Research Article pubs.acs.org/journal/ascecg
Screening Synthesis Pathways for Biomass-Derived Sustainable Polymer Production Dongda Zhang,*,† Ehecatl Antonio del Rio-Chanona,‡ and Nilay Shah† †
Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom
‡
S Supporting Information *
ABSTRACT: Sustainable polymers derived from biomass have been extensively investigated to replace petroleum-based polymers and fulfill the ever-increasing market demand. Because of the diversity of biomass and polymer categories, there exists a large number of synthesis routes from biomass to polymers. However, their productive and economic potentials 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. First, 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 that can be produced from biomass, respectively, because of their highest carbon recovery efficiency of ∼70%. Second, an external hydrogen supply is vital to guarantee the high yield of biopolymer, because, without enough hydrogen, biopolymer productivity can be reduced by half. Third, through sensitivity analysis, the current biopolymer ranking is verified to be stable, subject to a moderate change of reaction selectivities and hydrogen supply. Therefore, this study provides a clear direction for future biopolymer research. KEYWORDS: Biomass wastes, Sustainable polymers, Synthesis pathways, Reaction network flux analysis, Hydrogen utilization, Sensitivity analysis
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be utilized for future polymer production since the 20th century.3 In particular, biomass and its derived materials are considered to be the primary organic carbon source to replace fossil fuels, since they are generated from atmospheric CO2 through photosynthesis and have great potential to produce carbon-neutral polymers.1 Furthermore, their abundant annual production can also guarantee the stability of feedstock supply chain for industrial-scale polymer production.3 So far, research for the synthesis of biopolymers from biomass has been extensively conducted. Different biomass feedstocks, including citrus waste,6,7 forestry residues,8 agricultural waste,1,9 and microalgae biomass,10,11 have been demonstrated to be able to generate biopolymers through various pathways. In order to save energy costs and reduce process complexity, chemicals that can be easily extracted or synthesized from biomass are particularly investigated for sustainable polymer production.7,8,10 For instance, it has been concluded that limonene extracted from citrus waste,2,7 1,4-cyclohexadiene synthesized from plant oil,8,12 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
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 polymerization technologies, versatile polymers characterized by highly tunable properties have been synthesized,1 and they have been widely applied in coatings, engineering plastics, adhesives packaging materials, diagnostics, and electronics.1,2 It is estimated that the global production of polymers has exceeded 260 million tonnes in 2009 and will triple by 2015.3 For example, ∼31 million tonnes of polymers were produced in the United States and mainly used in packaging and durable goods in 2010,3 and the United Kingdom produced 2.5 million tonnes of polymers in 2012 with an annual turnover of £19 billion and the creation of over 180 000 job opportunities.4 Despite the ever-increasing global demand, however, currently, the dominant feedstocks for industrial polymer production are derived from nonrenewable 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 gas.2,5 Because of the dwindling of these resources and their effects on the environment, it has been suggested that other sustainable and environmentally friendly resources should © 2017 American Chemical Society
Received: February 10, 2017 Revised: April 7, 2017 Published: April 10, 2017 4388
DOI: 10.1021/acssuschemeng.7b00429 ACS Sustainable Chem. Eng. 2017, 5, 4388−4398
Research Article
ACS Sustainable Chemistry & Engineering
embedded into RNFA to extend its application for multicriteria decision analysis from technology, economic, and environmental aspects.22,24,26 Despite its success, it is notable that RNFA has been used almost exclusively for bioenergy, in particular, biofuels-related process design and optimization.22−24,26,27 Therefore, in this work, RNFA is applied as an efficient tool to identify several 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 eqs 1a−1c:
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 analyzed. Despite these achievements, three challenges still severely prevent the further production of biomass-based polymers. First, 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. Nevertheless, because of the lack of essential kinetic and economic information, this research has not been conducted. Second, although chemicals that are directly extracted or synthesized 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 a laboratory scale with little effort being paid on assessing their industrialization 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 commercialized 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 which is a methodology inspired from flux balance analysis (FBA),18 is applied in the current study. A literature review of RNFA is presented in the “Methodology” section, followed by the detailed discussion of the original contributions of this work presented in “Results and Discussion” section.
