Scholarly article on topic 'A critical analysis of paddlewheel-driven raceway ponds for algal biofuel production at commercial scales'

A critical analysis of paddlewheel-driven raceway ponds for algal biofuel production at commercial scales Academic research paper on "Chemical engineering"

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Abstract of research paper on Chemical engineering, author of scientific article — Jonathan N. Rogers, Julian N. Rosenberg, Bernardo J. Guzman, Victor H. Oh, Luz Elena Mimbela, et al.

Abstract Microalgae have been promoted as the next frontier of green biotechnology and gained widespread attention as desirable feedstocks for biofuels. Using conservative assumptions for microalgal growth rates (15gm−2 d−1) and total lipid content (25%), the entire “pond-to-pump” lifecycle of algal biofuels for 1000bbld−1 of crude algae oil production is modeled with approximately 4875ha of raceway ponds for solar collection and cultivation and 1463MLD (385MGD) of water handling capacity in the current analysis. Technoeconomic analysis based on an array of 6000 modular 0.8ha (2acre) paddlewheel-driven ponds in New Mexico identified several cost barriers and resources challenges (i.e., nutrient and water resources). For 10- and 20-year capital return scenarios, the cost of algal oil production – $4.10L−1 ($15.52gal−1) and $3.21L−1 ($12.14gal−1), respectively – requires substantial capital and facility maintenance investments with principal cost sensitivities attributed to extraction efficiency and lipid content. Baseline conditions result in an energy return on investment (EROI) of 2.73. Uncertainty in energy requirements for paddlewheels as well as water supply and circulation significantly affect the EROI and operating costs. Alternative strategies to address the major cost barriers are needed for algal biofuels to realize their full potential.

Academic research paper on topic "A critical analysis of paddlewheel-driven raceway ponds for algal biofuel production at commercial scales"

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ALGAL-00085; No of Pages 13

Algal Research xxx (2013) xxx-xxx

A critical analysis of paddlewheel-driven raceway ponds for algal biofuel production at commercial scales^

Jonathan N. Rogers a, Julian N. Rosenberg a,b, Bernardo J. Guzman a, Victor H. Oh a, Luz Elena Mimbelac, Abbas Ghassemic, Michael J. Betenbaugh a, George A. Oyler a,b,d, Marc D. Donohue a,b,e'*

a Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, United States b Synaptic Research, LLC, 1448 South Rolling Road, Baltimore, MD 21227, UnitedStates c Institute for Energy & the Environment, New Mexico State University, Las Cruces, NM 88003, United States d Department of Biochemistry, University of Nebraska-Lincoln, 1901 Vine Street, Lincoln, NE 68588, UnitedStates e Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, United States

ARTICLE INFO ABSTRACT

Microalgae have been promoted as the next frontier of green biotechnology and gained widespread attention as desirable feedstocks for biofuels. Using conservative assumptions for microalgal growth rates (15 g m-2 d-1) and total lipid content (25%), the entire "pond-to-pump" lifecycle of algal biofuels for 1000 bbl d-1 of crude algae oil production is modeled with approximately 4875 ha of raceway ponds for solar collection and cultivation and 1463 MLD (385 MGD) of water handling capacity in the current analysis. Technoeconomic analysis based on an array of 6000 modular 0.8 ha (2 acre) paddlewheel-driven ponds in New Mexico identified several cost barriers and resources challenges (i.e., nutrient and water resources). For 10- and 20-year capital return scenarios, the cost of algal oil production - $4.10 L-1 ($15.52 gal-1) and $3.21 L-1 ($12.14 gal-1), respectively - requires substantial capital and facility maintenance investments with principal cost sensitivities attributed to extraction efficiency and lipid content. Baseline conditions result in an energy return on investment (EROI) of 2.73. Uncertainty in energy requirements for paddlewheels as well as water supply and circulation significantly affect the EROI and operating costs. Alternative strategies to address the major cost barriers are needed for algal biofuels to realize their full potential.

© 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Article history:

Received 11 June 2013

Received in revised form 13 September 2013

Accepted 6 November 2013

Available online xxxx

Keywords:

Biofuel

Raceway ponds Sustainability Technoeconomic analysis

1. Introduction

The social, environmental, and economic pressures of human activity require ever increasing energy resources. The rise of developing nations coupled with a predicted expansion of the world population to at least 9 billion by 2050 [1] correlates to a global increase in energy use from 533 quadrillion (1015) kJ in 2008 to 812 quadrillion kJ by 2035 [2]. Photosynthetic biomass grown as a bioenergy crop has the potential to contribute a significant amount of renewable fuel while simultaneously absorbing point sources of carbon dioxide (CO2). The United

States has a goal of 17% reduction in CO2 emissions by 2020. Current projections show energy-related CO2 emissions in 2020 to be only 9% below their 2005 level [2]. Therefore, an intensified expansion of carbon neutral renewables will be necessary to meet the milestone within the coming decade.

Microalgae are perhaps the most prolific source of photosynthetic biomass on the planet. The controlled cultivation of microalgae on large-scale farms offers an avenue to enhance domestic energy production while minimizing land resources requirements. In 2010, liquid biofuels represented only 1% of the total U.S. fuel portfolio and are

Abbreviations: AD, Anaerobic Digestion; ANL, Argonne National Laboratory; APD, Algae Process Description Tool; ASP, Aquatic Species Program; BD, Biodiesel; BGY, Billion Gallons per Year; BLY, Billion Liters per Year; CO2, Carbon Dioxide; COP, Cost of Production; DAF, Dissolved Air Flotation; DARPA, Defense Advanced Research Projects Agency; DAP, Diammonium Phosphate; DOE, U.S. Department of Energy; DW, Dry Weight; EE, Extraction Efficiency; EERE, Energy Efficiency & Renewable Energy; EIA, Energy Information Administration; EISA, Energy Independence and Security Act of 2007; EPA, Environmental Protection Agency; EROI, Energy Return on Investment; FWC, Flue-gas and Wastewater Co-utilization; g L-1, Grams per Liter; GHG, Green House Gas; ha, Hectare; HDPE, High-Density Polyethylene; HRP, High Rate Ponds; kW, Kilowatts; kWh, Kilowatt Hours; LANL, Los Alamos National Laboratory; LCA, Life-Cycle Analysis; LEA, Lipid Extracted Algae; LLE, Liquid-Liquid Extraction; MGY, Million Gallons per Year; MGD, Million Gallons per Day; MLD, Million Liters per Day; NMSU, New Mexico State University; NREL, National Renewable Energy Laboratory; NWIS, National Water Information System; PA, Polyacrylamide; PBR, Photobioreactor; PE, Photosynthetic Efficiency; RA, Resource Assessment; RD, Renewable Diesel; RFS, Renewable Fuel Standard; TEA, Techno-Economic Analysis; USGS, U. S. Geological Survey; WWT, Wastewater Treatment; WWTP, Wastewater Treatment Plant.

☆ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author at: Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, United States. E-mail address: mdd@jhu.edu (M.D. Donohue).

2211-9264/$ - see front matter © 2013 The Authors. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.! 016/j.algal.2013.11.007

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expected to reach 4% by 2035 with nearly all of their consumption attributed to the transportation sector [2]. Algal biofuels may ease this inevitable energy transition by fulfilling a significant role in our portfolio of alternatives to fossil fuels.

Microalgae have been promoted as one of the more promising third-generation biofuels for their ability to accumulate substantial amounts of lipids, divide rapidly, grow in low quality water, absorb CO2, and grow on non-arable land [3]. There is a wealth of literature that documents the commercial scale growth of various microalgal species for natural products as well as the progression ofboth basic and applied biological research, improvements to photobioreactor (PBR) and pond design, and lifecycle analyses of algal biofuels [4-6]. The mass cultivation of microalgae was pioneered in the early 1950s with Chlorella [7] and quickly transitioned into a modular production process using Oswald's raceway design termed "high rate ponds" (HRPs) for large-scale recirculating algae cultivation [8]. Although there is some debate surrounding the "carbon neutrality" of biofuels in general, microalgae offer significant advantages over other alternatives. Yet, the lack of uniformity in these technologies and microbial crops still makes assessment of the costs and energy requirements complex. Some of the major factors affecting algal biomass productivity include inherent pho-tosynthetic constraints as well as the bioprocessing challenges related to large-scale cultivation of microbes in water.

When cultivating algae in an artificial environment (e.g., outdoor pond), it is essential that growth factors are plentiful in order to maximize growth rates [9]. While CO2 can be acquired from the atmosphere, it is commonly fed into algae media to improve production [10-12]. In addition to CO2, nitrogen and phosphorus are the major nutrients required for algae growth. It has become an accepted measure that marine plankton have relatively constrained elemental ratios of 106:16:1 (C:N:P) [13-15]. Although some algae species, primarily cyanobacteria, can fix nitrogen from the air [16,17], most microalgae require a soluble form, such as urea or ammonia [18].

