Renewable Rubber and Jet Fuel from Biomass: Evaluation of

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Renewable Rubber and Jet Fuel from Biomass: Evaluation of Greenhouse Gas Emissions and Land Use Tradeoffs in Energy and Material Markets Bahar Riazi, Mukund Karanjikar, and Sabrina Spatari ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b03098 • Publication Date (Web): 10 Sep 2018 Downloaded from http://pubs.acs.org on September 11, 2018

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ACS Sustainable Chemistry & Engineering

Renewable Rubber and Jet Fuel from Biomass: Evaluation of Greenhouse Gas Emissions and Land Use Tradeoffs in Energy and Material Markets Bahar Riazi1, Mukund Karanjikar2, Sabrina Spatari1* 1 Drexel University, Department of Civil, Architectural, and Environmental Engineering, 3141 Chestnut Street, Philadelphia, PA, USA, 2 Technology Holding LLC, Salt Lake City, UT, USA *Corresponding author, [email protected], 215-571-3557

1 ACS Paragon Plus Environment

ACS Sustainable Chemistry & Engineering 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract

Biomass holds great promise for producing fuels, chemicals, and polymeric materials to address climate change and energy security. Polyisoprene, the raw material used to produce rubber, can be produced from rubber trees and synthesized from the monomer that is derived from both petrochemical feedstock and fermentable sugars in biomass. We explore select life cycle environmental impacts of an alternative pathway for producing polyisoprene from corn stover and forest residue along with dimethyl cyclooctadiene, a high density jet fuel blend. Following feedstock pretreatment and hydrolysis, the sugars generated are fermented to methyl butenol (MBE) and then dehydrated to isoprene, which is further polymerized to polyisoprene. Within the same dehydration reactor, MBE undergoes catalytic conversion to dimethyl cyclooctadiene. We use life cycle assessment to evaluate the greenhouse gas (GHG) emissions and land requirements for producing polyisoprene produced from forest residue and corn stover and compare these to polyisoprene produced from petroleum and rubber trees. Both corn stover and forest residue-based polyisoprenes have negative global warming potential (GWP) between -5.7 to -1.3 kg CO2e/kg polyisoprene, even while considering sources of uncertainty. Moreover, polyisoprene from rubber tree plantations in Southeast Asia has the most significant land use intensity compared to other feedstocks.

Keywords: Biopolymers, Jet fuel, Natural rubber, Petroleum alternatives, Life cycle assessment, Greenhouse gas emissions, Land use intensity


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Introduction Polyisoprene is a commercially valuable material that it is used in the manufacture of more than 40,000 products.1-2 It can be extracted from natural rubber trees or synthesized from petroleum, currently the two main resources for the production of rubber products.3 One million tonnes of isoprene are produced globally each year from petrochemical resources, where most of the world’s synthetic isoprene is co-produced with ethylene cracking of light naphtha.4 To a lesser extent, it is also co-produced with pyrolytic gasoline processing of heavy naphtha.5 These production processes are energy intensive6 and have selectivities in the range of 2%-5% towards isoprene.7 If ethylene production or py-gas decline due to oversupply or due to changes in feedstock economics, as has been observed with recent use of surplus ethane from shale gas to produce ethylene,8 polyisoprene production suffers resulting in a higher market price. Similarly, the price of natural polyisoprene, one of the most important natural polymers commercially available, is volatile and depends on many factors including successful plant production, and the amount of polyisoprene exported from major producers such as Thailand, which recently decreased its export due to higher domestic demand.9-10 Due to increasing demand for polyisoprene, more than two million hectares of land were converted to rubber plantations from 2000 to 2010 in Southeast Asia.11 Conversion of forests to rubber tree plantations could worsen environmental conditions beyond deforestation alone as it affects water provision, productivity of soil, carbon stock dynamics, and biodiversity.10, 12 Moreover, global dependence on natural rubber is especially risky due to climate requirements that limit cultivation in tropical regions.2 Therefore, developing alternative sources for natural and synthetic rubber could satisfy the growing market demand for rubber products while sourcing feedstock from sustainable sources.13 To address resource, environmental, and economic sustainability of polyisoprene supply, two leading tire