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max c Tf
(1a)
f
subject to
M·f = b
(1b)
f, b ≥ 0
(1c)
where f is a vector of size n and represents the molar fluxes of the reactions, c the objective function coefficient vector of size n, M the stoichiometric matrix of r × n, and b the accumulation of the reactants (a vector of size r). The parameter r represents the number of chemicals and n is the number of reaction and exchange fluxes. In this study, c is a vector of size 78 (1 × 78 matrix), M is a 65 × 78 matrix, f is a vector of size 78 (78 × 1 matrix, corresponding to 76 reactions and two fluxes for additional hydrogen and CO2 supply, respectively), and b 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 in the Supporting Information. Specific to the current study, in order to maximize the biomass carbon utilization 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 byproducts or wastes. Once constructed, the reaction network is transformed to a mathematical model where the mass balance of each chemical (eq 1c) 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., minimizing heat supply). Since, in this study, all intermediates can be converted to biopolymers, b in eq 1b is equal to zero if it represents an intermediate, and it is non-negative for final products (polymers). n
ΔHR =
METHODOLOGY
∑ fi ·ΔHRi i=1
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 circumstances.17,20 Because of its steady-state assumption, reaction information such as enzyme kinetics or metabolites concentrations, which are always difficult to measure, is not needed.21 Therefore, FBA is considered as the first choice to simulate metabolic reaction networks whenever the assumption is valid.18,19 Inspired by this methodology, reaction network flux analysis (RNFA) was recently developed for sustainable chemical processes design.17 RNFA is introduced as an optimization-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 network.17 Because of its flexibility and efficiency, it has been applied to screen many 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 wastes.23,24 Moreover, multiobjective optimization algorithms25 have also been recently
(2)
where n is the total number of reactions in the reaction network, ΔHRi is the enthalpy of reaction i, and ΔHR is the entire energy generated in the reaction network. Generally, solutions of eq 1b are not unique, because the number of reactants is larger than the rank of the stoichiometric matrix M. To ascertain a particular molar flux distribution, an objective function (eq 1a) and additional constraints (eq 1c) must be determined. Based on different objective functions, such as maximizing product yield (mass balance criteria), maximizing process profit (economic criteria), and minimizing 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 utilize these carbon sources adequately, 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 (synthesized from plant oil),12 isoprene (excreted from microalgae),13 and lactic acid (excreted from 4389
DOI: 10.1021/acssuschemeng.7b00429 ACS Sustainable Chem. Eng. 2017, 5, 4388−4398
Research Article
ACS Sustainable Chemistry & Engineering
Figure 1. Reaction network from biomass to biopolymers. microalgae).28 However, because of the low content of these chemicals in biomass (carbon distribution of 40%, among which 3 products (PE, PPC, PCHDC) even yield an efficiency of >50%, indicating the great potential of using biomass for future industrial biopolymer production. When aiming to maximize total biomass carbon recovery efficiency, with regard to each polymer category and the entire system (listed in Table S4 in the Supporting Information), 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 4393
DOI: 10.1021/acssuschemeng.7b00429 ACS Sustainable Chem. Eng. 2017, 5, 4388−4398
Research Article
ACS Sustainable Chemistry & Engineering and Table S4 in the Supporting Information). However, note that, in this case, there are a set of multiple polymers where the total carbon utilization efficiency is 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 synthesized from biomass by utilizing solar energy. However, biopolymer production in this scenario is much lower than that in the latter scenario, with an average carbon utilization efficiency of 8.2% and a highest efficiency of 11.4%, corresponding to two individual biopolymers (PCE and PCHDC) when the external hydrogen is supplied. This efficiency is only 26.0% and 22.5% of that of PCE and PCHDC synthesized through the syngas scenario, respectively. As mentioned previously, the low polymer production from this scenario is caused by the low content of monomers in biomass.15,16 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 preprocessing 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 utilization efficiency and biopolymer ranking heavily rely on the amount of external hydrogen. Because in this study, syngas is considered to be the hydrogen pool (containing both external hydrogen and biomass derived hydrogen) for monomer synthesis; hence, it is necessary to estimate its optimal H2 to CO (H/C) ratio. For example, when the objective is to maximize total carbon utilization efficiency, the correlation between syngas H/C ratio and highest biomass carbon recovery efficiency is presented in Figure 3. From the figure, it is seen that total carbon utilization
RC = 34.071(H/C) + 5.660
(H/C) ≤ 2.0
RC = − 226.63(H/C) + 525.34
(H/C) ≥ 2.0
(4a) (4b)
However, if an excessive amount of hydrogen is supplied and the system is compelled to consume all the hydrogen, the reaction steps that 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 secondbest 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 Figure S1 in the Supporting Information). Nonetheless, because the selectivities of reactions in PPC synthesis route are lower than those in the PE synthesis route, e.g., r19 has a selectivity of only 45.4%,38 more biomass carbon is converted to byproducts and RC2 is dramatically reduced. This correlation is formulated as eq 4b (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 optimization 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. Because of the large number of reaction steps, the full rank of sensitivities of RC2, with respect to each reaction selectivity under the 10 different objective functions presented in Table S5 in the Supporting Information, are listed in Table S6 in the Supporting Information. 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: (1) In this reaction network, which lacks an 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 toward the synthesis of 1,4-cyclohexadiene, e.g., r6, r11, and r25, will have more influence on the system. (2) Since the amount of hydrogen is limited in the current system, reactions (e.g., r6, r11, and r16), which involve the
Figure 3. Highest total carbon recovery efficiency, with respect to syngas H/C ratio.