In raceway ponds, paddlewheels are used to maintain constant mixing of the algae. A single paddlewheel has been shown to provide sufficient mixing for algae biomass cultivation for arrays of connected ponds covering areas as large as 5 ha [19,20]. While this shows promise for paddlewheels in HRPs, larger ponds require scale-up of the number of paddlewheels to maintain sufficient mixing. With a turbulent mixing velocity, the culture can maintain a uniform density. The addition of eddies generated from the paddlewheel helps to reduce the residence time of the algae in the dark regime. At a low velocity, laminar flow will decrease the productivity of the pond; yet, as the velocity increases, the power required to generate the new velocity increases cubically. This presents problems for the mixing velocity at rates greater than 30 cm s-1, specifically manifested in increased energy costs [21].

Algae based biofuels received recent support as qualified feedstock, making algae-derived biofuels eligible for $1.01 tax credit per gal (Section 40 of United States Code). Despite the general public awareness of the various biological sources of liquid fuels and their potentially significant contribution to greenhouse gas (GHG) reduction [22], there remains little consensus on the lifecycle analyses (LCA) and TEA of these biofuels [23,24]. Some of the most recent information on algal biofuel technoeconomics shows that algae may have a favorable energy return on investment (EROI) compared to fossil fuels, first-, and even second generation biofuels [25]. However, the existence of multiple biofuel production pathways, different productivity assumptions and limited commercial-scale production makes it difficult to establish theoretical mass and energy balance equations [26].

In the present study, the production costs and EROI of a hypothetical algae farm and biocrude oil refinery of commercially relevant production capacity (1000 bbl d-1) were evaluated for a variety of operating scenarios. The overall feasibility of this facility and its scalability to meet 5 and 10 billion gallon per year (BGY) production goals,

equivalent to 18.9 and 37.8 billion liters per year (BLY), were explored for the geography and climate of New Mexico based on technical and economic analyses. In particular, this algae oil process model focuses on upstream cultivation using conventional technologies (i.e., raceway ponds) and locally sourced water and energy inputs. The impact of well-established harvesting, dewatering, and separating of methodologies on the sustainability of downstream processing was also assessed. The results of this technoeconomic study identify the major energy demands, water requirements, and capital investments associated with traditional algal biofuel production. The ultimate conclusions regarding cost of production (COP) and sustainability are held in comparison with empirical data from the New Mexico State University (NMSU) microalgae cultivation testbed.

2. Methods

2.1. Production assumptions and scalability

The model in the present study was developed in order to estimate the scale of a facility to produce 1000 barrels of crude algae oil per day (bbl d-1). Nutrient demand was determined from the Redfield ratio elemental composition of C106H181O45N16P, resulting in nutrient requirements of 525.1 mg C g- 1-algae DW, 91.9 mg N g-1-algae DW, and 12.7 mg P g-1-algae DW. All references made to algal biomass in this model pertain to the biomass dry weight (DW). As a simplifying assumption, a year averaged microalgal growth rate of 15 g m-2 d-1 was chosen. Estimates for growth culture density (0.5 g L-1), harvesting rate (10%), extraction efficiency (80%), and lipid content (25%) were used as the baseline condition of the model. Due to the uncertainty of lipid fractions and the effects of specific growth conditions, all lipids were assumed to be useable as a precursor to renewable diesel (RD) conversion. Based on the assumptions, a growth surface area of 4875 ha will produce 730,000 ± 2000 kg d-1 algal biomass, yielding 1000 bbl d-1 algae oil. So as not to exceed the paddlewheel capabilities individual ponds were sized at 0.81 ha each, requiring 6000 ponds for production. The growth surface area has a depth of 30 cm resulting in a total volume of 14.627 billion liters. By maintaining the culture density at 0.5 g L- 1 the ponds can contain 7.3 M kg-algae, of which only 10% of the total raceway volume will be harvested daily. This allows the ponds to maintain production and operate in a continuous steady state by balancing the harvesting rate with the photosynthetic growth rate. Wages were estimated to contribute 12% of the total operating costs. This estimate was chosen based on similar analyses of the industry [6]. Maintenance costs were estimated as 3% of the process equipment and raceway pond costs [27]. All equipment was assumed to operate for 24 h d-1 365 d yr-1. As a simplifying assumption, an average cost of electricity of $0.11 kWh-1 was used to account for seasonal variations.

One of the main goals of the current study was to evaluate the potential scalability of algae facilities. The U.S. Energy Independence Security Act of 2007 (EISA) has mandated 36 billion gallons (136B L) of renewable fuel by 2022 that will in turn be ramped up to 60 billion gallons (227B L) of biofuel by 2030 with provisions to emphasize the development of advanced, non-corn ethanol biofuels [28]. To evaluate the scale up feasibility of algae production, we assumed that algae oil will contribute a moderate portion of these mandates by producing 5 BGY (18.9 BLY) of algae oil by 2022 with an increase to 10 BGY (37.8 BLY) by 2030. The current model was designed with New Mexico as a possible site for algal production due to its climate (low seasonal variation and high solar irradiance) and geography (flat topography). The current study evaluated the capital and operating requirements associated with algae-oil production of 1000 bbl d-1. Results from a single model production plant were used to assess the scalability and key limitations of algal biofuels from both economic and sustainability perspectives [25]. The process flow of baseline estimates for the current model is shown in Fig. 2.1a.

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Hexane recycle (17.5 kg hr1)

(350,000 kg hr1)

Fig. 2.1a. Single plant baseline operating flow diagram. Dashed lines indicate possible pathways for LEA. Additional processing such as anaerobic digestion would be necessary for nutrient breakdown and the production of biogas. An alternative pathway would be to sell LEA as a nutritional supplement for livestock or aquaculture.

2.2. Economic, energy, and resource estimates

The plant capital and operating costs were estimated using industrial estimates and reports from similar processes. Three static mixers would facilitate CO2 and nutrient addition to the ponds totaling about $0.17 M capital investment (Westfall Manufacturing). Paddlewheel estimates were based on the requirements for a 0.81 ha raceway with a channel 12.2 m across and 30 cm deep. The 6000 ponds in the current model require a total of 24,000 paddlewheel units, and span over 4875 ha. Capital cost estimates for paddlewheels totaled a $120 M capital investment (Waterwheel Factory Inc.). The impacts of three energy conditions were evaluated in the model. In the baseline scenario, the paddlewheels were calculated to require 0.22 W m-2 (Appendix B.1) and contribute $10.1 M of annual operating costs. Paddlewheel energy scenario 2 requirements are 0.73 W m- 2 based on power usage specs from an industrial supplier (Waterwheel Factory Inc.), resulting in annual operating costs of $34.5 M. In paddlewheel energy scenario 3, assumptions from the current model are applied to pilot plant paddlewheel energy measurements from the NMSU testbed to assess scale up feasibility. Two pilot ponds, each with a growth surface area of 32 m2, were operated by paddlewheels with individual motors. Paddlewheel energy measurements were taken using a fluke meter while circulating water in the ponds continuously 24 h d- 1 to determine energy consumption under load. The total energy consumed by the two paddle wheel motors was measured to be 8.16 W m-2. Scaling up to the production capacity of that model would not be feasible for commercial scale and would result in $383.1 M of annual operating costs. Mixing energy inputs exceeded the theoretical growth mixing requirements at the test scale and represented a significant discrepancy between experimental ponds and commodity-scale ponds used for algal cultivation. Further optimization is needed to determine minimum mixing energy requirements and will assist in bridging the gap between pilot- and commercial-scale operations.

The current model assumes a polymer flocculantcostof$100 per ton of algae processed and contributes $26.7 M annually to the process (SNF Polydyne). Two static mixers are required for flocculant addition totaling $0.12 M (Westfall manufacturing). Estimates for two, 76.2 m diameter lamella clarifiers, added a capital investment of $2.5 M based on a project for the City of Detroit WWT, from Monroe Environmental [29]. The 5.96 kW rakes were assumed to run continuously for the operation of each clarifier, costing $11,500 annually. The algae water stream leaving

the clarifiers was assumed to be 30 g L-1 [30]. Centrifuges were used for additional dewatering of the algae water stream to 200 g L-1 [6]. The current model requires 12 centrifuges, totaling $6.6 M in capital investment, with each operating at 55 kW continuously and costing $0.64 M annually (GEA Westfalia). The current model requires 13 sonicators for cell disruption, totaling $3.25 M. The sonicators operate at 16 kW continuously and process up to 12 m3 h-1, costing $0.20 M annually (Hielsher Ultrasonics). In the baseline scenario, extraction is performed on the 20%-solid algae slurry using hexane at a cost of $3000 per ton. A small static mixer is used to combine the solvent and algae during lipid extraction costing $4500 (Westfall Manufacturing). A solvent to algae-DW mass ratio of 10:1 was assumed with a 1 h recycle time, totaling a 350 t requirement for extraction. Hexane was estimated to contribute $1.05 M of initial capital investment. It was assumed that there would be a daily solvent loss of0.005%, contributing to an additional $0.4 M of annual operating costs. In order to separate the LEA from the solvent-crude mix the current model uses an oil-water separator with 3000 GPM capacity and residence time of 1 h (Hydro-Flo Technologies), totaling a $0.25 M capital investment. The separator operates at 6 kW continuously and contributes about $6000 of annual operating costs. The present model uses a distillation column, $0.5 M capital investment [31], with a 595 GPM (2252 L min-1) capacity for solvent recovery and purification of the crude oil stream. Energy calculations (Appendix B.2) assume continuous operation of the column to contribute an annual energy cost of $2.7 M. In an alternative scenario the economic and energy impact of drying the algae slurry to 10%-water content is evaluated (Appendix B.2).