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companies, Goodyear Tire & Rubber Company and Bridgestone Corp., have embarked on research to produce tires from bioisoprene.7 While much literature has evaluated life cycle environmental impacts of biofuels from lignocellulosic sources like forest and agricultural residues,14-18 with the exception of a few studies,19-21 less has been written about biomass as a resource for polymeric materials. Recent studies have evaluated the life cycle environmental performance of producing natural rubber from guayule, a shrub native to the southwest U.S., as a substitute for Hevea natural rubbers.9, 22-23 While guayule-based rubber may fulfill US independence from imported natural rubber22-23, land use requirements and GHG emissions resulting from irrigation, fertilizer usage and other agricultural activities may be intensive.9, 22-23 Moreover, land for guayule production could be limited such that resulting rubber production may not meet rising demand for diverse rubber products in domestic US and global markets. Thus, a diversity of sustainable feedstocks to supplement polyisoprene production is needed in the near term. Petrochemical resource use and climate change impacts due to GHG emissions dominate the environmental impact of synthetic rubber and other polymers.20 Moreover, land occupation24 and GHG emissions resulting from land use change may dominate the environmental impact of natural rubber. Therefore, life cycle GHG emissions and land use intensity metrics are critical for evaluating new means of producing natural and bio-synthesized rubbers. The land use intensity of new and existing renewable and non-renewable energy technology has been evaluated in life cycle assessment (LCA) studies of electricity production25 unconventional oil and gas production26 and uses of biomass for renewable energy.27 Moreover, select studies have found the GHG impacts from land use change to be significant for select biomass and unconventional petrochemical resources.28-31


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The majority of environmental life cycle assessment (LCA) research on biomass as a feedstock substitute for petroleum has focused on its use in liquid transport fuel markets.15, 17-18, 32-43 Forest residues and corn stover, two lignocellulosic non-food/feed biomass resources for ethanol and other transport fuels, have been shown to reduce GHG emissions in the range of less than 60% (not meeting the U.S. Renewable Fuel Standard, RFS2) to over 100% relative to gasoline from petroleum.15, 17-18, 31-35 However, multiple co-produced bio-products that can substitute both fossil fuels and petroleum-based chemicals and materials hold potential for reducing GHG emissions as well as diversifying feedstocks for those markets. Therefore, our objective is to evaluate the environmental performance of biomass feedstocks that are prevalent in the U.S., corn stover and forest residues,44 for the production of polyisoprene compared with two dominant resources, petroleum- and natural rubber tree-based polyisoprene. We study global warming potential (GWP) in 100 years and land use intensity where the sensitivity of key model parameters is evaluated to test the limits of life cycle GHG emissions. Methods This study applies life cycle assessment (LCA) to evaluate the environmental impact associated with polyisoprene production from corn stover and forest residue based on experiments and chemical process simulations formulated with experimental observations. We use sequential mass and energy balances following the International Organization for Standardization (ISO 2006)45 to construct a life cycle inventory (LCI) model and define the functional unit as 1 kg of polyisoprene. The metrics studied are land use intensity and the 100-year global warming potential (GWP100), as an indicator of climate change, measured in kg CO2 equivalent per kg of polyisoprene for the greenhouse gases, CO2, N2O, and CH4, based on AR5 of IPCC 2013.46 While Morais et al.7 defined land use as the amount of good agricultural soil required for the production of polyisoprene, we



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used a broader definition from LCA literature26-28 that includes all occupied land to estimate land use intensity of polyisoprene. Scenarios are defined to examine the effect of co-producing jet fuel as well as uncertain model parameters on life cycle metrics. Forest residue and corn stover based polyisoprene Renewable polyisoprene and jet fuel co-products are modeled as cradle-to-gate processes that include feedstock production and harvest where soil N2O emissions and change in soil organic carbon (SOC) are also considered (for the corn stover scenarios), field operations and preprocessing (for the forest residue scenarios), and transport of the feedstock, pretreatment, hydrolysis, fermentation, separation, dehydration, and polymerization to polyisoprene and dimerization to jet fuel (Figure 1). While the use cycle (storage and combustion) of jet fuel results in biogenic CO2 emissions and other greenhouse gases that are accounted in the life cycle inventory, this study assumes that the polyisoprene product will be used for long-lived goods that store C over the life of the material.11 Therefore, polyisoprene usage is excluded from the defined life cycle system boundary.


