Integrating Algae with Bioenergy Carbon Capture and Storage (ABECCS) Increases Sustainability

Abstract

Bioenergy carbon capture and storage (BECCS) has been proposed to reduce atmospheric CO₂ concentrations, but concerns remain about competition for arable land and freshwater. The synergistic integration of algae production, which does not require arable land or freshwater, with BECCS (called “ABECCS”) can reduce CO₂ emissions without competing with agriculture. This study presents a technoeconomic and life-cycle assessment for colocating a 121-ha algae facility with a 2,680-ha eucalyptus forest for BECCS. The eucalyptus biomass fuels combined heat and power (CHP) generation with subsequent amine-based carbon capture and storage (CCS). A portion of the captured CO₂ is used for growing algae and the remainder is sequestered. Biomass combustion supplies CO₂, heat, and electricity, thus increasing the range of sites suitable for algae cultivation. Economic, energetic, and environmental impacts are considered. The system yields as much protein as soybeans while generating 61.5 TJ of electricity and sequestering 29,600 t of CO₂ per year. More energy is generated than consumed and the freshwater footprint is roughly equal to that for soybeans. Financial break-even is achieved for product value combinations that include 1) algal biomass sold for $1,400/t (fishmeal replacement) with a $68/t carbon credit and 2) algal biomass sold for $600/t (soymeal replacement) with a $278/t carbon credit. Sensitivity analysis shows significant reductions to the cost of carbon sequestration are possible. The ABECCS system represents a unique technology for negative emissions without reducing protein production or increasing water demand, and should therefore be included in the suite of technologies being considered to address global sustainability.

Plain Language Summary

We evaluated the sustainability of integrating algae production with bioenergy CCS (called ABECCS). Our motivation is to devise an affordable system that removes CO₂ from the atmosphere without negatively impacting food security. The International Panel on Climate Change suggested that in addition to zero-emissions systems (such as solar power or crops), negative-emissions systems are needed to mitigate global warming. Bioenergy CCS (BECCS) is a promising negative-emissions approach in which biomass is combusted to generate electricity in conjunction with CCS. However, on a scale relevant to mitigating global warming, the arable land and freshwater requirements for BECCS could be unviable and cause competition with food production. In the ABECCS system, soy cropland is replaced by eucalyptus forests used for BECCS that provides marine algae with CO₂, heat, and electricity. The integrated 2,800-ha facility produces as much high-quality protein as soy without increasing freshwater demand, and generates 61.5 TJ of electricity while sequestering 29,600 t of CO₂ per year. The system is economically viable when receiving $600/t of algae and $278/t of CO₂ sequestered. With favorable economic conditions, ABECCS could contribute to the reduction of CO₂ in the atmosphere in a sustainable way.

1 Introduction

Increased levels of carbon dioxide and other greenhouse gases in the earth's atmosphere (National Oceanic and Atmospheric Administration [NOAA], 2017a) have contributed to increased global temperatures (NOAA, 2017b) over the last century, raising concerns about the negative impacts of future climate changes (Karl & Trenberth, 2003; Oreskes, 2004). Greenhouse gases and global temperatures have increased along with global population, which is projected to reach 9.7 billion by 2,050 (United Nations [UN], 2017). In short, without mitigating technologies, the continued increase in global population is expected to yield more greenhouse gases in the atmosphere that could cause adverse economic and environmental effects (Allen et al., 2009; Hsiang et al., 2017; International Panel on Climate Change [IPCC], 2013; Meinshausen et al., 2009; Tol, 2014). In fact, the IPCC suggested that in addition to many low/zero emissions technologies (e.g., solar, wind, and nuclear power, fossil energy with carbon capture and storage [CCS], carbon neutral crops, etc.) net-negative-emissions technologies are needed to limit temperatures to less than 2°C above preindustrial conditions (Fuss et al., 2014; Greene et al., 2010, 2017; IPCC, 2013; Walsh et al., 2017). Net-negative-emissions technologies include bioenergy with CCS (BECCS), afforestation, direct capture of carbon dioxide (DAC) from the air, and other carbon dioxide removal technologies.

BECCS is a promising option because, unlike many others, it generates a valuable product (energy) in addition to sequestering carbon dioxide. However, widespread deployment of BECCS systems at a scale relevant to reversing global carbon dioxide concentrations would be constrained by the availability of arable land and freshwater (Azar et al., 2010; Greene et al., 2016). Globally, up to one-third of arable land and three-quarters of freshwater are already used to produce food (Aiking, 2011; Ranganathan, 2013). With a rapidly growing global population, diverting these resources for energy production, rather than food production, could be contentious and unviable. Furthermore, it is expected that global protein demand, in particular, will increase by more than 100% by 2050 (Erisman et al., 2008; Tilman et al., 2011). The ability to meet the global food (and protein) demand and reduce atmospheric carbon dioxide concentrations using conventional BECCS systems will be challenging (Smith et al., 2016; Williamson, 2016).

To address this challenge, we evaluate an alternative system that integrates algal biomass production with BECCS (called ABECCS) to reduce atmospheric carbon dioxide concentration without reducing agricultural protein production or increasing freshwater consumption. Although adding algal biomass production can increase the cost of BECCS, the goal for this system is to enable expanded BECCS deployment at global scales without competing with food production. Currently, algae are cultivated for a range of niche applications including human and animal nutrition, pigments, and other specialty products (Spolaore et al., 2006). Over the last few decades, there has been a large research effort to commercialize algal biofuels and commodity animal feeds (Beal et al., 2015; Brennan & Owende, 2010; Lum et al., 2013). There is growing interest in producing human food products from algal biomass as it can achieve higher price points (Borowitzka, 2013) and reduce the land footprint of food production (Walsh et al., 2016). However, at present-day commodity prices, large scale algae production has been stifled by high capital costs and the challenge of sourcing inexpensive water, carbon dioxide, electricity, and heat in locations suitable for high productivity (Beal et al., 2015). Colocating a biomass combustion unit with the algae facility provides heat and electricity for algae production and ensures a supply of CO₂ for the algae, which thus increases the range of sites that are suitable for algae cultivation (Walsh et al., 2015). The ABECCS system yields as much protein as soybeans grown on the same footprint, while generating excess electricity and permanently sequestering carbon dioxide. In this study, we conduct a technoeconomic assessment and life-cycle assessment of the integrated facility and compare it with other systems that generate energy, food, carbon dioxide sequestration or a combination of these products.