The proposed liner is a high-density polyethylene (HDPE) liner [32]. A 40-mil HDPE liner was modeled to line 6000 ponds at a price of $0.77 m-2, totaling $250 M [6]. The landscaping cost was estimated at $0.16 m-2, contributing a total of $57 M [33]. Covering the raceways cost an estimated $0.98 m-2, totaling $315 M [34], which included a transparent PE cover and greenhouse structure. Additional infrastructure costs needed for the plant include railways, roads, buildings, pipes, and construction. The current model assumes that railways and roads will be necessary for the transport of raw materials and access to the facility. The study estimates that a $1.85 M investment will provide 8.0 km of rail and 4.8 km of road for the facility [35,36]. Buildings are estimated to contribute $0.45 M for a chemical storage warehouse, a central operations facility, process buildings, and additional storage [37]. Construction costs are estimated to contribute $2.5 M for the

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facility, based on a similar study done on the profitability of soybean biodiesel from Iowa State [37]. Sizing estimates for piping were made using the volumetric flow rates throughout the process to maintain an average flow velocity of ~2.5 m s-1. Estimates were simplified by assuming 188,000 ft of piping requirements at an average cost of $11.43 m-1, totaling approximately $7 M [38,39].

Land was assumed to cost $3,707 ha-1 for property in New Mexico, totaling approximately $18 M [40], and did not consider CO2 (flue gas) or railway accessibility. Based on a resource assessment performed by The Solar Energy Research Institute, the Tularosa Basin and Crow Flats Basin are comprised of approximately 39,000 ha and a regional saline-water availability of750-1630 billion gallons (2839-6170 billion L) suitable for microalgal cultivation [41]. Although the current study operates under the assumption of using fresh-water algae, it assessed the feasibility of New Mexico for a theoretical strain of marine-algae with otherwise identical characteristics to that used in the present model.

Several simplifying assumptions were applied to the availability of water, which assumed to be accessible and that pumping was the only associated cost. No permit fees, taxes, or transportation considerations were applied to the baseline estimate for the cost of water. An average of ground water level measurements in New Mexico taken by the National Water Information System (NWIS) of 114 m (Table A.1) was used to calculate the pumping energy requirements to fill and maintain the raceway ponds. Using groundwater from this depth, filling the pond to the total operating volume of 14.63 BL (3.864 BG) was found to cost $2.9 M. Two scenarios of 6 m (baseline) and 12 m head were estimated for the on-site pumping circulation requirements of the 1463 MLD (386.5 MGD) processed, contributing annual operating costs of $5.7 M and $11.5 M for the scenarios respectively (Appendix B.3). Industrial turbine pumps were used as the basis for the capital cost estimates of the pumps throughout the plant, totaling $1.27 M of capital costs [42,43]. The current model estimates that approximately 1457 MLD (385 MGD) of water are recycled daily throughout the dewatering and back end processes. Despite the high percentage of process water recovered, a daily loss of about 0.03% of the total plant water volume equates to 3.67 MLD (0.97 MGD) for the 4875 ha plant. Far more critical however is the amount of water lost due to evaporation from the ponds. Evaporation loss for the plant is 243.8 MLD (64.4 MGD, 0.5 cm d-1) and 24.2 MLD (6.4 MGD, 0.05 cm d-1) for the two evaporation scenarios. Groundwater pumping for replacing 247.6 MLD (0.5 cmd-1 evaporation, 3.67 MLD process loss) and 28.0 MLD (0.05 cm d-1 evaporation, 3.67 MLD process loss) was found to have an annual cost of $18.3 M and $2.1 M respectively. The total annual water consumption per plant, producing 57.9 million liters (15.3 million gallons) of crude-algae oil/yr, is estimated at 90.5 billion and 10.2 billion liters of water for the uncovered and covered raceway pond designs, respectively. The two scenarios were found to require from 176 L-water to 1560 L-water per L-algae oil. Our wastewater treatment costs were estimated based on a facility of similar processing capacity totaling a $245 M capital investment [44]. Only relevant WWT steps were considered due to lower process requirements.

Nutrient consumption was estimated using the Aspen Plus v7.3 (Aspen Technology, Inc.) chemical process modeling software suite. The uptake was divided into photosynthesis and biosynthesis in order to adhere to the program's capabilities. Photosynthesis was modeled by combining CO2, water, and light to produce sugars and oxygen. Conversion efficiency considered poor light penetration in ponds, inherent photosynthetic loss, and cellular energy use; therefore, we chose a 4% overall conversion of solar energy [45] and a 50% conversion of CO2 to meet the demand of 525.1 mg C g- 1-algae DW in order to simplify mass balances. Biosynthesis modeled the conversion of sugar, a nitrogen source, and a phosphorus source into algal biomass (lipid, starch, protein), water, and oxygen. The current model found an annual requirement of 0.280 million tons of CO2 at a cost $40 per ton [46], contributing $11.2 M of annual operating costs. Diammonium phosphate (DAP, 18% N, 46% P, $499 ton-1 [47]) is used to meet the demand of

12.7 mg-P g-1-algae DW at an uptake efficiency of 50% [6], contributing $7.36 M of annual operating costs. In addition to DAP, the model uses urea (46% N, $379 ton-1 [47]) as a nitrogen source at an uptake efficiency of 76% to meet the 91.9 mg-N g-1-algae DW [6], contributing $24.43 M of annual operating costs (Table 2.2a).

3. Results

3.1. Results overview

The assumptions made for algal growth, dewatering, and extraction efficiency allowed us to estimate the costs and energy usage for each step of the process. The production goal of 1000 bbl-algae oil per day in New Mexico was found to require 730,000 kg ± 2000 kg-algae d- 1 (depending on inconsistencies in bioproduction) via a growth surface area of 4875 ha. The plant also produces 585 tons of lipid extracted algae (LEA) daily that can potentially be sold as a feed supplement or used for anaerobic digestion and nutrient recycle. The estimated cost of production that covers operating costs and a 10- or 20-year return of capital investment resulted in a baseline cost of $4.10 L- 1 ($15.52 gal-1) or $3.21 L-1 ($12.14 gal-1) of crude oil, respectively. The operating cost of production was estimated to be $134 M annually. Five operating factors - paddlewheels, nutrients, water, maintenance, and harvesting - were identified for their major impact on either cost or sustainability to the process. For the purpose of this study, the plant was classified as sustainable if it could annually achieve its nameplate capacity without any significant detriment to the surrounding

Table 2.2a

Key assumptions and model details.

Algae strain Chlorella vulgaris

Elemental composition of algae biomass C106H181O45N16P

Average annual areal productivitya 15gm-2d-1

Biomass lipid content 25 wt.%

Daily oil production 1000 bbl d-1 (159,000 ld-1)

Density 920 kg m-3 (146,268 kg-oil d-1)

Extraction efficiency (base) 80%

Required daily biomass (DW) 730,000 ± 1,500 kg d-1

Dilution rate 10%

Total biomass in raceways 7.31 M kg

Required growth surface area for daily 4875 ha

production

Growth surface area per pond 0.81 ha

Number of ponds in plant 6000

Maximum culture density 0.5 g L-1

Raceway depth 30 cm

Raceway volume 14.62 billion liters

CO2 recovery to culture 50%

Nitrogen recovery to culture 76%

P recovery to culture 50%

Net N demand 91.9 mg g-'-algae DW

Net P demand 12.7 mg g-1-algae DW

Pond mixing baseb 0.22 W m-2

Pond mixing 2c 0.73 W m-2

Pond mixing 3d 8.16 Wm-2

Lamella separators 3.91 x 10-4 kWh kg-1-algae DW

Centrifuge power 2.17 x 10-2 kWh kg-1-algae DW

Cell disruption 6.83 x 10-3 kWh kg-1-algae DW

Biomass-separator 1.97 x 10-4 kWh kg-1-algae DW

Hexane recovery 4.68 x 10-1 kWh kg-1-oil

Groundwater depth 114.3 m (375 ft)

Pumping water from off-site 2.12 x 10-3 kWh L-1

Evaporation (base) 0.05 cm d-1

On-site pumping circulation base 9.84 x 10-5 kWh L-1

(6 m head)

On-site pumping circulation 2 1.97 x 10-4 kWh L-1

(12 m head)

a The annual average areal productivity of 15 g m-2 d-1 in New Mexico s accounted for by assuming 19 g m-2 d-1 peak algal biomass production in the spring and summer months with 8 g m-2 d-1 during the fall and winter [46,48]. b Calculated in Appendix B.1.

c Based on power usage specs from an industrial supplier (Waterwheel Inc.). d Pilot plant paddlewheel energy measurements from NMSU testbed.

