Screening synthesis pathways for biomass derived sustainable
polymer production
Dongda Zhang1,*, Ehecatl Antonio del Rio-Chanona2, Nilay
Shah1
1: Centre for Process Systems Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, UK.
2: Department of Chemical Engineering and Biotechnology,
University of Cambridge, Pembroke Street, Cambridge CB2 3RA,
UK.
*: corresponding author, email: [email protected],
tel: 44 (0)7543785283.
Synopsis: This work aims to identify the most promising and
environmentally friendly synthesis routes to produce sustainable
biopolymers from biomass wastes.
Abstract
Sustainable polymers derived from biomass have been extensively
investigated to replace petroleum-based polymers and fulfil the
ever-increasing market demand. Because of the diversity of biomass
and polymer categories, there exist a large number of synthesis
routes from biomass to polymers. However, their productive and
economic potential have never been evaluated. Therefore, in this
study, a comprehensive reaction network covering the synthesis of
20 polymers including both newly proposed biopolymers and
traditional polymers is constructed to resolve this challenge for
the first time. Through the network, over 100 synthesis pathways
are screened to identify the most promising biopolymers. Three
original contributions are concluded. Firstly, from a carbon point
of view, polyethylene and 1,4-cyclohexadiene based polymers are
found to be the best petroleum-based polymer and newly proposed
biopolymers which can be produced from biomass, respectively, due
to their highest carbon recovery efficiency around 70%. Secondly,
external hydrogen supply is vital to guarantee the high yield of
biopolymer, as without enough hydrogen biopolymer productivity can
reduce by half. Thirdly, through sensitivity analysis, the current
biopolymer ranking is verified to be stable subject to a moderate
change of reaction selectivities and hydrogen supply. This study,
therefore, provides a clear direction for future biopolymer
research.
Keywords: biomass wastes, sustainable polymers, synthesis
pathways, reaction network flux analysis, hydrogen utilisation,
sensitivity analysis
Introduction
Polymers e.g. organic plastics are one of the most commonly used
chemicals in both daily life and industry. With the development of
modern industrial polymerisation technologies, versatile polymers
characterised by highly tuneable properties have been synthesised1
and their applications have be widely found in coatings,
engineering plastics, adhesives packaging materials, diagnostics
and electronics1,2. It is estimated that the global production of
polymers has exceeded 260 million tonnes in 2009 and will triple by
20153. For example, around 31 million tonnes of polymers were
produced in the US and mainly used in packaging and durable goods
in 20103, and the UK produced 2.5 million tonnes of polymers in
2012 with an annual turnover of £19 billion and creation of over
180 thousand job opportunities4.
In spite of the ever-increasing global demand, however,
currently the dominant feedstocks for industrial polymer production
are derived from non-renewable fossil fuels. The monomers (e.g.
ethylene, propylene, benzene) used for the synthesis of commonly
used polymers such as polyethylene (PE), polyethylene terephthalate
(PET) and polypropylene carbonate (PPC) are predominantly produced
from petroleum and natural gas2,5. Due to the dwindling of these
resources and their effects on the environment, it has been
suggested that other sustainable and environmentally friendly
resources should be utilised for future polymer production since
the 20th century3. In particular, biomass and its derived materials
are considered to be the primary organic carbon source to replace
fossil fuels, as they are generated from atmospheric CO2 through
photosynthesis and have great potential to produce carbon-neutral
polymers1. Furthermore, their abundant annual production can also
guarantee the stability of feedstock supply chain for industrial
scale polymer production3.
So far, research for the synthesis of biopolymers from biomass
has been extensively conducted. Different biomass feedstocks
including citrus waste6,7, forestry residues8, agricultural
waste1,9 and microalgae biomass10,11 have been demonstrated to be
able to generate biopolymers through various pathways. In order to
save energy cost and reduce process complexity, chemicals that can
be easily extracted or synthesised from biomass are particularly
investigated for sustainable polymer production7,8,10. For
instance, it has been concluded that limonene extracted from citrus
waste2,7, 1,4-cyclohexadiene synthesised from plant oil8,12, and
isoprene and lactic acid excreted by microalgae and
bacteria10,11,13 can be directly used as the monomers or monomer
precursors for green polymer production. Based on these chemicals,
a variety of bio-based polymers e.g. polylimonene carbonate
(PLC)6,14, polycyclohexadiene phthalate (PCEP)8, and
polycyclohexadiene carbonate (PCHDC)8,12 have been created with
their physical properties being thoroughly analysed.
Despite these achievements, three challenges still severely
prevent the further production of biomass based polymers. Firstly,
because of the diversity of polymers and biomass feedstocks, there
exist a significant amount of synthesis routes converting biomass
to biopolymers, most of which, however, are economically
infeasible. Therefore, it is vital to identify a small set of most
promising synthesis routes for detailed process design and
analysis. Nonetheless, due to the lack of essential kinetic and
economic information, this research has not been conducted.