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Figure 1. Cradle-to-gate life cycle process flow diagram for the production of polyisoprene from forest residue and corn stover. Data from literature14-15,

32-34

are used for feedstock

production/handling, pretreatment, and hydrolysis; and experimental data parameterized into Aspen simulations are used to characterize the conversion of fermentable sugars in biomass to the final product and coproduct. Feedstock logistics The biorefinery modeled herein processes 2000 dry metric tons of biomass per day (MTPD) similar to the National Renewable Energy Laboratory’s (NREL) biorefinery design.47 For both feedstocks, the supply area is within an 80.5 km radius of the biorefinery.14, 16, 48-49 Hence, we assumed the average of 80.5 km transportation distance and include the return trip. Data from literature15 are used for the LCI inputs (nutrients and diesel for farm operations) of corn stover production and harvesting. The location chosen for the corn stover supply scenario is Iowa, as corn stover in that area can supply the chosen scale of the bioplant.15 The soil GHG emissions are obtained from prior literature15 where emissions were modeled in Boone county, Iowa based on climate data and historic yields using the DayCent model.50 We assumed that modeled N2O emissions and soil carbon change from Boone County represent all counties supplying the biorefinery.15 While nutrient inputs and energy for harvest will not vary greatly by corn stover supplying county, soil GHG emissions (N2O and SOC change) will vary based on local



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climatic conditions and soil type.51 Hence, we included a sensitivity analysis to examine the variability in soil GHG emissions. Maine forest residues can supply a 2000 MTPD biorefinery52 and are chosen for the forest residue based scenarios modeled herein, where preprocessing and field operation emissions are calculated based on prior research.34 However, other locations in the U.S. could supply a similarly sized biorefinery. For example, Martinkus et al.

53-54

identified several feasible locations within

the Pacific Northwest region for a jet fuel supply chain using forest residue, including in Washington, Montana, Oregon, and Idaho. Biomass conversion The biorefinery was modeled using a combination of experimental data to parameterize unit operation mass balances analyzed using Aspen Plus55 and Aspen HYSYS56 simulation software, where several unit operations were taken from literature14-15, 32-33 to build mass balances for the LCI. Pretreatment and hydrolysis Data from literature15, 17 are used for pretreatment and hydrolysis steps assuming dilute acid pretreatment, and enzymatic hydrolysis. The lignin portion of the feedstock is assumed to be fractionated after pretreatment and combusted to generate electricity and steam onsite providing energy required for the bioplant similar to prior models.32,

47

Based on our calculations, the

electricity generated is greater than that required for operating the biorefinery resulting in a surplus electricity credit that is assumed sold to the electricity grid 17, 33 that supplies the biorefinery region. Given the central Iowa location for corn stover feedstock supply, the Midwest Reliability Organization (MRO) electricity grid is used for LCA calculations of corn stover based scenarios.


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Similarly, the Northeast Power Coordinating Council (NPCC) electricity grid is applied for forest residue based scenarios in the northeast. Fermentation Fermentation using genetically engineered E. coli converts sugar to methyl butenol (MBE). Following strain development, media formulation, and industrial feedstock tolerance testing, this process is scaled up to a 10L reactor to determine initial operating parameters and ensure that MBE production is maintained in a fermentation environment. The energy requirements for fermentation are dominated by the heat required to warm the feed from room temperature (20º C) to the reaction temperature (37º C). Using fermentation experimental data [See Figure S1 in the Supporting Information (SI) for fermentation experimental details], the energy required for this step is calculated and used as an input to the LCI model. Also direct CO2 emissions from the fermentation step are calculated using the net stoichiometric fermentation reaction. (Equation 1). These emissions add to total GHG emissions of bioconversion and are related to biogenic uptake of the feedstock. 3Glucose + 4O2  8CO2 + 8H2O + 2MBE

Equation 1

Separation Since the fermenter product stream consists of 10% MBE by mass, purification to 99% MBE is necessary for further treatment. The McCabe-Thiele method was employed for initial modeling of MBE/benzene binary distillation. This analysis provided starting parameters (reflux and boilup ratios, theoretical stages, and distillate target composition) for the distillation model in Aspen Plus.55 The Aspen Plus model results provided mass and energy balances required for the LCI model [Figure S2 and Table S1] for the process flow diagram of separation modeled in Aspen Plus55 and distillation column parameters respectively).



