ISSN: 0738-8551 (print), 1549-7801 (electronic) Crit Rev Biotechnol, Early Online: 1–14 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/07388551.2013.835301


Biofuel production from microalgae as feedstock: current status and potential Critical Reviews in Biotechnology Downloaded from by Kansas State University Libraries on 06/07/14 For personal use only.

Song-Fang Han1, Wen-Biao Jin1, Ren-Jie Tu1, and Wei-Min Wu2 1

School of Civil and Environment Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, P. R. China and 2Department of Civil and Environmental Engineering, Center for Sustainable Development and Global Competitiveness, Stanford University, Stanford, USA Abstract


Algal biofuel has become an attractive alternative of petroleum-based fuels in the past decade. Microalgae have been proposed as a feedstock to produce biodiesel, since they are capable of mitigating CO2 emission and accumulating lipids with high productivity. This article is an overview of the updated status of biofuels, especially biodiesel production from microalgae including fundamental research, culture selection and engineering process development; it summarizes research on mathematical and life cycle modeling on algae growth and biomass production; and it updates global efforts of research and development and commercialization attempts. The major challenges are also discussed.

Biofuel, feedstock, lipid, microalgae, modeling

Introduction Great interest has been shown in the development of renewable, clean and sustainable energy sources including biofuels. Among biofuels, biodiesel has been applied widely in recent years. Microalgae have been proposed as the third and fourth generation biodiesel feedstock (Brennan & Owende, 2010; Lu¨ et al., 2011). Microalgae are classified as prokaryotes (cyanobacteria) and eukaryotes, with about 200 000–800 000 species, with over 40 000 species that have already identified, including green algae, diatoms, yellow– green algae, golden algae, red algae, brown algae, dinoflagellates and others (Hu et al., 2008; Richmond, 2004). As an energy source, microalgae are superior over other feedstocks based on their productivity footprint (Chisti 2007; Mata et al., 2010). Biodiesel productivity of microalgae is estimated from 52 000–121 000 kg ha1 yr1, and this is much greater than that of soybean (562 kg ha1 yr1) and other feedstocks. The potential of microalgae as a bioenergy source is considered to be exponential, and there have been several optimistic estimates. If algae fuel replaced all of the petroleum fuel in the United States, it would require 15 000 square miles or 39 000 km2 for algae farming, which is only 0.42% of the US map (Hartman, 2008) and which is less than one-seventh of the area of corn harvested in the United States in 2000. In 2011, a study by Pacific Northwest National Laboratory (PNNL) of the US Department of Energy (DOE)

Address for correspondence: Wei-Min Wu, Department of Civil and Environmental Engineering, Center for Sustainable Development and Global Competitiveness, Stanford University, Stanford, USA. Tel: 1-650724-5310. E-mail: [email protected]; Wen-Biao Jin, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, P. R. China. Tel: 86-755-26033512. E-mail: [email protected]

History Received 8 May 2013 Revised 13 July 2013 Accepted 12 August 2013 Published online 18 March 2014

indicated that 17% of the United States imported oil (79.5 billion liters) for transportation could be replaced with domestic grown biofuels from algae in strategic locations, including the Gulf Coast, the Southeastern Seaboard and the Great Lakes ( Zhang et al. (2012) estimated that the annual potential energy production from algae in total marginal land of China is 4.19 billion tons standard coal equivalent, far more than the total annual energy consumption equivalent in China in 2007. However, these claims remain unrealized and still have to be proven commercially. Other potential advantages of biodiesel from microalgae are the following: (a) the production of algal oil does not have adverse effects on traditional agriculture because it is not competitive with food crops for arable land; (b) the algae can grow in various environments, such as seawater; (c) the algal growth removes CO2 and phosphorous and can be helpful in wastewater treatment (Abdel-Raouf et al.,2012; El-Sheekh et al.,2005; Harun et al., 2010); and (d) the algal biomass can also be used for production of a broad range of biofuels including methane, hydrogen and syngas (Hu et al., 2008; Li et al., 2008). A number of excellent review papers and book chapters have been published previously on microalgae as a biofuel and bioenergy source (Ahmad et al., 2011; Brennan & Owende, 2010; Chen et al, 2011; Chisti, 2007, 2008; Gouveia 2011; Gouveia & Oliveira 2009; Huang et al., 2010; Lu¨ et al., 2011; Mata et al., 2010; Milledge, 2011; Singh & Dhar 2011; Smith et al., 2010). This article’s goal is to update progress on the fundamentals, engineering processes for microalgae fuel, global R&D efforts and attempts at commercialization. Especially, the work on mathematical and life cycle models will be reviewed, as it has not been summarized previously in the above review articles.


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Table 1. Lipid content and productivities of some lipid-rich microalgae species.

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Microalgae species Chlorella protothecoides Chlorella sorokiniana Chlorococcum sp. Dunaliella salina Ellipsoidion sp. Microcystis aeruginosa NPCD-1* Nannochloris sp. Nannochloropsis sp. Neochloris oleoabundans Pavlova salina Scenedesmus sp. Scenedesmus obliquus X5-H13y

Lipid content (%, w/w)

Productivity of biomass (g L1d1)

Lipid productivity (mgL1d1)

14.6–57.8 19.0–22.0 19.3 6.0–25.0 27.4 28.1 20.0–56.0 12.0–53.0 29.0–65.0 30.9 19.6–21.1 46.9

2.00–7.70 0.23–1.47 0.28 0.22–0.34 0.17 0.0469 0.17–0.51 0.17–1.43 – 0.16 0.03–0.26 0.127

1214 44.7 53.7 116.0 47.3 13.2 60.9–76.5 37.6–90.0 90.0–134.0 49.4 40.8–53.9 59.8

The data from Mata et al. (2010) except for those with makers * and y. *Da Ro´s et al. (2012). yJin et al. (unpublished data).

Microalgae R&D progress Selection of promising strains The research on microalgae as a potential fuel source has been conducted since the 1940s as described in detail in Supporting Information (SI). Selection of high lipid-containing algal species has been a primary target since 1990s. Microalgae contain several kinds of lipids, most of which are extractable. However, only triglycerides (TAGs) are easily transesterified into biodiesel. The lipid content in different strains varies, ranging from 1% to approximately 85% of dry cell weight (Li et al., 2008). The carbon number of fatty acids (FAs) in algal TAGs ranges from C10 to C24, depending on the species or strains (Hu et al., 2008). Most blue algae Microcystis in phylum of Cyanobacteria, which causes harmful algal blooms in lakes and rivers, have a low lipid content (52%) and are not considered as target cultures except for non-microcystin producing Microcystis aeruginosa NPCD-1, which has a biomass productivity of 0.0469 g L1 d1, with a lipid content up to 28.1% (Da Ro´s et al., 2012). The lipid content and FA composition of the individual strains are also influenced by growth and environmental conditions (Khozin-Goldberg & Cohen, 2006). Therefore, the selection of oleaginous microalgal strains for biodiesel production is focused not only on lipid content or TAGs content but also on growth conditions. In the United States, most promising strains are those identified via the US DOE Aquatic Species Program (ASP) in the 1990s. Mata et al. (2010), Chen et al. (2011) and others have reviewed lipid content and productivities of different microalgae species in detail. Several microalgal species with high lipid productivities are listed in Table 1. The algal species studied for mass-production of biofuel with high lipid content (% dry weight) include Botryococcus braunii (25– 75%), Chlorella (18–57%), Dunaliella primolecta (23%), Nannochloropsis sp. (31–68%) and others (Chisti 2007; Mata et al., 2010). Heterotrophic microalgae Chlorella protothecoides has the highest lipid content and biomass productivity, and Chlamydomonas is used as a reference organism for understanding algal triacylglycerol accumulation (Merchant et al., 2012). Other than lipid productivity and lipid quality, the selection criteria also include the adaptation to the local

