Global evaluation of biofuel potential from microalgaeJeffrey W. Moodya, Christopher M. McGintyb, and Jason C. Quinna,1
aMechanical and Aerospace Engineering and bDepartment of Wildland Resources, Utah State University, Logan, UT 84322
Edited by Stephen Polasky, University of Minnesota, St. Paul, MN, and approved April 29, 2014 (received for review November 18, 2013)
In the current literature, the life cycle, technoeconomic, and resourceassessments of microalgae-based biofuel production systems haverelied on growth models extrapolated from laboratory-scale data,leading to a large uncertainty in results. This type of simplisticgrowth modeling overestimates productivity potential and fails toincorporate biological effects, geographical location, or cultivationarchitecture. This study uses a large-scale, validated, outdoor photo-bioreactor microalgae growth model based on 21 reactor- andspecies-specific inputs to model the growth of Nannochloropsis. Thismodel accurately accounts for biological effects such as nutrient up-take, respiration, and temperature and uses hourly historical meteo-rological data to determine the current global productivity potential.Global maps of the current near-term microalgae lipid and biomassproductivity were generated based on the results of annual simula-tions at 4,388 global locations. Maximum annual average lipid yieldsbetween 24 and 27 m3ha1y1, corresponding to biomass yields of13 to 15 gm2d1, are possible in Australia, Brazil, Colombia, Egypt,Ethiopia, India, Kenya, and Saudi Arabia. The microalgae lipid pro-ductivity results of this study were integrated with geography-spe-cific fuel consumption and land availability data to performa scalability assessment. Results highlight the promising potentialof microalgae-based biofuels compared with traditional terrestrialfeedstocks. When water, nutrients, and CO2 are not limiting, manyregions can potentially meet significant fractions of their transporta-tion fuel requirements through microalgae production, without landresource restriction. Discussion focuses on sensitivity of monthly var-iability in lipid production compared with annual average yields,effects of temperature on productivity, and a comparison of resultswith previous published modeling assumptions.
algae | global model | geographic information system |life cycle assessment | dynamic map
Recent volatility in oil prices, attributed to increased demandand limited resources, has led to the development of un-conventional petroleum reserves, such as oil sands, and increasedexploration of alternative and renewable fuel sources. Scalabilitylimitations associated with traditional terrestrial biofuel feed-stocks have renewed interest in next-generation feedstocks, suchas microalgae. Microalgae offer many potential advantages overtraditional terrestrial oil crops, including higher lipid productiv-ities, a lack of competition for arable land, year-round cultivation,integration with saline and low-quality water sources, and a viabledrop-in equivalent fuel product (15). These scalable advantagesmake microalgae a promising feedstock for biofuel production anda potential sustainable alternative to traditional petroleum fuels.The current near-term productivity potential for microalgae at
large-scale currently is being estimated through the linear scalingof laboratory-based growth and lipid data, which has led to a largevariance in reported values (4, 6). This type of scaling has beenintegrated into various life cycle, technoeconomic, and resourcemodels of the microalgae-to-biofuels process, leading to unrealisticassumptions about industrial function, and is a source of largeuncertainty (2). Current near-term algal lipid productivity valuesreported in life cycle, technoeconomic, and resource modelingliterature range from 2.3 m3ha1y1 reported by Ramachandraet al. (6) to 136.9 m3ha1y1 reported by Mata et al. (4), with avariety of researchers reporting values between these two extremes(1, 326). Large uncertainty in the reported productivity potentialsstems from the use of simplistic growth modeling through simple
solar conversion calculations or linear scaling of laboratory data;both fail to incorporate biological function and geographic di-versity. Propagation of errors in microalgae production modelingat large-scale skew life cycle, economic, and scalability assess-ments, because lipid yield typically represents the functional unit inthese assessments.Decreasing uncertainty in the current productivity potential from
microalgae requires increased fidelity in growth modeling throughtemporal and biological resolution combined with geographicallyspecific climatic and resource data (27). This study integratesa microalgae growth model with hourly historical meteorologicaldata from various global locations for the assessment of the currentnear-term lipid and biomass productivity potential of microalgaecultivated in a traditional closed-system photobioreactor. Themicroalgae growth and lipid content is simulated on an hourly timescale over the course of 1 y at 4,388 global locations through theuse of 1225 y (depending on site) of meteorological data. Resultsfrom annual simulations were surface interpolated to producea dynamic global map of the current near-term microalgae lipidproductivity and are intended to represent the current large-scaleproduction potential based on a photobioreactor architecture.Discussion focuses on the effects of temperature on productivity,a geographically specific scalability assessment, monthly variabilityin productivity, and a comparison of modeled results with currentnear-term productivity potentials reported in microalgae biofuellife cycle, technoeconomic, and scalability literature.
Results and DiscussionThe results from this study are divided into four sections: (i)baseline global lipid productivity and variability, (ii) tempera-ture sensitivity to lipid productivity, (iii) global scalability, and (iv)comparison of results with literature-based modeling assumptions.
Research into microalgae as a feedstock for biofuels continuesto increase because of the inherent potential advantages itholds over traditional terrestrial feedstocks. However, the truenear-term large-scale productivity of microalgae remains un-certain. This study integrates a large-scale, outdoor growthmodel with historical meteorological data from 4,388 globallocations to estimate the current near-term lipid and biomassproductivity potential from microalgae cultivated in a photo-bioreactor architecture. Results show that previous life cycle,technoeconomic, and resource assessments dramatically over-estimated lipid yields. A scalability assessment that leveragesgeographic information systems data to evaluate the currentproductivity potential from microalgae with global fuel con-sumption and land availability shows that microalgae can havea positive impact on the transportation energy portfolios ofvarious countries.
Author contributions: J.C.Q. designed research; J.W.M., C.M.M., and J.C.Q. performedresearch; and J.W.M., C.M.M., and J.C.Q. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.1To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
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www.pnas.org/cgi/doi/10.1073/pnas.1321652111 PNAS | June 10, 2014 | vol. 111 | no. 23 | 86918696
Global Productivity Potential and Variability. Hourly biomass andlipid productivity results from simulation locations were aver-aged for an annual geographically resolved result. Results fromthe 4,388 simulation locations were surface interpolated toproduce a dynamic map illustrating the current near-term lipidproductivity potential from microalgae across the globe (Fig. 1).The closed photobioreactor system modeled represents a prom-ising production system compared with that of an open racewaypond, based on increased stability and improved volumetricproductivity from extended surface area and a short light path.Results from this closed system will be greater than those of anopen system (1, 22, 28). As expected, locations in the northernand southern parts of the globe, where the light intensity andtemperature are lower, result in decreased lipid productivitycompared with regions more centrally located. Although loca-tions around the equator typically are considered optimum cul-tivation locations because of their annual temperature stability,results show these locations do not necessarily produce thelargest lipid yields; this is the result of other climatic phenomenathat affect biological growth. A detailed comparison of fourcountries, India, China, Brazil, and Australia, is presented inFig. 2. Manaus, Brazil, is closer to the equator than AliceSprings, Australia, but experiences more rain and cloud cover,and as a result, Manaus produces a lower lipid and biomassyield, 18.9 m3ha1y1 and 10.2 gm2d1, respectively, comparedwith the lipid and biomass yield in Alice Springs, 24.2 m3ha1y1
and 13.1 gm2d1, respectively. India and China are neighboringcountries, but India has better average lipid yields, primarily be-cause of temperature differences. In general, the temperature inIndia is closer than that of China to the optimal growth tem-perature and demonstrates the growth models ability to accu-rately capture