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Dehydrogenation Experiments were performed with the industrial catalyst, Amberlyst®, to find the optimum temperature that would lead to the highest conversion [Figure S3 shows methyl betenol conversion to isoprene as a function of temperature and flow rates]. Based on experimental results, maximum conversion takes place at 110 °C which is below the boiling point of methyl butenol, therefore, the dehydrogenation reaction is in the liquid phase. A Heater model was used in Aspen Plus55 to determine the energy required to heat the distillation tower’s bottoms stream. Then a RStoic reactor model was used to determine the energy required for the reaction, assuming complete conversion of MBE to isoprene by calculating the enthalpy difference between a liquid stream of the column bottoms composition and a vapor stream with the corresponding amounts of isoprene, benzene, and water. Polymerization Polymerization takes place at 70 °C using chlorobenzene as solvent.57 Since the isoprene stream output of dehydrogenation is 90 °C, one cooling step before polymerization is necessary, the energy of which is calculated using Aspen HYSYS56 software. The energy required for polymerization is calculated using an activation energy of 3.3 kcal/mol based on literature.58 Dimerization MBE can be converted to dimethyl cyclooctadiene (jet fuel blend) through thermal dimerization of isoprene. The required energy of dimerization is calculated using thermodynamic equations assuming dimerization takes place at 200 °C. Three process configurations are considered: 1) the production of only polyisoprene where there is no isoprene dimerization step; 2) the co-production of jet fuel blend with polyisoprene, assuming the production in equal quantities (mass basis) of


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polyisoprene and jet fuel; and 3) the production of jet fuel only, where all MBE goes through isoprene dimerization. Life cycle assessment (LCA) model development The energy and chemical input requirements for all process stages (Figure 1) are calculated using Aspen Plus55-56 simulations, stoichiometry, thermodynamics, and kinematic equations as explained above. The mass and energy balances derived from the Aspen Plus model are used as LCI inputs for biomass conversion to isoprene and then polyisoprene. The LCA software tools SimaPro59 and GREET60 are applied to calculate the 100-year GWP measured as kg CO2 equivalent (CO2e) per kg of product based on AR5 of IPCC 2013.46 Three life cycle scenarios based on process configurations are defined where 1 kg of polyisoprene is the functional unit for scenarios in which polyisoprene only and both polyisoprene and jet fuel are produced; and the functional unit of 1 kg of jet fuel is applied for the scenarios where only jet fuel is produced. In the scenarios where jet fuel is co-produced, life cycle system expansion is applied assuming the displacement of conventional jet fuel from crude oil. Due to the increasing global population, demand for agricultural land for food production is expected to rise leading to rising competition for land27. Land use intensity is defined here as land required (in hectares) to produce 1 kg of polyisoprene. Data from literature15, 61-62 along with experimental data and process simulation results are used to calculate land use intensity of corn stover- and forest residue-based polyisoprene. The corn stover-to-polyisoprene pathway is based on removal of 50% corn stover from existing cropland in the US Midwest, while the forest residue based conversion pathway is based on yields of 25 to 62 metric ton per hectare wood biomass 63 for forestry thinning operations in Maine where soil organic carbon (SOC) loss is not considered.



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Petroleum based polyisoprene We consider the dominant polyisoprene production from petroleum, which is converted from the cracking of light naphtha rather than co-produced with pyrolytic gasoline processing of heavy naphtha. Therefore, our life cycle system boundary for polyisoprene from petroleum includes distillation of crude oil to generate light naphtha, steam cracking where isoprene is one of the output fractions, and polymerization of isoprene to produce polyisoprene (Figure 2). The GWP of butadiene (1.17 kg CO2e/kg butadiene) from the Ecoinvent databa64-65 in SimaPro59 software is used as a basis for calculating the GWP of isoprene as both are outputs of steam cracking; thus we applied the steam cracker output percentages for butadiene (4.5%) and isoprene (2%) along with their molecular weights to approximate the GWP emissions of isoprene (0.65 kg CO 2e/kg isoprene). The energy required for polymerization is calculated using Aspen HYSYS56 software and chemical kinetics equations in a method similar to polymerization of isoprene from biomass explained in the previous section.

Figure 2. Cradle-to-gate life cycle process flow diagram for the conversion of crude oil to polyisoprene. SimaPro and Aspen HYSYS simulations are used to calculate the GWP of isoprene from crude oil and polyisoprene respectively.


