screening methodologies,
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one of them is the RNFA methodology [12], the process performance of various fuel candidates
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can be evaluated and the most promising ones are elaborated. In the following the RNFA
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methodology is briefly outlined before comparing the process performance of various suggested
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fuel candidates and their associated pathways. The top-scorer of each fuel family will be selected
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for a final comparison.
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2. Screening Methodology
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Reaction Network Flux Analysis (RNFA) was developed as an optimization-based screening
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tool aiming at a fast evaluation of a high number of conceivable reaction pathways [12,19]. A
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reaction network containing all known reaction pathways starting from biomass to targeted
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products
is
set
up
and
subsequently
analyzed
by
means
of
mole
balances.
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2.1 Review of RNFA
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The RNFA is based on methods developed in metabolic engineering [20]. A reaction network
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is set as a graph constituting of arcs and nodes representing reactions and substances,
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respectively. The reaction network can be analyzed by various performance criteria. Green
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metrics like atom or more specific carbon efficiency or hydrogen consumption can be applied as
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well as economic criteria like raw material cost. Typically, candidate pathways are investigated
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to analyze the tradeoff between total annualized cost (TAC) and environmental impact (EI).
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The total annualized cost TAC is defined as
∙ = + ∙ 1 − 1 +
(1)
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and comprises two terms. The first term covers the investment cost IC discounted for a period of
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n=10 years. The interest rate i is set to 8%. The calculation of the raw material cost in the second
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term involves the stoichiometric matrix A as well as the molar fluxes f, the molar masses Mj and
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the prices Cj of the reactants j. While the raw material cost can be determined easily from mass
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balances, the investment cost are difficult to assess without a detailed process design. Therefore,
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an empirical correlation suggested by Lange [21],
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Energy & Fuels
1 [Mill. $ 1993] = "#$%&1 ∙ ∆([MW]*+,-./ ,
(2)
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is used instead. It has been derived by fitting the investment cost to the energy consumption of a
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large number of existing processes. The empirical constants are determined as Invest1=3 and
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Invest2=0.84 [21].
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As a full life-cycle analysis is not applicable due to missing data in the early design stage,
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various metrics and concepts have been proposed in literature to at least determine the
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environmental impact, like the Eco-Indicator 99 [22], the environmental health and safety index
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[23], the ReCiPe method [24] or the eco-efficiency analysis [25]. In this work, the environmental
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impact EI is determined using a simplified version of the eco-efficiency analysis by Uhlmann
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and Saling [25]. Its computation involves energy consumption (EC), resource consumption (RC),
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emission impact (Em), toxicity potential (TP), land use change and risk potential. The latter two
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factors are not considered in the analysis in this work, because their assessment relies on data,
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which are not accessible before a detailed process design is completed. Missing data and
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predictive models render the application of most of the other concepts listed above even more
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challenging in the very early phase of process design. An exception is the heuristic approach of
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Koller et al. [23] for assessing safety (e.g. mobility, explosion probability or decomposition),
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health (irritation, chronic toxicity) and environmental aspects (e.g. pollution, degradation,
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accumulation) based on accessible pure-component properties and classifications like R-phrases,
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which could as well be utilized in the RNFA to evaluate even more aspects beyond the applied
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EI.
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A modification of the original RNFA [12,19] is applied in this work to overcome the need for a
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time-consuming, iterative calculation procedure of the EI and to facilitate the comparison of
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different case studies (see supplementary information for more detail). The factors contributing
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to the dimensionless environmental impact EI are accounted for by the weighted sum
5 ( = 012 ∙ ( + 032 ∙ 4 + 015 ∙ (6 + 078 ∙ 9.
(3)
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Ethanol is taken as a reference with an EI set to the value of one, because it is an established
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biofuel with a well-known production process. The weights in Eq. (3) are calibrated such that all
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the factors contribute equally to the EI for the reference fuel ethanol, which is produced from a
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cellulosic feedstock in a plant with a capacity of 100,000 tons/year corresponding to a total
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enthalpy of combustion of 2.77·1012 kJ per year. The resulting weights for fuel production are
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summarized in Table 1. Details on the calculation procedure are compiled in the supplementary
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information. Note, in case of a multi-product biorefinery, the allocation of all products to an
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overall EI needs to be considered. Different allocation methods should then be carefully
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analyzed, as the choice of allocation method, like partitioning, system expansion or substitution,
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strongly influences the results [26,27].
17 012 [kg/MJ] 0.0124 18
032
015
078
[-]
[kg product/kg CO2eq.]
[year/kg]
0.09017
0.2614
8.35∙10-6
Table 1: Weighting factors in Eq. (3)
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The objective of the process design is the minimization of both, TAC and EI at the same time
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for a production with a design specification of 2.77·1012 kJ per year enabling a comparison of
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fuel candidates with respect to the enthalpy of combustion [19]. The design problem can be
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captured by the following multi-objective optimization problem: 6" = > :, < ( %. &. ∙ = ? 6@A$