Secondly, although chemicals that are directly extracted or
synthesised from biomass have been researched for biopolymer
production, it is notable that their yields from biomass are very
low (e.g. 3.8% yield of limonene from citrus waste)15,16. Thus,
biopolymers generated from these chemicals can hardly meet the
ever-increasing polymer demand, and the disposal of the remaining
biomass wastes remains a challenge. Finally, although novel
biopolymers have been created recently, their synthesis is mainly
focused on laboratory scale with little effort being paid on
assessing their industrialisation feasibility and global market
potential. Hence, their applicability is still unclear. As a
result, it is necessary to explore reaction pathways from biomass
to currently commercialised polymers (e.g. PE, PPC, PET).
In order to resolve the aforementioned challenges, it is of
critical importance to screen a large number of potential reaction
pathways from biomass to various polymers through the construction
of a comprehensive reaction network, and then rank the pathways
with respect to different criteria (e.g. productivity,
environmental impact and economic cost) so that a small group of
promising pathways can be identified for further research. This
forms the research goal of the current study. To efficiently
complete this aim, reaction network flux balance (RNFA)17, a
methodology inspired from flux balance analysis (FBA)18, is applied
in the current study. A literature review of RNFA is presented in
Section 2, followed by the detailed discussion of the original
contributions of this work presented in Section 3.
Methodology
FBA and RNFA
Flux balance analysis (FBA) is a methodology primarily used in
biochemistry to reconstruct the metabolic reaction networks of
microorganisms18,19 and determine their major metabolic pathways
under different circumstances17,20. Because of its steady-state
assumption, reaction information such as enzyme kinetics or
metabolites concentrations which are always difficult to measure is
not needed21. Therefore, FBA is considered as the first choice to
simulate metabolic reaction networks whenever the assumption is
valid18,19.
Inspired by this methodology, reaction network flux analysis
(RNFA) was recently developed for sustainable chemical processes
design17. RNFA is introduced as an optimisation based methodology
to evaluate and subsequently identify the feasibility of potential
reaction pathways for the synthesis of a set of products with
respect to specific selection criteria in a given chemical reaction
network17. Because of its flexibility and efficiency, it has been
applied to screen a number of newly constructed reaction networks
to identify a variety of potential sustainable products, ranging
from microorganism based bioproducts e.g. 1,3-propanendiol and
3-hydroxypropionic acid22 to different renewable biofuels derived
from agricultural and forestry based biomass wastes23,24. Moreover,
multi-objective optimisation algorithms25 have also been recently
embedded into RNFA to extend its application for multi-criteria
decision analysis from technology, economic and environmental
aspects22,24,26. Despite its success, it is notable that RNFA has
almost exclusively used for bioenergy, in particular biofuels,
related process design and optimisation22–24,26,27. Therefore, in
this work, RNFA is applied as an efficient tool to identify a
number of promising reaction pathways from biomass to different
biopolymers at the very early stage of the research for sustainable
polymer production, and its detailed principle is introduced
below.
The mathematical formulation of RNFA is shown in Eq. 1(a) to Eq.
1(c).
subject to:
where is a vector of size and represents the molar fluxes of the
reactions, is the objective function coefficient vector of size ,
is the stoichiometric matrix of , and is the accumulation of the
reactants (a vector of size ). is the number of chemicals and is
the number of reaction and exchange fluxes. In this study, is a
vector of size 78 (1×78 matrix), is a 65×78 matrix, is a vector of
size 78 (78×1 matrix, corresponding to 76 reactions and two fluxes
for additional hydrogen and CO2 supply, respectively), and is a
vector of size 65 (65×1 matrix, corresponding to 65 chemicals).
A reaction pathway is defined as a sequence of reaction steps
starting from biomass to a targeted biopolymer through the link of
possible intermediates. Reactions are collected from an extensive
literature research to obtain the essential kinetic information
including reaction stoichiometry, single-pass conversion
efficiency, reaction selectivity, and enthalpy of reaction. This
information is listed in Table S7. Specific to the current study,
in order to maximise the biomass carbon utilisation efficiency, all
the intermediates presented in the reaction network are allocated
to several reactions so that they can be eventually converted to
biopolymers instead of being sold as by-products or wastes.
Once constructed, the reaction network is transformed into a
mathematical model where the mass balance of each chemical (Eq.
1(c)) is used as a constraint and the energy balance of the system
(Eq. 2) is considered as either a constraint (e.g. no additional
heat supply) or an objective function (e.g. minimising heat
supply). Since in this study all intermediates can be converted to
biopolymers, in Eq. 1(b) is equal to zero if it represents an
intermediate, and nonnegative for final products (polymers).
where is the total number of reactions in the reaction network,
is the enthalpy of reaction , and is the entire energy generated in
the reaction network.
In general, solutions of Eq. 1(b) are not unique as the
number of reactants is larger than the rank of the stoichiometric
matrix . To ascertain a particular molar flux distribution, an
objective function, Eq. 1(a), and additional constraints, Equation
1(c), have to be determined. Based on different objective functions
such as maximising product yield (mass balance criteria),
maximising process profit (economic criteria) and minimising energy
cost (energy criteria), solutions of the model can be distinct from
each other.
