Process design and economics for production of advanced biofuels from genetically modified lipid-producing sorghum

Applied Energy 239 (2019) 1459–1470

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Process design and economics for production of advanced biofuels from genetically modified lipid-producing sorghum Peyman Fasahatia,b, J. Jay Liuc, John B. Ohlrogged, Christopher M. Saffrona,b,e,

T

⁎

a

DOE Great Lakes Bioenergy Research Center, Michigan State University, 1129 Farm Lane, East Lansing, MI 48824, USA Department of Biosystems & Agricultural Engineering, Michigan State University, 216 Farrall Hall, East Lansing, MI 48824, USA c Department of Chemical Engineering, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 608-739, Republic of Korea d Department of Plant Biology, Michigan State University, East Lansing, MI 48824-1312, USA e Department of Chemical Engineering & Materials Science, Michigan State University, 428 S. Shaw Lane, East Lansing, MI 48824, USA b

HIGHLIGHTS

GRAPHICAL ABSTRACT

process model synthesized for co• Aproduction of diesel and ethanol from sorghum.

process is economically • Coproduction favorable over ethanol-only. lipid content in sorghum fur• Higher ther improves the economics. MESP of $2.46 and $3.08/gal is • An calculated for coproduction and ethanol-only.

ARTICLE INFO

ABSTRACT

Keywords: Biorefinery Biodiesel Bioethanol Process design Technoeconomic analysis Genetically modified sorghum

This study evaluates the potential for making advanced biofuels from genetically modified (GM) lipid–producing sorghum. A biodiesel coproduction process is developed to extract, purify, and upgrade lipids to diesel fuel while carbohydrates are utilized for making ethanol through acid thermal pretreatment, enzymatic hydrolysis, and fermentation. To assess the advantages of coproducing biodiesel from GM–sorghum, process economics are compared to a cellulosic ethanol biorefinery receiving non-GM sorghum. Minimum ethanol selling prices (MESP) that reach a breakeven point after 30 years of service life are calculated as an economic index to compare the two processes. Results indicate that biodiesel coproduction improves the economics by lowering the MESP from $3.08/gal for the ethanol-only process to $2.46/gal. Sensitivity analyses reveal that increasing sorghum’s lipid content, increasing the lipid extraction efficiency, and reducing the solvent-to-solids ratio in lipid extraction columns are the most important process parameters to further enhance technoeconomics. Analyses indicate that a lipid content above 13 wt% (dry basis) or a biomass price less than $65/Mg (dry basis) will result in a 2014 ethanol wholesale price of $2.25/gal for the coproduction process.

⁎ Corresponding author at: Department of Biosystems & Agricultural Engineering, Michigan State University, 524 S. Shaw Ln., 204 Farrall Hall, East Lansing, MI 48824, USA. E-mail address: [email protected] (C.M. Saffron).

https://doi.org/10.1016/j.apenergy.2019.01.143 Received 21 August 2018; Received in revised form 14 December 2018; Accepted 19 January 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Nomenclature ATH bmr CEPCI DC EPA FCI FOC GM HMF IC IRR

LHV M MESP MFSP n PS RFS RMC TCI TE TAG wt

acid thermal hydrolysis brown midrib chemical engineering plant cost index direct costs Environmental Protection Agency fixed capital investment fixed operating cost genetically modified 5-hydroxymethylfurfural indirect costs internal rate of return

1. Introduction

lower heating value million minimum ethanol selling price minimum fuel selling price scaling exponent photoperiod sensitive Renewable Fuel Standard raw material cost total capital investment technoeconomic triacylglyceride weight

at lower prices and yet large scales to meet the RFS goals. Triacylglycerols (TAGs) are the vegetable oil precursor of biodiesel and main building block of lipids accumulated by plants such as soybean. Plant species can be genetically modified (GM) to increase their lipid accumulation yield [16,17] or to produce high energy-content lipids instead of lower energy-content carbohydrates [18,19]. Genetic modifications can alter biodiesel’s composition [20] to improve its physical (viscosity, density, as well as melting, freezing, pour, and cloud points) [21] and chemical properties (caloric content, ratio of saturated to unsaturated fatty acids, chain length, double bond positions, and functional units) [22]. In recent years, metabolic engineering has successfully increased TAG accumulation for several biomass sources. Among many plant types, sorghum has unique characteristics that makes it ideal for genetic modification to direct fixed carbon towards lipids in leaves and stems instead of starch or other carbohydrates. Sorghum has high productivity (33 Mg acre−1 at 65% moisture) [23] and is more water-use efficient (33% less water consumption compared to corn) and drought tolerant [24]. In addition, cultivation costs of sorghum are typically lower compared to other crops [25] and it is more compliant to currently practiced cultivation and production systems in the U.S. [26]. Furthermore, sorghum breeding and genetic structure are well-known and its genome has been completely sequenced [27]. Through breeding programs, sorghum’s diverse array of traits has led to elite and diverse grain, forage, and sweet sorghum genotypes that are useful for bioenergy production [26]. These characteristics have attracted large interest in sorghum for advanced biofuels production [26,28–30]. However, despite a few studies for ethanol and diesel coproduction from genetically modified lipid-accumulating sugarcane [14,31] and microalgae [13], there are no studies on the economic potential of lipid accumulating lignocellulosic biomass. GM lipid producing lignocellulosic biomass could provide a sustainable alternative for coproducing advanced biofuels to meet the RFS goals while bypassing criticism when using crop-based biomass such as sugarcane and corn, which threatens global food security and causes land competition [32]. Considering the ongoing research effort to engineer lignocellulosic biomass such as sorghum for lipid accumulation [33,34], we first designed a biorefinery to simultaneously produce ethanol and diesel from lipid-accumulating sorghum. Second, the impact of adding a trait for lipid accumulation to sorghum is evaluated by developing a detailed technoeconomic model to calculate the minimum ethanol selling price (MESP) as an economic index for comparing GM sorghum to a non-GM sorghum that only makes ethanol. Results from an Aspen Plus model (V.10) are fed to an economic analysis used to identify the main cost drivers and bottlenecks to guide the technology toward eventual commercialization.

About 28% of annual U.S. greenhouse gases are emitted by the transportation sector, amounting to 1,854 Tg CO2 equivalent GHGs in 2016 (96.7% CO2, 2.4% HFCs, 0.8% NOx, and 0.1% CH4) [1]. To curb these negative impacts and promote energy independence and security, the Renewable Fuel Standard (RFS) was established in the U.S. by the Energy Policy Act of 2005 and later expanded in 2007 by the Energy Independence and Security Act, which started with four billion gallons of renewable fuel in 2006 and aims for 36 billion gallons by 2022 [2]. The statute considers four renewable categories of conventional biofuel: corn starch ethanol, advanced biofuel that are biofuels other than corn starch ethanol, cellulosic biofuel produced from holocellulose and lignin, and biomass-based diesel. In 2015, corn starch ethanol production reached the blend wall of 15 billion gallons, though advanced biofuels have failed to reach the statutory target by a large margin [2]. As a result, renewed urgency is needed to increase advanced biofuels to achieve the RFS goals. In addition, current limiting key challenges such as developing more efficient enzymes and microorganisms [3], enhancing microbial tolerance to their products for biological conversion [4], design of more durable and efficient catalysts for catalytic deconstruction and conversion [5], and development of more atom- and energy-efficient conversion strategies must be overcome to achieve a sustainable and economical bioeconomy [6]. Compared to other biofuel categories, biodiesel offers several advantages. It is a biodegradable [7] and nontoxic fuel that contains no sulfur and aromatics [8]. Except for nitrogen oxides, biodiesel combustion produces lower engine exhaust emissions [9,10]. Furthermore, within the RFS, biodiesel qualifies to meet the annual standard for multiple RFS fuel categories and receives a credit factor proportional to its energy ratio to ethanol in calculating annual production obligations determined by the Environmental Protection Agency (EPA) [2]. An energy and carbon analysis for biodiesel production with conventional biofuels such as ethanol indicates that biodiesel producing pathways could displace more fossil fuel by being more energy-economical despite being less carbon-economical if the diesel engine’s efficiency is considered in the analyses [11]. Furthermore, coproduction of diesel and ethanol leads to a better well-to-wheel environmental profile than a single biofuel by better approaching a circular economy between cultivation and conversion phases [12]. However, biodiesel adoption is limited because of high biomass cost and low biomass availability making it more expensive than fossil diesel [9]. Biomass cost contributes more than 65% of total production cost for biodiesel [13,14]. The EPA reported [15] that in 2016, vegetable oil (soybean 72%, canola 15%, and corn oil 13%), animal fat (white grease 51%, tallow 29%, and poultry 19%, and others), and recycled feeds (yellow grease and others) were the main biomass sources for biodiesel at values of 3.88, 0.51, and 0.63 million metric ton (Mg), respectively. Therefore, alternative sources of biomass with higher lipid yields and lower cultivation costs per area are required to improve the economics and produce biodiesel 1460