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J.N. Rogers et al. / Algal Research xxx (2013) xxx-xxx

environment. Additionally, capital investment was estimated to be approximately $1035 M. Five capital expenses; paddlewheels, wastewater treatment equipment, land/landscaping, pond liners, and pond covers were found to contribute over 95% of the total capital investment for the plant. A comparison of the operating and capital costs is shown in Figs. 3.1a and 3.1b respectively. Complete tables of the results (presented in both $ L-1 and $ gal-1) can be found in the supplementary material (Tables A.2, A.3).

3.2. Economic sensitivity analysis

Multiple scenarios were modeled for adjustments to the major operating and capital factors. The sensitivity analysis was performed for the 10- and 20-year return of capital investment scenarios and can be seen in Figs. 3.2a and 3.2b, respectively. Tables A.4 and A.5 display the COP sensitivity analysis results in terms of $ L-1 and $ gal-1 respectively.

The sensitivity analysis shows that the cost of production is the most heavily influenced by the useable lipid content of the algae and the extraction efficiency (EE) of lipids in the process. Changes to the algal lipid content and EE directly impact productivity and do not affect the scale of the growth infrastructure and only slightly impact the processing requirements of the model. Therefore, as a simplifying assumption only productivity was varied and all operating and capital costs were kept constant for the sensitivity analysis of lipid content and EE. By increasing the algal lipid content to 35% productivity would increase from the baseline scenario (25% lipid content) of 57.9 MLYto 81.4 MLY and lower COP by $1.17 L-1 (10-yr) and $0.92 L-1 (20-yr). Contrarily, decreasing the algal lipid content to 15% lowers the production to 34.8 MLY and substantially increases COP by $2.73 L-1 (10-yr) and $2.14 L-1 (20-yr). This range of oil contents (± 10%) extended from our baseline of 25% total lipids represents a single biological variable with the ability to surpass DOE's 2014 target of reducing the "modeled mature plant cost of algal open pond oil" [49]. Therefore, one priority of future research will be to evaluate different approaches to increasing oil content, whether by genetic intervention or controlled nutrient regimens, such as nitrogen deprivation. Additionally, future research can increase productivity by improving the average areal growth rate of the algal biomass used. A decrease in the growth rate to 10 g m- 2 d- 1 from the baseline scenario (15 g m-2 d-1) would reduce the productivity to 38.6 MLY and substantially increase COP by $2.05 L-1 (10-yr) and $1.60 L-1 (20-yr). Increasing the growth rate to 20 g m-2 d-1 would increase the productivity to 77.2 MLY and lower COP by $1.02 L-1 (10-yr) and $0.80 L-1 (20-yr). The three EE scenarios would result in either an increase to 72.7 MLY (100% EE) or decrease to 43.5 MLY (60% EE) production of

Fig. 3.1a. Baseline annual operating cost comparison. Operating cost of production was estimated to be $134 M annually. Paddlewheels, nutrients, water, maintenance, and harvesting were identified for their major impact on either cost or sustainability to the process.

Fig. 3.1b. Baseline capital cost comparison. Capital investment was estimated to be approximately $1035 M. Paddlewheels, wastewater treatment equipment, land/landscaping, pond liners, and pond covers were found to contribute over 95% of the total capital investment for the plant.

algae crude. Increasing EE in the 10- and 20-yr capital return scenarios lowers COP by $0.82 L- 1 and $0.64 L- 1 respectively. Lowering EE has a more pronounced impact on the cost L-1; increasing the COP by $1.37 L-1 (10-yr) and $1.07 L-1 (20-yr). The significant investment required for pond liners and covers result in a strong dependence to the capital return scenarios. It can be seen that the impact on cost L-1 is relaxed significantly in the 20-yr capital return case. Whether or not the pond uses a liner impacts the COP by ±$0.43 L-1 and ±$0.22 L-1 in the 10-yr and 20-yr scenarios respectively. Whether or not to cover the ponds also shows a reduced impact on COP in the 20-yr return scenario at $0.27 L-1 as opposed to $0.54 L-1 in the 10-yr return case. The paddle wheel energy requirements are independent of capital expense and are therefore unchanged for the two capital return scenarios. Raising the paddlewheel energy requirements from 0.22 W m-2 to 0.73 W m-2 resulted in a $0.42 L- 1 increase. The decrease in net harvesting efficiency uses the simplifying assumption that all operating and capital costs remain the same and final production is decreased from 57.9 MLY (100%) to 49.3 MLY (85%) of algae oil. The lower harvesting efficiency results in a $0.72 L-1 (10-yr return) and $0.57 L-1 (20-yr return) increase. It can be seen clearly that flocculant costs constitute nearly all of the net harvesting costs. The flocculant costs contribute ±$0.23 L-1 (both return scenarios) of the total impact, ±$0.24 L-1 (both return scenarios) when adjusting total harvesting costs ± 50%. The economic impact of N and P recycling operated under a simplifying assumption that eliminated the cost of DAP and urea addition, resulting in a $0.55 L-1 decrease. The extraction costs only considered the solvent costs and the energy costs required to run the extraction equipment. The extraction costs in the current model assumed that the use of hexane at a 10:1 solvent to algae DW ratio would achieve 80% extraction efficiency from 20%-solids algae [6].lt can be seen that decreasing the baseline extraction costs by 50% resulted in a COP decrease of $0.03 L-1 in both scenarios. There is not much information available about extraction techniques being used at the commercial scale. Conventional solvent extractions used in agricultural processes require the biomass to have less than 10% water content [4]. Drying the algae paste leaving the centrifuges (185.4 tons, 20%-solids) to 10% water content requires 122 M kWh of energy annually, and causes COP to increase by $0.23 L-1 (10- and 20-yr scenarios). The costs associated with evaporation rate are based on the calculations performed for the energy requirements of pumping replacement groundwater from a depth of 114 m. Using the assumption that water levels and pumping costs would remain constant, increasing the evaporation rate from 0.05 cm d-1 to 0.5 cm-1 had a $0.28 L-1 increase on COP. Increasing the on-site pumping capacity for 1463 MLD (386.5 MGD) from a 6 m to 12 m head resulted in a $0.10 L- 1 increase on COP.

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Fig. 3.2a. Sensitivity analysis on COP (10-yr return of capital investment). Estimates are based on the baseline operating costs of $134 MM and annual capital payments of $104 MM to cover a 10 year return of the $1.04 billion capital investment. The baseline COP was found to be $4.10 L-1 ($15.52 gal-1).

3.3. Energy sensitivity analysis

Maintaining a positive net energy balance is a major issue for biodiesel production. The amount of energy generated must be greater than the amount of energy that is input to make it. The current model produces 53.4 M kg-algae oil yr-1. Algal biodiesel has been reported to have a higher heating value of 41 MJ kg-1 [50]. Under this assumption we can estimate that the annual oil production as approximately 608.2 M kWh of energy. The energy return on investment (EROI) must be above 1, otherwise the plant will consume more energy than it generates and is inherently unsustainable. Alternative energy scenarios were evaluated for the amount of replacement water required due to evaporation, on-site pumping capacity, and paddlewheels. The major energy requirements of the plant for both the baseline and alternative scenarios can be seen in Fig. 3.3a.

The annual energy requirements show the energy dependence of different processes throughout the model. The combined energy of all plant processes must be less than the energy generated through oil production. The baseline scenario shows that the most energy intensive

aspects of the plant (listed from high to low) are due to the paddlewheels, on-site pumping circulation, solvent recovery, pumping to fill the ponds, and pumping replacement water. The baseline scenario results in an EROI of 2.73. The alternative energy graph shows the impact for increases in the requirements for replacement water pumping (147.2 M kWh annual energy increase), on-site pumping circulation (52.3 M kWh annual energy increase), paddlewheels (221.6 M kWh annual energy increase), and algae dewatering to 90%-solids (122.3 M kWh annual energy increase). The current model found that in the worst-case scenario, in which the energy increases from all of the alternative scenarios were combined, the EROI dropped to 0.80. This is an unsustainable scenario in which the plant consumes more energy than it produces and highlights the importance of minimizing energy inputs for commercial-scale algal cultivation.