2 Methods

2.1 System Design

The integrated system evaluated in this study (Figure 1) includes a 121-ha algae production facility, a 2,680-ha eucalyptus forest, combined heat and power (CHP), and amine-based CCS. Because algae can generate 27-times more protein per hectare than soybeans (Huntley et al., 2015; United States Department of Agriculture [USDA], 2016) the ABECCS facility is sized to produce the same amount of protein from algae as would otherwise be produced by soy on the same total footprint (0.99 t protein/ha-yr) (note: the unit t represents metric tonnes). For the first deployment modeled here, we assume existing soy land is purchased and converted to eucalyptus and algae production, thereby avoiding deforestation. The eucalyptus forest absorbs carbon and serves as the fuel source for CHP, which generates electricity, heat, and carbon dioxide for algae cultivation and other system components. Excess carbon dioxide is sequestered by injection, yielding negative carbon dioxide emissions for the system. Land is purchased for $2900/ha (USDA, 2015). The components of the ABECCS facility are described below.

Figure 1

Process flow diagram for the integrated algae-and-forestry bioenergy carbon capture and storage (ABECCS) facility. Capital costs for each component are shown beneath the component label in millions of dollars. The overall facility includes 2800 ha (6920 ac), which is 96% eucalyptus forest and 4% algae production.

2.1.1 Algae Cultivation and Harvesting

The cultivation facility is based on the design by Beal et al. (2015) and consists of a hybrid system of photobioreactors (PBRs) and raceway ponds for cultivation of Desmodesmus sp. The system includes 114,000 m³ of growth volume and 91.9 ha of sunlit cultivation area. Algae production has a 95% capacity factor (347 d/yr). Seawater is supplied from a 5 km pipeline (requiring 1.88 kJ/L for pumping). Nutrients are provided as diammonium phosphate (DAP) and ammonia. Carbon is obtained from the post-carbon-capture flow and compressed to 1 MPa for transport and supply to the growth volume (requiring 248 kJ/kg of gas). Carbon uptake efficiency is 79%. The algal biomass productivity is 82.5 t/ha-yr (23.8 g/m²-d) with an elemental composition consisting of 48% carbon, 6.3% nitrogen, and 0.6% phosphorus (Huntley et al., 2015). The biomass is 39% protein, 37% lipid, 21% carbohydrate, and 3% ash. The daily harvest volume is 48,000 m³ with an algal concentration of 0.43 g/L. Electricity is consumed for seawater supply, circulating the growth media in the PBRs and ponds, transporting carbon dioxide, and mixing nutrient tanks, with a total electrical input of 276 kWh/ha-d. PBR polyethylene plastic is replaced every 3 months at a cost of $0.81/m² based on retailer quotes.

Algal biomass is harvested by settling, filter press, and finally drying to 90% solids. The two-step passive settling process described by Beal et al. (2015) recovers 94% of the algal biomass at a concentration of 20 g/L. A filter press further concentrates the slurry to 200 g/L (80% moisture) with 98% recovery efficiency and 1.1 kJ/L of electrical input. Solar drying beds (10 ha, LDPE liner) are used to reduce the moisture content to 20% (Lundquist et al., 2010), followed by a ring dryer to achieve 10% moisture content (Beal et al., 2015). The heat required by the ring dryer to evaporate remaining water was calculated to be 2.6 MJ/kg of water removed (with 2,800 kg of water per day removed for drying from 80% to 90% solids).

The total capital costs for algae cultivation and harvesting are $53.1 M and $5.6 M, respectively. Thus, the total capital cost for generating harvested biomass is $485,000/ha of the algae facility area (121 ha), which is within 1% of that published by Beal et al. (2015) and Huntley et al. (2015) ($484,000/ha). The labor cost for operating the algae facility is $1.4 M/yr (Beal et al., 2015). The operating, capital, and labor costs are incorporated into the cumulative discounted cash flow calculations described below. However, for comparison, if the capital costs are amortized linearly over 30 years and combined with operating and labor costs, the cost of producing algal biomass in a stand-alone system is $826/t, which is within 2% of the results by Huntley et al. (2015) of $840/t, but significantly higher than the estimate by Davis et al. (2016) of $541/t. As such, we consider this to be a conservative model.

2.1.2 Eucalyptus Production and Processing

Eucalyptus production occupies 2,680 ha and yields 11.7 t/ha-yr (Gabrielle et al., 2013; van Oel & Hoekstra, 2012). Nutrient inputs include ammonia (4.8 g NH₃/kg biomass), phosphate (2.1 g P₂O₅/kg biomass), and potassium (0.9 g K₂O/kg biomass) (Gabrielle et al., 2013; Raymond & Muneri, 2000; Stape et al., 2004). Water footprint is estimated for a subtropical climate to be 496 L/kg biomass (equivalent to 464 mm/yr or 18.3 in/yr) (van Oel & Hoekstra, 2012). Total diesel consumption for forestry, harvesting, and chipping is estimated to be 36 g/kg biomass (Gabrielle et al., 2013). Assuming the eucalyptus biomass is 49% carbon, the forest absorbs 153 t of carbon dioxide per day.

The capital cost of forestry equipment is estimated to be $0.86 M (Gonzalez et al., 2011). Labor and maintenance operations include land preparation, seeding, weed control, fire management, and fertilizing at $13.1/t of biomass yield. The operating, capital, and labor costs described above are incorporated into the cumulative discounted cash flow calculations described below. However, as a point of comparison, if the capital costs (including land) are amortized over a straight-line 30-year facility lifetime and combined with operating and labor costs, the cost of eucalyptus production in this study is calculated to be $48/t, which is within the range of current timber prices and literature results (Gonzalez et al., 2011; Howard & Jones, 2016; Idaho National Laboratory, 2014). The cost per tonne of biomass is also dependent on the model assumptions, which are evaluated further in the sensitivity analysis below (Section 2.2.5).

2.1.3 Combined-Heat-and-Power

Eucalyptus wood chips are used as fuel for CHP in a gasification CHP plant (Bain & Overend, 2002; Caputo et al., 2005; Francois et al., 2013; van den Broek et al., 1996). The forest operation yields 85.8 t/d of wood chips with a lower heating value of 18.1 MJ/kg (Francois et al., 2013). The base plant net electrical and thermal efficiencies are 27% and 39%, respectively. Freshwater consumption is 0.14 m³/t wood. Combustion is modeled as 95% complete combustion, generating 1.8 kg of CO₂/kg of wood in flue gas. Heat is recovered at a temperature of 120°C, which is sufficient quality for carbon capture steam generation and algal biomass drying. The total capital cost for the CHP plant is calculated to be $1610/kW and labor costs are ¢0.5/kWh (Bain & Overend, 2002; Turton et al., 1998; van den Broek et al., 1996). The operating, capital, and labor costs are incorporated into the cumulative discounted cash flow analysis below. However, for comparison, if the CHP facility was a stand-alone operation that purchased wood chips for $48/t, labor cost was ¢0.5/kWh, and capital costs were amortized over a straight-line 30 year period, the cost of electricity production would be $0.05/kWh, which is reasonable for small-scale production prior to transmission and fees (EIA, 2017).