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Natural rubber tree based polyisoprene The life cycle system boundary of polyisoprene from natural rubber tree includes application of fertilizer in rubber tree plantations as well as energy usage for tillage and transportation (Figure 3).

Figure 3. Life cycle flow diagram of polyisoprene from natural rubber tree where emissions associated with production, usage, and leaching of N and P fertilizers, and diesel usage for tillage and transportation are considered.66 We consider a scenario of natural rubber tree plantations in Thailand because it is the largest natural rubber tree producer worldwide.67 Most of the rubber plantations in Thailand were first planted 60 to 80 years ago. After seven years of plantations, these trees have about 13 to 18 productive years and afterwards, new rubber plantations are required. 66 Since after 20 years carbon stocks reach a new equilibrium (IPCC, 2004)68, no change in soil carbon stock is assumed for these plantations. However, with the recent trend of converting tropical forests in Thailand to rubber plantations discussed by Jawjit et al., we consider two scenarios: 1) GHG emissions of polyisoprene from natural rubber trees planted in lands that have been converted over 60 years ago; and 2) GHG emissions of polyisoprene from natural rubber trees wherein forests are converted to new rubber tree plantations.66 13 ACS Paragon Plus Environment


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The fraction of polyisoprene in latex is in the range of 0.3-0.5;69-70 therefore, we assume that the fraction of poly-cis-isoprene in latex is 0.4 and data from the literature67 are used to calculate land use intensity of polyisoprene from natural rubber tree. Sensitivity analysis A sensitivity analysis is performed for model parameters judged to significantly affect life cycle GHG emission results. Similar to prior research17, 19, 32 we collected data from experimental results, literature, and model simulations to represent parameter upper and lower bounds. Table 1 shows parameter ranges, data sources, and assumptions applied to the life cycle sensitivity analysis. For all six scenarios studied, upper and lower bounds of total GHG emissions are calculated using the highest and lowest GHG emissions related to each parameter (see Figure S4), assuming a uniform distribution between the lower and upper bounds. For the baseline model, we assumed that the electricity generated from lignin combustion, is used within the bioplant to provide the required electricity for the operation of equipment and surplus electricity, a coproduct of the process, is sold to the regional electricity grid. However, for the sensitivity analysis, the possibility of not displacing electricity from the grid, due to uncertainties in time-of-day electricity dispatch and substitution,15,

17

an extreme case of 0%

substitution, is also considered.


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Table 1. Parameter ranges for life cycle sensitivity analysis

Parameter

Unit

Reference case

Lower bound

Upper bound

Fermentation conversion

%

32

25

33

Experimental data were applied in the reference case and upper and lower bounds

99.9

Aspen Plus simulation (reference case and upper bound are simulated at the pressure of 0.5 bar assuming recycle while lower bound is simulated at 0.5 bar); a high recovery efficiency is assumed for the reference and upper bound; a conservative measurement for lower bound

1533

Condenser duty taken from Aspen Plus simulation (reference case and upper bound) are based on 99.9% MBE recovery simulation and lower bound is based on 84% MBE recovery simulation)

MBE recovery in separation

Condenser duty in separation

%

KJ/kg MBE

99.9

1533

84

1511

Data sources and assumptions

Reboiler duty in separation KJ/kg MBE

1636

1636

1711

Reboiler duty taken from Aspen Plus simulation (reference case and upper bound are based on 99.9% MBE recovery simulation and lower bound is based on 84% MBE recovery simulation)

Dehydration conversion

%

79

69

89

Experimental results are applied to the reference case, assuming ±10% mass basis for upper and lower bounds

Polymerization conversion

%

99.9

92.4

99.9

Experimental data from Alnajrani et al.57 are applied to reference, upper, and lower bounds.