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

respectively. Sucrose hydrolyzes completely to form fructose and glucose, followed by a complete degradation of fructose to 5-hydroxymethylfurfural (HMF) and water. After ATH, the hydrolysate is flashed and vapors are condensed by exchanging heat with a fresh water stream. Later, hydrolysate is sent to conditioning reactors where ammonia is added to increase the pH to 5 prior to enzymatic hydrolysis. Area 300, enzymatic hydrolysis and fermentation: The conditioned hydrolysate enters a continuous high-solid saccharification reactor followed by batch hydrolysis in stirred tanks to convert cellulose to glucose using cellulase enzymes that were made onsite. After enzymatic hydrolysis is completed, fermentation vessels are inoculated with Zymomonas mobilis, an ethanologen, to co-ferment glucose and xylose to ethanol. Laboratory- and pilot-scale experiments performed on sorghum conversion to ethanol indicate that a typical high-biomass sorghum variety performs very similar to corn stover and has similar yields of ethanol [26,28,30]. Therefore, similar conversions as reported by NREL for corn stover [35] are considered for individual components in sorghum as provided in Table 2. Area 400, cellulase enzyme production: This process area makes cellulases, including endoglucanases that reduce cellulose polymer chain length, exoglucanases that target the chain ends of highly crystalline cellulose fibers, and β-glucosidase that hydrolyzes small cellulose oligomers and dimers to glucose. Trichoderma reesei, a fungus that secretes cellulase enzymes under aerobic conditions when grown on cellulose, is the organism used for enzyme production. Area 500, ethanol recovery: Ethanol is recovered to its azeotropic concentration using two sequential beer and rectification columns, and then further dehydrated to 99.5% purity using molecular sieves. Most of the water and solid residues of fermentation are recovered as stillage from the beer column bottoms. Area 600, wastewater treatment: The wastewater produced in the biorefinery is processed through anaerobic and aerobic digestion, membrane filtration, reverse osmosis, evaporation, dewatering, and gravity belt thickening with the aim to reduce fresh water demand and discharge to the environment. The outputs include: biogas from anaerobic digesters, concentrated brine through reverse osmosis and mechanical-vapor-recompression, sludge from anaerobic and aerobic digesters dewatered through centrifuging, and clean water which is recycled to the process. Area 700, storage: Onsite storage is provided for raw materials and products, including: diammonium phosphate, corn steep liquor, sulfuric acid, ammonia, sodium hydroxide, hexane, ethanol, biodiesel, glycerin, and methanol. Area 800, heat and power generation: Solids remaining after lipid extraction along with biogas from anaerobic digesters and sludge in wastewater treatment are burned to produce steam for heating and electricity in a combustor, boiler, and turbogenerator. The excess electricity is sold as a byproduct to the grid. Area 900, utilities: This unit produces cooling water, chilled water,

2. Materials and methods 2.1. Sorghum composition Chemical composition data for sorghum are obtained from a study performed by the U.S. Department of Energy from the “Sorghum to Ethanol Research initiative” [26,28]. In this study, the chemical composition of 22 commercially available sorghum hybrids was measured, including: forage sorghum, sorghum-sudangrass, brown midrib (bmr) sorghum, non-bmr sorghum, photoperiod sensitive (PS) sorghum, and non-PS sorghum, all at various states of maturity. Table 1 shows the average chemical compositions of sorghum obtained from the study. Identification of a chemical composition for lipid-producing sorghum is difficult as they are not currently available. Even so, the chemical composition of a lipid producing sorghum can be predicted based on an energy balance method [31]. In this calculation, it is assumed that lipid production substitutes sucrose and starch on an energy basis as these two components are the plant’s primary energy reserves. After all of the sucrose and starch is substituted with lipid, further increases in lipid content dilutes the remaining biomass components on a mass basis. Table 1 shows the chemical composition of GM sorghum calculated after assuming different lipid concentrations. A 10 wt% lipid content in sorghum is considered as the base case in this study. 2.2. Process synthesis The coproduction process considered here receives sorghum biomass as feed and produces ethanol through fermentation of sugars obtained from pretreatment and enzymatic hydrolysis of carbohydrates [35]. Solid residues from fermentation are extracted to obtain lipids [13], which are further purified and transesterified to produce diesel and glycerol [6,7,36]. After lipid extraction, the solid residues are combusted along with biogas from anaerobic digesters in wastewater treatment to produce heat and power for the biorefinery. The excess electricity is sold to the grid as another byproduct of the process. Fig. 1 portrays the process flow diagram for the coproduction process and Table 2 shows the main process parameters and key assumptions. Area 100, biomass handling: Sorghum biomass is delivered in a uniform format after preprocessing, densification, and homogenization in a depot as designed by the Idaho National Laboratory [37]. The delivered sorghum is assumed to have a uniform composition with a specific particle size, ash content, and moisture content. Energy and capital costs of the biomass handling unit are assumed to be included in the biomass purchase cost [38]. Area 200, pretreatment and conditioning: Biomass pretreatment occurs by two main steps, acid thermal hydrolysis (ATH) and conditioning. In the ATH, hydrolysis reactions are catalyzed by sulfuric acid using high-pressure steam to reach 165 °C. As shown in Table 2, most of the xylan and arabinan hydrolyze to xylose and arabinose,

Table 1 Chemical composition (wt%) of non-genetically modified and genetically modified lipid-producing sorghum at different lipid compositions reported as percent on a dry basis.

Cellulose Structural starch Soluble starch Soluble Sucrose Extractives Xylan Galactan Arabinan Lignin Lipid Protein Ash

LHV (MJ/kg)

Average non-GM

5% lipid

10% lipid (Base case)

15% lipid

20% lipid

15.98 15.98 15.98 15.67 14.18 16.43 15.98 16.43 24.05 37.19 22.40 −

15.02 12.23 2.04 1.61 20.6 16.52 1.07 2.68 13.30 − 5.26 9.66

16.21 4.22 − − 22.23 17.83 1.16 2.89 14.36 5.00 5.67 10.42

16.07 − − − 22.04 17.68 1.15 2.87 14.23 10.00 5.63 10.33

15.18 − − − 20.82 16.70 1.08 2.71 13.44 15.00 5.31 9.76

14.29 − − − 19.59 15.71 1.02 2.55 12.65 20.00 5.00 9.18

1461

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Fig. 1. Process flow diagram for a biorefinery that co-produces biodiesel and ethanol from GM lipid-producing sorghum.