Biopolymer synthesis reaction network
In this reaction network, biomass feedstock including citrus
waste, forestry residues and microalgae biomass is presented as an
averaged chemical formula for convenience. In order to adequately
utilise these carbon sources, two biopolymer synthesis scenarios
are included. In the first scenario, important monomers and their
precursors are directly separated from biomass, including limonene
(extracted from biomass and excreted from microalgae)13,14,
1,4-cyclohexadiene (synthesised from plant oil)12, isoprene
(excreted from microalgae)13 and lactic acid (excreted from
microalgae)28. However, due to the low content of these chemicals
in biomass (carbon distribution less than 2%)15,16, the majority of
biomass cannot be effectively used for polymer production. Hence,
in the second scenario, all biomass is initially converted to
syngas through gasification29,30, and then syngas is used to
generate different monomers via industrially feasible existing
reactions.
The reaction network comprises 20 biopolymers including both
commonly used industrial polymers (e.g. PE, PPC) and bio-derived
new polymers (e.g. PLC, PCHDC). They are categorised into three
polymer classes (polyalkene, polyester, and polyether) and listed
in Table S1. To complete the conversion from biomass to
biopolymers, this reaction network consists of 76 reactions and
their reaction conditions and kinetic information are selected to
be close to general industrial operating conditions. This is
because the current research aims to identify a number of feasible
reaction routes for industrial scale sustainable polymer
production. As a result, although some reactions have been reported
to show a higher selectivity under other circumstances, they are
not considered here. Finally, the current constructed reaction
network is presented in Fig. 1.
Constraints and objective function
As introduced before, the mass balances of chemicals are used as
constraints, and their molar flux is nonnegative. In this study,
due to the lack of essential information such as the energy cost of
operation units, price of biopolymers, and cost of feedstock and
facilities, it is not feasible to carry out a detailed
techno-economic-environmental assessment at the early stage of
process design. Therefore, in this conceptual design, (entire
energy generated in the reaction network) and RSN (number of
reaction steps for biopolymer production) are selected as the
energy and economic criterion, respectively. It is expected that
the reaction pathway with a higher amount of generated energy and a
lower number of reaction steps can reduce the demand of energy
supply and process investment cost.
Figure 1: Reaction network from biomass to biopolymers
Furthermore, although biomass can be served as both carbon
source and hydrogen source for bioproduct synthesis, additional
hydrogen gas may have to be supplied into the system to tune the
composition of syngas. However, because at present more than 90% of
hydrogen comes from fossil fuel based industries 31, in order to
mitigate total CO2 emission, the amount of hydrogen provided into
the system should be minimised. In addition, it is also
advantageous if the current biopolymer production routes can store
CO2 such as that from the atmosphere. Therefore, both hydrogen
demand and capability of carbon storage are selected as the key
factors for reaction pathway decision-making.
In terms of the objective function, the current study aims to
determine the highest biomass carbon utilisation efficiency with
respect to (i) each individual biopolymer candidate, (ii) each
polymer category, and (iii) entire reaction network with and
without the supply of external hydrogen. As the by-products of
reactions included in this reaction network are mainly hydrocarbon,
they can be either sold as raw materials to other industrial users
or combusted as a fuel to provide energy for the reaction pathway.
Therefore, depending on the scope of the reaction network, the
objective function can be chosen maximise:
1. RC1: direct biomass carbon utilisation efficiency (not
including the fate of by-products);
2. RC2: total biomass carbon recovery efficiency (assuming that
by-products are combusted and converted to CO2, and then re-enter
the reaction network for biopolymer production).
Sensitivity analysis
Sensitivity analysis (SA) is used to measure the effect of model
parameters on the system performance20. A normalised sensitivity ()
has been defined by a previous study and shown in Eq. 332. It
estimates the proportional change of the system’s performance (,
e.g. bioproduct yield) with respect to the proportional change of a
model parameter ()33. A positive sensitivity indicates that
increasing the parameter can enhance the system’s performance,
whilst a negative sensitivity suggests that increasing the
parameter will diminish the system’s performance. Moreover, a
greater sensitivity also indicates that the effect of this
parameter on the system’s performance is more significant.
SA is in general related to the data uncertainty. In the current
study, reaction stoichiometry is assumed to be correct and thus
exempted from SA. Although the single-pass conversion efficiency
can affect molar fluxes, in practice unconverted reactants will be
separated from products and recycled into the reactor for further
conversion. Hence, the uncertainty of single-pass conversion
efficiency has more impact on the process economic cost (e.g.
addition of separation units) rather than carbon utilisation
efficiencies. As a result, reaction selectivity is chosen as the
representative of each reaction for SA. Moreover, the amount of
supplied external hydrogen gas is also selected as a parameter for
SA, so that the significance of hydrogen utilisation on biopolymer
production can be identified.
Overall, the current SA is applied to address the following
questions:
· what are the most influential reactions that affect the
ranking of reaction pathways with respect to (i) each individual
biopolymer candidate, (ii) each polymer category, and (iii) entire
reaction network;
· is the rank order of reaction pathways still reliable when the
most influential parameters are changed within a certain range;
· does the supply of additional hydrogen significantly change
the ranking result and total carbon utilisation efficiency;
· what is the optimal hydrogen to carbon (H/C) ratio of syngas
for biopolymer production?