Polymerization activation energy

Kcal/mol

3.3

3.3

6.7

Reference case and upper bound from Hadjichristidis et al.58 and lower bound from Ceausescu71

99.9

Reference and upper bound are based on assumption of selling electricity coproduct to the grid while the lower bound is based on zero substitution of coproduct electricity (e.g., no electricity grid credit)15, 17

Surplus electricity coproduct

%

Soil N2O emissions (corn stover case)

gCO2/kg isoprene

324

-4

816

DayCent model72 results from Pourhashem et al. 15 were applied to reference, upper, and lower bounds

Change in soil carbon (corn stover case)

gCO2/kg isoprene

1182

2350

5

DayCent model72 results from Pourhashem et al.15 were applied to reference, upper, and lower bounds

99.9

0



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Results and discussion Life cycle greenhouse gas emissions Figure 4 shows the life cycle GWP considering uncertainty ranges described in Table 1 (left vertical axis) for producing only polyisoprene, both jet fuel and polyisoprene, and only jet fuel from corn stover and forest residue and the percentage contribution to GWP (right vertical axis). SI Table S3 summarizes the GWP of each contributing parameter presented in Fig. 4 by percentage; note that percentages in the figure are not scaled to the left axis. The GWP is negative for all studied scenarios, but for the jet fuel-only scenario it is the highest due to combustion of the fuel during use. The contributions to GWP and trend for the two feedstocks are different for the scenario of adding jet fuel as coproduct. Adding jet fuel as the coproduct, requires a larger quantity of feedstock input per kg of polyisoprene and results in higher emissions for the corn stover pathway and lower emissions for forest residue pathway relative to the polyisoprene only scenarios due to different ratios of biogenic carbon in each feedstock; the larger feedstock requirement for corn stover compared to forest residues magnifies GHG emissions generated during process conversion.


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Figure 4. GWP (left axis) and percentage contribution (right axis) of select parameters for the scenarios of producing only polyisoprene, only jet fuel blend, and equal portions of polyisoprene and jet fuel from corn stover (CS) and forest residue (FR). The reference case (Total GWP) and upper and lower bounds (error bars) are included for all six scenarios. Note: percentage contributions in the figure are scaled to the right axis only; refer to Table S3 for the breakdown of life cycle process inputs. Recent studies73-74 have argued that the removal of corn stover for biofuel production decreases SOC and as a result, increases CO2 emissions. The GWP from the production of only polyisoprene, both polyisoprene and jet fuel, and only jet fuel from corn stover based on SOC loss estimated by Liska et al74 are calculated and depicted in Figure 5 where upper and lower bounds are based on 100% corn stover removal, the limit of approaching 0% stover removal and point estimates (represented as black dots) from the reference case of our study (Figure 4) based on the SOC loss estimated by Pourhashem et al.15. While the Liska et al. study did not take into account some important factors including, prior land history, soil properties, and precipitation as well as different 17 ACS Paragon Plus Environment


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management scenarios and biorefinery co-product credits that can reduce GHG emissions17, 75 using the SOC loss from Liska et al., an extreme high risk case, the GWP of production of only polyisoprene from corn stover remains in the negative range. For the extreme case of 100% stover removal for the production of both jet fuel and polyisoprene the GWP is 0.2 kg CO2e/kg isoprene, which, although positive, is lower than the GWP of natural rubber tree and petroleum-based polyisoprene, which are 0.5 and 0.7 kg CO2e/kg isoprene, respectively. The upper bound for the scenario in which only jet fuel is produced from corn stover with 100% stover removal (considering the extreme SOC loss scenario of Liska et al.) has the highest GWP bound, 2 kg CO2e/kg among all biomass-based cases. Although positive, it is still lower than the GWP of jet fuel from crude oil, which is 3.83 kg CO2e/kg fuel.60


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Figure 5. 100-year Global warming potential (GWP) of three product based scenarios where the upper and lower bounds are calculated based on SOC loss of 100%, approaching the limit of 0% corn stover removal, respectively from Liska et al.74. Point estimates (black dots) correspond with our main results based on Pourhashem et al.15. Tradeoffs and hotspots among land use intensity and life cycle GWP We examine the tradeoff of land use and climate change related environmental impacts of historical and marginal polyisoprene production from the feedstocks studied herein and guyule from literature, and discuss the hotspots within these pathways arising due to life cycle process inputs and co-product credits (Table 2). We consider land use intensity defined as the spatial land coverage for feedstock acquisition, measured in ha/metric ton of polyisoprene from different feedstock sources and consider the total land occupied for feedstock acquisition, whereas earlier work considered only agricultural land occupied7. The land use intensity of petroleum based