environment and their capability of using nutrients, environmental and economic benefits and the production of by-products with commercial value as described in detail recently by Gong & Jiang (2011). In the authors’ laboratory, after changes in growth conditions, a lipid yield of Scenedesmus obliquus of up to 36.7 mg L1d1 was achieved, which was higher than that of same microalgae species growing in wastewater and reported previously (Table 1). By using UV irradiation, a mutant strain of S. obliquus strain X5-H13 was obtained (Figure 1). The growth rate of the mutant strain was faster by 21% with a lipid content of 46.9% versus 36.8% in comparison with the wild strain. When the mutant was grown on sewage, lipid production was 59.8 mg L1d1 (Jin et al., unpublished data). Microalgal growth Effects of growth conditions Factors influencing growth include temperature, light, CO2, available nutrients, salinity, etc., and these factors also influence microalgal lipid production. Most oil-producing microalgae have an optimal growth temperature of 25–30  C. Selection of algal strains grown at high temperatures, such as 40  C, may be needed, for example, in South China, Thailand, etc. The lipid content of microalgae is strongly influenced by temperature and depends on the strains. An increase in temperature from 20 to 25  C practically doubled the lipid content of Nannochloropsis oculata (from 7.9 to 14.9%), while an increase from 25 to 30  C caused a decrease in the lipid content of Chlorella vulgaris, from 14.71 to 5.90% (Converti et al., 2009). Light is the strongest factor affecting growth and storage products of microalgae. Low light intensity causes a reduction in dry weight, while high intensity causes biochemical damage to the photosynthetic machinery or photoinhibition (Scott et al., 2010). Under most incubation conditions, especially with relatively high cell concentration, the light is limited without photoinhibition. The higher the light intensity applied (from 35 to 400 mE m2 s1), the higher fatty acid and arachidonic acid content in the cells of the microalgae Parietochloris incisa (Solovchenko et al., 2008). Similarly, the microalgae B. braunii had the highest lipid

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DOI: 10.3109/07388551.2013.835301

Biofuel production from microalgae as feedstock


Figure 1. Effect of UV on selection of mutant strain of Scenedesmus obliquus. (A) Wild strain. (B) Scenedesmus obliquus strain X5-H13 (right corner showing microscopic picture of the mutant).

yield of 0.45 g L1 as the intensity tested was increased from 0.3 to its highest 538 mE m2 s1 (Ruangsomboon, 2012). The results also suggested that a relatively high light intensity limited algal growth, but favored an increase in the lipid content and yield. The production of excessive photoassimilates, which can then be stored in the form of lipid, is probably a means to convert excess light to chemical energy in order to avoid photooxidative damage (Solovchenko et al., 2008). The effect of wavelength on the growth of B. braunii indicated that blue light and red light were more effective for growth, photosynthetic CO2 fixation and hydrocarbon production than the green light, and that the red light is the most efficient light source (Baba et al., 2012). A slightly higher lipid content was found in the blue light and red light grown cells. Nitrogen deprivation limits protein synthesis and thus increases the lipid content in Chlorella and Nannochloropsis strains (Converti et al., 2009; Illman et al., 2000). A strong increase in total FA content was observed in a P. incisa culture under nitrogen starvation (Solovchenko et al., 2008). Lipid production by microalgae isolated from freshwater sources in Thailand slightly declined in a nitrogen-rich medium (Yeesang & Cheirsilp, 2011). However, the effect of nitrogen was not significant on the growth of the microalgae B. braunii with KNO3 ranging from 16.5 to 344 mg N L1 (Ruangsomboon, 2012). Slightly high phosphorus concentrations appears to help the growth of biomass, but the impact on lipid content is not clear. A maximum lipid content of 54.7% and yield of 0.47 g L1 was found at 222 mg P L1 when B. braunii was cultivated in media containing phosphorus from 22 to 444 mg L1 (Ruangsomboon, 2012). The effect of iron concentration appears to be algae strain dependent. A negative effect on the growth of green microalga strain KB, identified as Botryococcus sp., was reported (Hu, 2004; Yeesang & Cheirsilp, 2011). No significant effect on biomass and lipid content was observed with B. braunii (Ruangsomboon, 2012). An increase in salinity might result in a slight increase in the lipid content of some algae (Ben-Amotz et al., 1985; Hu, 2004) but had no effect on B. braunii (Ruangsomboon, 2012; Yeesang & Cheirsilp, 2011). Recently, research is focused on algae cultivation with wastewater as the medium (Li et al., 2011; Sydney et al., 2011; Wu et al., 2012a). Utilizing N and P sources in

wastewater for algae growth could therefore reduce the cost of the substrate and also help in the removal of N and P in wastewater to prevent eutrophication. More detailed information on wastewater as a resource is provided by Abdel-Raouf et al. (2012) and Olguin (2012). Cultivation conditions Cultivation conditions can be divided into four major types based on their different energy and carbon sources: photoautotrophic, heterotrophic, mixotrophic and photoheterotrophic (Chen et al., 2011). To date, photoautotrophic cultivation is the most frequently used cultivation condition for microalgae production (Yoo et al., 2010). Phototrophic cultivation means that the microalgae use light, such as sunlight, as the energy source and inorganic carbon (e.g. CO2) as the carbon source through photosynthesis (Huang et al., 2010). When microalgae use organic carbon as both an energy and carbon source, the cultivation is heterotrophic (Chojnacka & Marquez-Rocha, 2004). The oil productivity via phototrophic cultivation is usually much lower than that of heterotrophic cultivation, due to slow cell growth. However, the low cost and favorable environmental effects (i.e. removal of CO2 from off gas of factories or power plants) make it a promising and attractive option. Heterotrophic conditions could offer better oil productivity than phototrophic cultivation conditions, by avoiding the problems associated with light limitations, which hinder high cell density in large scale photobioreactors (PBRs) during phototrophic cultivation (Huang et al., 2010). The highest lipid productivity from heterotrophic cultivation could be nearly 20 times of that obtained under phototrophic cultivation, e.g. biomass productivity of 2.00–7.70 g L1d1 by C. protothecoides (Table 1). However, heterotrophic algal cultures can easily be contaminated by bacteria or non-lipid producing algae, especially in open cultivation systems, resulting in failures in lipid production at the large-scale. In addition, the cost of supplementing an organic carbon source is also a major concern from a commercial aspect. The prevention of contamination and use of low cost organics are still a challenge for application. On the other hand, the contamination problem is less severe when using autotrophic growth. Outdoor scale-up microalgae cultivation systems (such as open ponds or raceway ponds) are usually operated under phototrophic cultivation conditions (Mata et al., 2010).