2.1.4 Carbon Capture and Storage

Carbon capture is based on a monoethanolamine (MEA) scrubber design (Bartela et al., 2014; Fout et al., 2015; Mariz, 1998; Rochelle, 2009; Romeo et al., 2008; Singh et al., 2003). Carbon capture efficiency is 90%, requiring 3.11 MJ of heat and 0.40 MJ of electricity per kg of CO₂ recovered. Heat and electricity are obtained from the CHP plant. MEA losses are estimated to be 1.6 g/kg of CO₂. Consumption of cooling water and scrubber chemicals are estimated at 3.7 L/kg of CO₂ and 3.3 g/kg of CO₂, respectively. A slipstream of the concentrated CO₂ (49.4 t/d) is pumped to the algae facility as described above and the remainder (85.4 t/d) is injected underground at 15 MPa. Compression for injection requires 886 kJ of electricity per kg assuming 85% compression efficiency. The capital cost is estimated at $86/t of CO₂ recovered per year (with 46,800 t recovered per year for a total cost of $4.0 M). Labor cost is $4.63/t of carbon captured. As with the other system components, these costs are incorporated into the cumulative discounted cash flow analysis below. For comparison, as a stand-alone unit, the CCS process would cost $45.2/t of CO₂ recovered, which agrees well with the results from literature sources (Bartela et al., 2014; Fout et al., 2015; Mariz, 1998; Rochelle, 2009; Romeo et al., 2008; Singh et al., 2003).

2.2 Assessment Methods

2.2.1 Technoeconomic Assessment

To evaluate the economic feasibility of the integrated system, we first calculate the net present value (NPV) for the system after 30 years of operation assuming baseline prices (see Table 1) are received for each of the three output products (algal biomass, electricity, and carbon sequestration). Then, we calculate the minimum revenue required for each output product to break-even after 30-years of operation when the other products receive baseline market prices. Thus, we calculate the minimum selling price for algal biomass ($/t), electricity ($/MJ), or carbon sequestration ($/t net CO₂ sequestration)—when the other products receive baseline prices—that yield a net present value (NPV) for the facility equal to zero (break-even) after 30 years. NPV is calculated using the cumulative discounted cash flow model from the National Renewable Energy Laboratory (Short et al., 1995), which was used previously by Beal et al. (2015) and Gerber et al. (2016). Table 1 lists the critical input parameters for the discounted cash flow analysis.

Table 1. Key Parameters for the Technoeconomic Analysis

Parameter	Value
Discount rate	10%
Tax rate	20%
Capital	$71.5 M
Equity	40%
Interest rate	8%
Loan term	10 years
Depreciable investment (% total capital)	81%
Maintenance (% of depreciable investment)	1% per year
Insurance (% of depreciable investment)	1% per year
Land price	$2900/ha
MACRS % depreciationaa IRS (2016). , years 1–8	14.3, 24.5, 17.5, 12.5, 8.9, 8.9, 8.9, 4.5
Facility life	30 years
Start-up	1 year
Algal biomass baseline market valuebb Beal et al. (2015), Davis et al. (2016), and Gerber et al. (2016).	$600/t
Electricity baseline market valuecc EIA (2017).	¢1.9/MJ (¢6.74/kWh)
Carbon sequestration baseline market value	$0/t

• a IRS (2016).
• b Beal et al. (2015), Davis et al. (2016), and Gerber et al. (2016).
• c EIA (2017).

Annual operating costs include energy and materials (C_E & M), maintenance (C_mtn), insurance (C_ins), loan payments (C_loan), tax (C_tax), and labor (C_labor). Therefore, the annual operating cost (C_aop) is expressed as

(1)

For each year, taxes are calculated as the product of the tax rate (t) and the difference between the net income (NI) and the losses carried forward (LF) from the previous year (if any), represented as

(2)

where D is depreciation and I is loan interest. The time it takes for the facility to achieve a break-even point (NPV = 0) is determined using the discounted payback period (DPB). The DPB is calculated as

(3)

where DCF_k is the discounted cash flow associated with the facility for year k. The DCF_k is calculated as

(4)

where C_eq is the equity portion of the total capital cost (40%) and

(5)

where R_tot is the annual revenue and i is the discount rate.

The minimum selling price of algal biomass, electricity, or carbon sequestration represents the price at which one product has to be sold for the facility to break-even after 30 years, while the price of other revenue sources are held constant at the baseline market values.

2.2.2 Energy Return on Investment

The second-order energy return on investment (EROI) is calculated as the ratio of the energy impact of outputs (algal biomass and electricity) to the energy impact of inputs (all material and energy inputs) (Beal et al., 2015; Mulder & Hagens, 2008). Cumulative energy demand intensities for each material and energy flow were taken from ecoinvent© version 3.3 (Wernet et al., 2016). The energy impact of each energy or material input/output is the product of the amount of that input/output (e.g., kg ammonia, MJ electricity, etc.) and the cumulative energy intensity (e.g., MJ/kg ammonia, MJ/MJ electricity, etc.).

2.2.3 Water Consumption

The water impacts for the ABECCS system are evaluated two ways: (1) as the freshwater footprint (blue and green water, including precipitation) for the system divided by the amount of algal biomass produced (m³/t algae) and (2) applying the water depletion potential impact from ecoinvent© database version 3.3 (ReCiPe Midpoint H, ecoinvent) (Wernet et al., 2016) for each material and energy flow to evaluate the life-cycle water depletion impacts. Water for eucalyptus production is assumed to come from precipitation (496 L/kg; van Oel & Hoekstra, 2012). This study assumes seawater is used for algae production; however other sources of low-quality water could be used, such as saline aquifers, municipal wastewater, or produced water from oil and gas production. The life-cycle water depletion potential is calculated per tonne of algae produced and displacement credits are awarded for electricity exported from the system based on the water depletion potential of grid electricity that is avoided (0.80 L/MJ, production mix Texas Regional Entity).

2.2.4 Greenhouse Gas Accounting

Greenhouse gas impacts are evaluated two ways: (1) calculating the amount of CO₂ sequestered for the entire facility (reported as t CO₂/yr) and (2) applying the greenhouse gas impacts from ecoinvent© database version 3.3 (IPCC, 2013; GWP 100a) (Wernet et al., 2016) for each material and energy flow to evaluate the life-cycle greenhouse gas impact. The life-cycle GHG impact is calculated per tonne of algae produced and displacement credits are awarded for electricity exported from the system based on emissions avoided from grid electricity that is displaced (189 g CO₂e/MJ, production mix Texas Regional Entity). Biogenic carbon in the algal biomass (originally absorbed by eucalyptus) is assumed to be eventually released as carbon dioxide during metabolism or combustion, and therefor represents a net-zero GHG impact. Emissions for PBR plastic are included, but those associated with capital inputs, transport, or decommissioning are not.

