7.2.1 Identification of Algae Lipid Profiles

The steps involved in the upstream process for algal oil-based biodiesel production include strain identification, optimization for higher lipid yield, and mass produc­tion. For quantifying the lipids available in microalgae, any one of the following chromatography techniques may be followed: high-pressure liquid chromatogra­phy (HPLC), gas chromatography (GC), or chromatography-mass spectrometry (LC/GC-MS). Usually, GC is employed for analyzing the algae lipid profiles after conversion to FAMEs. GC-FID (flame ionization detection) or GC-MS may be used for the identification of fatty acid profiles of algae lipids. In case of GC-FID, the retention time for the individual components of FAMEs is compared to known stan­dards (Mansour, 2005; Lin et al., 2007; Paik et al., 2009). The lipid profile of algal oils can be analyzed via GC-FID using the ASTM D6584 and EN 14105 standard methods. The methylated lipid/FAMEs of algae may contain traces of contaminants such as chlorophylls, catalyst, or water, and samples injected in GC must be free from these contaminants to prevent GC column damage.

The broad application profile in the food industry and the increasing interest in fluorescent products showcase the diverse and promising potential of phycobilip — roteins in number of applications. Table 10.5 exhibits several other novel proper­ties of phycobiliproteins that have the potential for commercialization, but have only been accounted for in patents. Other biomedical properties included are anti­inflammatory, antioxidant, liver protection, anti-tumor, lipase activity inhibitor, and serum reducing agent; all of which have been reported in patents and applied research but have not yet been commercially explored (Sekar and Chandramohan, 2008).

Despite extensive research spanning 150 years, and the thousands of microalgal species that are known to exist, only a few hundred have been screened for chemi­cal compositions and only a handful have been exploited on an industrial scale (Spolaore et al., 2006; Sekar and Chandramohan, 2008). To make phycobiliproteins more market competitive and economically feasible, basic screening is imperative in order to source the organisms that are responsible for significant production of phycobiliproteins—but may not necessarily be the fastest-growing strains (Sekar and Chandramohan, 2008). Genetic modification of microalgae holds great promise, along with pursuing other methods of cultivation (heterotrophic and mixotrophic). With the ever-increasing range of potential products and applications pending commercialization, it is imperative to pursue these avenues of research to advance microalgal biotechnology.

Wide variations exist in units of measurement, and standardization is required with regard to the growth conditions of algae to permit comparison of outputs (Coronet, 2010). On a volume basis, biomass in several species of autotrophic algae varied considerably between 0.002 and 4 g L-1d-1 and 1.7 to 7.4 g L-1d-1 in the heterotrophic algae (Table 13.1); on an areal basis, values ranged from 0.57 to 150 g m-2d-1 (Table 13.1). The highest production of algal biomass (120 to 150 g m-2d-1) has been reported in PBRs under artificial light (Tsoglin and Gabel, 2000).

The success of microalgal biotechnology entrepreneurship depends on the opti­mization of biomass and production yields. It is necessary to establish to what extent these variations are intra-specific or inter-specific, whether or not these yields are based on optimal growth conditions, and how to prime the algal production. Between several species of Dunaliella, cell division rates ranged from 0.12 to 3.0 div d-1 (Subba Rao, 2009). Within the one species, Chlorella sorokiniana, biomass produc­tion rates (div d-1) varied between 0.32 and 4.0 div d-1; and in Dunaliella teriolecta, rates varied between 0.15 and 3.0 div d-1 (Subba Rao, 2009). Such variations could be due to differences in strains of isolates and/or culture conditions. Even in the most commonly used strain, Neochloris oleoabundans UTCC 1185, biomass varied between 0.03 and 1.50 g L-1d-1 (Table 13.2).