In this study, Mathematica® 11 is used to implement the RNFA
modelling and estimate parameter sensitivity. The model contains 78
variables, 136 constraints, and the objective is to maximise the
carbon utilisation efficiency from biomass (e.g. 100 mol) under
different circumstances. This is solved by Linear Programming in
approximately 3.5 seconds. The optimised fluxes of biopolymers are
solved from Eq. 1(a) to Eq. 1(c) and the results are listed in the
Results and discussion section. The process energy consumption and
sensitivity are estimated by Eq. 2 and Eq. 3, respectively, and the
results are listed in the corresponding tables. Once the optimal
fluxes of reaction pathways are estimated, the number of reaction
steps, the amount of hydrogen and CO2 required and the optimal
bioprocessing sequence are automatically calculated based on the
constraints in the model. These results are also listed in the
Results and discussion section.
Results and discussion
The original contribution of this paper is to compare a large
number (approximate 540) of potential reaction pathways from
biomass to polymers, and then identify the most promising polymer
candidates and synthesis routes for further process design and
optimisation. The detailed results are introduced below.
Polymer selection with additional hydrogen supply
Direct biomass carbon utilisation
Table 1 lists the RNFA optimisation results when aiming to
maximise carbon utilisation efficiency with respect to individual
polymers with enough hydrogen supply (top three polymers in each
category, full list can be found in Table S2). From the table, it
is seen that when the fate of by-products is not included (results
in RC1 and AC1), the biopolymer which utilises the highest amount
of carbon from biomass is PE (73.7%, no CO2 emission), one of the
most commonly used plastics in industry and daily life. This is
followed by three polymers (PCE, PCHDC, PCEDO), each of which has a
carbon utilisation efficiency of 43.4% and meanwhile emits CO2.
Overall, there are 10 candidates out of the 17 investigated
biopolymers capable of utilising over 40% of carbon from biomass,
and 2 candidates (PPC and PPP) can capture additional CO2 for their
production. Most of the polymers, however, still emit CO2 during
their production.
When aiming to maximise biomass carbon utilisation efficiency
with respect to each polymer category, both polyalkene and
polyether result in a single polymer production, which is PE
(73.7%) and PCEDO (43.4%), respectively. However, for polyester,
the best scenario corresponds to the production of multiple
polymers consisting of PPC (3.8%), PCEP (31.0%) and PET (15.4%),
which in total recovers 50.2% biomass carbon (Fig. 2(a)). It is
followed by a series of mixed polyester products comprising PPC,
PCEP and PET with a carbon utilisation efficiency decreasing from
50.2% to 43.4% where the mixture intersects the same efficiency
compared to PCHDC (single polyester). This result suggests that for
bio-based feedstocks, where a number of potential reaction pathways
and products are available, the synthesis objective is not
necessary to be identifying the best route to generate one
particular product, but rather the best way to convert the specific
feedstock.
Figure 2: Optimal biopolymer synthesis routes. (a): with
external hydrogen supply. Blue line: reaction pathway for PE
production, red line: reaction pathway for PPC, PCEP and PET
production. (b): without external hydrogen supply. Thick red line:
reaction pathway for PE production. Thin red line: reaction pathway
for PCEP and PET production.
Table 1: Maximum biopolymer production from biomass with
external hydrogen supply. AC1: absorbed CO2 (mol/100 mol biomass)
through the reaction pathway; AC2: absorbed CO2 (mol/100 mol
biomass) through the entire system; RH: required hydrogen supply
(mol/100 mol biomass); ∆H: energy required from the reaction
pathway (kJ/mol); RSN: number of reaction steps from biomass to
polymer.
Polymer
RC1
AC1
RC2
AC2
RH
∆H
RSN
Polyalkene
PE
73.7
0.0
73.7
-26.3
116.0
-52.7
5
PCE
43.4
-16.6
43.4
-56.6
52.1
-51.7
8
PC
40.4
-16.6
40.4
-59.6
59.4
-52.4
9
Polyester
PPC
39.3
13.1
52.4
-47.6
129.7
-59.1
6
PCHDC
43.4
-9.4
50.6
-49.4
52.1
-53.0
9
PCHC
40.3
-9.9
47.0
-53.0
59.3
-53.9
10
Polyether
PCEDO
43.4
-16.6
43.4
-56.6
52.1
-53.0
9
PCEO
40.3
-16.6
40.3
-59.7
59.3
-53.9
10
Total biomass carbon utilisation
The above section concludes that for biopolymer production the
highest direct carbon utilisation efficiency is 73.7%, whilst most
of the polymers can only utilise less than half of the total
biomass carbon. It is therefore necessary to take into account the
fate of by-products generated in the system. As mentioned before,
most of the by-products are hydrocarbons such as C4 to C934–37,
which can be either sold or directly combusted. In this research,
these by-products are assumed to be burned to provide power and
heat, and the produced CO2 is sent back to the system for polymer
production if possible. The results are presented as RC2 and AC2 in
Table 1.
From the table, it is observed that unlike the results obtained
before, CO2 is emitted by the system in all cases (negative numbers
in AC2). The highest biomass carbon recovery efficiency for
individual biopolymer production still comes from PE (73.7%).