2.2.5 Sensitivity Analysis

A sensitivity analysis was conducted to evaluate the range of potential outcomes for the ABECCS system. Detailed sensitivity and uncertainty analyses have also been presented previously for the algae production system (Beal et al., 2015; Gerber et al., 2016). Based on the results from those studies and consideration of the most impactful parameters in this model, Table 2 presents the parameters included in the sensitivity analysis along with their worst-case, baseline case, and best-case values. Most parameters are evaluated at twice and half the baseline value except for electricity generation efficiency, carbon uptake rate, and cooling water source, which are engineering estimates.

Table 2. Sensitivity Parameters

Parameters	Worst	Baseline	Best
Cultivation capital cost ($M)	106	53.1	26.6
Harvesting capital cost ($M)	11.2	5.60	2.80
Forestry capital cost ($M)	1.72	0.86	0.43
CHP capital cost ($M)	15.7	7.83	3.92
CCS capital cost ($M)	8.08	4.04	2.02
Algae productivity (t/ha-yr)	41.3	82.7	165
Eucalyptus productivity (t/ha-yr)	5.85	11.7	23.4
Algal biomass sale price ($/t)	300	600	1200
Labor ($M/yr)	4.60	2.30	1.15
Insurance and maintenance ($M/yr)	0.58	1.16	2.33
Interest rate (%)	0.16	0.08	0.04
Electricity generation efficiency (%)	0.20	0.27	0.35
Algal carbon uptake efficiency (%)	0.50	0.79	0.90
Forestry diesel consumption (kg/kg biomass)	0.07	0.04	0.02
Ammonia for algae cultivation (kg/kg algae)	0.13	0.06	0.03
Cultivation electricity demand (MJ/kg algae)	10.1	5.05	2.52
CCS electricity (MJ/t CO2)	1930	964	482
Forestry water consumption (m^3/t biomass)	993	496	248
PBR plastic lifetime (yr)	0.13	0.25	0.50
CCS cooling water source (−)	NA	Fresh	Salt

3 Results

A summary of the model results is presented in Table 3 for each model component and Table 4 presents the results for the integrated facility control volume (see Figure 1). Table 5 presents the overall technoeconomic analysis, EROI, greenhouse gas impact, and water depletion potential results.

Table 3. Component-Level Input/output Model Results

	Amount
Algae cultivation
Electricity (MJ/d)	111,000
Carbon dioxide supplied (kg/d)	49,400
Seawater used (m^3/d)	27,400
Ammonia consumed (kg/d)	1,540
DAP consumed (kg/d)	538
PBR plastic consumed (m^2/d)	3,060
Algae produced (kg/d)	21,900
Carbon dioxide released (kg/d)	10,500
Cultivation CapEx ($M)	53.1
Algae harvesting
Algae input (kg/d)	21,900
Electricity (MJ/d)	1,140
Heat (MJ/d)	186,000
Seawater discharged (m^3/d)	27,400
Whole algal biomass produced (kg/d)	20,200
Algae processing CapEx ($M)	5.6
Forestry production
Ammonia (kg/d)	410
P2O5 fertilizer (kg/d)	183
K2O fertilizer (kg/d)	74
Freshwater (green water) (m^3/d)	42,600
Diesel consumed (kg/d)	3,110
Carbon dioxide absorbed (kg/d)	153,000
Biomass yield (kg/d)	85,800
Forest CapEx ($M)	0.9
Combined heat and power
Biomass consumed (kg/d)	85,800
Freshwater (blue water) (m^3/d)	12
Electricity generated (MJ/d)	419,000
Heat generated (MJ/d)	606,000
Carbon dioxide generated (kg/d)	150,000
Incomplete combustion products (CO) (kg/d)	829
CHP CapEx ($M)	7.8
Carbon capture and storage
Carbon dioxide from CHP (kg/d)	150,000
Carbon capture electricity (MJ/d)	54,400
Carbon capture heat (MJ/d)	419,000
Amine loss (kg/d)	216
Scrubber chemicals (kg/d)	442
Freshwater (blue water) (m^3/d)	494
Electricity for carbon storage (MJ/d)	75,700
Carbon dioxide for algae (kg/d)	49,400
Carbon dioxide emitted (kg/d)	15,000
Incomplete comb. prod. (kg/d)	829
Carbon dioxide sequestered (kg/d)	85,400
CCS CapEx ($M)	4.0

• Note. Inputs are shown in black, outputs are shown in italic, and capital costs are listed in bold.

Table 4. Net Flows of Energy and Materials Across the Control Volume Boundary. The energy impact, cost/revenue, GHG impact, and water depletion potential for each input/output are calculated as the product of the daily amount of each input/out (X/d) and the cumulative energy intensity (MJ/X), price ($/X), GHG intensity (CO₂e/X), or water depletion intensity (m³/X), respectively (intensity values are not shown). *Carbon contained in algal biomass is assumed to eventually be released as CO₂ via metabolism or combustion.

	Amount (X/d)	Energy impact (MJ/d)	Cost/revenue ($/d)	GHG impact (kg CO2e/d)	Water depletion potential (m^3/d)
Inputs (X/d)
Seawater (m^3/d)	27,400	0	0	0	0
Ammonia (kg/d)	1,950	80,200	1,530	3,890	3
DAP (kg/d)	538	16,600	381	792	7
PBR plastic (m^2/d)	3,060	26,100	2,470	833	1
Green water (rain) (m^3/d)	42,600	0	0	0	0
Atmospheric CO2 (kg/d)	153,000	0	0	−153,000	0
Freshwater (blue water) (m^3/d)	505	3,030	126	238	523
P2O5 fertilizer (kg/d)	183	7,220	128	334	2
K2O fertilizer (kg/d)	74	565	49	34	0
Diesel (kg/d)	3,110	178,000	1,820	11,100	4
Scrubber chemicals (kg/d)	442	29,600	221	644	0
Amine loss (kg/d)	216	14,800	209	604	1
Total inputs		356,000	6,940	−134,000	542
Outputs (X/d):
CO2 returned to atmosphere (kg/d)	25,500	0	0	25,462	0
Seawater discharged (m^3/d)	27,400	0	0	0	0
Whole algal biomass produced (kg/d)	20,200	412,000	12,100	34,800*	NA
Electricity exported (MJ/d)	177,000	514,000	3,320	−33,600	−143
Heat exported (MJ/d)	0	0	0	0	0
Incomplete combustion products (CO) (kg/d)	829	0	0	2,490	0
Carbon dioxide sequestered (kg/d)	85,439	0	0	0	0
Total outputs		926,000	15,400	29,200	−143
		EROI = 2.60 (−)	Revenue =  $8490/d	GHG = −5.21 (t CO2e/t)	WDP = 19.7 (m^3/t)