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S.-F. Han et al.

Growth attempts for heterotrophic algae have been tested in bioreactors with wastewater and organic by-products. Chen & Walker (2011) reported on the biomass and lipid production of C. protothecoides by using biodiesel-derived crude glycerol. In a batch mode, the biomass and lipid concentration of the cells cultivated in a crude glycerol medium were 23.5 and 14.6 g L1 on the 6th day and reached 45.2 and 24.6 g L1, respectively, after 8.2 days. With a fedbatch mode, the maximum lipid productivity reached 3 g L1 d1. This work demonstrates the feasibility of a crude biodiesel glycerol, which is product of transesterification during the biodiesel process, as a carbon resource. Mixotrophic cultivation is when microalgae undergo photosynthesis and use both organic compounds and inorganic carbon (CO2) as carbon sources for growth (Chen et al., 2011). This requires that the selected microalgae are able to live under either phototrophic or heterotrophic conditions, or both. Photoheterotrophic cultivation is defined as the microalgae requiring light when using organic compounds as the carbon source (Chen et al., 2011). The main difference between the two cultivations is that the latter requires light as the energy source, while the former can use organic compounds to serve this purpose (Chojnacka & MarquezRocha, 2004). However, both of the above cultivations are rarely used in microalgae oil production, because of contamination risk and light requirements (Chen et al., 2011). Approaches for enhancement of lipid productivity Different approaches have been tested to enhance lipid productivity. One practicable way is to optimize the cultivation conditions as described in ‘‘Effects of growth conditions’’ section. Efforts to change the cell’s metabolism have also been conducted via genetic engineering (Lu¨ et al., 2011). The recombinant DNA technique for algae has proven to be capable of creating constructs for both prokaryotes and eukaryotes, which can replicate and possess novel functions. Algal metabolic engineering also involves targeted improvement of cellular activities by manipulation of enzymatic, transport and regulatory functions of the photosynthetic cells using other biological and engineering approaches. Using recombinant DNA technology to improve the production of biodiesel has been tried recently, but with little effect. Radakovits et al. (2010) reviewed different methods of genetic engineering. However, more research is still needed to verify the effectiveness of this approach.

Microalgal farm and biodiesel refinery Microalgal biomass production facility To date, two typical devices have been used at the pilot and large scale, i.e. open ponds and PBRs. Each of them has advantages and limitations throughout the production process. Several review papers have reported on these two reactor configurations in detail (Chisti, 2007; Gong & Jiang, 2011). Open ponds or raceway ponds An open oxidation pond has been used for wastewater treatment, but it is not designed for algae production. An open raceway pond shaped like a horse track is a continuously

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operated, closed-loop recirculation channel used to cultivate algal biomass (Chisti, 2007). Raceway ponds for microalgae cultivation have been in use since the 1950s and are used to produce nutraceutical algae, e.g. Spirulina as their primary method of cultivation. More than 440 000 m2 (44 hectares or 108.7 acres) of ponds are in use globally (Spolaore et al., 2006). Production of microalgae fuel has been evaluated in studies of open-channel raceways sponsored by the US DOE (Sheehan et al., 1998). A typical raceway pond is 0.1–0.3 m deep (James & Boriah, 2010). The system is built as individual ponds or as groups of ponds arranged in a series connection (Shen et al., 2009). The raceway is constructed of concrete or compacted earth and may be plastic lined to mitigate seepage losses. A paddlewheel is generally used to recirculate the biomass, nutrients and water. Mixing is achieved through a combination of the effects of the paddlewheel and the interaction of the flowing water with the bottom and sides of the raceway. The flow rate and the depth of the raceway affect the distribution of nutrients, and these factors should be regulated to maintain the algal suspension and for mixing. Often, the algal culture is fed ahead of the paddlewheel and is harvested behind the paddlewheel upon completion of the circulation loop. CO2 can be bubbled through the system to improve aeration and mixing, increase CO2 consumption and enhance algal biomass growth. However, the drawbacks are that open ponds or raceways cannot be sterilized or kept under axenic conditions (Packer, 2009), and natural sunlight is affected by daily and seasonal fluctuations (Chisti, 2008) making it difficult to control contamination and cultivation conditions such as light intensity and temperature, which results in relatively low productivity. Because algae strains with lower lipid content may grow as much as 30 times faster than those with a high lipid content (Becker, 1994), the prevention of contamination of unfavorable algae strains has been an operational challenge. Moreover, accounting for the shallow water depths that are needed to provide sufficient sunlight for most microalgal cells and low mass density, the costs of land use and the harvesting of cells are increased. Photobioreactors The PBR is closed system used to cultivate algae in bags, tubing or other transparent materials that are not exposed directly to the atmosphere (Shen et al., 2009). Because algae are grown in a closed system, culture conditions are easier to control and there is less contamination and evaporative losses when compared with open pond systems, and PBR has much higher biomass productivity (Posten & Schaub, 2009). Flat-plate, tubular and column bioreactors are the three main types of PBR. In addition, a fiber PBR is highly effective but too expensive (Javanmardian & Palsson,1991). PBR includes a light system, temperature control, pH control, CO2 supply and O2 removal (Chisti, 2008). Moreover, mixing is conducted with a mechanical pump or airlift pump. The major drawbacks of PBR, as discussed by Gong & Jiang (2011), are the high costs of construction, operating and maintenance. A successful PBR system should be inoculated with algal strains with a high lipid content, a fast growth rate, that are

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not too difficult to harvest, and that has a cost-effective reactor configuration, as well as a source to provide concentrated CO2 to increase the rate of production.

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Hybrid system Recently, a hybrid system comprised of a PBR and open ponds was proposed as a promising option. Sufficient contaminant-free inocula can be produced by PBR, followed by transferring to open ponds or raceway ponds to attain the biomass needed for biodiesel production (Gong & Jiang, 2011). Bioalgene Inc., a Seattle US-based energy company, said that they prefer utilizing closed reactors, such as PBR, as nurseries to grow inoculation strains in pure form before introducing them to open ponds for mass production. In August 2012, the National Algae Association (NAA) of the United States announced a new commercial production system, which eliminated contamination and low production issues found with raceway ponds, while reducing the high cost of commercial closed-loop PBR. However, no details have been published about the system. Biomass harvest and oil refinery The harvest of algae biomass, cell dewatering, extraction of lipids and conversion of lipids to biodiesel are all very important procedures in algal biofuel production and can contribute 40–60% of cost for biodiesel. An excellent review focused on this aspect was recently published by Gouveia (2011). It is only briefly discussed below. Harvest of algal biomass from open ponds is completely different from PBR. Microalgae can be harvested by sedimentation, flotation, filtration and centrifugation from open ponds. Flotation is a widely used technique for algal cell harvest (Chen et al., 1998; Chen et al., 2011). Other methods include filtration with rotary drum precoat filtration and press filters (Gong & Jiang, 2011). Harvesting of microalgal biomass is a costly process, accounting for approximately 20–30% of the total cost (Gong & Jiang, 2011). More research will likely be needed to develop a cost-effective approach. Different methods have been reported for extracting lipids from dewatered microalgal biomass, including presses, solvent extraction, enzymatic extraction and other methods used only at a laboratory scale (osmotic shock, supercritical CO2 extraction, microwave technology, etc.). Prior to the extraction, the cells must be dried via sun drying, drum drying, spray drying, fluidized bed drying, freeze drying or using refractance window dehydration technology (Gong & Jiang, 2011). The use of solvents (such as hexane and chloroform) is a quick and efficient commercial method for the extraction of lipids from whole cells. The technique of converting extracted microalgal oil or lipid to biodiesel uses a transesterification process, which is one of the most common processes for producing biodiesel from vegetable oils, animal fat and microalgal oils (Demirbas, 2002; Schuchardt, et al., 1998). Transesterification is a multistep, consecutive reaction, where TAGs are usually reacted with methanol (methanolysis) and converted to diglycerides, monoglycerides in the added catalysts, resulting in a yield of the corresponding fatty acid methyl ester (FAME) as biodiesel and with glycerol as by-product (Supplementary Figure S1).