4. Discussion

Scalable production capability is a critical element in analyzing the viability of algae oil as a renewable fuel source. Algae may play a crucial

Lipid content (35% : 25% : 15 %) Extraction efficiency (100% : 80% : 60%) Areal productivity g/m2/d (20 : 15: 10) Pond liner (no liner : liner : replace liner once) Cover (no cover : cover : replace cover once) Paddle wheels (0.22 W per m2 : 0.73 W per m2) Net harvesting efficiency (100% : 85%) Net Harvesting costs (50% : base : 150%) Flocculant ($50 : $100 : $150 per ton-algae) $ per ton CO2 ($20 : $40 : $60) N+P recycle (100% : 0%) Extraction costs (50% : base : Dry algae to 90% DW) Evaporation rate (0.05 : 0.5 cm/day) On-site pumping capacity (20 ft : 40 ft head)

-$1.50 -$1.00 -$0.50 $3..21 +$0.50 +1.00 +$1.50 +$2.00 +$2.50 +$3.00

Fig. 3.2b. Sensitivity analysis on COP (20-yr return of capital investment). Estimates are based on the baseline operating costs of $134 MM and annual capital payments of $52 MM to cover a 20 year return of the $1.04 billion capital investment. The baseline COP was found to be $3.21 L-1 ($12.14 gal-1).

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> 20000

H 15000

■ Baseline □Alternative

Fig. 3.3a. Annual plant energy comparison. Alternative energy scenarios for the amount of replacement water required due to evaporation (28.0 MLD to 247.6 MLD), on-site pumping capacity (20 ft to 40 ft head), paddlewheels (0.22 W m-2 to 0.73 W m-2), and the additional dewatering of the algae to 90%-DW for extraction are compared with the baseline energy values.

role in contributing to the EISA biofuels mandate of 36 billion gallons (136B L) of renewable fuel by 2022 and 60 billion gallons (227B L) by 2030. In order for algae oil to make a significant contribution to the renewable fuel industry, we proposed 5 BGY (18.9B LY) of algae oil by 2022 with an increase to 10 BGY (37.8B LY) by 2030. New Mexico was evaluated as a possible site for algal production to meet these demands. The current model evaluated the COP impact of both 10-yr and 20-yr capital investment return scenarios. The baseline algae oil COP was found to be $4.10 L-1 ($15.52 gal-1) (10-yr) and $3.21 L-1 ($12.14 gal-1) (20-yr). The COP results of this model fall in line with the priorities set by the DOE Office of Energy Efficiency & Renewable Energy (EERE) for Bioenergy Technologies to reduce the cost of open pond algal oil to $14.31 per gallon ($3.78 L-1) ofgasoline equivalent by 2014 [49]. Although this model does not take conversion costs of the algae crude to useable fuel, it identifies key areas that have the potential to reduce COP and help realize the EERE goals.

4.1. Paddlewheels

Paddlewheels are needed for the cultivation of algae, but their main drawbacks come from their operating costs. The present model assumes that the paddlewheels will maintain a mixing velocity of 30 cm s-1 for the full 365 days with constant operation and an average productivity of 15 g d-1. Raceway ponds are inherently subject to varying intensities of natural light throughout the course of the day. Absence of light during the night is inevitable and could result in biomass losses as high as 25% [51]. Sufficient light penetration is variable in any algae cultivating system, due to the density of the algae culture. Thus, there is an inherent light attenuation regime in raceway ponds (i.e., algae cells near the bottom of the ponds may be in the dark regime) [52]. Therefore, ample mixing should be provided to minimize residence time in the dark regime [53-55]. Alternatively, one method of reducing the operating costs of the paddlewheels is to decrease the mixing velocity during the night. Techniques such as operating the paddlewheels on a variable schedule (e.g. run at 30 cm s-1 for 14 h, and then reduce it to 25 cm s-1 for 108 h) can be used to increase paddlewheel productivity and efficiency [56]. Another method to reduce paddlewheel-operating costs would be to cease operations during the winter. During the winter, algae productivity decreases due to the low temperatures. This method is an extreme case, when the operating costs far exceed the expected return [56].

In both operating cases (0.22 W m-2 and 0.73 W m-2) the paddlewheels represent a significant portion of the total annual

operating costs and result in an annual difference of 221.6 M kWh. In the third scenario, the paddlewheel energy measurements for the NMSU pilot pond totaled 8.16 W m-2 and require further optimization to determine the minimum mixing energy requirements for experimental growth. The uncertainty when scaling up production is clearly shown by the difference between the theoretical and measured paddlewheel energy requirements and indicates the need for the development of alternative options. Other mixing devices, such as air lift pumps, mixing boards, Archimedes screws, and mechanical pumps have all been found to suffer from inflexibility in operation and high capital costs despite relatively good efficiency [19]. Promising alternatives may eliminate mechanical mixing by utilizing large-scale open-ponds with sloped or corrugated designs [57,58].

42. Pond liners

The liner is one of the most expensive items for raceway ponds. Liners enable a raceway pond to be built in otherwise unsuitable terrain. Liners can be made from different materials such as clay, concrete, asphalt, fiberglass, and HDPE [59]. Although ponds can be located in areas where clay is abundant and inexpensive, it can crack when the ponds are dry and cause the ponds to lose essential nutrients and water. Clay ponds cannot be cleaned like those utilizing a HDPE liner and can lead to an increase in potential contamination [6,56]. Despite being costly, liners may be necessary to help mitigate these problems. In the current model, liners constitute a massive portion ($250 M, 24%) of the capital costs for the facility. Liners represent a crucial cost prohibitive element in the use of raceway ponds for algal biomass production and a recent demonstration plant construction by Sapphire Energy in New Mexico without liners has indicated the viability of proceeding successfully in the cultivation of algae without.

43. Flocculant options

Harvesting single cells from liquid suspensions is energy intensive and difficult when applied to continuous operation of large-scale bioprocessing facilities [60]. Membrane filtration by size exclusion is a simple and cost effective separation technique; however, even advanced filtration methods are subject to caking and erosion based on the throughput of biomass needed for biofuel production, which can lead to significant reductions in efficiency over the lifetime of the membrane [61]. Low-speed centrifugation may be a more feasible alternative for algae if a flocculant agent is used to increase the average particle size of microalgae slurries from 10 |am to 10 mm by forming large masses of many small particles to facilitate solid separation from a liquid media [62]. The use of cationic polymers is a well-established method of flocculating particulate matter from large volumes of liquid as in wastewater treatment facilities or paper processing plants [63,64] Dissolved air flotation and settling clarifiers have also been successfully implemented to harvest flocculated algal biomass at demonstration scales. The algal biomass harvesting process modeled in the present study relies on a flocculation step followed by lamella clarifiers to bring the ~ 2 g L- 1 algae suspension to a 30 g L- 1 slurry, followed by centrifugation of this slurry to a 200 g L-1 paste.

Polyacrylamide (PA) flocculants are most widely used and can be effective with microalgal cells; however, if the biomass residues are to be used as LEA for other co-products, PA may foul the biomass from a health perspective. This process was designed using a flocculant dose estimated at $100 per ton of DW algae harvested based on the operating parameters. While flocculant costs in excess of $100 per ton of DW algae are prohibitive for biofuel production, a target of less than $40 per ton may be an achievable production target with alternative flocculant materials. Metal ion based flocculants have been deemed incompatible with end biofuel standards and pH-induced changes are unfeasible for algal biofuel production at commercial scales; thus, biological approaches to flocculation have gained significant attention in

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recent years. These processes can occur either as a natural "auto-flocculation" mechanism in mature or nutrient deprived algae cultures or instigated by a biological agent, such as the introduction of another microorganism (algae, bacteria, cyanobacteria, predators or grazers) [65-68] or a biomolecule with the bridging characteristics similar to a polymer or adhesive qualities of a coagulant [69]. By employing these methods, some estimates project that operating costs can be reduced by $200-400 per million gallons of process water treated by transitioning from chemical flocculants to bioflocculation [70]. Furthermore, the production of biological floc-culants is highly scalable and can be incorporated into the algae biofuel production plant.

4.4. Extraction efficiency, lipid content, and areal growth rate

The extraction efficiency, algae lipid content, and areal growth rate directly correlate to the overall productivity of the operation. The sensitivity of these three factors was shown to have the highest economic implications and represents a valuable opportunity for future research to improve. It is crucial for the extraction process to be as efficient as possible in order to minimize costs and maximize productivity. Some dewatering is important to reduce processing volumes, however extensive drying must be avoided and techniques for efficient extraction from aqueous biomass (less than 20%-solids) must be optimized to avoid costs and energy requirements. Because such a substantial portion of COP is in the capital investment required for the growth infrastructure, and increases to the algae lipid content from 25% to 35% or microbial growth rate from 15 g m-2 d-1 to 20 g m-2 d-1 improve production without a need for the development of additional growth area. As such, there is a strong incentive to genetically engineering microalgae for augmented areal growth and oil productivity [71], which can significantly impact the cost of production, as evidenced by our sensitivity analysis. There is an equally strong motivation to control the unintended release of such genetically modified (GM) organisms into the natural environment; such scenarios for GM algae have been investigated recently [72-75]. Additionally, the areal growth rate of microalgae has been shown to be dependent on seasonal variations [48] averaged on an annual basis for simplicity in this model. These seasonal variations create a difference between production and processing capacity, resulting in excess algal production in peak months and excess in processing capacity in winter, which must be considered when sizing future commercial scale developments. The current model assumes that all extracted material is useable for conversion to fuel; however, high triglyceride content would be the most suitable for fuel conversion via transesterification. Alternative techniques such as hydrothermal processing or pyrolysis may allow for higher yields via the conversion of the entire algal biomass into fuel, however no such systems are currently being used at the industrial scale [76,77].