However, the second-best candidate switches from the three polymers
(PCE, PCHDC, PCEDO, with an efficiency of 43.4%) to PPC (52.4%). It
is attributed to the fact that CO2 is a raw material for PPC
synthesis, and thus part of the by-product CO2 can re-enter the
system for polymer production. Similarly, the total carbon recovery
efficiency of PCHDC increases from 43.4% to 50.6%, ranking it as
the third best candidate. Overall, 11 candidates out of the 17
polymers have a total carbon recovery efficiency higher than 40%,
amongst which 3 products (PE, PPC, PCHDC) even yield an efficiency
over 50%, indicating the great potential of using biomass for
future industrial biopolymer production.
When aiming to maximise total biomass carbon recovery efficiency
regarding to each polymer category and the entire system (listed in
Table S4), a single biopolymer product is always estimated to be
the best choice, followed by the synthesis of multiple polymers as
the second-best scheme. This result further suggests that when a
single polymer is not of the main interest of the industry,
producing multiple biopolymers simultaneously from biomass can
serve as an alternative to diversify the product types and yield
similar carbon recovery efficiency. It is also concluded that
identifying a suitable network boundary (e.g. whether or not
including the fate of by-products) before decision-making is
important due to its significant impact on product ranking.
Polymer selection without external hydrogen supply
For bioproduct synthesis, hydrogen is provided to remove biomass
oxygen content and tailor final product composition. In particular,
both PE and PPC, the best two candidates identified above, require
a significant amount of additional hydrogen supply. However, as
hydrogen is predominantly generated by fossil fuels, in order to
produce carbon-neutral biopolymers, it is worth investigating the
biopolymer ranking when hydrogen is purely provided from biomass
through gasification, and if necessary, also from the water-gas
shift reaction (r71).
Table 2 shows the simulation results without additional hydrogen
supply (top three polymers in each category, full list can be found
in Table S3). From Table 2, it is found that PE is still the best
option when aiming to maximise the carbon utilisation efficiency of
individual polymers. Whilst its carbon utilisation efficiency is
significantly reduced by 38.6%. For direct carbon utilisation
efficiency, same as before, the second-best candidate is any of the
three polymers (PCE, PCHDC, PCEDO), each of which has an
utilisation efficiency of 33.8%, reduced by 22.1% compared to the
previous case. However, when considering the total carbon recovery
efficiency, the second-best option switches from PPC to PCHDC. In
this case, not only is the biopolymer candidate changed, but also
the carbon recovery efficiency is decreased by almost 40%.
Although the conclusion that the biopolymer ranking results are
subject to the change of reaction network scope is also valid in
this case, both direct carbon utilisation efficiency and total
carbon utilisation efficiency of all the investigated biopolymers
are found to reduce significantly. This is due to the fact that
without extra hydrogen supply, a large amount of the oxygen inside
the biomass has to be removed by forming CO2, thus much less carbon
can be utilised for biopolymer production. Moreover, because
different biopolymers need different amounts of hydrogen for their
synthesis, the lack of available hydrogen can also switch the
biopolymer ranking. For example, for polyester production, the rank
of PPC drops from 1st to 5th due to it requiring almost twice as
much external hydrogen as other polyesters.
Table 2: Maximum biopolymer production from biomass without
external hydrogen supply.
Polymer
RC1
AC1
RC2
AC2
∆H
RSN
Polyalkene
PE
45.2
-38.6
45.2
-54.8
-48.3
6
PCE
33.8
-35.0
33.8
-66.2
-49.4
9
PC
30.5
-36.9
30.5
-69.5
-49.7
10
Polyester
PCHDC
33.8
-29.3
39.4
-60.6
-50.4
10
PCHC
30.5
-31.8
35.6
-64.4
-50.8
11
PCHC/PCHDC
32.1
-30.7
34.8
-65.2
-50.6
11
Polyether
PCEDO
33.8
-35.0
33.8
-66.2
-50.4
10
PCEO
30.5
-36.9
30.5
-69.5
-50.8
11
In terms of each polymer category and the entire reaction
network, it is found that for both RC1 and RC2, the best candidate
is always a single biopolymer. Same as the case where extra
hydrogen is provided, the second-best candidate is always multiple
polymers (shown in Fig. 2(b) and Table S4). It is notable, however,
that in this case there are a set of multiple polymers of which the
total carbon utilisation efficiency is in between the best single
polymer and the second-best single polymer.
Comparison of two biopolymer synthesis scenarios
Comparing the two synthesis routes (direct vs. via syngas), the
former one has the significant advantage of reducing energy
consumption, since monomers are synthesised from biomass by
utilising solar energy. However, biopolymer production in this
scenario is much lower than that in the latter scenario, with an
average carbon utilisation efficiency of 8.2% and a highest
efficiency of 11.4% corresponding to two individual biopolymers
(PCE and PCHDC) when external hydrogen is supplied. This efficiency
is only 26.0% and 22.5% of that of PCE and PCHDC synthesised
through the syngas scenario, respectively.
As mentioned before, the low polymer production from this
scenario is caused by the low content of monomers in biomass15,16.
Therefore, although the two synthesis strategies in this reaction
network are set in parallel, in practice the direct synthesis
strategy can be used as a pre-processing procedure to separate
monomers from biomass under mild operating conditions and to reduce
investment cost as well as energy demand (e.g. 20% of energy
requirement for monomer synthesis).