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polyisoprene was calculated from marginal California crude oil land disturbance data from Yeh et al.28, which estimated that 2800 ha produce 260 milion bbl oil on the margin. This results in approximately 0 ha for petroleum based polyisoprene (assuming California API gravity of 18 degrees, the land use for oil production will be 7.1 * 10-5 ha per metric ton of produced oil). We assume negligible differences in land consumption at the industrial refinery and polymerization stages for isoprene and coproducts derived from biomass and petroleum feedstocks.7 The land use intensity range for corn stover based polysioprene is calculated assuming 50% stover removal15, 61 at the upper bound (0.7 ha/metric ton polyisoprene) and the extreme case of 100% stover removal74 at the lower bound of land use intensity, wherein the range is lower than the land use intensity of natural rubber tree based polyisoprene. Table 2 also compares the GWP of all polyisoprene feedstock cases. For corn stover, the GWP is based on 50% corn stover removal, the baseline scenario (Table S3). If stover removal increases from 50% to 100%, the land use intensity of corn stover-based polyisoprene decreases by 57% but would lead to an 88% increase in GHG emissions as noted in Table 2. At the upper limit of stover removal with jet fuel co-production, the GWP would increase further due to jet fuel combustionrelated emissions. The range in land use intensity for forest residue-based polyisoprene assumes an average wood biomass yield of commercial thinning in Maine63. Guayule, a bio-based feedstock that can grow in arid climates, has a comparatively high land use intensity among the alternatives, even after reaching optimal production yields after years of cultivation. Its range of GWP as investigated by Soratana et al.9 varies from as little as -4 to as high as 15.9 kg CO2/kg polyisoprene owing to co-products made from guayule bagasse. At the low end, the bagasse is used to coproduce electricity and offset electricity supplied by the regional electricity grid, but at the upper end, if the bagasse is used to co-produce bio-oil and charcoal, the GWP is among the highest of


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polyisoprene pathways shown in Table 2. 9In spite of the low GWP estimate relative to petroleum and rubber-tree based isoprene, the land use intensity of guayule based isoprene is high compared to corn stover based isoprene. When examining hotspots across life cycle process inputs of lignocellulosic and guayule feedstocks, co-product selection has a major influence on final GWP, but for corn stover-based isoprene, stover removal dominates the magnitude of life cycle GWP. Based on our results, the GWP of polyisoprene produced from both forest residue and corn stover are negative resulting in a decrease in GHG emissions to the atmosphere relative to the two main current sources of polyisoprene, natural rubber tree and petroleum (Table 2). Considering that the two feedstocks are residues and rely on existing forestry and corn production markets, neither one is assumed to incur additional land or nutrients for production, only replacement nutrients in the case of corn stover removal.17 As shown in Table 2, converting forests to new lands for new rubber tree plantations, due to increasing demand for polyisoprene,66 leads to significant GHG emissions from deforestation, on par with the LUC-related GHG emissions described by Fargione et al30 for palm-based biodiesel in Southeast Asia. Hence from an environmental perspective, it is prudent to use alternative biomass resources to avoid further deforestation.



Page 22 of 42

Table 2. Comparing land use intensity and GWP of polyisoprene produced from different feedstock sources

Land use

GWP

(ha/metric ton isoprene)

(kg CO2/kg isoprene)

0b

0.7

1.6c

0.5d to 15.9e

Corn stover

0.3 to 0.7f

-3.4 to -0.4; 0.2g

Forest residue

0.6 to 1.1h

-3.6 to -3.4 i

1.2j

-4 to 15k

Polyisoprene feedstock source Petroleuma Rubber tree (without and with forest conversion)

Guayule a

From light naphtha

Using California crude oil land disturbance intensity for marginal supply,28 assuming California API gravity of 18 degrees, the land use for oil production will be 7.1 * 10-5 ha per metric ton oil) and assuming that both petrochemical refinery and biorefinery occupy similar comparable land areas7 b

c

Based on Jawjit et al.67 land use and assuming 40% polyisoprene in the latex69

Existing rubber tree plantations in Thailand (rubber tree plantations in lands converted over 60 years ago)66-67 d

e

Conversion of forest to new rubber plantation in Thailand66-67

f

Based on 100% to 50% stover removal

g

The values -3.4 and -0.4 kg CO2e/kg isoprene are based on 50% and 100% stover removal; 0.2 kg CO2e/kg isoprene is the upper bound of 100% stover removal with jet fuel co-production h

Based on 10 to 25 metric ton per acre wood biomass yield63 for forestry thinning operations in Maine, and no SOC loss i