Table 2 Main process assumptions and conversions of the biorefinery. Area

Assumptions

Ref.

200

Acid thermal hydrolysis: 158 °C, 5 min residence time, 30 wt% solid loading, 22.1 mg H2SO4 /g dry biomass (Glucan)n + n H2O → n Glucose, conversion: 9.9% (Xylan)n + n H2O → n Xylose, conversion: 90.0% (Arabinan)n + n H2O → n Arabinose, conversion: 90.0% Sucrose → Glucose + HMF + 2 H2O, conversion: 100.0% Separate hydrolysis and fermentation at 20 wt% solid loading. Enzymatic hydrolysis: 48 °C, 3.5 days, cellulase loading 20 mg/g cellulose, (Glucan)n + n H2O → n Glucose, conversion: 90.0% Fermentation: Co-fermenting bacteria (Z. mobilis), 32 °C, 1.5 days Glucose → 2 Ethanol + 2 CO2, conversion: 95.0% 3 Xylose → 5 Ethanol + 5 CO2, conversion: 85.0% 3 Arabinose → 5 Ethanol + 5 CO2, conversion: 85.0% Enzyme mass yield: 0.24 kg/kg glucose Combustor (93.4% efficiency), Boiler (80% efficiency), Turbogenerator (85% efficiency), Flue gas desulfurizer (92% efficiency) Cooling tower: cooling water supply at 28 °C and 9 °C temperature rise in process, tower temperature drop from 37 °C to 28 °C, 0.005% windage loss, and 0.15% blowdown at basin Chilled water: centrifugal chillers with 0.56 kW/ton of refrigeration efficiency Agitated counter-current extractor: hexane:solids loading of 5:1, 95% extraction efficiency, 2% water carryover to solvent/lipid phase, 0.5% hexane loss to aqueous phase, power demand of 30 kW/extractor Hexane-lipid separation: distillation column and 0.01% hexane loss. Degumming: phosphoric acid (0.19 wt% of feed) and water wash (10 wt% of feed) Demetallization: silica (0.10 wt% of feed) Bleaching: clay (0.20 wt% of feed) Total lipid loss: 5% Transesterification: 60 °C and 95% conversion, 94% methanol recovery using liquid-liquid-vapor distillation Water washing using a liquid-liquid extractor at a water loading of 4 wt% of feed to remove 100% glycerol and NaOH, 95.5% water, and 98.5% methanol 99% diesel recovery using a distillation column 100% NaOH catalyst removal using a H3PO4 neutralization reactor 99% glycerol recovery using a distillation column 99% methanol recovery using a methanol–water distillation column

[28]

300

400 800 900 1000 1100

1200

1462

[2628]

[35] [35] [35] [13] [13]

[39,55]

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

and instrument air, while providing the biorefinery’s clean-in-place system. In addition, the unit tracks and balances biorefinery water and power consumption to calculate fresh process water demand and excess power sold to the grid. Area 1000, lipid extraction and recovery: The stillage from ethanol recovery is first cooled by exchanging heat in the beer column preheater. Cooled stillage is then fed to the top of an agitated liquid-liquid extraction column. Hexane is used as the solvent in a 5:1 ratio to cooled stillage to extract lipids from the solids in a multi-stage counter-current extraction column. Hexane is a non-polar solvent which is known to extract neutral fatty acid lipids over polar lipids. The extracted light oil phase contains solvent, lipids (both fatty acid lipids and polar lipid impurities), and a small amount of water. This phase is then routed to a stripping column to recover the solvent, leaving a high purity (∼99.7% total lipids) lipid stream. Area 1100, lipid purification: This unit removes impurities through a series of steps to provide a clean lipid stream. A cleaning step is needed to remove polar lipids because of their potential to form gums and to remove metals, salts, and nutrients sent from upstream pretreatment, conditioning, and fermentation steps [13]. In the degumming step, phosphoric acid, followed by centrifugation with wash water, is used to remove gums. Subsequently, silica is added for demetallization producing a slurry stream that is filtered to remove spent silica. Consequently, bleaching is performed by adding clay to remove remaining metals and impurities. Filtration is performed to remove spent clay from the slurry formed. Removed cakes containing gums and other solids are sent to anaerobic digesters in wastewater treatment to produce biogas. Area 1200, biodiesel production and recovery: Cleaned lipids are transesterified by reacting with methanol in the presence of sodium hydroxide catalyst at 60 °C to produce biodiesel and glycerol [39,40]. Unreacted methanol is recovered using a three-phase distillation column. About 94% of the methanol entering the distillation column is recovered and recycled to the transesterification reactors. The methanol recovery column operates under vacuum (0.2 bar) to keep the boiling temperature in the reboiler low enough to prevent reverse transesterification. Biodiesel is then washed by water in an extraction column to remove glycerol, catalyst, and remaining methanol. Washed biodiesel and lipids are separated in a distillation column under vacuum to prevent biodiesel degradation and to raise the biodiesel purity above 99.6% as required by ASTM specifications. Unreacted lipids are recycled from the column bottom to transesterification reactors while biodiesel is recovered from the top. A partial condenser is used to remove remaining water and methanol as vapors in the column overhead stream. The aqueous stream from the extraction column is sent to neutralization reactors where phosphoric acid is used to neutralize the NaOH. The neutralization product, Na3PO4, is separated in a gravity separator. After removing the catalyst, glycerin, at a purity above 99%, is separated from water and methanol using a vacuum distillation column. The top product containing water and methanol is also distilled to separate water and methanol. Finally, methanol is recycled back to the transesterification column and water is sent to wastewater

treatment. 2.3. Modeling assumptions and technoeconomic analysis NREL’s 2011 cellulosic biorefinery model [35], simulated in Aspen Plus (Aspen Technology Inc., Massachusetts, USA) for converting 2,000 dry Mg/day of corn stover into ethanol serves as the basis case for comparison. The biorefinery model was modified in Aspen Plus v10 to receive sorghum at 20% moisture content. Lipid extraction, purification, and diesel production units are integrated to formulate the coproduction scenario that produces biodiesel from lipid-containing GM sorghum. Mass and energy balances from the Aspen Plus simulation were used to formulate a technoeconomic (TE) model to predict the MESP in 2014 U.S. dollars. Most of the economic model parameters are obtained from NREL’s TE models and are presented in Table 3. The TE model uses nthplant assumptions that reflect a mature industry rather than a pioneer plant; risks and uncertainties associated with a first-of-a-kind plant are neglected. Note, only the biorefinery is modeled in this analysis, i.e. no submodels for biomass cultivation, harvesting, and associated human labor costs are included. Similar to NREL assumptions, it was assumed that biomass supply chain costs are included in the feedstock price at the biorefinery gate. Capital costs of lipid extraction and purification are scaled from equipment cost reports [13], transesterification reactors [41,42], and all other units [35], which are quoted by specialized vendors in industry using a characteristic scaling exponent (n) shown in Eq. (1):

New cost = (Base cost) × (New size/Base size) n

(1)

The exponent “n” typically ranges between 0.6 and 0.7 and is reported for different types of equipment in the literature [35]. The installation costs are calculated by multiplying new costs by an equipment specific installation factor typically between 1.5 and 3.1 [35]. Installed costs are updated to 2014 U.S. dollars using the chemical engineering plant cost index (CEPCI) [43]. Direct and indirect costs are calculated as functions of installed costs using the methodology provided in Table 4 in supplementary data. Later, fixed capital investment (FCI) is calculated as the sum of direct and indirect costs and total capital investment (TCI) as the sum of FCI, land, and working capital. The operating cost is the sum of the raw material cost (RMC) and the fixed operating cost (FOC). RMC is calculated using the material flowrates obtained from the Aspen Plus model and unit costs given in Table 5. FOC is calculated as the sum of labor, maintenance, property, insurance, and tax costs (Table 3). Both RMC and FOC are updated to 2014 U.S. dollars using the inorganic chemical index [44] and the labor index [45], respectively. In addition to making ethanol, biodiesel, glycerol, and electricity are coproducts of the biorefinery. Revenue from biodiesel is calculated by multiplying the MESP by the ratio of lower heating values for biodiesel (37,864 kJ/kg) and ethanol (26,964 kJ/kg) [46]. Surplus electricity was sold to the grid at the average 2014 whole sale price ($0.523/kWh) [47]. Glycerol market prices are characteristically

Table 3 Key technoeconomic model parameters considered in the analysis. Parameter

Conditions

Ref.