Optimal syngas H/C ratio
From the above sections, it is concluded that hydrogen supply is
vital to biopolymer production, as both carbon utilisation
efficiency and biopolymer ranking heavily rely on the amount of
external hydrogen. Because in this study syngas is considered as
the hydrogen pool (containing both external hydrogen and biomass
derived hydrogen) for monomer synthesis, it is necessary to
estimate its optimal H2 to CO (H/C) ratio.
For example, when the objective is to maximise total carbon
utilisation efficiency, the correlation between syngas H/C ratio
and highest biomass carbon recovery efficiency is presented in Fig.
3. From the figure, it is seen that total carbon utilisation
efficiency increases linearly with the increasing H/C ratio from
0.8 up to 2.0 where the utilisation efficiency peaks at 73.7%,
beyond which a dramatic decrease of RC2 is observed. In this study,
as PE is estimated to be the best candidate for biomass carbon
recovery, it is found to be the only product during the first stage
(H/C ratio ranges from 0.8 to 2.0). The linear increase of PE
production may be introduced by the increasing flux of methanol and
ethanol synthesis reactions (r12 and r16 shown in Fig. S3) in its
synthesis pathway, since the stoichiometry ratio of H2 to CO in
both reactions are 2:1. This linear correlation is represented by
Eq. 4(a) (R2 = 1.0).
Figure 3: Highest total carbon recovery efficiency w.r.t. syngas
H/C ratio
However, if an excessive amount of hydrogen is supplied and the
system is compelled to use up all the hydrogen, the reaction steps
which can consume hydrogen for biopolymer synthesis will be
activated and their flux will be enhanced markedly. For instance,
in the current study PPC is identified to be the second-best single
biopolymer candidate in terms of recovering feedstock carbon, and
its stoichiometry ratio of H2 to CO is 2.3:1. Thus, when an
effectively unrestricted amount of hydrogen is added, its synthesis
pathway becomes active and the final polymer produced within this
stage (H/C ratio higher than 2.0) is a mixture of PE and PPC (shown
in Fig. S1). Nonetheless, because the selectivities of reactions in
PPC synthesis route are lower than those in PE synthesis route,
e.g. r19 has a selectivity of only 45.4%38, more biomass carbon is
converted to by-products and RC2 is dramatically reduced. This
correlation is formulated as Eq. 4(b) (R2 = 0.99). Furthermore, it
is important to clarify that the optimal C/H ratio (2:1) identified
in this study is not a decision variable, but rather an observed
consequence of optimisation specific to the current reaction
network.
Sensitivity analysis
Effects of selectivity on reaction network
Since sensitivity reflects the importance of reaction steps on
the performance of reaction network, by ranking sensitivities the
significance of each reaction step on the current system can be
obtained. Due to the large number of reaction steps, the full rank
of sensitivities of RC2 with respect to each reaction selectivity
under 10 different objective functions presented in Table S5 are
listed in Table S6.
From Table S6, it can be concluded that in most cases, an
individual reaction step only has an effect on the system under
some specific objective functions, implying that it may be
important to the synthesis of some specific biopolymers. However,
it is also found that regardless if additional hydrogen is
provided, all the objective functions are sensitive to r1, r12 and
r16. This suggests that the synthesis of syngas (r1), methanol
(r12), and ethanol (r16) are of critical importance to the current
reaction network, since the majority of monomers are derived from
these starting materials. Furthermore, most of the objectives are
also sensitive to r6, r11 and r25. This is probably because these
reactions are related to the synthesis of methane (r6),
1,3-butadiene (r25) and acetylene (r11), which are the initial
specified intermediates for the generation of important monomers
such as 1,4-cyclohexadiene (monomer precursor of polyalkene (PCE),
polyether (PCEDP) and polyester (PCHDC and PCHC/PCHDC)).
In addition, these reactions are observed to show more
significance to the system when external hydrogen is not provided.
Such an observation can be attributed to two reasons. The first one
is that in this reaction network without additional hydrogen
supply, 1,4-cyclohexadiene based polymers such as PCHDC, PCEDO, and
PCE are the best or second-best polymer candidates in each polymer
category. Therefore, reaction steps directly towards the synthesis
of 1,4-cyclohexadiene, e.g. r6, r11 and r25, will have more
influence on the system. The second reason is that because hydrogen
is limited in the current system, reactions (e.g. r6, r11 and r16)
which involve the consumption or generation of hydrogen will have
greater significance to the reaction network.
In this study, in order to regulate the composition of syngas,
two reactions, methane steam reforming (r5) and water-gas shift
reaction (r71) are included to allow the system to increase its
hydrogen content. From sensitivity analysis, it is seen that when
hydrogen is provided, neither of the reactions have effects on the
system (0 sensitivity), since the system has enough hydrogen to
remove biomass oxygen and convert biomass carbon into polymers.
However, when hydrogen is not provided, r71 is immediately
activated to generate hydrogen for the further synthesis of
biopolymer. With the activation of this reaction, r5 remains
inactive as its sensitivity is still 0. Whilst if the water-gas
shift reaction is not embedded into the system, methane steam
reforming can also be significantly stimulated to produce hydrogen
and its sensitivity can rise sharply (presented in Table S6).