The values -3.6 and -3.4 kg CO2e/kg isoprene are based on scenarios of coproducing jet fuel and producing only polyisoprene respectively j

Calculated based on 11200 kg/ha guayule biomass yield9 and assuming 7.5% rubber content in guayule biomass (average of 3% to 12%13) k

Based on Soratana et al.9, where negative GWP scenarios assume combustion of bagasse to generate electricity that displaces Arizona’s electricity supply mix and positive (high) GWP scenarios assume the co-production of bio-oil and/or charcoal from bagasse. 22 ACS Paragon Plus Environment

Page 23 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Prospects for meeting renewable energy and rubber demand To address shortcomings in petroleum- and natural rubber tree-based isoprene and diversify isoprene resources, leading tire companies have begun research on the production of bioisoprene. Fermentable sugars from sustainably harvested biomass can be considered an environmentally preferable feedstock for its production. While the majority of current research on uses of biomass for petroleum substitution has focused on liquid fuels,15, 17-18, 31-43 novel bioconversion routes for polyisoprene production alone or in combination with jet fuel, as demonstrated herein, offer the prospect of substituting both energy for long-haul transport and rubber based materials. Given that bioconversion technologies are at an early stage of development, at pre-commercial scales, there are technological challenges affecting the profitability and hence commercialization of such biorefineries. One way to improve the economics of fuel facilities is coproducing value-added products such as polymers. The proposed process described in this study can produce both polyisoprene, a high-value chemical, as well as jet fuel, a drop-in biofuel, while maintaining negative range GWP. This is important for commercialization of the process as depending on the market demand, the process can switch from 100% polyisoprene to 100% jet fuel. Increasing demand for polyisoprene could result in deforestation and threaten biodiversity if forests continue to be converted to rubber tree plantations,10, 12 which has implications for both land use intensity and GWP (Table 2). However, if corn stover or forest residues are used as feedstock to meet growing polyisoprene demand, both the land use intensity and the GWP of polyisoprene would decline at the margin. Although the calculated land use intensity of petroleum based polyisoprene is nearly 0, petroleum wells deplete over time and petrochemical infrastructure needs permanent land allocation while the same lands can be used over time to cultivate corn, maintain forests, and even cultivate on existing rubber tree plantations. When considering the



Page 24 of 42

tradeoff between land and GWP, biomass resources increase the land intensity of all bio-derived products, however they offer a pathway to address climate change. Other LCIA metrics such as eutrophication, acidification, human toxicity, and water intensity as well as economics should be evaluated further with site specific data on nitrogen loadings76-77, estimated water withdrawal78-79, and production costs to better assess the range of sustainability tradeoffs. For the metrics studied herein, corn stover- and forest residue-based polyisoprene reduce GHG emissions substantially compared to existing commercial production routes while consuming less land at the margin.

Associated Content Supporting Information 4 figures and 6 tables that summarize supporting experimental details, Aspen Plus modeling assumptions, sensitivity analysis, energy requirements of different steps, contribution of different life cycle components on total GWP, life cycle inventory input data, and carbon balance of the system

Author information Corresponding Author S. Spatari, Tel.: +1-215-571-3557. E-mail: [email protected] ORCID: Bahar Riazi: 0000-0001-9693-987X Mukund Karanjikar: 0000-0002-9714-6190 Sabrina Spatari: 0000-0001-7243-9993

Acknowledgements This work was supported by the U.S. Department of Agriculture under USDA-NIFA USDANIFA 2012-10008-20263.


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Abstract graphic

The global warming potential (GWP) of biomass-based polyisoprene is lower than that for conventional polyisoprene that uses petroleum and rubber tree feedstocks.


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ACS Paragon Plus Environment



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2

60%

0

40%

Isoprene(CS)

Isoprene + Jet(CS)

Jet (CS)

Isoprene(FR)

Isoprene + Jet(FR)

-2

Jet (FR)

20%

-4

0%

-6

Jet fuel-credit

Percentage contributions to GWP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

GWP (kg CO2/kg polyisoprene)


-20%

-8

Jet fuel combustion Electricity Credit Fermentation CO2 Chemicals Pretreatment Boiler Feedstock transport Biogenic carbon

-40% CS: Corn stover FR: Forest residue

-10

-60% ACS Paragon Plus Environment

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Renewable Rubber and Jet Fuel from Biomass: Evaluation of

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