4.5. Lipid extracted algae

A potential benefit of the use of algae for biofuel production is the potential value of the spent biomass as animal feed. The use of spent biomass as a side product has significant contributions to the value of algae biomass. Nutritional and toxicological evaluations have demonstrated that algae biomass is suitable as a feed supplement or substitute for conventional animal feed sources [78,79]. For LEA to be considered for use as a feedstock the algae strain must have a composition suited for both nutritional and fuel production purposes [80]. For example despite the high nutritional value of Spirulina, it contains very low lipid content and is not considered a viable feedstock for biofuel production [81]. Although lipid extraction will remove beneficial fatty acids (such as omega-3 PUFAs) and lower the overall nutritional value of the algae biomass, the LEA may still be used as a high protein supplement.

The theoretical commercial scale algae facility in this study is estimated to produce 585 tons d-1 of lipid-extracted algae. As mentioned the quality of LEA as a protein supplement in animal and aquaculture

feed is still being researched, leading to an uncertainty in selling price estimated to be from $250-$1000 ton-1 [82]. Each 1000 bbld-1 facility could therefore potentially generate between $53.3 and $213.5 million from the sale of LEA as a protein supplement.

4.6. Nutrient consumption

Nutrients represent an essential element of algae growth. The costs and sustainability of which depend heavily on the nutrient source and recyclability. As algae-oil technology continues to develop, it will be crucial to supplement CO2, nitrogen, and phosphorus requirements by recycling nutrients and co-locating algae farms with power plants and wastewater [4]. Increased demand of limited resources and the exhaustion of optimal growth locations are some of the difficulties associated with scaling up production.

Carbon is the largest nutrient requirement and is fed into the autotro-phic process in the form of CO2, in which 768 tons are required daily for production in the plant. The production process examined in this study requires roughly 0.28 million tons of CO2 annually. At the price of $40 per ton, CO2 annually contributes $11.2 M of the annual operating costs. The source of CO2 can have a significant impact on price and the cost of production. CO2 sourced from an ethanol refinery may be provided for as low as $20 whereas CO2 sourced from flue gas or other industrial sources may range from $40-$60 [39]. Therefore, operating expenses can be minimized co-locating commercial-scale algal biofuel developments with suitable CO2 sources. Scaling up to achieve the contribution goals of 5 BGY and 10 BGY to the EISA biofuel mandates would require 92 million and 183 million tons of CO2, respectively. According to the EPA, coal-fired power plants in the United States emitted 1.6B metric -tons of CO2 in 2009 [83]. The 5 BGY and 10 BGY scale up contributions would require 5.7% and 11.4% of all coal-fired power plant emissions in the United States, respectively. Algae have fantastic potential to sequester the emitted CO2 to be recycled into biofuel [84], however many of the coal-fired plants are located in areas not suitable for algae-growth. Additional work needs to be done at existing coal-fired plants to assess the potential construction of algae-farms at their location. Efforts should also be focused on building future coal-fired plants in areas that will also be suitable for algae-oil production.

The present model relies on urea as a nitrogen source, which contributes $24.43 million of the annual operating costs. Ammonia is another optional nitrogen source that is similar in price and availability to urea [6]. A single 1000 bbl d-1 plant that produces 57.9 MLY (15.3 MGY) of oil is estimated to require 64,459 tons of urea annually for operation. Scaling up in attempts to contribute to the EISA biofuels mandates would require 21.1 million and 42.2 million tons of urea annually for the production of 5 BGY and 10 BGY of algae-oil, respectively. Natural gas is the main input used to produce ammonia and with the major increase in supply of natural gas over the past few years' nitrogen supply is not currently an area of concern [85] and may lower the associated operating costs. In this study, the phosphorus requirement is met with 14,746 tons of DAP annually. The demand was found to be 12.7 mg-P/g-algae DW at an uptake efficiency of 50%. 40.4 tons d-1 of DAP are used to meet the gross requirement 18.6 tons d-1 of phosphorus. In the current model, DAP annually contributes $7.36 million of the annual operating costs. Scale up for the 5 and 10 BGY production scenarios would increase the demand to 4.82 and 9.64 million tons of DAP, respectively. Phosphate rock is a non-renewable resource that has taken 10-15 Ma to form. World population growth ensures the need for phosphate fertilizer to grow crops for food and biofuels [86]. The largest deposits are found in northern Africa, China, the Middle East, and the United States. Large resources have been identified on the continental shelves and seamounts in the Atlantic Ocean and the Pacific Ocean however they currently cannot be recovered economically with the current technology. At the current rate of production, the phosphate reserves in the United States will be depleted in about 50 yr [87]. There are no substitutes for phosphorus in agriculture. This dependence

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will force the United States to rely on imported fertilizers to grow crops. The growth of biofuels will only serve to expedite the depletion of existing phosphorus reserves. From 2006 to 2012 the average value of phosphate rock increased from $27.89 to $96.90 per ton [86,88]. While chemical processes can produce synthetic nitrogen fertilizers, this process does not provide a sustainable outlook for algal biofuels due to the fossil fuel demand of the Haber-Bosch process. It is critical to algae biofuels to continue to consider growth conditions that utilize recycled nitrogen and phosphorus sources.

Efforts must continue to identify methods that will lower the costs and improve the sustainability associated with nutrient consumption. Inexpensive sources of CO2 such as from a flue gas provide an appealing option for culture enrichment. Nutrient-rich wastewater can supplement or eliminate the need for additional N and P and the recovery of these vital elements through anaerobic digestion of algae biomass offers another important option to reclaim the fertilizer components used in algal biofuel production [89,90]. This flue-gas and wastewater co-utilization (FWC) strategy will not only lower costs of raw materials but also offset waste products and contribute to the sustainability of microalgal biofuels [91]. Despite the benefits of FWC, the maximum potential of algae production from FWC may still only make a moderate impact on United States petroleum dependence for transportation fuels. A GIS based national assessment considering the relative abundance of the input resources and proximity for the economic and biofuel production potential of FWC found that less than 200 MGY (757 MLY) could be produced annually

[92]. Additional strategies for increasing biofuel production must include nutrient recycling and the utilization of nutrients from livestock waste

4.7. Land and water requirements: covered vs. uncovered raceways

Algal production is heavily dependent on large volumes of water for production. The costs and energy associated with replenishing lost water to the process were found to be highly dependent on evaporative losses and groundwater levels. This section assesses the sustainability and productivity of scaling-up an algal-facility as it pertains to water availability.

Despite the reductions in COP for uncovered raceways, the water requirements pose serious sustainability issues when scaling up production. For equivalent water availability of a plant using covered raceways can support 4.2 times the production capacity as a plant operating with the uncovered design. While evaporative loss of culture volume can seriously affect production due to water availability in arid regions, evaporative cooling plays an important and necessary role in maintaining appropriate pond temperature [6]. Water loss in the range of 0.30.6 cm d-1 [6] is not uncommon and accounts for a significant hurdle to sustainable algal biofuel production [25]. Thus, a balance must be struck between the natural discharge of heat by evaporation and the reclamation of this valuable water that is lost in the process [94,95]. Additional research must account for seasonal variations to optimize growth conditions without jeopardizing plant sustainability due to evaporative losses [96].

As a preliminary metric, it was desired to evaluate freshwater requirements of the current model and scale up compared to the total freshwater consumption in both New Mexico and the United States as a whole. The 2005 New Mexico Water Report found that 4.88 trillion -liters (1.29 trillion gallons) of freshwater are consumed every year [97]. The state's freshwater is extremely limited, of which 77.86% is used agriculturally. Due to limited supply, both fresh and slightly saline waters are now used for domestic, industrial, and agricultural purposes. Water rights holders are also unlikely to support the demands of an increase in algal production [41]. In 2005, the United States freshwater consumption totaled 482.3 trillion liters (127.4 trillion gallons) annually [98]. Assuming sufficient land for production, it is evident that the freshwater required to build facilities in New Mexico rapidly matches and surpasses all other freshwater consumption in the state. The 5 BGY goal is completely unattainable in New Mexico alone

as it requires 168% and 704% of all of the freshwater consumed in the state for the covered and uncovered raceway scenarios, respectively. The impact on the total freshwater consumed in the United States is also evident as algae production is increased to 10 BGY, foreshadowing potential water versus fuel debate by reaching levels equivalent to 14% of all current U.S. freshwater consumption. The scale up effects of this algae facility pertaining to total freshwater consumption in New Mexico [97] and the United States [98] can be seen in Table A.6.