Biorefinery capacity Year of analysis Depreciation method Equity Tax rate Internal rate of return (IRR) Operating hours per period Construction period Startup period

2,000 Mg/day dry sorghum 2014 U.S. dollars Double declining balance method, 7 years recovery for general plant, 20 years for boiler/turbogenerator unit 40% (8% loan interest, 10 year loan term) 35% per year 10% 7,880 h/year Three years (8% 1st year, 60% 2nd year, 32% 3rd year) Three months (50% product/raw material, 100% fixed costs)

[35]

1463

[35] [35] [38] [35] [35]

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Development of new uses for glycerol is expected to increase its consumption and increase its market price. In this study a base price of $1,000/Mg was assumed for glycerol. Even so, a sensitivity analysis was performed to assess the impact of price variations on the MESP.

Table 4 Cost factors for estimating direct and indirect costs [35]. Direct costs (DC)

% of installed costs

Installed costs Warehouse Site development Additional piping

100% 4% 9% 4.5%

Indirect costs (IC)

% of total direct costs (TDC)

Prorateable costs Field expenses Home office and construction Project contingency Other costs Fixed capital investment (FCI) Total capital investment (TCI)

10% 10% 20% 10% 10% FCI = DC + IC TCI = FCI + working capital (5% of FCI) + land*

3. Results and discussion 3.1. Comparison to the ethanol-only process To understand the potential advantages of using GM-sorghum engineered to produce vegetative triacylglycerol, results for the coproduction of diesel and ethanol (the coproduction scenario) are compared with an ethanol-only scenario. In the ethanol-only scenario, the average chemical composition of non-GM sorghum, shown in Table 1, is used to produce ethanol as the only product. The process includes the traditional unit operations used to make cellulosic ethanol, but not those used in the coproduction scenario, such as lipid extraction, purification, and diesel production. In the ethanol-only scenario, the solid residues from fermentation are recovered from the beer column bottoms using pressure filters. These solid residues are combusted to produce heat and power.

* A land cost of 1.8 M$ was considered based on [35].

volatile, changing significantly while biodiesel production has increased worldwide during the last decades. From 1970 until 2004, high purity glycerol had a stable price between $1,200–1,800/Mg. The price of glycerol reached $600/Mg in 2006 and started to fall to $40–110/Mg in 2011 with a global increase in biodiesel production. In November 2012, glycerol from vegetable oil and animal fat was quoted at $925–1,080/Mg and $892–1,069/Mg, respectively, while pharmaceutical grade glycerol was quoted between $1,410–1,565/Mg [48].

3.2. Capital and utility costs Table 5 shows an inventory of inputs and outputs for the biorefinery along with their annual costs obtained from Aspen Plus simulations for the coproduction and ethanol-only scenarios. Results show that

Table 5 Raw materials/product selling prices and mass flowrates obtained from Aspen Plus simulations of coproduction and ethanol-only processes at a plant scale of 2,000 dry Mg/day sorghum. Area

Material name

Price $/Mg (2014)

Coproduction (Diesel + Ethanol)

Ethanol-only

Ref.

Flow kg/h

Cost M$/y

Flow kg/h

Cost M$/y

100

Feedstock (Dry)

88.2a

83,333

57.91

83,333

57.91

[38]

200

Sulfuric Acid, 93% Ammonia

117 588

1,980 640

1.83 2.96

1,980 640

1.83 2.96

[38] [38]

300

Corn Steep Liquor Diammonium Phosphate Sorbitol

74 1,307 1,492

1,019 130 40

0.60 1.34 0.47

1,135 139 44

0.67 1.44 0.52

[38] [38] [38]

400

Glucose Corn Steep Liquor Ammonia Host Nutrients Sulfur Dioxide

760 74 588 1,076 402

1,305 75 53 31 8

7.82 0.04 0.24 0.26 0.02

2,378 137 96 56 14

14.25 0.08 0.45 0.48 0.04

[38] [38] [38] [38] [38]

600

Caustic (as pure)

196

1,322

2.04

1,345

2.08

[38]

800

Boiler Chemicals FGD Lime Disposal of Ash

6,542 261 42

0.248 1,056 10,498

0.01 2.17 3.45

0.257 982 9,808

0.01 2.02 3.22

[38] [38] [38]

900

Cooling Tower Chemicals Makeup Water

3,920 0.235

2.07 144,113

0.064 0.27

2.256 153,963

0.07 0.29

[38] [38]

1000

Hexane

1,270

852

8.53

[13]

1100

H3PO4 Silica Clay

847 2,354 706

15 8 16

0.10 0.15 0.09

[13] [13] [13]

1200

Methanol Caustic (as pure) H3PO4

541 196 847

818 80 65.3

3.49 0.12 0.44

[56] [38] [13]

Product

Ethanol Biodiesel Glycerol Electricity to Grid

1,158b 1,000 0.0523 ($/kWh)

13,629 7,557 773 20,451 (kW)

88.53 68.95 6.10 8.43

a b

$80 per dry U.S. ton. Calculated based on relative LHV of diesel to ethanol and MESP of ethanol ($2.46/gal) for the coproduction process. 1464

18,722

152.18

16,449 (kW)

6.78

[48] [47]

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

coproduction in the base case (10% lipid) makes 40 and 17.9 million (M) gal/year ethanol and diesel fuels, respectively, while the ethanolonly process makes 49.4 Mgal/year ethanol. Table 6 shows the plant cost worksheet for the total capital investment (TCI), operating costs, and revenues of both processes. TCI for the coproduction and ethanolonly processes is calculated to be 500 and 451 M$. As expected, coproduction has higher capital costs because of additional units for lipid extraction (38.4 M$), purification (3.5 M$), and diesel production (3.2 M$). Lipid extraction has a high capital cost (38.4 M$), mainly because the unit receives a large volumetric flow from the bottom of the beer column and a large hexane-to-solids ratio (5:1) required to extract lipids. The capital cost of the ethanol recovery unit in the ethanol-only process is twice that of the coproduction process because of two reasons: (1) a larger amount of ethanol produced and (2) the capital cost of a pneumatic pressure filter to separate solid residues of fermentation from the beer column bottoms. In the coproduction process, the capital cost of the pneumatic pressure filter is included in the lipid extraction and solvent recovery unit. The ethanol-only process has higher capital costs for enzyme production and ethanol recovery because of larger starch and cellulose contents in non-GM sorghum and thereby, larger ethanol production. Raw material cost in coproduction is about 6 M$/y more than the ethanol-only process because of additional costs associated to purchase hexane (8.5 M$/y) and methanol (3.5 M$/year). However, the annual glucose cost for enzyme production in the ethanolonly process is about 6.4 M$ higher than in the coproduction process. Fig. 2 compares heating, cooling, and power demand of the coproduction and ethanol-only processes. Heat demand for the ethanol-only process is 71.5 MW, of which about 70% is consumed for ethanol recovery and dehydration and 30% in pretreatment reactors for xylan hydrolysis. The coproduction process has a similar heat demand in the pretreatment reactors. However, because of its smaller ethanol production, the heat demand for ethanol recovery is about 16 MW less than for the ethanol-only process. The coproduction process has additional heat demands of 19.6 MW in the hexane-lipid distillation column reboiler and 4.7 MW in the reboilers for the methanol, diesel, and glycerol distillation columns. Total power produced in the coproduction and ethanol-only process are 44.4 and 43 MW, respectively, of which 20.4 and 16.3 MW are in excess of what is internally needed and are thus sold to the grid. The GM sorghum has slightly higher lignin content (17.7%) compared to non-GM (16.5%) as shown in Table 1. Therefore, a larger amount of heat and power is produced in the coproduction process. Furthermore, the ethanol-only process requires more power to agitate the enzyme production vessels and to operate the air coolers on the beer column condenser due to larger starch and cellulose contents in the non-GM sorghum.