The above conclusion also suggests unsurprisingly that the
system prefers to choose the water-gas shift reaction to tailor
syngas composition instead of methane steam reforming. Moreover,
since the operation cost of water-gas shift reaction is less
expensive than that of methane steam reforming, the water-gas shift
reaction should be used as the first choice for further process
design. Finally, it is also concluded that when external hydrogen
is not provided, more reactions become inactive for polymer
production.
Effect of key reactions on biopolymer production
To further explore the reaction network stability and
demonstrate whether the biopolymer ranking is still reliable when
the most influential parameters are changed, the selectivity of r6,
r11, r12, r16 and r25 are increased by 10%. In addition, due to the
importance of r71 under no extra hydrogen supply conditions, it is
also included here.
From Table 3, it is found that with enough hydrogen supply, PE
always ranks as the first candidate, and its carbon recovery
efficiency is not very sensitive to key reactions. Similarly, PPC
is not sensitive to any reactions except r12. On the contrary,
PCHDC is highly sensitive to all the reactions, mainly because all
of they are involved in its synthesis pathway. Hence, in many cases
because of the increase in reaction selectivity, PCHDC surpasses
PPC and becomes the second-best biopolymer candidate. It indicates
that under the conditions of sufficient hydrogen supply, the
current reaction network is somewhat sensitive as its biopolymer
ranking strongly depends on the selectivity of key reactions.
Table 3: Rank of biopolymers with respect to the change of key
reaction selectivity. The sensitivity of biopolymer RC2 w.r.t.
reaction selectivity is listed in brackets.
With enough hydrogen supply
r16
r12
r6
r25
r11
1st
PE (1.0)
PE (0.475)
PE (0.0)
PE (0.0)
PE (0.0)
2nd
PCHDC (0.573)
PPC (1.0)
PCHDC (0.395)
PCHDC (0.553)
PCHDC (0.395)
3rd
PPC (0.0)
PCHDC (0.277)
PPC (0.0)
PPC (0.0)
PPC (0.0)
Without external hydrogen supply
r16
r12
r6
r25
r11
r71
1st
PE (0.995)
PE (0.512)
PE (0.0)
PE (0.0)
PE (0.0)
PE (0.203)
2nd
PCHDC (0.736)
PCHDC (0.368)
PCHDC (0.330)
PCHDC (0.737)
PCHDC (0.330)
PCHDC (0.103)
3rd
PCHC (0.816)
PCHC (0.379)
PCHC (0.309)
PCHC (0.702)
PCHC (0.309)
PCHC (0.142)
Although PPC can be ranked as the second-best candidate under
current recorded reaction selectivities and by increasing the
selectivity of r12, it is notable that r12 already has a high
selectivity (0.989)39 and therefore increasing its selectivity will
not significantly increase biopolymer production. Nonetheless, as
many of the reaction steps for PCHDC synthesis have lower
selectivities (e.g. r16 with a selectivity of 0.74440 and r25 with
a selectivity of 0.69041), there is significant space to further
improve the selectivities of these reactions and enhance the
production of PCHDC. Thus, compared to PPC, PCHDC is more promising
for research into biomass based polymer production.
In contrast to the conditions where additional hydrogen is
provided, the biopolymer ranking is stable when there is no
external hydrogen supply. This is because although both PCHDC and
PCHC are sensitive to all the reactions included in Table 3, their
sensitivities with respect to each reaction are highly similar and
thus the order of rank is kept the same. Therefore, even if their
carbon utilisation efficiency can be improved by enhancing reaction
selectivities, the ranking order is unlikely to change.
Polymer selection based on RSN
Finally, the number of reaction steps for biopolymer production
(RSN) is considered as the economic criterion and its effect on
biopolymer selection is evaluated. This is because for each
reaction step, there is in general one reactor and at least one
separation unit. Thus, the lower the RSN of a reaction pathway, the
lower the process investment cost will be. Table 4 summaries the
rank of biopolymers for maximising total carbon utilisation
efficiency per reaction step (RC2/RSN).
Table 4: Rank of biopolymer w.r.t. total biomass carbon
utilisation efficiency per reaction step
With external hydrogen supply
Without external hydrogen supply
Rank
Polymer
RC2/RSN
Rank
Polymer
RC2/RSN
1st
PE
14.74
1st
PE
7.53
2nd
PPC
8.73
2nd
PPC
4.40
3rd
PP
8.04
3rd
PP
4.18
4th
PE, PCE
6.50
4th
PCHDC
3.94
5th
PCHDC
5.62
5th
PCE
3.76
From the table, it is found that under either condition, the
first three biopolymers are always PE, PPC and PP, although their
carbon utilisation efficiencies per reaction step are both reduced
by almost 50% if external hydrogen is not provided. This decrease
is not only caused by the fact that more carbon has to be used for
biomass oxygen removal (i.e. emitted as CO2), but also attributed
to the activation of the water-gas shift reaction which increases
the number of reaction steps. In addition, the best polyester
candidate switches from PCHDC to PPC because of the lower RSN.
Moreover, it is concluded that although producing multiple polymers
and single polymer can obtain similar carbon yields, most of the
single polymer synthesis pathways have a RSN less than 10, whilst
that of multiple polymers is mainly over 20. This means that
compared to producing multiple polymers, synthesising a single
biopolymer may be more economically viable.