Table 5. Integrated System Results for the ABECCS System

Parameter	Value
Capital cost ($M)	$71.5
Operating costaa This operating cost value excludes loan payment and taxes, which vary year-to-year. ($M/yr)	$5.87
Labor ($M/yr)	$2.30
Total land footprint (ha)	2,800
Protein yield (t protein/ha-yr)	0.99
EROI (−)	2.60
NPV at year 30 at baseline prices ($M)	-$72.7
Minimum algal biomass selling pricebb Minimum selling/sequestration prices are calculated to yield NPV = 0 (break-even) after 30 years when other products receive baseline market prices. ($/t)	$1,780
Minimum electricity selling pricebb Minimum selling/sequestration prices are calculated to yield NPV = 0 (break-even) after 30 years when other products receive baseline market prices. ($/kWh)	$0.55
Minimum carbon sequestration pricebb Minimum selling/sequestration prices are calculated to yield NPV = 0 (break-even) after 30 years when other products receive baseline market prices. ($/t CO2)	$278
Carbon dioxide sequestered (t CO2/yr)	29,600
Life-cycle GHG impactcc The carbon contained in algal biomass exiting the system boundary is assumed to be eventually released back to the atmosphere as CO2 via metabolism or combustion. See Table 4. (kg CO2e/t algae)	−5,210
Freshwater footprint (blue and green) (m^3/t algae)	2,250
Water depletion potential (m^3/t algae)	19.7

• a This operating cost value excludes loan payment and taxes, which vary year-to-year.
• b Minimum selling/sequestration prices are calculated to yield NPV = 0 (break-even) after 30 years when other products receive baseline market prices.
• c The carbon contained in algal biomass exiting the system boundary is assumed to be eventually released back to the atmosphere as CO₂ via metabolism or combustion. See Table 4.

3.1 Technoeconomic Results

Operating costs over the facility lifetime ($234 M) are roughly three times greater than capital costs ($71.5 M) for the integrated facility. Aggregated over the 30-year facility life, the largest operating costs include: labor ($69.0 M), PBR plastic ($25.7 M), loan interest ($21.0 M), insurance ($17.4 M), maintenance ($17.4 M), diesel ($19.0 M), ammonia ($15.9 M), CCS chemicals ($4.5 M), and DAP ($4.0 M). The largest contributors to the capital cost include: pipes ($16.1 M), pond liner ($12.8 M), CHP ($7.8 M), land ($8.1 M), CCS ($4.0 M), solar drying beds ($2.2 M), buildings ($1.9 M), filter press ($1.8 M), ring dryer ($1.2 M), and project coordination ($1.0 M). Meanwhile, at baseline market prices ($0.07/kWh for electricity and $600/t for algal biomass), electricity and algal biomass revenues over the 30-year facility lifetime total $34.6 M and $126.0 M, respectively.

At baseline market prices, which provide no carbon sequestration credit (see Table 1), the facility loses $72.7 million after 30 years of operation. However, the system is specifically designed to sequester carbon, so it is unrealistic to consider scenarios without a significant carbon credit. The facility breaks-even for many combinations of product prices (algae; electricity; CO₂ sequestration), including: (1) $1,780/t algae; $0.07/kWh; $0/t CO₂, (2) $600/t algae; $0.55/kWh; $0/t CO₂, and (3) $600/t algae; $0.07/kWh; $278/t CO₂. Soybean prices have ranged from roughly $350 to $600/t since 2008 (USDA, 2017a). As shown in Figure 2, if algae fetch comparable prices to soy, the cost of carbon sequestration would be around $300/t CO₂. While it is unlikely that the price of electricity would ever reach $0.55/kWh, it is possible that algal biomass could be worth up to $1,800/t as a specialty health food product or as an aquafeed ingredient (Borowitzka, 2013; Shah et al., 2017). Many groups are currently pursuing the production of algae for high value products based on the added value of high-quality protein and omega-3-rich oils in algae (U.S. Department of Energy [U.S. DOE], 2010). At higher price points, algal biomass would no longer substitute for low-cost soy protein, which could result in more soy being planted and unintended land use changes. However, as mentioned above, ABECCS is designed to sequester carbon, and the higher price points for algal biomass correspond with low or zero carbon sequestration credits and are therefore not representative of expected market conditions for ABECCS deployment. Figure 2 presents a range of break-even price combinations for algal biomass and carbon sequestration, assuming an electricity price of $0.07/kWh.

Figure 2

Cost of carbon sequestration for (a) ABECCS with a range of algal biomass sale prices and (b) for various technologies. Results for ABECCS are based on a market price for electricity at $0.07/kWh and, for plot b, for algal biomass at $600/t. Sources: DAC (Sanz-Pérez et al., 2016; Zeman, 2014), BECCS (Smith et al., 2016), Amine CCS (Rochelle, 2009), O₂/CO₂ (Singh et al., 2003).

While the carbon sequestration credit required for the system at baseline prices ($278/t CO₂) in this study is 4–5 times greater than near-zero emissions technologies including amine-based CCS, it is similar to that of other negative-emissions technologies, including conventional BECCS (with cost estimates ranging from $130 to $400/t; Smith et al., 2016) and DAC (with cost estimates ranging from $300 to $1000/t; Sanz-Pérez et al., 2016; Zeman, 2014). Of the technologies compared in Figure 2, ABECCS is the only one that generates protein and negative carbon emissions. As such, a more probable opportunity for solvency exists with a carbon sequestration credit of $50 or $100/t CO₂, requiring algal biomass to be sold for $1,570/t or $1,350/t, respectively, to reach break-even after 30 years. With reduced capital and operating costs for algae production, which dominates the TEA, break-even prices could be driven even lower, as shown in the sensitivity analysis below. However, creating a carbon sequestration subsidy of $50 or $100/t remains challenging, as current cap-and-trade systems in Europe and California price carbon under $15/t (California Carbon Dashboard, 2017; European Energy Exchange, 2017; Intercontinental Exchange, 2017).

These results also demonstrate that the integrated system is more expensive than conventional algae production on the one hand and also more expensive than traditional BECCS on the other. For producing algae, it would be most economical to acquire carbon dioxide at no cost (Beal et al., 2015), followed by purchasing it from a fossil-based CCS plant (∼$50/t), and lastly by purchasing it from a BECCS facility (∼$250/t)—analogous to ABECCS. Similarly, as shown in Figure 2, the ABECCS system adds to the cost of sequestration by traditional BECCS. However, the stand-alone algae facility does not permanently sequester carbon and the conventional BECCS facility does not generate food; there is an added cost to do both—a sustainability nexus of economic feasibility, energy production, food production, and climate change mitigation—which is discussed further in Section 3.6.