$0.34/gal on the MESP with capital costs of fermentation vessels ($0.21/gal) for the former and sugar purchase cost ($0.23/gal) for the latter being the main contributors to each unit. Similar to the coproduction scenario and after biomass cost, the boiler and turbogenerator have the highest contribution in the ethanolonly process at $0.54/gal because of high capital costs of boiler/turbogenerator. The next main cost is from cellulase enzyme production with $0.44/gal of which 70% of the costs belong to sugar purchase. This indicates that a reduction in enzyme loading could significantly improve the process economics. Wastewater treatment and pretreatment units are the next costly units with a contribution of $0.41/gal and $0.35/gal to the MESP, respectively. Capital (48%) and raw material (28%) costs are the main pretreatment costs which originate from using sulfuric acid as catalyst. Use of sulfuric acid requires the pretreatment reactor be cladded with expensive material such as Incoloy to prevent corrosion and also needs ammonia conditioning to neutralize the pH to a level appropriate for enzymatic hydrolysis. 3.4. Sensitivity analysis Because the coproduction process is in the concept stage and not ready for commercialization, there are uncertainties and risks associated with the process and economic parameters used in this system’s design. To appropriately address these uncertainties, single-point sensitivity analyses are performed using Aspen Plus and the TE model. These sensitivity analyses are performed by changing each variable between an expected minimum and maximum while fixing the other variables to their base value. The tornado chart in Fig. 5 summarizes the sensitivity analysis for key economic and process parameters. Results show that IRR has the highest impact on MESP and an increase in IRR from 10% to 15% and 20% increases the MESP by $0.35/ gal and $0.74/gal, respectively. The next parameter that most impacts MESP is the uncertainty in calculating FCI. A ∓ 25% change in FCI changes MESP by ∓$0.26/gal to $2.20/gal and $2.73/gal, respectively. Lipid extraction efficiency also has a high impact on the MESP primarily because it directly alters diesel fuel yield. A reduction in lipid extraction efficiency from the base value of 95% to 85% and 75% could increase MESP by $0.11/gal and $0.24/gal. Another parameter that affects lipid extraction efficiency is the hexane-to-solid ratio which is Table 6 Plant cost worksheet for total capital investment, operating costs, and revenues.

3.3. Minimum ethanol selling price The minimum ethanol selling prices for coproduction and ethanolonly scenarios are calculated to be $2.46 and $3.08/gal, respectively. Figs. 3 and 4 show cost contribution details from each process area per gallon ethanol produced for coproduction and ethanol-only processes. Results show that coproduction of diesel could reduce MESP by about $0.62/gal compared to an ethanol-only process. In both processes, biomass cost has the highest contribution to the MESP at $1.61 and $1.17/gal for coproduction and ethanol-only. In coproduction, the highest contribution belongs to the boiler/turbogenerator area at $0.78/gal, with the capital costs of the boiler and turbogenerator being the main driver. However, electricity generation reduces the total impact of the unit by about $0.51/gal. The next highest cost drivers in coproduction are lipid extraction and recovery ($0.56/gal), wastewater treatment ($0.53/gal), and pretreatment ($0.48/gal). In lipid extraction and recovery, the large hexane:solids ratio of 5:1 makes the hexane purchase cost ($0.24/gal) and capital costs of the extraction columns ($0.25/gal) the major contributors. Enzymatic hydrolysis and fermentation, and cellulase enzyme production units have similar impacts of

Item

Coproduction

Ethanol-only

Biorefinery scale (Mg/day) Ethanol yield (Mgal/year) Diesel yield (Mgal/year)

2000 40.0 17.9

2000 49.4 −

Installed costs (M$) Area 100: Feedstock handling* Area 200: Pretreatment/conditioning Area 300: Enzymatic hydrolysis & fermentation Area 400: Cellulase enzyme production Area 500: Ethanol recovery Area 600: Wastewater treatment Area 700: Storage Area 800: Boiler/turbogenerator Area 900: Utilities Area 1000: Lipid extraction and solvent recovery Area 1100: Lipid purification Area 1200: Biodiesel production and purification

26.5 35.9 31.9 9.5 12.1 49.7 5.3 76.6 6.6 38.4 3.5 3.2

26.5 36.2 32.7 16.6 24.1 53.4 4.8 73.1 7.2 − − −

Total capital investment (M$) Biomass cost (M$/year) Raw material cost (M$/year) Fixed operating cost (M$/year) Glycerol credit (M$/year) Electricity to grid credit (M$/year)

499.7 57.9 36.5 12.5 −6.1 −8.4

450.9 57.9 30.4 11.5 − −6.8

* The capital costs of feedstock handling is assumed to be incorporated in biomass cost. 1465

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Fig. 2. Biorefinery utility distribution by process area for coproduction and ethanol-only processes.

assumed to be 5:1 in the coproduction design. Fig. 3 shows that hexane loading impacts MESP because it affects the total mass flowrate circulating between extraction and distillation columns, which affects the capital and energy costs to recover hexane and lipids. Results show that a change in hexane:solid ratio to 3:1 and 7:1 impacts MESP by ∓$0.11/ gal, respectively. This indicates that improving the extraction efficiency is an important process parameter that could significantly improve process economics. As indicated in Figs. 2 and 3, cellulase enzyme production has a large contribution to the MESP for both processes. Therefore, a sensitivity analysis on enzyme loading is performed by changing it from base value of 20 mg protein per g cellulose to 10 and 30 mg/g, recognizing that changes in enzyme loading changes capital costs of seed trains and sugar costs. Results reveal that the MESP changes by about ∓$0.10/gal to $2.37/gal and $2.56/gal for 10 and 30 mg/g enzyme loading, respectively. Uncertainties in glycerol price have the least impact on the MESP compared to the other evaluated parameters. A ∓ 50% change in glycerol selling price changes the MESP from its base value of $2.46/gal by about ∓$0.05/gal to $2.42/gal and $2.51/gal.

GM sorghum during cultivation has not been experimentally assessed and the possibility that biomass yield (Mg/ha) reduces when plants produce lipids exists. Biomass purchase price and thus feedstock costs will be increased as a larger harvest area would be required to cultivate adequate biomass, which also leads to increased transport costs. Therefore, a sensitivity analysis is performed on the impact of lipid content and biomass price on the MESP. Aspen Plus simulations are updated for sorghum composition from a base value of 10% lipid to 5%, 15% and 20%. Mass and energy balance results are used to update capital costs and the TE model. Results shown in Fig. 6(a) indicate that MESP changes linearly with variations in lipid content. A higher lipid content is favorable as it results in higher biodiesel production and lower MESP values. It also benefits lipid extraction, purification, and biodiesel production units because of a higher economy of scale. Fig. 6 also shows the base case MESP value and the ethanol wholesale price of $2.25/gal in 2014 [49]. From Fig. 6(a), to reach a MESP of $2.25/gal, the biomass price should be reduced below $65/Mg at 10 wt% lipid content, or the lipid content should be increased to above 13 wt% at the base biomass price of $88.2 per dry Mg.