Biofuel production from microalgae as feedstock


A review paper by Lee et al. (2009) summarizes the transesterification process and catalysts related to biodiesel synthesis. During recent years, most of the research on transesterification has focused on the development of costeffective catalysts and on the enhancement of FAME recovery efficiency (Kouzua et al., 2008; Lee et al., 2009; Li et al., 2007). In China, transesterification of algal oil is also performed with ethanol and sodium ethanolate serving as the catalyst. An integrated approach of biodiesel production from heterotrophic C. protothecoides focusing on scale-up in bioreactors was reported by Li et al. (2007). The cell density of C. protothecoides achieved 15.5 g L1 in 5 L to 14.2 g L1 in 11 000 L bioreactors with a lipid content of 46.1% and 44.3%, respectively. With immobilized lipase and a 3:1 molar ratio of methanol to oil, 98.15% of FAME was achieved. The expanded biodiesel production rates were 7.02 g L1 and 6.24 g L1 in 5 L and 11 000 L bioreactors, respectively. The properties of the biodiesel complied with the US Standard for Biodiesel (ASTM 6751). The results suggest feasibility to expand heterotrophic Chlorella fermentation for biodiesel production at an industry level.

Models for microalgal growth, reactor process and life cycle Development of proper models to describe microalgal growth provides a better understanding of the interaction of algal biofuel production with the environment and is quite useful for designing an efficient bioreactor, predicting process performance and optimizing operating conditions (Aiba, 1982; Ever, 1991). Models for life cycle assessment (LCA) provide the information about technology, economics and sustainability. Growth rate with substrate uptake The growth kinetics of algae is similar to most photosynthetic microorganisms (Aiba, 1982). The kinetic model for bacterial growth developed by Monod (1950) has been fitted to algal growth by many researchers since 1970s. The Monod equation is expressed as ¼

max S Ks þ S

where,  is specific growth rate, max is maximum specific growth rate, S is substrate (phosphorus, nutrient or carbon source) and Ks is saturation constant. After studying a wide variety of taxa and nutrients including phosphorus, silicon and carbon sources, the growth of algae in fresh water and sea water has for years been deemed as following Monod kinetics (Goldman & Carpenter, 1974; Grover, 1989). The simplest model only takes initial cell concentration into account (Blanchard et al., 2001; Zeng et al., 2001). The kinetic parameters of algal growth have been described by several researchers. Grover (1989) measured phosphorus-dependent kinetics of 11 species of freshwater algae at 12  C. The maximum growth rate (max ) for known lipid-rich species, including Chlorella sp., Scenedesmus sp. and Nitzschia sp., were relatively high, ranging from 0.63 to 0.88 d1, while also consisting of Ks as low as 0.002 to

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0.047 mM. Kwon et al. (2013) found that the Ks values of Nitzschia sp. and other three microalgae were around 8 mM for nitrate and 3–4 mM for phosphate at blue, yellow, red and mixed wavelength. In general, the low Ks values for P, NO 3 and NHþ -N reported are at a micromolar level, suggesting 4 that these nutrients may not be rate-limiting factors during biomass production with nutrient supply. However, microalgal growth limited by phosphorus may not always be in accordance with the Monod model, as many microalgae can grow using intracellular phosphorus (Droop, 1983; Wu et al., 2012b,c). Lin et al. (2003) developed a kinetic model to describe inorganic carbon utilization and cell synthesis by microalgae biofilms in a laboratory flat plate PBR. The microalgal biofilm grows upon utilization of the inorganic carbon. The growth rate can be expressed as   dX YkS ¼ b X dt Ks þ S where S is the concentration of inorganic carbon, X is the concentration of microalgae, Y is the yield coefficient of microalgal biofilm and b is the decay coefficient of microalgal biofilm. To use this model, the initial microalgal biofilm thickness must be assumed to be a small, yet reasonable value, in order to simulate the growth. Hilaly et al. (1994) developed a model for industrial microalgae fermentation including substrate and inhibition products as S Kt  ¼ m  ðKS þ S þ S3 Þ Kt þ Ct2 where mm is the maximum specific growth rate; S is substrate concentration; Ks and Kt are saturation constant; and C is the inhibition product. General growth model without nutritional limits When the nutrient supply (such as N, P, C and other trace elements) is not limited, the algal growth is dependent upon parameters such as cell concentration, growth duration, light intensity, temperature, etc. The growth rate versus algae cell concentration A generalized logistic curve can model the ‘‘S-shaped’’ behavior (abbreviated S-curve) of growth of some populations and is used to describe dynamic changes in the number of microalgae cells or algal biomass, which approaches a limit (Supplementary Figure S2). Blanchard et al. (2001) analyzed the dynamic behavior of a microphytobenthic biomass in a European intertidal mudflat, comparing field and laboratory measurements and described it as XðtÞ ¼

X e m t  0 1  XXm0 ð1  em t Þ

where X0 is the concentration of cells seeded, Xm is the maximum cell concentration and t is time. In an experimental mesocosm, where the effects of grazing by deposit-feeders and resuspension by tides had been significantly decreased, the benthic microalgal biomass followed a logistic-type growth curve, and thus converged toward a maximum value

at which production was theoretically equal to zero. Based on a logistic model, Li (1999) used the data from cultivation of Spirulina in a 40 L airlift inner-loop PBR to characterize the parameters for the logistic growth kinetic model as Cx ðtÞ ¼