3.2 EROI Results

Shown in Table 4, the EROI for the system in this study is 2.60. The largest energy input is diesel for forestry production (50% of total), followed by ammonia (23% of total). Energy output is split between electricity (56% of total) and algal biomass (44% of total). The ABECCS facility offers advantages over single-output technologies because (unlike soybeans, fossil-fuel electricity generation, or other carbon removal technologies) this system yields protein, electricity, and sequesters carbon—thereby providing three valuable outcomes simultaneously with a substantial return on energy investment. However, as shown in Figure 3, the EROI for this system is about 75% lower than soybeans, but higher than a stand-alone algae facility (Beal et al., 2015; Sills et al., 2012) or protein production from livestock (USDA, 2017b; Wernet et al., 2016). The EROI is less than that for fossil energy resources (Murphy & Hall, 2011), but greater than corn ethanol (Shapouri et al., 2002). Generally speaking, our industrialized society uses energy resources with high EROI (such as coal, oil, gas, nuclear power, solar power, and agriculture crops) to enable the production of goods and services with low-or-zero EROI (such as meat, freshwater, housing, entertainment, and CCS). The synergies of the ABECCS system evaluated here enable a unique process by which high and low EROI technologies are combined to generate three previously unrelated products (electricity, algal protein, and carbon sequestration) with a positive net energy balance (i.e., EROI > 1).

Figure 3

(a) EROI results for several energy and protein products. The line indicates EROI = 1 (energy neutral). Sources: Soybeans, milk, and chicken (USDA, 2017b; Wernet et al., 2016), PV solar power and electricity from oil and gas (Murphy & Hall, 2011), algae fuel and feed (Beal et al., 2015), corn ethanol (Shapouri et al., 2002). (b) Water footprint (blue and green water; units of m³/t biomass), including natural precipitation, for several biomass sources. Sources: Soybeans and corn (Critchley and Siegart, 1991; USDA, 2016), eucalyptus (Gabrielle et al., 2013; van Oel & Hoekstra, 2012), and marine algae (Beal et al., 2015).

3.3 Greenhouse Gas Results

The ABECCS facility sequesters over 29,600 t of CO₂ per year with a 2800-ha footprint. The US generates 6.6 billion tonnes of CO₂e per year (U.S. Environmental Protection Agency, 2017) and there are 34 million hectares of planted soybeans (USDA, 2016). To get a sense of scale, replacing 50% of the soybeans grown in the United States with the ABECCS system would sequester roughly 2.8% of the total U.S. GHG emissions without any reduction in protein supply and a negligible increase in water consumption (see below). Although the algae production system in this model is dependent on saltwater and a temperate climate with ample sunlight—and thus constrained to coastal areas and areas with saline aquifers in the southern United States—woody biomass production could replace soybeans throughout the United States and timber could be shipped to the algae facilities for CHP and CCS. In that scenario, economic and environmental impacts of shipping would need to be added to the assessment.

Shown in Table 3, the life-cycle GHG impact of the integrated facility is −5,210 kg CO₂e per tonne of algae generated, as compared to 390 kg of CO₂e emitted per tonne of soybeans produced (with no deforestation and similarly assuming that biogenic carbon in soybeans is eventually released) (Wernet et al., 2016). The greatest emissions are associated with diesel (53%), ammonia (19%), and incomplete combustion products (12%). The GHG credits are dominated by the net carbon uptake into the control volume (73%), followed by displacement credits for exported electricity (27%). Based on these results, the ABECCS design represents a novel approach to generating negative GHG emissions and can be considered as one of the many options to help limit the increase of atmospheric CO₂.

3.4 Water Results

The main water use components for this system are: (1) seawater or saline water for algae cultivation, (2) natural precipitation (green water) for eucalyptus production, and (3) freshwater (blue water) required for cooling in CCS. The direct freshwater footprint (including blue and green water) for the system is 2,250 m³/t of algal biomass generated, which is within 2% of that for soybeans and roughly 4 times greater than that for corn or eucalyptus. The water footprint—of eucalyptus or soybeans—can vary significantly depending on the climate of a particular location. For example, in tropical locations, the water footprint for eucalyptus has been estimated to be roughly twice as much as that in subtropical locations (van Oel & Hoekstra, 2012). The summary finding of this analysis is that because eucalyptus and soybeans require a similar amount of water, it is possible to replace existing soybean crops with eucalyptus forest without increasing the water footprint of protein production. The life-cycle water depletion potential (which omits rain) for the ABECCS system is 19.7 m³/t of algae—less than half of that for soybeans (51 m³/t soybean; Wernet et al., 2016)—and due almost exclusively to the large cooling water requirement of CCS.

3.5 Sensitivity Analysis Results

As shown in Figure 4, the cost of carbon sequestration is heavily influenced by the productivity of both eucalyptus and algae. The eucalyptus productivity determines the overall carbon uptake and electricity yield, while the algal productivity drives the algal biomass revenue. The only other two parameters that yield sequestration prices below $200/t are a 50% reduction in the cost of cultivating algae and a 100% increase in the price received for algal biomass. Achieving this cost reduction or revenue increase, or a combination of the two, might be accomplished with genetically modified strains, income generated from coupling this system with municipal wastewater treatment, major increases in the price of protein worldwide, or targeting niche high-value algae products. Sequestration price also demonstrated some sensitivity to algal carbon uptake rate, labor, and interest rate, while being fairly insensitive to the other parameters investigated.

Figure 4

Sensitivity analysis results for carbon sequestration cost. Best and worst case values for each parameter are listed in Table 2. Parameters with no impact are not shown. The horizontal line corresponds to the base case.

Figure 5 shows that the EROI is most sensitive to the electricity generation efficiency, diesel consumed for forestry, CCS electricity demand, algal productivity, and algal cultivation electricity demand. These results are to be expected as exported electricity and algal biomass make up 100% of the energy output, while diesel, CCS electricity, and algae cultivation electricity dominate the energy consumption (see Table 3). Heat does not impact the EROI in this model because there is excess heat produced and it is used to assist solar drying. The remainder of the parameters investigated yielded changes to the EROI less than 10%. In all cases, the EROI is greater than 1 (indicating net energy production) and less than 4.

Figure 5

Sensitivity analysis results for EROI (a) and water footprint (b). Best and worst case values for each parameter are listed in Table 2. Parameters with no impact are not shown. *Sensitivity artifact, see text. The horizontal lines correspond to the base case.