3.4.1. Impact of lipid content Lipid production potentials of sorghum biomass is another variable with significant uncertainty. Depending on the geographic cultivation site, weather conditions, soil characteristics, and extent of genetic modifications, sorghum cultivation could yield different lipid contents. In addition, the price of sorghum, when cultivated under different conditions, could vary significantly. Furthermore, the productivity of

3.4.2. Impact of plant scale Increasing plant scale improves biorefinery economics through benefits of economies-of-scale, in which larger biorefinery scales are always favored. It was of interest to study lower plant scales on the MESP because, currently, GM-sorghum is not cultivated at a large scale and mass cultivation is expected to become feasible over a longer period of time. For this purpose, Aspen Plus simulations and TE models

Fig. 3. Cost contribution ($/gal) of process areas to the minimum ethanol selling price for the coproduction scenario. 1466

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Fig. 4. Cost contribution ($/gal) of process areas to the minimum ethanol selling price for the ethanol-only scenario.

are updated for plant scale changes from 500 to 2,000 dry Mg/day at every 250 Mg/day increase. Results shown in Fig. 6(b) indicate that, because of economies-of-scale, MESP changes nonlinearly with plant scale. Results show that MESP could change between a minimum of $1.97/gal at a plant scale of 2,000 dry Mg/day and a dry biomass price of $40/Mg and a maximum of $3.67/gal at a plant scale of 500 dry Mg/ day and a dry sorghum price of $110/Mg. Furthermore, results show that MESP is very sensitive to plant scale variations between 500 and 2,000 dry Mg/day, but less sensitive to scales above 2,000 dry Mg/day. Therefore, to achieve an ethanol selling price of $2.25/gal (2014 wholesale price) increasing the plant scale to above 2,000 dry Mg/day would not suffice and reducing biomass price by about $20/Mg dry is required.

8,000 Mg stems/day, and 70% moisture content was considered [31]. The lipid-cane process includes five stages: biomass shredding, oil and sugar extraction using hot water, juice treatment and oil-sugar separation, sugar fermentation to ethanol, and oil transesterification to biodiesel and glycerol. Bagasse, the solid residue after sugar/oil extraction, is combusted to provide heat and power for the process while excess electricity is sold to the grid. The microalgae process [13] receives 1,215 dry Mg/day of algae at 20 wt% moisture content and includes dilute acid pretreatment to hydrolyze glucan carbohydrates to glucose, fermentation of sugars and ethanol recovery, lipid extraction and solvent recovery, lipid purification, upgrading and hydrotreating to produce primarily a diesel-range paraffinic product and a small amount of naphtha as byproduct. The algae process includes anaerobic digestion to produce biogas from the solid residues, which is combusted in a gas turbine to generate heat and electricity. Solid digestate cake, along with effluent water, contain nitrogen and phosphorus and are sold as nutrient sources providing additional coproduct revenue. CO2 from flue gas and fermenter vents is consumed by algae cultivation ponds and provides another source of revenue. The soybean biodiesel process [31,40] receives 1,500 Mg/day feedstock at 12% moisture content. The process includes soybean crushing/conditioning, solvent extraction of lipids, conditioning, and transesterification to biodiesel and glycerol. Solid residues after solvent

3.5. Comparison with other biomass-to-biodiesel technologies Base case results are compared with other biomass-to-biodiesel technologies to benchmark the performance of sorghum coproduction and identify potential ways to improve its economics. For this purpose, two coproducing technologies, lipid cane [31] and microalgae [13], along with a biodiesel-only approach using soybeans [31,40] are considered. The lipid-cane biomass is a genetically engineered sugarcane capable of producing lipids instead of sugars. Similar to the lipid content in this study’s base case, lipid cane with 10 wt% lipid content,

Fig. 5. Impact of single point variations in the main process parameters on MESP for coproduction process. 1467

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

Fig. 6. Impact of (a) biomass price and lipid content and (b) biomass price and plant scale on the MESP for the coproduction process.

extraction contain protein and carbohydrates which are milled and sold as soybean meal for animal feed. The soybean biodiesel process purchases electricity and steam to meet its utility demands. As with the sorghum scenarios already portrayed, technoeconomic models were developed for these three additional processes. The capital and raw material costs are updated to 2014 US dollars using the CEPCI and the inorganic chemical index. 40% equity financing and 10% internal rate of return are used to calculate minimum fuel selling prices (MFSP) to reach a breakeven point after 30 years of plant life. Because different types of fuels are produced in each process, MFSPs are calculated on a gallon of gasoline equivalent (GGE) basis, considering an LHV value of 80.5, 126.1, and 122.5 GJ/gal for ethanol, biodiesel, and conventional gasoline, respectively [50]. The MFSPs, in ascending order for the lipid-cane, sorghum, microalgae, and soybean processes are calculated to be $3.27, $3.74, $4.59, and $4.68/gal, respectively. The microalgae and sorghum processes are more capital and energy intensive than the lipid-cane and soybean processes, partly because of acid thermal pretreatment required in the former compared to the milder crushing and shredding in the latter. The much lower energy consumption of lipid cane and soybean leads to larger net electricity sales to the grid for lipid cane and lower utility costs for the soybean process. In addition, the lipid-cane and soybean processes do not include capital costs for wastewater treatment. Finally, the soybean process does not have a boiler/turbogenerator, leading to the lowest capital cost of these four processes. The main cost driver in the microalgae process is biomass purchase cost which contributes 67% of the total costs of $13/GGE because of the large cost of cultivation ponds. Furthermore, hexane solvent for lipid extraction and hydrogen for lipid hydrothermal upgrading are two major chemical costs contributing 32% and 22%, respectively, to the total raw material costs. In the soybean process, biomass is the major cost driver contributing $10/GGE to MFSP, equating to 90% of the total production cost. The soybean process receives a large credit of -$6.8/ GGE from selling soybean meal byproduct. However, large soybean costs hinder the economics, making soybean biodiesel the most expensive process. A critical factor that must be considered in the comparisons presented in Fig. 7 is that lipid cane and soybean are food crops, which raise food vs. fuel debates and potentially threaten global food security and raise competition for arable land [32]. However, sorghum as lignocellulosic biomass and microalgae as a third generation biomass source [51] curb the negative aspects of food crop biomass making them more appealing alternatives for biofuel production.

3.6. Practical implications Insights from this study regarding biodiesel and bioethanol production from lignocellulosic biomass can guide future research and development. Adding a lipid accumulation trait to sorghum improves the economics in a manner that correlates with lipid content. However, the effect of lipid production on cultivation yield per acre is unknown. As lipid producing sorghum would have higher energy density than regular sorghum, this might reduce the maximum growth, thus requiring a larger cultivation area for the same amount of biomass. A larger cultivation area will consequently increase cultivation, collection, and transportation costs [52]. As biomass is a major cost contributor to the fuel selling price and that land availability is limited [32], a larger cultivation area could negatively affect the development of coproduction technology. Although the impact of biomass price on process economics is captured in the sensitivity analyses (Fig. 6), experimental studies are needed to assess the effect of lipid production on sorghum growth and cultivation yield. Another main cost contributor is lipid extraction using hexane, which at a lipid to hexane ratio of 5:1 leads to high capital and energy costs. The design of lipid extraction is based on the extraction of lipids from microalgae cells [13], which are very small and dispersed in the stillage. In contrast to microalgae, sorghum, lipid cane, and soybeans consist of larger particle size, which allows for recovery by less