Cxo  e0:0464t  Cxo 1  1:628 ð1  e0:0464t Þ

where, C is the algal cell concentration. Experimental results showed that the overall relative average error of the model was less than 8%. Another generalized logistic curve or function – also known as Richards’ curve – is a widely used and flexible sigmoid function for growth modeling. It is also used for the description of algal growth as 1

x ¼ Að1  B expkt Þ1m where, X is the algal concentration; A is growth limit parameter; B is cell initial growth parameter; m is alienation or degradation rate parameter; and t is time. Zeng et al. (2001) used this model to describe the relationship between a Spirulina cell concentration and the cultivation time in a batch culture, in a 15 L airlift inner-loop PBR. The experimental data fitted to the model with the method of least squares with correlation coefficient R2 ¼ 0.9996 as 1

x ¼ 2:583ð1  0:0745 exp0:026t Þ10:992 Based on the data, the growth curve included the stagnation phase, exponential growth phase and stable growth phase. The growth rate versus light intensity Light intensity is a parameter significantly influencing algal growth and accumulation of lipids. When the nutrients are not limited, the growth of microalgae depends on the light conditions. Several models have been proposed and are divided by containing the term of photoinhibition. Under light-limiting condition, photoinhibition of algal growth can be ignored. Models with a term of average irradiation generally do not include photoinhibition. Tamiya (1951) developed a model as ¼

m I m þ I

where,  is a specific growth rate; m is the maximum rate; and I is average irradiance inside the culture. Van Oorschot (1955) and Steele et al. (1977) proposed respective alternatives without photoinhibition as    I  ¼ m 1  exp  Im  and  ¼ m

  I 1 exp 1  Im Im

where Im is the maximum irradiance. Grima et al. (1994) developed a model similar to the Monod equation for lightlimited continuous growth of microalgal culture as ¼

n max Iav n n Ik þ Iav

Biofuel production from microalgae as feedstock

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DOI: 10.3109/07388551.2013.835301

Figure 2. Growth model as function of incident light intensity. Adapted from the figure and source of Zhang (2009).

where,  is specific growth rate; Iav is average irradiance inside the culture; Ik is irradiance constant; and n is characteristic parameter of the hyperbolic growth model. Zhang (2009) studied the relationship between growth rate of Synechococcus and light in a 1.5 L airlift glass PBR, and used the Tamiya-Grima model to describe batch culture growth with characterized parameters and R2 ¼ 0.998 (Figure 2) as ¼

0:0553 I 1:21 0:7051:21 þ I 1:21

When incident intensity was o.4  4.5 klx, the specific growth rate increased with the light intensity increase. However, when the light intensity further increased, the rate of growth slowed down significantly. Models have been also developed with the term of photoinhibition. Bannister (1979) developed a model for algal growth under nutrient-saturated, steady state condition as ¼

m I ðKin


þ I n Þn

where, Kin is constant with photoinhibition and n is inhibition factor. A more widely cited model is that developed by Aiba (1982), using the data from experiments of the effect of solar light intensity on photosynthetic activities of 14 species of planktonic marine algae including Chlorophyta, Diatoms and Dinoflagellates. The specific growth rate related to light intensity as ¼

m Iav 2 KI þ Iav þ Ki Iav

where,  is specific growth rate; Iav is average irradiance inside the culture; KI is photoaffinity coefficient; and Ki is photoinhibition coefficient. Xu et al. (2001) used this model to simulate the specific growth rate of Spirulina in relation to the average light intensity in a batch culture in a laboratory plate PBR to fit the data by the method of least squares, obtaining ¼

1:136Iav 2 238:29 þ Iav þ 0:00493Iav


Figure 3. Specific growth rate versus light intensity described by Monod equation and Aiba model in comparison with experimental data. Adapted from the figure and data of Xu et al. (2001). The data fits Monod model at low light intensity but fits Aiba model at higher light intensity.

The correlation did not fit well (R2 ¼ 0.8653), especially in a low light intensity range. As shown in Figure 3, when the light intensity was below saturation, the relationship between specific growth rate versus the intensity was closer to the Monod model; and when the light intensity was greater than the saturation, the relationship fit the Aiba model well (Figure 3). Further modification of the model may be needed. Multiple parameter models Attempts to develop a model for the effect of multiple parameters on algal growth have been conducted. Based on the fact that the growth rate is dependent on the average irradiance (Iav) and varies with the incident irradiance level (Io), a model was developed with an outdoor culture of Phaeodactylum tricornutum strain UTEX 640, by using airlift-type tubular PBR, with an external-loop solar receiver (Ferna´ndez et al., 1998) as ðn2þ n3 Þ max  Iav Iwm ¼ n3  n1 ðn2þIwm Þ ðn2þ n3 Þ 0 Iwm I k þ K1 þIav Iwm where  is specific growth rate, Iav is average irradiance inside the culture, Iwm is mean daily photosynthetic irradiance inside the water pond, max is the maximum specific growth 0 rate (which was 0.063 h1 based on the test results), Ik is 2 1 specific irradiance constant (94.3 mE m s ), K1 is photoinhibition constant (768.4 mE m2 s1), n1, n2, n3 are characteristic parameters of photolimitation–photoinhibition, n1 ¼ 3.04, n2 ¼ 1.209 and n3 ¼ 514.6. The data fitted well with the model with R2 ¼ 0.9945. Del Rio et al. (2005) reported a growth model incorporating the effect of both irradiance and nitrate uptake for the growth of a culture of Haematococcus pluvialis in continuous photoautotrophic condition as   NNmax n  Iav KN þN  ¼ n n a þ Nb þIav


S.-F. Han et al.

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where, KN is the saturation constant for nitrate; Nmax is the specific growth rate independent of nitrate supply; a, b are adjust parameters; N is nitrate concentration;  is specific growth rate; Iav is average irradiance inside the culture; and n is characteristic parameter of the hyperbolic growth model. According to the model, the specific growth rate increases with the increase in light intensity and nitrate concentration. Li & Wang (2010) constructed a growth model with the explicit incorporation of light and nutrient availability to characterize both carbon and phosphorus limitations and analyzed how light and nutrient availability regulated algal dynamics. The effect of temperature on growth rate The algal growth rate is temperature dependent. In general, as temperature increases, algal growth rate increases, reaches an optimal level and then decreases rapidly. The temperature coefficient (Q10) represents the factor by which the growth rate increases for every 10  C rise in the temperature. Most models were developed empirically except those based on the Arrhenius equation. It remains widely debated whether the relationship between the growth rate and temperature is exponential or linear before it reaches optimal level, with no conclusive evidence in support of either model (Montagnes & Lessard, 1999; Raven & Geider, 1988). One exponential equation was developed by Eppley (1972) as  ¼ n  AT in which  is specific growth rate, day1; n and A are constants; and T is temperature ( C). Eppley (1972) used this model to plot data compiled by Hoogenhout & Amesz (1965), for growth rate versus temperature (540  C), from a large number of batch culture studies of freshwater and marine algae growing photoautotrophically with CO2 and water in the laboratory. The results were n ¼ 0.851 and A ¼ 1.066 (i.e. Q10 of 1.88). This model has been a highly cited empirical equation. Another exponential model was developed based on the Arrhenius equation ^ ¼ AeE=RT where A is a constant, d1; E is activation energy, cal mole1; R is universal gas constant, 1.987 cal  K1 mole1; and T is temperature ( K). Goldman & Carprenter (1974) used the Monod equation to analyze the growth kinetics of  3 12 strains of fresh algae with NHþ 4 , NO3 , PO4 and carbon as the substrate at different temperatures and suggested that when light intensity was held constant, the maximum growth rate was solely a function of temperature, by applying the Arrhenius equation. After analyzing the data obtained from continuous culture studies with marine and freshwater algae, the equation obtained was ^ ¼ ð5:35  109 Þe6472=T with Q10 of 2.08. Linear equations have also been used to describe the effect of temperature on algal growth. Montagnes & Franklin (2001) acclimated eight diatoms for approximately five generations and then cultivated them for approximately five more generations at five different temperatures from 9 to 25  C