efficiency increases linearly with the increasing H/C ratio from 0.8 up to 2.0 while the utilization efficiency peaks at 73.7%, beyond which a dramatic decrease of RC2 is observed. In this study, since 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 Figure S3 in the Supporting Information) in its synthesis pathway, since the stoichiometry ratios of H2 to CO in both reactions are 2:1. This linear correlation is represented by eq 4a (R2 = 1.0): 4394
DOI: 10.1021/acssuschemeng.7b00429 ACS Sustainable Chem. Eng. 2017, 5, 4388−4398
Research Article
ACS Sustainable Chemistry & Engineering Table 3. Rank of Biopolymers, with Respect to the Change of Key Reaction Selectivitya r16
a
r12
1st 2nd 3rd
PE (1.0) PCHDC (0.573) PPC (0.0)
PE (0.475) PPC (1.0) PCHDC (0.277)
1st 2nd 3rd
PE (0.995) PCHDC (0.736) PCHC (0.816)
PE (0.512) PCHDC (0.368) PCHC (0.379)
r6
r25
r11
With Sufficient Hydrogen Supply PE (0.0) PE (0.0) PCHDC (0.395) PCHDC (0.553) PPC (0.0) PPC (0.0) Without an External Hydrogen Supply PE (0.0) PE (0.0) PCHDC (0.330) PCHDC (0.737) PCHC (0.309) PCHC (0.702)
r71
PE (0.0) PCHDC (0.395) PPC (0.0) PE (0.0) PCHDC (0.330) PCHC (0.309)
PE (0.203) PCHDC (0.103) PCHC (0.142)
The sensitivity of biopolymer RC2, with respect to reaction selectivity, is listed in parentheses.
since 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 utilization 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 generally 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 summarizes the rank of biopolymers for maximizing total carbon utilization efficiency per reaction step (RC2/RSN).
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 are included to allow the system to increase its hydrogen content: methane steam reforming (r5) and water−gas shift reaction (r71). From sensitivity analysis, it is seen that when hydrogen is provided, neither of the reactions have effects on the system (zero 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, since its sensitivity is still 0. While 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 the 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, with regard to 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, because of the importance of r71 under no extra hydrogen supply conditions, it is also included here. Table 3 shows that, with a sufficient 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. In contrast, PCHDC is highly sensitive to all the reactions, mainly because all of them 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, because its biopolymer ranking is strongly dependent on the selectivity of key reactions. 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,
Table 4. Rank of Biopolymer, with Respect to Total Biomass Carbon Utilization Efficiency Per Reaction Step (RC2/RSN) With External Hydrogen Supply
Without External Hydrogen Supply
rank
polymer
RC2/RSN
rank
polymer
RC2/RSN
1st 2nd 3rd 4th 5th
PE PPC PP PE, PCE PCHDC
14.74 8.73 8.04 6.50 5.62
1st 2nd 3rd 4th 5th
PE PPC PP PCHDC PCE
7.53 4.40 4.18 3.94 3.76
Table 4 shows that, under either condition, the first three biopolymers are always PE, PPC, and PP, although their carbon utilization efficiencies per reaction step are both reduced by almost 50% if no external hydrogen is provided. This decrease is not only caused by the fact that more carbon must 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 value of