Fig. 7. Comparison of sorghum coproduction at 10 wt% lipid content with coproduction from lipid cane at 10 wt% lipid, microalgae, and soybean biodiesel. 1468

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al.

expensive grinding, heating, and phase separation. Alternatively, if the stillage after alcohol recovery contains most of the lipids, then this stillage can be filtered and sent for lipid extraction. This will significantly reduce the capital and energy costs of the extraction columns by decreasing the volumetric flow after filtration by about eight times. Furthermore, the hexane extraction efficiency will improve because of increased contact between lipids and hexane, which could reduce hexane-to-solids loading. The minimum selling prices calculated in this study provide a conservative estimate and ample potential for further improvement exists by optimizing the process configuration. The type of lipids produced by sorghum can affect the extraction efficiency. Polar lipids, such as phospholipids, have a propensity to form gums that can cause problems during transesterification or deactivate catalysts in hydrotreating processes [53]. Unfortunately, removal of polar lipids reduces the total available lipid for biodiesel production. Therefore, it is important that genetically engineered sorghum accumulate nonpolar lipids (fatty acids) and minimize polar lipid production. On the process side, using a nonpolar solvent, such as hexane, reduces the amount of polar lipids extracted [13] and thereby, reduces the total phosphoric acid required to remove polar lipids during downstream degumming. Finally, pretreatment and enzyme loading can greatly affect the fuel selling price. Experimental studies on microalgae show that lipid extraction after acid pretreatment and enzymatic hydrolysis increase an order of magnitude from 6%-8% to 77%-93% compared to lipid extraction prior to any pretreatments [54]. In addition, solvent use prior to enzymatic hydrolysis and fermentation can reduce enzymatic and microbial activity [4]. The acid loadings, enzyme loadings, and conversions considered in this study are similar to the values reported by NREL for corn stover [35], as unmodified sorghum has been found to perform similarly [28]. However, as lipid producing sorghum will have a smaller carbohydrate content, this might reduce demand for acid and enzyme loading leading to reduced costs. Nevertheless, these potential improvements depend heavily on GM-sorghum’s characteristics and requires further verification through experimentation.

References [1] EPA (U.S. Environmental Protection Agency). U.S. Transportation Sector Greenhouse Gas Emissions 1990–2016; 2018. [2] Bracmort K. The Renewable Fuel Standard (RFS): An Overview; 2018. [3] Schwab A. Bioenergy technologies office multi-year program plan, March 2016; 2016. doi: 10.2172/1245338. [4] Mukhopadhyay A. Tolerance engineering in bacteria for the production of advanced biofuels and chemicals. Trends Microbiol 2015;23:498–508. https://doi.org/10. 1016/j.tim.2015.04.008. [5] Grotewold E, Jones Prather KL, Peters K. Lignocellulosic Biomass for Advanced Biofuels and Bioproducts: Workshop Report, Washington, DC, June 23-24, 2014; 2015. doi: 10.2172/1471542. [6] Dutta A, Iisa MK, Tan EC, Mukarakate C, Griffin MB, Schaidle JA, et al. Ex Situ Catalytic Fast Pyrolysis of Lignocellulosic Biomass to Hydrocarbon Fuels: 2018 State of Technology and Future Research. Golden, CO (United States); 2018. doi: 10. 2172/1481364. [7] Mishra S, Anand K, Santhosh S, Mehta PS. Comparison of biodiesel fuel behavior in a heavy duty turbocharged and a light duty naturally aspirated engine. Appl Energy 2017;202:459–70. https://doi.org/10.1016/j.apenergy.2017.05.162. [8] Lin L, Cunshan Z, Vittayapadung S, Xiangqian S, Mingdong D. Opportunities and challenges for biodiesel fuel. Appl Energy 2011;88:1020–31. https://doi.org/10. 1016/j.apenergy.2010.09.029. [9] Gebremariam SN, Marchetti JM. Economics of biodiesel production: Review. Energy Convers Manage 2018;168:74–84. https://doi.org/10.1016/j.enconman.2018.05. 002. [10] Chen H, Xie B, Ma J, Chen Y. NOx emission of biodiesel compared to diesel: Higher or lower? Appl Therm Eng 2018;137:584–93. https://doi.org/10.1016/j. applthermaleng.2018.04.022. [11] Fasahati P, Maravelias CT. Advanced biofuels of the future: atom-economical or energy-economical? Joule 2018. doi:10.1016/j.joule.2018.09.007. [12] Souza SP, Seabra JEA. Environmental benefits of the integrated production of ethanol and biodiesel. Appl Energy 2013;102:5–12. https://doi.org/10.1016/j. apenergy.2012.09.016. [13] Davis R, Kinchin C, Markham J, Tan E, Laurens L, Sexton D, et al. Process design and economics for the conversion of algal biomass to biofuels: algal biomass fractionation to lipid- and carbohydrate-derived fuel products; 2014. doi: 10.2172/ 1159351. [14] Kumar D, Long SP, Singh V. Biorefinery for combined production of jet fuel and ethanol from lipid-producing sugarcane: a techno-economic evaluation. GCB Bioenergy 2018;10:92–107. https://doi.org/10.1111/gcbb.12478. [15] U.S Energy Information Administration. U.S. Inputs to biodiesel production; 2018. https://www.eia.gov/biofuels/biodiesel/production/table3.pdf. [16] Topfer R, Martini N, Schell J. Modification of plant lipid synthesis. Science (80-) 1995;268:681–6. https://doi.org/10.1126/science.268.5211.681. [17] Slocombe SP, Cornah J, Pinfield-Wells H, Soady K, Zhang Q, Gilday A, et al. Oil accumulation in leaves directed by modification of fatty acid breakdown and lipid synthesis pathways. Plant Biotechnol J 2009;7:694–703. https://doi.org/10.1111/j. 1467-7652.2009.00435.x. [18] Sanjaya A, Miller R, Durrett TP, Kosma DK, Lydic TA, Muthan B, et al. Altered lipid composition and enhanced nutritional value of arabidopsis leaves following introduction of an algal diacylglycerol acyltransferase 2 published by: American Society of Plant Biologists (ASPB) Linked references are available on JSTOR for th 2017. [19] Zale J, Jung JH, Kim JY, Pathak B, Karan R, Liu H, et al. Metabolic engineering of sugarcane to accumulate energy-dense triacylglycerols in vegetative biomass. Plant Biotechnol J 2016;14:661–9. https://doi.org/10.1111/pbi.12411. [20] Ma W, Kong Q, Grix M, Mantyla JJ, Yang Y, Benning C, et al. Deletion of a Cterminal intrinsically disordered region of WRINKLED1 affects its stability and enhances oil accumulation in Arabidopsis. Plant J 2015;83:864–74. https://doi. org/10.1111/tpj.12933. [21] Liu J, Tjellström H, McGlew K, Shaw V, Rice A, Simpson J, et al. Field production, purification and analysis of high-oleic acetyl-triacylglycerols from transgenic Camelina sativa. Ind Crops Prod 2015;65:259–68. https://doi.org/10.1016/j. indcrop.2014.11.019. [22] Knothe G. “ Designer ” Biodiesel : Optimizing Fatty Ester Composition to Improve. Fuel Properties † 2008:1358–64. [23] Griffith AP, Haque M, Epplin FM. Cost to produce and deliver cellulosic feedstock to a biorefinery: Switchgrass and forage sorghum. Appl Energy 2014;127:44–54. https://doi.org/10.1016/j.apenergy.2014.03.068. [24] McCollum III T, Mccuistion K, Bean B. Brown mid-rib and photoperiod-sensitive forage sorghums. Agric Res Ext Cent Amarillo 2005. [25] Zhang C, Xie G, Li S, Ge L, He T. The productive potentials of sweet sorghum ethanol in China. Appl Energy 2010;87:2360–8. https://doi.org/10.1016/j. apenergy.2009.12.017. [26] Dahlberg J, Wolfrum E, Bean B, Rooney WL. Compositional and agronomic evaluation of sorghum biomass as a potential feedstock for renewable fuels. J Biobased Mater Bioenergy 2011;5:507–13. https://doi.org/10.1166/jbmb.2011.1171. [27] Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, et al. The Sorghum bicolor genome and the diversification of grasses. Nature 2009;457:551–6. https://doi.org/10.1038/nature07723. [28] Technical N, Ed C. Sorghum to Ethanol Research Initiative, Cooperative Research and Development, Final Report, NREL/TP-7A10-52695; 2011. [29] Chandler C. Plant scientists aim to turn sorghum into jet fuel; 2017. https://news. unl.edu/newsrooms/today/article/plant-scientists-aim-to-turn-sorghum-into-jet-