In 1990, of the estimated 2.467 x 1013 m3 of groundwater reserves in New Mexico, 1.85 x 1013 m3 were characterized as moderately saline, very saline, or brine. Only Tularosa Basin and Crows Flats were deemed suitable for large-scale microalgal cultivation based on the selection criteria established by the Solar Energy Research Institute (SERI) (water quality, land slope, and climatic conditions) [41]. The selected regions were found to contain about 2.84-6.17 x 109 m3 of available saline water with just over 39,000 ha of suitable land [41,99]. Studies have been performed to determine the salinity tolerance of algae for biofuel production as utilizing saline water can help to curb egregious freshwater requirements [41,99]. Maintaining all previous assumptions for a marine algal species, the land available would allow for the construction of eight 1000 bbl d-1 facilities at 4875 ha each, requiring from 229-870 billion liters of water annually for the covered and uncovered raceway designs, respectively. The facilities in the current model would therefore be able to produce an estimated 463.3 million liters (122.4 million gallons) of oil annually based on the 1990 SERI resource assessment. More current resource assessments have indicated that additional saline or "brackish" water resources may be available to facilitate additional development algal biofuels in New Mexico, however favor locations situated around the Gulf Coast, the eastern seaboard, and areas adjacent to the Great Lakes to meet water requirements [6].

The low seasonal variation, high solar irradiance, and flat topography in New Mexico make it a valuable location for the development of algal biofuels, however groundwater depletion in New Mexico is already a serious issue and increased withdrawals could further aggravate freshwater scarcity in the state. Therefore, algal biofuel development efforts in the state must use algal strains with the ability to utilize saline or "brackish" water. Scaling algal biofuel production in New Mexico must focus on reducing water losses and maximizing available resources. From an environmental engineering perspective, the water reduction and sustainabil-ity gained by using covered raceways most likely outweighs the increase in cost of production. Covering the raceway ponds will also help to mitigate contamination and environmental damage however it must be designed to facilitate cooling and allow for optimal growth.

5. Conclusions

The current study evaluates sustainability and economic requirements of a 1000 bbl d-1 algal biofuel facility based in New Mexico. Several concerns arose during the development of this model; however, alternatives and solutions exist and must continue to be developed in order to realize the full potential of algal biofuels. Current technology and production techniques limit the scalability of algal biofuel production. Energy, water, CO2, and nutrient requirements in particular represent significant obstacles to the sustainability of algal biodiesel at the industrial scale. A critical analysis of the available resources in New Mexico has shown that these challenges of water, CO2, and nutrient sourcing at large-scale production must be carefully considered. Development must focus on reducing water losses and maximizing available saline and "brackish" water resources. Multiple pathways are available for producing fuels from algae and additional research needs to be done to develop the most economic and sustainable solutions. Both brackish water tolerant freshwater and marine algae strains must be cultivated using various water sources to identify the strains ideally suited for production under a variety of conditions.

Several options are available to mitigate the sustainability challenges that were identified by the present model. Flue-gas and wastewater

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utilization will help to supplement some of the nutrient demands of algal growth. Integration with livestock production will provide nutrients to the system that can be used for algal production; however, locating these facilities in close proximity also depends on the available land and resources. The systems that are the most ideal for integration with algal production (flue gas sources, WWT, etc.) already exist for the large part. Therefore, they must be evaluated for the feasibility of incorporating algae growth into their facilities [91]. Future development of algal biofuels will be best served if new sources of unconventional nutrients are developed in conjunction with algal growth in an integrated biorefinery model [20,93]. Additionally, despite the potential economic impact of utilizing LEA as an animal feed supplement, nutrient sustainability concerns may necessitate the use of LEA for nutrient recycle.

Freshwater availability represents a serious issue to algal biofuel production without robust water reclamation methods and evaporative loss minimization. Utilizing saline or brackish water will be necessary to sustainably grow algae in New Mexico [95]. Depleting ground water depth levels will increase the pumping energy requirements to fill and maintain the ponds. Efforts must focus on reducing evaporative losses, recovering water during processing, and maximizing available saline and "brackish" water resources.

The disparity between the theoretical and measured paddlewheel energy use reflects the uncertainty in scale-up and demonstrates the importance of minimizing energy requirements. Paddlewheel-driven raceway ponds have historically been the method of choice for algae cultivation due to their presumed low-cost compared to more sophisticated photobioreactor designs. However, raceway ponds are severely limited by their operating efficiencies and physical aspect ratios to promote maximal algal biomass productivity. While these ponds can accomplish the objective of tertiary wastewater treatment with microalgae at small scales, the capital costs, operating costs, and energy requirements of paddlewheels at large scales indicate the need for alternative bioreactor designs for algal biofuel production [100-102].

Algae growth and harvesting costs dominate the economic investment in facility scale-up. The amount of growth area required for production correlates directly to the largest capital expenditures. A pond liner is a necessity for the majority of geographic locations suitable for algae growth and a partial cover will reduce evaporative loss while allowing necessary cooling to occur. Cultivating strains with a more rapid growth rates and augmented total lipid contents have the highest potential to decrease COP as these biological parameters increase productivity without imposing additional capital investment to the growth infrastructure [103].

Collectively, the modeled data pertaining to energy, nutrient, and water requirements for expansive deployment of microalgal biofuel production in New Mexico pose certain challenges but are likely to be addressable with technological advances in algae cultivation methods. The continued development of mitigation strategies for each of these concerns will build a foundation to withstand the inevitable and necessary scrutiny for emerging energy technologies, such as algal biofuels. Ongoing research in this active area of renewables will need to refine algae production systems in order to gain both public acceptance and secure the economic viability of biofuels.

Acknowledgements

This work was supported, in part, by funds from the U.S. Department of Energy's National Energy Technologies Laboratory through grant number DE-0E0000098 to New Mexico State University. Partial support was also provided by a fellowship to JNR from the Johns Hopkins Environment, Energy, Sustainability & Health Institute (E2SH1). We appreciate the constructive input from Dr. Scott Williams and his generous help reviewing this manuscript.

Appendix A. Tables

Table A.1

New Mexico groundwater depth measurements.

New Mexico groundwater depth measurements [104]

National water information system

Site number

312323108293302

312503108332801

312705108304801

312722108350401

312804108332301

312951108295101

320104103120301

Average

Depth ft

375 ft = 114 m

Table A.2

Baseline operating cost results for a single plant. Comparison of annual costs and contribution to COP in $ gal-1 and $ L-1. Annual production: 58.03 MLY (15.33 MGY).

Baseline operating costs

$MM yr-

Cost $ gal-

Cost $ L-

Paddlewheels $10.07 $0.66 $ 0.17

Lamella separator $0.01 $0 .00 $0.00

Centrifuge $0.64 $0 .04 $0.01

Cell disruption $0.20 $0 .01 $0.00

Biomass-separator $0.01 $0.00 $ 0.00

Hexanes recovery $2.75 $0.18 $ 0.05

On-site pumping circulation $5.76 $0.38 $ 0.10

DAP $7.36 $0.48 $ 0.13

Urea $24.43 $1.59 $ 0.42

Pumping replacement water $2.07 $0.13 $ 0.04

Replacement hexane $0.40 $0.03 $ 0.01

Absorber CO2 $11.21 $0.73 $ 0.19

Flocculant $26.70 $1.74 $ 0.46

Wastewater treatment $1.10 $0.07 $ 0.02

Maintenance $28.55 $1 .86 $0.49

Wages $13.05 $0 .85 $0.22

Total operating $134.30 $8 .76 $2.31

Table A.3

Baseline capital cost results for a single plant. Total investment and cost contribution to

COP in $ gal-1 and $ L-1 for 10-yrand 20-yr capital return scenarios. Annual production:

58.03 MLY (15.33 MGY).

Baseline capital costs

$MM 10 yr $ gal-1 10 yr $ L-1 20 yr $ gal-1 20 yr $ L-1

CO2 absorber (static mixer) $0.17 $0.001 $0.000 $0.001 $0.000

Paddlewheels $120.00 $0.783 $0.207 $0.391 $0.103

Flocculant addition (static mixer) $0.11 $0.001 $0.000 $0.000 $0.000

Lamella separators $2.50 $0.016 $0.004 $0.008 $0.002

Centrifuges $6.60 $0.043 $0.011 $0.022 $0.006

Cell disruption (sonicators) $3.25 $0.021 $0.006 $0.011 $0.003

Lipid extraction (static mixer) $0.00 $0.000 $0.000 $0.000 $0.000

Biomass-solvent separator $0.25 $0.002 $0.000 $0.001 $0.000

Distillation column $0.50 $0.003 $0.001 $0.002 $0.000

Pumps $1.27 $0.008 $0.002 $0.004 $0.001

Operating water $2.96 $0.019 $0.005 $0.010 $0.003

Hexane $1.05 $0.007 $0.002 $0.003 $0.001

Wastewater treatment equipment $245.00 $1.598 $0.422 $0.799 $0.211

Land $18.00 $0.117 $0.031 $0.059 $0.016

Liners $250.00 $1.631 $0.431 $0.815 $0.215

Landscaping $57.00 $0.372 $0.098 $0.186 $0.049

Cover $315.00 $2.055 $0.543 $1.027 $0.271

Railways $1.25 $0.008 $0.002 $0.004 $0.001

Roads $0.60 $0.004 $0.001 $0.002 $0.001

Operating buildings $0.45 $0.003 $0.001 $0.001 $0.000

Construction costs $2.50 $0.016 $0.004 $0.008 $0.002

Pipes $7.08 $0.046 $0.012 $0.023 $0.006

Total capital $1035.55 $6.76 $1.78 $3.38 $0.89

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Table A.4

Cost of production sensitivity analysis ($ per liter). Estimates are based on the baseline operating costs of $134 MM and annual capital payments of $52 MM to cover a 20 year return of the $1.04 billion capital investment.