with a 14:10 light:dark cycle at  50 mmol photons m2 s1 in glass flasks. The specific growth rates of the diatoms with the increase in temperature showed a linear relationship in all cases, i.e. ¼aT þc where,  is the growth rate, T is temperature, and a and c are constants. Same observation was also found in the previous experiments with Strombidinopsis multiauris (Montagnes & Lessard 1999). Based on the view that many biological processes are probably controlled by not only biochemical processes but also by physical processes such as diffusion and viscosity, Be˘lehra´dek (1926) proposed an empirical equation  ¼ aðT  Þb where  is the growth rate, a, b and  are constants and T is temperature. The exponent b is mostly between 1.0 and 3.0. Ahlgren (1977) used this model to describe the results of the growth of the green alga Scenedesmus quadricauda under batch, P-limited chemostat conditions at T between 3 and 25  C and found that Be˘lehra´dek’s equation gave the best fit to the  values (R2 ¼ 0.945, n ¼ 18) compared with Eppley (1972) and other empirical equations as  ¼ 0:0307ðT  0:18Þ1:13 However, it is not clear whether this empirical equation fits the growth of other algae or if it only works in a specific case. Algae growth in an open-channel raceway pond Raceway ponds have been widely used for algal growth and biomass production for years. James et al. (2010) used modified versions of the US Environmental Protection Agency’s Environmental Fluid Dynamics Code, in conjunction with the US Army Corp of Engineers’ water-quality code, to simulate the hydrodynamics coupled to the growth kinetics of algae (P. tricornutum) in an open-channel raceway pond. The governing equation for algal biomass growth is     B ¼ P  BM  PR  Ws, B t z where B is the algal biomass expressed as carbon equivalents (g C m3), P is the production rate (d1), BM is the basal metabolism rate (d1), PR is the predation rate (d1) and ws is the settling velocity (m day1). The biomass production rate was determined by the availability of nutrients, the intensity of light and the ambient temperature. The effect of each is multiplicative P ¼ PM f ðNÞgðIÞhðTÞ where PM is the production under optimal conditions (d1), f(N) is the effect of non-optimal nutrient concentration (0  f (N)  1), g(I) is the effect of non-optimal illumination (0  g(I)  1) and h(T) is the effect of non-optimal temperature (0  h(T)  1). Nutrients are assumed to be in excess

Biofuel production from microalgae as feedstock

DOI: 10.3109/07388551.2013.835301

(i.e. f(N) ¼ 1). The formulation of the correction due to non-optimal illumination was derived from Steele’s equation

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f ðIÞ ¼

I 1II e s Is

where, I is the instantaneous illumination rate (W m2) and Is is the optimal illumination rate (W m2). Algal production increases as a function of light intensity until an optimal intensity is reached, and beyond that optimal value, production varies in accordance with the type of light source. The effect of temperature is assumed to be an exponential variation for limitation based on Cossins & Bowler (1987). The model can manipulate a host of variables associated with raceway-design, algal-growth, water-quality, hydrodynamic and atmospheric conditions and provides the results wherein growth rates follow the diurnal fluctuation of solar irradiation and temperature. The numerical simulation of the flow system can help the design of the raceway before construction and evaluates the impacts of various changes to system conditions (e.g. depth, temperature and flow speeds). Sompech et al. (2012) used computational fluid dynamics (CFD) modeling to characterize the energy demands for mixing full-scale raceways of various configurations. The locations of the dead zones and the conditions required for eliminating them were identified. They compared existing geometric configurations of the raceways to identify the best configurations and found that an inexpensive raceway configuration with a minimum of three semicircular deflector baffles and a modified end of the central divider was the most energy efficient, while also being able to completely eliminate the dead zones. Algae growth in PBR Models have been developed for PBR design, based on both the kinetics of the photochemical reaction and the distribution of light intensity in the reactor from the Lambert– Beer law for homogeneous photochemical systems. Koizumi & Aiba (1981) developed the first model for a PBR to grow Rhodopseudomonas spheroides, a kind of purple bacteria, which can obtain energy through photosynthesis. Additional substrate (acetate) was supplied as the carbon source in this model. Since an algae bioreactor or PBR is used for cultivating algae on purpose to fix CO2 and produce biomass, CO2 is added to the PBR. Except for the parameters described above, a model describing PBR should contain kinetics of mass transfer of CO2 and CO2 uptake as a substrate. To date, three typical PBRs have been used at a pilot scale, i.e. plate, tubular and bubble column PBR. A PBR model should incorporate the parameters related to the reactor configuration and hydraulic characteristics. Wu & Merchuk (2004) simulated the growth of the red marine alga Porphyridium sp. in an internal loop airlift (ALR) PBR. The model integrated a dynamic formulation of the kinetics of photosynthesis, photoinhibition and the fluid dynamics, including shear stress effects on the kinetics of growth, with the kinetic parameters obtained from a system under defined light/dark cycles. The maintenance term was


modified to take into account the detrimental effects of shear stress in the bioreactor on the rate of growth. This model can be used to predict the effect of gas flow rate, column height, column diameter and cross-sectional areas on the productivity of a photosynthetic process in an airlift bioreactor, as well as the optimal diameter for an assembly of ALR PBR. More detailed microalgae growth modeling for ALR PBR was described by Popova & Boyadjiev (2008). The CO2 distribution in the liquid phase was simulated using a convection–diffusion equation with volume reaction and the substrate (CO2) utilization that followed the Monod equation. The model predicated kinetic parameters under different superficial gas velocity at 1.944, 5.76 and 11.88 u m1 h–1 and accorded well with the experimental data with the microalgae Porphyridium. Quinn et al. (2011) presented a model of microalgae biomass production and lipid accumulation in an outdoor, industrial-scale PBR. The model incorporated a time-resolved simulation of microalgae growth and lipid accumulation based on solar irradiation, species specific characteristics and PBR geometry. This model was validated utilizing nine weeks of stochastic weather and growth data from a large scalable outdoor PBR cultivating N. oculata and historical weather data for the idealized solar location of Yuma, AZ. The results showed that the current productivity potential was 5.72  104 kg ha1 yr1 of biomass or 26.4 m3 ha1 yr1 of oil given optimum thermal conditions. Modeling for the bubble-column type PBR was conducted to use CFD for quantitative comparison of the PBR design of a bubble-column PBR for the mass cultivation of microalgae (Seo et al., 2012). Four different multiphase models were investigated to select an appropriate modeling technique and to achieve the bubble shape and fluid flow made by bubble injection inside the 2 -L PBR. The model verification tests, using laboratory experiments and CFD simulations, showed that the surface tension factor was 0.048 N-m. The CFD model was used to apply the evaluation methods of mixing efficiency for the quantitative comparison of PBR performance. Life Cycle Assessment Life cycle assessment (LCA) is a compilation and evaluation of the inputs and outputs and the potential environmental impacts of a product system throughout its life cycle (Pfromm et al., 2011). The outcome of LCA of microalgae-based biodiesel production varies with different culture systems, and the different methods used for biomass harvest and oil refinery (Frank et al., 2013; Resurreccion et al., 2012). Based on a LCA of the whole process of producing microalgaebased biodiesel, Shirvani et al. (2011) evaluated that current algae biodiesel production was 2.5 times as energy intensive as conventional diesel and biodiesel from advanced biomass can realize its inherent environmental advantages of greenhouse gas (GHG) emission reductions once every step of the production chain was fully optimized and decarbonized. In addition, to reduce energy consumption and improve the environmental benefits, using wastewater and CO2 from power plants as nutrients, recovering energy from oilcake residues, using glycerol as a livestock feed and producing