Ninety-nine percent of the direct water demand for the ABECCS system comes from rain needed for producing eucalyptus. Increasing the precipitation demand by 100% raises the direct water demand to over 4,000 m³/t algae, while reducing the precipitation demand by 50% lowers the direct water demand to nearly 1,000 m³/t. The water footprint for eucalyptus is proportional to the eucalyptus yield (496 L/kg), so increasing eucalyptus productivity, without a similar increase in algae production, causes an increase in water footprint as an artifact. Increased algae productivity also causes an artifact, but to a lesser extent. CCS cooling water source has a minor impact on water footprint. Water depletion potential is almost entirely dependent on the cooling water for CCS (Tables 3 and 4). Replacing fresh cooling water with the salt water discharged from algae harvesting reduces the water depletion potential to −5.6 m³/t of algae (compared to 51 m³/t soybean; Wernet et al., 2016). In summary, the water impacts for this system relate directly to the rain and cooling water requirements.

3.6 Comparison with Other Negative-Emissions Protein-Generating Technologies

The assessments provided in Sections 3.1 – 3.5-3.1 – 3.5 compare ABECCS to an assortment of technologies that do not generate equivalent goods and services. The intersection of economic and environmental sustainability requires technologies that satisfy an array of performance criteria. Many of the energy, food, or carbon sequestration technologies considered above have economic and/or environmental tradeoffs. For example, amine-based CCS captures carbon, but increases the cost of electricity; soy production generates valuable biomass, but requires significant land, water, and fertilizer resources; milk production provides protein, but consumes more energy than it produces. As a result, these technologies serve as benchmarks for ABECCS, but they are not equal comparisons. There are, however, numerous technology combinations that can sequester carbon dioxide, generate electricity, and produce protein-rich biomass. Two leading examples include (1) the combination of BECCS with soy production and (2) DAC powered by natural-gas-powered CHP with CCS combined with soy production. To obtain apples-to-apples comparisons, these systems (among others) should be comprehensively modeled within the same technoeconomic and life-cycle assessment framework. Unfortunately, that modeling is beyond the scope of this proof-of-principle evaluation of ABECCS and remains as future work. Instead, as a preliminary comparison, we gathered literature data for the comparable technology combinations roughly to assess their relative impacts across economic, energetic, and environmental criteria (Table 6). The objective of this comparison is not to claim superiority for any one of these technology combinations, but rather to demonstrate that they offer economic and environmental tradeoffs.

Table 6. Comparison of ABECCS to BECCS + Soy and DAC + Soy. Soy production yields 7,250 t/yr and algae production yields 7,000 t/yr.

	Land (ha)	Cost ($M/yr)	Revenue ($M/yr)	Energy out (TJ/yr)	Energy in (TJ/yr)	GHG impact (t/yr)	GHG credit (t/yr)	Water footprint (thou. m^3/yr)
Algae production	121	5.79	4.20	143	37	1,630	0	0
(A)BECCS	2,680	4.38	1.21	178	87	−26,500	−11,700	15,700
ABECCS total	2,800	10.2	5.41	321	124	−36,500		15,700
Soy production	2,790	2.17	2.17	116	11	2,830	0	16,000
BECCS	1,760	9.32	1.08	168	60	−29,700	−10,900	10,300
BECCS + Soy Total	4,540	11.5	3.26	284	71	−37,800		26,300
Soy Production	2,790	2.17	2.17	116	11	2,830	0	16,000
NG CHP w/ CCS	0	4.66	1.15	178ee Electricity and heat from CHP used for DAC cancel and are omitted from the table. Energy output is for excess electricity (61.5 TJ/yr).	559	2,680	−11,700	92
DAC	0	11.9	0.00	0	0ee Electricity and heat from CHP used for DAC cancel and are omitted from the table. Energy output is for excess electricity (61.5 TJ/yr).	−31,700	0	0
DAC + Soy Total	2,790	18.7	3.37	294	570	−37,800		16,100

	Landaa The unit “t” refers to metric tonnes of either soy or algae produced. (ha/t)	Direct CO2 seq (t CO2/yr)	Cost, directbb Unlike the cumulative discounted cash flow analysis results (Table 5), these costs do not include taxes or a discount rate applied over the facility lifetime, which therefore reduces the cost of ABECCS sequestration significantly. ^,cc Cost, direct is the net cost divided by the amount of direct CO2 sequestration. ($/t CO2)	Cost, dir. and ind.bb Unlike the cumulative discounted cash flow analysis results (Table 5), these costs do not include taxes or a discount rate applied over the facility lifetime, which therefore reduces the cost of ABECCS sequestration significantly. ^,dd Cost, dir. and ind. is the net cost divided by overall GHG impact, which includes upstream impacts for energy and material inputs. ($/t CO2)	EROI (−)	GHG impactaa The unit “t” refers to metric tonnes of either soy or algae produced. (kg CO2e/t)	Water footprintaa The unit “t” refers to metric tonnes of either soy or algae produced. (m^3/t)
ABECCS	0.4	29,600	$161	$130	2.60	−5,210	2,250
BECCS + Soy	0.6	32,900	$250	$218	4.01	−5,210	3,630
DAC + Soy	0.4	31,700	$489	$408	0.52	−5,210	2,230

• a The unit “t” refers to metric tonnes of either soy or algae produced.
• b Unlike the cumulative discounted cash flow analysis results (Table 5), these costs do not include taxes or a discount rate applied over the facility lifetime, which therefore reduces the cost of ABECCS sequestration significantly.
• c Cost, direct is the net cost divided by the amount of direct CO₂ sequestration.
• d Cost, dir. and ind. is the net cost divided by overall GHG impact, which includes upstream impacts for energy and material inputs.
• e Electricity and heat from CHP used for DAC cancel and are omitted from the table. Energy output is for excess electricity (61.5 TJ/yr).

For this comparison, all three systems were specified to yield a net GHG impact of −5.21 t CO₂ per tonne of either soy or algal biomass produced and export roughly 60 TJ/yr. of electricity. For Table 6, the cost of algae production was set to $826/t (Section 2.1.1) with revenue of $600/t. The cost of BECCS when integrated with algae production (ABECCS) is calculated as the annualized total cost of the total ABECCS facility ($71.5 M capital plus $234 M operating over 30 years) less the annualized cost of algae production ($5.8 M). Revenue is generated for exported electricity (61.5 TJ/yr). Energy, GHG, and water impacts for ABECCS in Table 6 are the same as those in Table 4. Soy production in Table 6 is assumed to cost $300/t with equal revenue, and the energy, GHG, and water estimates are based on data in Figure 3 and (Wernet et al., 2016). The net cost of BECCS operated independently is estimated to be $250/t of CO₂ (Smith et al., 2016), while energy, GHG, and water impacts are based on the model of BECCS developed for this study (see Tables 3 and 4). DAC is based on the methodology presented by Zeman (2014) ($437/t CO₂), but modified with a natural-gas-powered CHP facility that generates excess electricity, which results in a slight cost increase over the referenced study.