When considering the economic criterion, it is also found that
the rank of best single biopolymer is kept constant (1st: PE, 2nd:
PPC, 3rd: PP, 4th: PCHDC) even if the reaction selectivities listed
in Table 4 are increased by 10%. This suggests that although PCHDC
can recover more carbon than PPC if the selectivity of key
reactions is improved, its high investment cost may still remain a
challenge to prevent its industrialisation compared to PPC.
Therefore, by including the effect of RSN, it is concluded that the
ranking of current biopolymers and their associated reaction
pathways are reliable when the key reaction selectivities are
changed within a certain range.
In addition, although the amount of energy required throughout
the reaction pathway can be considered as another selection
criterion, it is found that in the current study, all biopolymer
production pathways generate/require similar amount of energy on a
nett basis. Thus ∆H should not be chosen as a reaction pathway
selection criterion, due to its low sensitivity. To effectively
utilise this factor, it is necessary to embed separation units into
the promising biopolymer production pathways selected in the
current reaction network, so that more detailed energy consumption
of these pathways can be revealed for future comparison. Moreover,
with more detailed energy consumption information, process
profitability can also be estimated23. This will further quantify
the economic criterion which cannot be completed here due to the
limited information at this early stage of process design and
screening.
Conclusions
In the current research, a reaction network consisting of 3
polymer categories, 20 polymers, 65 chemicals and 76 reactions is
constructed to screen and narrow down a large number of biopolymer
synthesis pathways for the identification of the most promising
sustainable polymers. It is found that the ranking of biopolymers
is sensitive to the selection criteria such as network boundary,
hydrogen supply, and process economic potential. However, in all
cases polyethylene is estimated to be the best candidate because of
its high reaction selectivities and lowest number of reaction
steps. It is also concluded that when the selection criterion is
changed (e.g. maximising the production of a category of polymers),
a combination of multiple polymers rather than a single polymer may
serve as the best option. When including both material utilisation
efficiency and process economic potential, polypropylene carbonate
is identified to be the second-best candidate regardless if
external hydrogen is supplied. As both of them are commonly used
polymers mainly synthesised via the petroleum industry, it is
expected that more attention should be paid on their bio-synthesis
routes in near future.
In terms of newly proposed biopolymers which are generated
directly from biomass via synthesis and extraction, more effort
should be given to the 1,4-cyclohexadiene based polymers, in
particular polycyclohexadiene carbonate, instead of limonene
derived products, due to their much higher carbon utilisation
efficiency. Based on the sensitivity analysis, it is found that
there is significant space to increase the yield of these
candidates. In addition, although multiple polymers can also result
in a similar material utilisation efficiency, when considering the
potential economic cost, these products become less profitable
compared to single biopolymers.
Furthermore, the current study demonstrates that hydrogen supply
is of critical importance to facilitate the conversion from biomass
to biopolymers. Without external hydrogen supply, biopolymer
production will be reduced by 50%, with more CO2 emitted into the
environment. This means that in order to generate carbon-neutral
biopolymers, it is necessary to seek sustainable hydrogen
production routes (e.g. electrolysis) to replace the current fossil
fuel based hydrogen synthesis pathway. Moreover, to guarantee the
maximum biomass carbon utilisation efficiency, it is important to
keep the syngas H/C ratio at 2:1. In addition, when aiming to
reduce the amount of external hydrogen supply, incorporating
water-gas shift reaction rather than methane steam reforming into
the current system can be considered as a better strategy.
Finally, it is notable to emphasise that this research only
focuses on the biopolymers (containing only carbon, hydrogen and
oxygen) which can be synthesised purely from biomass wastes. Thus,
other commercialised polymers which require a significant amount of
additional elements (e.g. nitrogen) for their synthesis are not
included. For example, although polyhexamethylene adipamide (Nylon
66) has been widely used in daily life and industry42, due to its
much higher nitrogen content (12.4 wt%) than that of biomass
(0.1~2.5%)42,43, external nitrogen source has to be sought and its
synthesis route is not embedded here. In terms of future work, this
reaction network will be extended to include both polyamides and
environmentally friendly nitrogen sources to determine more
promising green polymers. Moreover, more detailed economic analysis
including operation cost and raw materials cost will be conducted
by using the recently proposed method23.
Acknowledgement
This research is supported by the EPSRC project under grant
EP/L017393/1, “Sustainable Polymers”.
Supporting Information
Number of pages: 18 pages.
Table S1 (page S2): List of current investigated
biopolymers.
Table S2 (page S3): Maximum biopolymer production from biomass
with external H2 supply.
Table S3 (page S4): Maximum biopolymer production from biomass
without external H2 supply.
Table S4 (page S5): Ranking of the biopolymer candidates in the
reaction network.
Table S5 (page S6): Different objective functions investigated
in the current study.
Table S6 (page S7): Rank of sensitivity of total biomass carbon
utilisation efficiency w.r.t. each reaction selectivity under
different objective functions.
Table S7 (page S9): Summary of reaction kinetic information in
the reaction network.
Figure S1 (page S12): Biopolymer synthesis routes with
unrestricted hydrogen supply.
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For Table of Contents Use Only
Synopsis: This work aims to identify the most promising and
environmentally friendly synthesis routes to produce sustainable
biopolymers from biomass wastes.
TOC/Abstract Graphic
32