S.-F. Han et al.

co-products was suggested (Shirvani et al., 2011; Sills et al., 2013). For more information, one can refer to the publications by Kumar et al. (2010) and Olguin (2012).

Global algae fuel activities and commercial attempts

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Global algae organization and resources International organizations play important roles in the development of algae-based energy technology. Algae Biomass Organization, Preston, MN, is a not-for profit organization, formed by multiple leading airline industries and Montana State University, and provides information related to algae research and commercial applications ( The NAA, Houston, TX, is a trade organization for renewable energy companies and researchers involved in the production and distribution of biofuels made from algae ( It hosts annual conferences on algal biofuels. The European Algae Biomass Association, Florence, Italy, is the association representing both research and industry in the field of algae technologies ( The Chinese Society of Phycology has more than 1000 members with the research on algal energy as one of its major programs ( The Japan Association for Microalgae Fuels, established in 2012, focuses to commercialize microalgae biofuel production and to facilitate joint efforts across industry, government and academia on a national basis. The Culture Collection of UTEX (the University of Texas at Austin) hosts approximately 3000 different strains of living algae, representing most major algal taxa and some strains have been recently utilized for algae biofuel development. The Canadian Phycological Culture Centre, housed at the University of Waterloo, Canada, has a collection of approximately 400 strains of freshwater and marine algae and cyanobacteria. A commercial service, Algae Depot, Eau Claire, WI, supplies several algae cultures worldwide, including lipid-producing C. vulgaris at price of $29.95 (50 ml). The Culture Collection of Algae, at Goettingen University (international acronym SAG), Germany, is a living microalgae culture resource with about 1600 species of microscopic algae (about 2400 strains) currently available. The Australian National Algae Culture Collection, Hobart TAS 7000, Australia, provides microalgal cultures, with the collection consisting of 1000 strains of more than 300 microalgae species. The Culture collection of Algae and Protozoa, Oban, Argyll, Scotland, UK, hosts more than 2000 strains. The Pasteur Culture Collection of Cyanobacteria, Institute Pasteur in Paris, France, has more than 750 axenic strains. The Culture Collection of Algae of Charles University in Prague, Czech Republic, holds 234 strains of algae and cyanobacteria. The FACHB-Collection at Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China, has mainly fresh water algal strains and over 1500 of these ( The Ocean University of China, Qingdao, China, has a marine algal culture collection with 700 strains of microalgae. The NIES-Collection of Microbial Culture Collection at the National Institute for Environmental Studies (NIES), Tsukuba, Japan, maintains a great number of culture strains of Cyanobacteria, eukaryotic microalgae and endangered algae (

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The website of US DOE ( continuously reports on the progress of algal biotechnology funded by the US government. An online trade publication Algae Industry in the USA (www.Algae actively addresses the progress and development of the algae biofuels and co-product industry. Oilgae located in Tamil Nadu, India, is a global information support resource for the algae fuels industry (www.oilgae. com). contains a database about different species of algae. Commercial application efforts The algae production industry is moving technologies out of the laboratories and into commercial-scale algae production. Currently, commercial algae product costs are high per unit mass (as in 2010, food grade algae costs $5 kg1) due to high capital and operating costs, yet are claimed to yield between 10 and 100 times more energy per unit area than other second-generation biofuel crops (Greenwell et al., 2010). Attempts to commercialize microalgae biofuels cost-effectively are being implemented around the world. Activities in US The United States is the world leader in the development of algal biofuel. In 2007, the US DOE launched the algae energy plan, called ‘‘Mini-Manhattan Project’’ with a plan to realize the commercialization of microalgae-based biodiesel by 2010. The US DOE released the final National Algal Biofuels Technology Roadmap developed from the National Algal Biofuels workshop in May 2010, which was intended to guide future work and investments in algae-based biofuels. Currently, research on algal energy is mainly performed at US DOE National Renewable Energy Laboratory, Golden, CO and PNNL. The US DOE made an investment of up to $24 million, for three research groups, aimed at commercializing algae-based biofuels in June of 2010. Among them, $6 million was granted to The Sustainable Algal Biofuels Consortium of Mesa, AZ, led by Arizona State University to investigate the biochemical conversion of algae to biofuels and products, as well as the chemical properties of algal fuels and intermediates; $9 million was received by a team led by the University of California, San Diego, for developing algae as a robust biofuels feedstock; and $9 million was granted to a group led by Cellana, a joint venture formed in 2007 between Royal Dutch Shell PLC and HR BioPetroleum Inc., to investigate large-scale production of fuels from microalgae grown in seawater. In February of 2010, the Defense Advanced Research Projects Agency announced that the US military planned to begin the large-scale production of oil from algal ponds into jet fuel. After extraction at a cost of $2 per gallon or $0.53 L1, the oil could be refined at less than $3 per gallon or $0.79 L1. A larger-scale refining operation, producing 50 million gallons (179 million liters) a year, was expected to go into production in 2013. The projects, operated by the companies SAIC and General Atomics, were expected to produce 1000 gallons (3785 liters) of oil per acre per year from algal ponds.