Cost of production sensitivity analysis ($ per liter)

10 yr baseline COP $4.10 L-1

20 yr baseline COP $3.21 L-1

decrease

COP increase

decrease

COP increase

Lipid content (35%: 25%: 15%) -$1.17 $2.73 -$0.92 $2.14

Extraction efficiency (100%: 80%: 60%) -$0.82 $1.37 -$0.64 $1.07

Areal productivity g/m2/d (20:15:10) -$1.02 $2.05 -$0.80 $1.60

Pond liner -$0.43 $0.43 -$0.22 $0.22

(no liner: liner: replace liner once)

Cover -$0.54 $0.54 -$0.27 $0.27

(no cover: cover: replace cover once)

Paddle wheels - $0.42 - $0.42

(0.22 W m-2: 0.73 W m-2)

Net harvesting efficiency (100%: 85%) - $0.72 - $0.57

Net harvesting costs (50%: base: 150%) -$0.24 $0.24 -$0.24 $0.24

Flocculant ($50: $100: $150 ton-algae-1) -$0.23 $0.23 -$0.23 $0.23

$ ton-1 pure CO2 ($20: $40: $60) -$0.10 $0.29 -$0.10 $0.29

N + P recycle (100%: 0%) -$0.55 - -$0.55 -

Extraction costs -$0.03 $0.23 -$0.03 $0.23

(50%: base: dry algae to 90% DW)

Evaporation rate (base 0.05: 0.5 cm d-1) - $0.28 - $0.28

On-site pumping capacity - $0.10 - $0.10

(base 20 ft: 40 ft head)

Table A.5

Cost of production sensitivity analysis ($ per gallon). Estimates are based on the baseline operating costs of $134 M and annual payments of $104 M to cover a 10 year return of the $1.04 billion capital investment.

Cost of production sensitivity analysis ($ per gallon)

10 yr baseline COP $15.52 gal-1

20 yr baseline COP $12.14 gal-1

COP COP COP COP decrease increase decrease increase

Lipid content (35%: 25%: 15%) -$4.43 $10.34 -$3.47 $8.09

Extraction efficiency (100%: 80%: 60%) -$3.10 $5.17 - $2.43 $4.05

Areal productivity g/m2/d (20:15:10) -$3.88 $7.76 -$3.03 $6.07

Pond liner (no liner: liner: replace liner once) -$1.63 $1.63 -$0.82 $0.82

Cover -$2.05 $2.05 -$1.03 $1.03

(no cover: cover: replace cover once)

Paddle wheels - $1.59 - $1.59

(0.22 W m-2: 0.73 W m-2)

Net harvesting efficiency (100%: 85%) - $2.74 - $2.14

Net harvesting costs (50%: base: 150%) -$0.92 $0.92 -$0.91 $0.91

Flocculant ($50: $100: $150 ton-algae-1) -$0.87 $0.87 -$0.87 $0.87

$ ton-1 pure CO2 ($20: $40: $60) -$0.37 $1.10 -$0.37 $1.10

N + P recycle (100%: 0%) -$2.07 - -$2.07 -

Extraction costs -$0.11 $0.88 -$0.11 $0.88

(50%: base: dry algae to 90% DW)

Evaporation rate (0.05: 0.5 cm d-1) - $1.06 - $1.06

On-site pumping capacity (20 ft: 40 ft head) - $0.38 - $0.38

Table A.6

Scale up assessment of algae freshwater requirements. Compares the water requirements when scaling the model for two evaporation conditions to the total freshwater consumption in New Mexico and the United States.

Scale up assessment of algae freshwater requirements

New Mexico annual freshwater consumption (BGY): 1287.2 United States annual freshwater consumption (BGY): 127,385.0

1 Plant (15.3 MGY production) Covered (0.05 cm d-1 evaporation) Uncovered (0.5 cm d-1 evaporation) 327 Plants (5 BGY production) Covered (0.05 cm d- 1 evaporation) Uncovered (0.5 cm d-1 evaporation) 654 Plants (10 BGY production) Covered (0.05 cm d- 1 evaporation) Uncovered (0.5 cm d-1 evaporation)

Water consumption (BGY) 6.57 27.73

2160 9060

4320 18,120

% of NM 0.5% 2.2%

167.7% 703.7%

335.3% 1407.4%

% of US 0.005% 0.022%

1.7% 7.1%

3.4% 14.2%

Appendix B. Calculations

B.1. Energy requirements for raceway ponds [56] The head loss in bends is calculated by,

(K-v2)

2 ■ g

(B.1.1)

in which K is the kinetic loss coefficient for 180° bends (theoretically = 2), v is the velocity of the raceway (0.3 m s-1), and g is the acceleration due to gravity (9.8 m s-2). Resulting in hb = 0.01834.

The friction loss across the length of the raceway is calculated using Manning's Equation,

hc = v ■ n ■

(B.1.2)

in which n is the roughness factor (0.015 for polyethylene), R is the channel hydraulic radius (2112), and L is the channel length (643.9). Resulting in hc = 0.46635.

The energy requirement per pond is calculated by,

W = 9.8-

Q ■ w ■ h

(B.1.3)

in which Q is the volumetric flowrate(1.1 m3 s-1), w is the unit mass of water (998 kg m3), h is the total head loss (htotal = 0.64984), e is the paddle wheel and drive system efficiency (40% assumed), and 9.8 is the conversion factor in W-s kg-m-1. The energy calculations resulted in 1741.5 W pond-1 (0.22 W m-2) or 10,448.8 kW for the whole plant. When operated continuously the total paddlewheel energy use totals 91.531 M kWh annually.

B.2. Energy required for solvent recovery and drying algae to 10% water content

Cp ■ w ■ (Tb-20) 3600

(B.2.1)

For solvent recovery: E is the energy in kWh, cp is the specific heat of hexane (2.26 KJ kg-1 °C-1 [105]), w is the number of kg of hexane entering the column every hour (92,696 kg hr-1), Tb is the boiling point of hexane (69.0 °C [106]), 20 is the starting temperature of the hexane, and 3600 is a conversion factor from KJ to kWh. Under continuous operation the column will require 25.0 M kWh annually.

For drying algae to 10% water content: E is the energy in kWh, cp is the specific heat of water (4.18 KJ kg-1 °C-1), w is the number of kg of water entering the dryer every hour (150,290 kg hr-1), Tb is the boiling point of water (100.0 °C), 20 is the starting temperature of the water, and 3600 is a conversion factor from KJ to kWh. Under continuous operation the column will require 122.3 M kWh annually.

B.3. Energy required for pumping groundwater and on-site circulation [107]

E = 9 ■ W ■ h

(B.3.1)

in which E represents the total energy in Watt hours, W is the pumping out W cubic meters of groundwater (or on-site circulation), h is the

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J.N. Rogers et al. / Algal Research xxx (2013) xxx-xxx

average groundwater depth (or m of head), and ç> is a coefficient defined by:

Y ■ P ■ g 1000

(B.3.2)

in which y is the pumping efficiency (0.5), p is the density of water (1000 kg m-3), and g is the acceleration due to gravity (9.8 m s-2). The current model assumes a groundwater depth of 114 m and two conditions for replacement water of 28,000 m3 (0.05 cm d-1 evaporation) and 247,450 m3 (0.5 cm d-1 evaporation). The two replacement conditions require 18.8 M kWh yr-1 and 165.9 M kWh yr-1 respectively. On-site pumping circulates 1.46 M m3 d-1 for the two scenarios of 6 m and 12 m head. The two pumping capacities are calculated to require 52.3 M kWh yr-1 and 104.6 M kWh yr-1 respectively.

Appendix C. Supplementary data

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.algal.2013.11.007.

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