The most notable result from Table 6 is the reduced cost of ABECCS ($161/t CO₂) in comparison to the cumulative discounted cash flow results presented in Table 5 ($287/t CO₂). In absence of a discount rate applied over the 30-year facility life and by omitting taxes, the cost of sequestration is significantly reduced. In layman's terms, because a $278/t revenue received 15 years from now is worth only $67/t if it were received today (and invested with 10% annual interest), for the facility to reach a NPV of 0 after 30 years with taxes and a discount rate, the actual revenue required for each t of CO₂ sequestered over the 30-year facility life is inflated to offset the time-value of money—an effect that is omitted from the estimates in Table 6. As a result, on a cost basis, ABECCS outperforms BECCS + Soy and DAC + Soy by 36% and 67%, respectively. Because all three of these proposed systems are nascent concepts without commercialized industries, cost estimates in the literature vary for all three. The cost for BECCS has been estimated as low as $125/t (Smith et al., 2016) and that for DAC as low as $300/t (Sanz-Pérez et al., 2016; Zeman, 2014). In addition, the technology readiness level for these technologies varies and can impact the eventual cost of deployment. Conducting side-by-side cumulative discounted cash flow assessments of these technologies, among others, remains as future work.

The land and water footprints of BECCS + Soy are both roughly 50% greater than ABECCS, demonstrating the resource constraints posed by this technology combination and the motivation for this study. However, the EROI of for BECCS + Soy is roughly 50% greater than that of ABECCS, which demonstrates the large energetic cost of algae production as compared to soy. Meanwhile, DAC + Soy has equivalent land and water footprints as ABECCS, but consumes about twice as much energy as it produces (EROI = 0.52). These comparisons demonstrate the synergistic advantages of ABECCS and support the need for more thorough technoeconomic and life-cycle assessments of all three systems under a common framework. Benefits of integrating ABECCS with municipal wastewater treatment or other waste resources could be also evaluated to further improve sustainability (Beal et al., 2012, 2016; Lundquist et al., 2010).

4 Conclusions

The motivation for this study was to evaluate the potential for an alternative BECCS system that integrates algal biomass production to sequester carbon dioxide without reducing agricultural output. The purpose of ABECCS is not to reduce the costs of either algae production or BECCS; rather it is to enable an increase in BECCS deployment at scales required for significant CO₂ sequestration without threatening food security. The general concept explored is to replace soy production with the ABECCS system. Our evaluation included economic, energetic, and environmental aspects.

From a sustainability perspective, the ABECCS facility generates more energy than it consumes (EROI = 2.60), permanently sequesters atmospheric carbon dioxide (net negative emissions), and produces the same amount of protein as soybeans cultivated on the same footprint without increasing freshwater demand. Economically, we found that the proposed system, in its current form, requires a sale price for algal biomass that is significantly greater than that for soybeans or many other terrestrial crops. With carbon sequestration credits of $0, $50, or $100/t, the algal biomass needs to receive $1,780, $1,570, or $1,350/t, respectively, for the facility to break-even after 30 years.

There are several resource constraints to consider for siting an ABECCS facility, including access to low-quality water, ample sunlight, and warm temperatures for algae production, precipitation for the eucalyptus forest, and access to geological formations suitable for direct injection of carbon dioxide or carbon dioxide pipelines for carbon storage or reuse. In addition, siting assumptions for the facility size, shape, slope, soil composition, distance to water source, and labor costs can impact the cost and environmental impacts of the facility. The feasibility of ABECCS depends on the CHP facility being colocated with algae production to recover electricity, heat, and CO₂ from CHP. However, the woody biomass fuel could be produced off-site and transported to the CHP facility, albeit with increased cost and environmental impacts for transportation. As an example, the island of Hawaii represents an ideal site for ABECCS, with extensive basalt formations for CO₂ injection (NETL, 2017), existing eucalyptus plantations on the wet Hamakua Coast (eastern coast, Hilo), and existing commercial algae production on the warm and arid Kohala Coast (western coast, Kona)—all of which are separated by distances less than 150 km. Therefore, commercial deployment of ABECCS systems should consider site-by-site resource constraints to determine specific economic and environmental conditions.

While BECCS systems and algae production have received significant interest in recent years as independent technologies, this assessment demonstrates the added benefits of synergistic integration of the two systems. By colocating the algae production facility with the CHP/CCS plant, algae cultivation receives electricity, heat, and CO₂, which are critical (and otherwise expensive) for algae production. Meanwhile, by coupling these technologies, the ABECCS system contributes to the agricultural demands of a growing world, thereby potentially avoiding the commercialization pitfalls that have loomed over traditional BECCS systems. From a thermodynamics perspective, the synergies increase exergy efficiency of both technologies by capturing valuable exergy products (heat and concentrated low-entropy CO₂) from CHP/CCS that would otherwise dissipated to the environment. Conducting a life-cycle exergy assessment and illustrating the exergy losses with Sankey diagrams would be useful for further optimizing the ABECCS system. Furthermore, there is room for boosting sustainability by integrating ABECCS with other industries, including municipal wastewater treatment (Beal et al., 2012; Lundquist et al., 2010), cofiring the CHP facility with flare gas (Beal et al., 2016), recovering oxygen from algae cultures for oxyfuel combustion, and by producing biochar for fertilizer and complementary carbon sequestration (Woolf et al., 2010).

Microalgae have been tested successfully as feed ingredients for poultry, swine, sheep, cattle, and seafood (Burnett et al., 2017; Lum et al., 2013; Morrill et al., 2017; Sørensen et al., 2017). The high-quality protein and omega-3 fatty acids provide added value over other protein sources—particularly for aquafeeds (Chauton et al., 2015; Shah et al., 2017)—and select strains are already consumed by humans at commercial scale (Spolaore et al., 2006). The commercialization of algal biomass as a commodity feed ingredient will not only determine the true market value of algal feed products in a world with a growing protein demand, but should also drive the cost of production lower with further research and industrial scale-up (Walsh et al., 2015). Expanded profit margins for algal biomass would increase the ability for the ABECCS system to be an economical option for achieving negative emissions. Based on these results, with favorable economic conditions, ABECCS could be a leading candidate to contribute to the reduction of carbon dioxide in the atmosphere in a sustainable way.

Acknowledgments

Funding was provided by the U.S. Department of Energy award DE-EE0007091 and we are grateful to the entire Duke Marine Algae Industrialization Consortium (MAGIC). We thank Deborah Sills, Leda Gerber Van Doren, Michael Walsh, Joe Granados, and Robert Hebner for discussions related to this research and review of the manuscript. We also thank two anonymous reviewers, whose feedback improved the manuscript. The data used for this study can be found in the references, tables, and/or figures.