DOI: 10.3109/07388551.2013.835301

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Activities in other countries In 2008, the UK launched a public-funded algae biofuel project. This project was planned to cost £26 million for the production of transportation fuels using algae to replace traditional fossil fuels in 2020. The Sustainable Fuels from Marine Biomass project (Biomara) is a UK and Irish joint project with E6 million budget, which aims to demonstrate the feasibility and viability of producing third generation biofuels from marine biomass at laboratory and small scale to potentially use both macroalgae and microalgae as alternatives to terrestrial algal fuel production (http:// Enalg S.P.A., an Italian company, has operated industrial pilot plant in Alicante (Spain) since 2010, for the continuouscycle production of algae based Blue Petroleum. Through the accelerated absorption and conversion of CO2 emissions from the fumes released by Cemex cement factory in Spain to produce a biopetroleum with a selected microalgae and to convert the CO2 into energy ( In June of 2010, the first flight by an airplane using algal biofuels was demonstrated by European Aeronautic Defense and Space (EADS) Company at the Berlin Air Show. EADS has partnered with IGV GmbH in the development of algaebased biofuels. An IGV PBR for microalgae was also exhibited at the Berlin Air Show. In 2011, Abengoa Bioenergy, a Spanish bioethanol company, started construction work at the ECOALGA project plant in Cartagena, Spain. The 5000 m2 experimental plant was supplied with CO2 generated by the neighboring bioethanol facility. The project was to evaluate strains of microalgae and Cyanobacteria, harvesting technique, optimum CO2 concentrations, etc., for the production of biofuels and animal feed. The ECOALGA Project is funded by the Spanish government agency (http:// In July of 2012, Subitec GmbH, a German biotechnology company, announced that they had raised E4.5 million for manufacture of algae PBR using CO2 from industrial production. This company is still working at a pilot-scale. In Senftenberg, Germany, the Vattenfall Group, which is headquartered in Solna Sweden, operates a closed algaebreeding facility (PBR) supplied by Ecoduna Produktions – GmbH, an Austrian company. The facility uses CO2 from a neighboring power plant. This company built up a 90 000 liter PBR for algal production in 2012. The FeyeCon D&I BV, the Netherlands, specializes in the commercialization of innovative CO2 technology and has created two business ventures within the algae sector. Algae Biotech SA creates innovative products and processes in the field of micro-algae and aims to improve all aspects relating to growing, harvesting, extraction and other downstream processes. It works closely with a sister company Clean Algae SA, which specializes in the growing of microalgae at competitive cost and maintains growing facilities on Gran Canaria. In Japan, in 2010, researchers at Tsukuba University invited Toyota and oil refiner Idemitsu Kosan to join an algae R&D project aiming to bring down the cost of algal-based fuel from $32.54 per gallon ($8.61 L1) to parity with the oil price by 2022. They have proposed a $16 million open-pond algal production system after achieving yields of 1000 metric tons

Biofuel production from microalgae as feedstock


per hectare a year at the laboratory scale. Heavy machinery maker IHI Corp., launched a research and development company with a university venture in August 2011 and planned a ¥400 million investment in the firm over 2 years. In 2006, the Chinese government promulgated the ‘‘Renewable Energy Law’’ and placed the development of biodiesel as a priority in energy projects. In 2009, the Chinese government listed ‘‘Key technologies in CO2 to oil algae to biodiesel’’ in their High-Technology Development Plan (863 Plan) and it was funded by RMB20.7 million ($3.3 million) via state and provisional agencies. As a result, a culture collection and data base of marine oil algae in China was established, 13 algal strains capable of fixing CO2 with industrial lipid production potential were selected, high CO2 adsorption PBRs were developed and pilot-scale tests were conducted in Langfang, Hebei, Inner Mongolia, and Haina Province. In 2010, the Chinese government joined the ‘‘microalgae energy’’ projects into the ‘‘973 Plan’’ aimed at making major breakthroughs in the core technology of large-scale production of algae-based biodiesel. The projects include six series of research and development topics such as microalgae harvesting, oil extraction, etc., and the plan is to solve the bottleneck of the high cost in the algal biodiesel production process by 2015 ( content/2011-02/21/content_276734.htm). In 2010, the ENN Group, an energy company in Langfang, China, released a brief about the scale up of their microalgal farm in Inner Magnolia to 280 hectares by 2013. This company obtained a loan of RMB300 million ($48.4 million) from the China Development Bank for algal biodiesel and planned to produce 5000 tons per year by 2015 ( detail/1056481291.html). The company estimated the sale price of biodiesel would be RMB7000 ($1111) per ton with a cost of RMB6500 RMB ($1032) per ton in 2014. Recently, the Qingdao Institute of Biomass Energy and Bioprocess Technology, Chinese Academy of Sciences, claimed that they selected high oil-producing microalgal strains and developed a cost-effective oil extraction and transesterification technology that can recover almost 100% lipid from algal biomass. The production of biodiesel from algae has been tested at the pilot-scale, and a demonstration plant will be built up with an annual productivity of 5000 tons in 2015. Perspective and challenges Based on the above review, microalgae are considered as promising commercial feedstocks with the options of (a) using screening and molecular engineering to improve the ability of oil production and to enhance the tolerance of microalgae to extreme environments (Chen & Shi, 2000; Wang et al., 2004); (b) further improving the productivity of bio-reactor to reduce costs (Liu et al., 2006); (c) sequestering CO2 from power plants to enhance biomass productivity and using nutrients and organic carbons from wastewater to decrease the cultivation cost and enhance productivity (Liu et al., 2011); (d) recovering high value by-products from microalgae biomass; and (e) recovering energy from residues. The technological challenges and biological constraints in the exploitation of microalgae for biofuels have been

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S.-F. Han et al.

discussed (Day et al., 2012; Scott et al., 2010). The main barriers to microalgae technology include: (a) lack of knowledge at the large-scale to mass produce algae costeffectively and free of contamination (in the open pond case); (b) a large footprint of land and water resources are required by the current open pond systems; (c) a high cost in algal biodiesel production, especially in harvesting and oil extraction; (d) the large amount of alcohol (ethanol or methanol) for the transesterification process; and (e) high capital expenditures (CAPEX), especially for PBRs and operational expenditures (OPEX) based on the current data base. Economic concerns remain for large-scale applications. Unfortunately, there is still a lack of data in the public domain on the production rates as well as on production and processing costs, due to the industry withholding research results as indicated by Gouveia (2011). Richardson et al. (2012) estimated the costs of production and chances of economic success for a commercial sized algal biofuel facility in the Southwest United States and they simulated algae production by open ponds and PBR. They indicated that the financial feasibility of PBRs was substantially lower than for open ponds, i.e. average total costs of production for lipids, including financial costs, were $3.78 L1 and $8.3 L1 for open ponds and PBRs, respectively. The financial feasibility analysis showed that the only way to achieve a 95% probability of economic success in the PBR system and open pond system was to reduce the CAPEX by 80% or more and the OPEX by 90% or more, and CAPEX by 60% and OPEX by 90%, respectively. As more and more pilotand large-scale systems are put into operational tests, more accurate cost estimates must be generated and cost-effective technological approaches can be developed.

Conclusions Microalgae are a promising biofuel feedstock because they do not compete with the food supply, and have a rapid growth rate, a high lipid productivity, a capacity to reduce CO2 emission and the potential for a large variety of highvalued bio-products in medicine, food and in the cosmetic industry. Biodiesel production from microalgae has been one of the hottest research topics and has reached the pilot-scale level. To develop a cost-effective, sustainable industrial process, it requires integration of multidisciplinary knowledge including phycology, molecular biology, genetic engineering, chemistry, bioprocess engineering and knowledge of material science, economics and management. Challenges to microalgae biofuel are mainly due to the high capital investments and operational costs, as well as contamination problems.

Acknowledgements We thank Xiao-ye Li for her help in laboratory and Jun-Hong Lv and Yang-Guang Chen for manuscript preparation.

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

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Biofuel production from microalgae as feedstock: current status and potential.

Algal biofuel has become an attractive alternative of petroleum-based fuels in the past decade. Microalgae have been proposed as a feedstock to produc...
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