4. Conclusion A new process design was synthesized for coproduction of ethanol and diesel from a genetically modified lipid producing sorghum. Technoeconomic analysis showed that coproduction of diesel results in a lower MESP value of $2.46/gal calculated for a 10% sorghum lipid content, which showed improved economics compared to an MESP value of $3.08/gal calculated for a non-GM sorghum producing ethanol only. Results indicated that lipid extraction and solvent recovery contribute the most cost of the additional units needed to an ethanol-only process for biodiesel production, mainly because of the high capital costs of extraction columns and the large solvent (hexane) to solids ratio (5:1) required to reach 95% extraction efficiency. Adding diesel production units to the biorefinery increases total capital investment by $50 million compared to an ethanol-only process. However, increased revenue from selling biodiesel exceeds added energy and capital costs. Furthermore, results showed that higher sorghum lipid contents lead to larger biodiesel fuel production and could further improve process economics. Acknowledgments This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494). Dr. Saffron’s contribution was supported in part by the USDA National Institute of Food and Agriculture, Hatch project 1018335, and Michigan State University AgBioResearch. 1469

Applied Energy 239 (2019) 1459–1470

P. Fasahati, et al. fuel/. [30] Dahlberg J, Wolfrum E. Sorghum to Ethanol Research; 2010. [31] Huang H, Long S, Singh V. Techno-economic analysis of biodiesel and ethanol coproduction from lipid-producing sugarcane. Biofuels, Bioprod Biorefining 2016;10:299–315. https://doi.org/10.1002/bbb.1640. [32] Nanda S, Azargohar R, Dalai AK, Kozinski JA. An assessment on the sustainability of lignocellulosic biomass for biorefining. Renew Sustain Energy Rev 2015;50:925–41. https://doi.org/10.1016/j.rser.2015.05.058. [33] EPA (U.S. Environmental Protection Agency). Renewable Fuel Standard Program: Grain Sorghum Oil Pathway. Off Fed Regist 2018. https://www.federalregister. gov/documents/2018/08/02/2018-16246/renewable-fuel-standard-programgrain-sorghum-oil-pathway (accessed 09.01.18). [34] U.S Department of Energy. DOE Bioenergy Research Centers. Jt Genome Inst; 2017. https://jgi.doe.gov/our-science/doe-bioenergy-research-centers/. [35] Humbird D, Davis R, Tao L, Kinchin C, Hsu D, Aden A, et al. Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol: diluteacid pretreatment and enzymatic hydrolysis of Corn Stover, vol. 303. Golden, CO (United States); 2011. doi:10.2172/1013269. [36] Zong L, Ramanathan S, Chen C. Fragment-Based approach for estimating thermophysical properties of fats and vegetable oils for modeling biodiesel. Ind. Eng. Chem. Res. 2010;49(2):876–86. https://doi.org/10.1021/ie900513k. [37] Searcy EM, Hess JR. Uniform-format feedstock supply system: a commodity-scale design to produce an infrastructure-compatible biocrude from lignocellulosic biomass; 2010. doi: www.inl.gov/bioenergy/uniformfeedstock. [38] Davis R, Tao L, Scarlata C, Tan ECD, Ross J, Lukas J, et al. Process design and economics for the conversion of lignocellulosic biomass to hydrocarbons: diluteacid and enzymatic deconstruction of biomass to sugars and catalytic conversion of sugars to hydrocarbons. Golden, CO (United States); 2015. doi: 10.2172/1176746. [39] Zhang Y, Dubé MA, McLean DD, Kates M. Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresour Technol 2003;89:1–16. https://doi.org/10.1016/S0960-8524(03)00040-3. [40] Haas MJ, McAloon AJ, Yee WC, Foglia TA. A process model to estimate biodiesel production costs. Bioresour Technol 2006;97:671–8. https://doi.org/10.1016/j. biortech.2005.03.039. [41] Ringer M, Putsche V, Scahill J. Large-scale pyrolysis oil production: a technology assessment and economic analysis; 2006. [42] Wright MM, Satrio JA, Brown RC, Daugaard DE, Hsu DD. Techno-economic analysis

[43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54]

[55] [56]

1470

of biomass fast pyrolysis to transportation fuels. Golden, CO (United States); 2010. doi: 10.2172/993332. Chemical Engineering Magazine Plant Cost Index. Chem Eng Mag 2014. http:// www.che.com/pci/. U.S. Producer Price Indexes – Chemicals and Allied Products/Industrial Inorganic Chemicals Index. Menlo Park, CA; 2014. National Employment, Hours, and Earnings Catalog, Industry: Chemicals and Allied Products, 1980-2014. US Bur Labor Stat 2014. https://data.bls.gov/cgi-bin/srgate. DOE. Biodiesel Fuel Basics, Alternative Fuels Data Center; 2018. https://www.afdc. energy.gov/fuels/biodiesel_basics.html. U.S Energy Information Administration. Wholesale Electricity and Natural Gas Market Data. Anal Proj 2014. http://www.eia.gov/electricity/wholesale/. Quispe CAG, Coronado CJR, Carvalho JA. Glycerol: Production, consumption, prices, characterization and new trends in combustion. Renew Sustain Energy Rev 2013;27:475–93. https://doi.org/10.1016/j.rser.2013.06.017. U.S. Energy Information Administration. Annual energy outlook 2016 with projections to 2040. Washington; 2016. DOE Hydrogen Analysis Resource Center. Lower and higher heating values of hydrogen and fuels 2018. https://h2tools.org/hyarc/calculator-tools/lower-andhigher-heating-values-fuels [accessed 10.03.18]. Fasahati P, Woo HC, Liu JJ. Industrial-scale bioethanol production from brown algae: Effects of pretreatment processes on plant economics. Appl Energy 2015;139:175–87. https://doi.org/10.1016/j.apenergy.2014.11.032. Ng RTL, Maravelias CT. Economic and energetic analysis of biofuel supply chains. Appl Energy 2017;205:1571–82. https://doi.org/10.1016/j.apenergy.2017.08.161. Kubička D, Horáček J. Deactivation of HDS catalysts in deoxygenation of vegetable oils. Appl Catal A Gen 2011;394:9–17. https://doi.org/10.1016/j.apcata.2010.10. 034. Laurens LML, Nagle N, Davis R, Sweeney N, Van Wychen S, Lowell A, et al. Acidcatalyzed algal biomass pretreatment for integrated lipid and carbohydrate-based biofuels production. Green Chem 2015;17:1145–58. https://doi.org/10.1039/ C4GC01612B. AspenTech. Aspen Plus Biodiesel Model. Components; 2008. p. 1–9. doi:10.1016/j. jenvman.2013.06.008. Methanex. Methanex Monthly Average Regional Posted Contract Price History 2014. https://www.methanex.com/our-business/pricing.