In Dunaliella tertiolecta, a green alga often used in biotechnology, Duarte and Subba Rao (2009) discussed the relationship between biomass (B determined as Chl-a), photosynthesis (P), and light energy I (pmol m-2s-1):

PB = {PBs[1 — exp(-aB//PBs)]exp(- pB//PBs>) + PBd

where PBs is the maximum potential photosynthesis in the absence of photo­inhibition, and PBd is the intercept of the P-I curve on the y-axis and has the same units as PBm. In D. teriolecta, PBm varied between 3.3 and 7.43 mg C mg Chl-a h-1 (Duarte and Subba Rao, 2009). They showed that the photosynthesis and respira­tion activities were dependent on the light energy and the cell density; that is, over a 21-day period, gross production and respiration decreased by sevenfold and fourfold, respectively, at 42 pmol m-2s-1. The optimal light energy for photosynthesis ranged between 627 and 1,356 pmol m-2s-1. Also, the gross primary production:respiration ratio decreased with higher cell densities. It will be crucial in biotechnology opera­tions to optimize the relationships among high biomass yields, photosynthetic effi­ciencies, and yield of bioactive compounds. These criteria are crucial and could greatly improve commercial algal harvest.

Grobbelaar (2010), while discussing the light energy relationships in algae, sug­gested that by optimizing light, photosynthetic yield could be doubled from 1.79 g (DW) m-2d-1 and pointed out that several factors determine volumetric yields of mass algal cultures. Furthermore, Grobbelaar pointed out that many biotechnology start-up companies make the mistake of simple extrapolation of controlled labora­tory rates to large-scale outdoor production systems.

TABLE 13.1 Summary of Variations in Microalgal Biomass Production Criteria Example Minimum Maximum Biomass Haematococcus pluvialis 0.06 1.2 Haematococcus pluvialis 0.06 0.55 Haematococcus pluvialis 0.28 Haematococcus pluvialis 1.2 Chlorella sorokiniana 0.32 Chlorella sorokiniana 0.5 Chlorella sorokiniana 1.8 Chlorella sorokiniana 4 Six microalgae 0.09 0.21 0.04 0.37 0.03 0.48 Haematococcus oleoabundans 0.63 Haematococcus oleoabundans 0.4 Chlorella protothecoides 0.002-0.02 Chlorella protothecoides 1.7-7.4 Production Dunaliella 2 Nannochloropsis sp. 20 120-150 Haematococcus pluvialis 50-90 Several species 0.91-38 20 species 0.57-130
Remarks Bubble reactor 130 pmol photons mr2s_1 Tubular reactor 2,500 pmol photons mr2s_1 1,200 pmol photons mr2s_1	Ref. Garcia-Malea et al., 2006 Garcia-Malea et al., 2005 Esperanza Del Rio, 2005 Hunt et al., 2010 Janssen et al., 2003 Chang and Yang, 2003 Lee et al., 1996 Gouveia et al., 2009 Chen et al., 2011 Pitman et al., 2011 Li et al., 2008 Chen et al., 2011 Chen et al., 2011 Ben — Amoz, 2009 Ben-Amoz, 2009 Tsoglin and Gabel, 2000 Olaizola, 2000 Chisti, 2007; Khan et al., 2009; Harun et al., 2010; Pitman et al., 2011; Chen et al., 2011 Mata et al., 2010
10 mM Sodium nitrate enrichment 5 mM Sodium nitrate enrichment Phototrophic cultivation Heterotrophic cultivation Rue gas enriched Bioreactors, artificial light
Note: Variations in microalgal biomass (g L 1d *) and production (g m 2d 1). * All values are for autotrophic cultivation unless specified as heterotrophic.

Optimal supply of nutrients, mainly carbon, nitrogen, and phosphorous, along with various other macro — and micronutrients required for algal growth, is a prerequisite for high growth rates. Deficiencies in any nutrient cause disturbances in metabo­lism, physiological changes, and decreased productivity (Pulz, 2001). The supply of nutrients to the culture is relatively simple, but the supply of nutrients to individual cells depends on efficient mass transfer, which is related to mixing and gas sparging (Grobbelaar, 2009). Nutrients are also a significant cost in microalgal cultivation; therefore, design of the reactor system to allow for efficient recycling of culture medium is essential (Greenwell et al., 2010).

Nutrients, with the exception of light and carbon, are generally provided in the liquid growth medium. Carbon is a major constituent of algal cells (often com­prising 50% of the dry weight), usually obtained from carbon dioxide (CO2) gas (Chisti, 2007). The concentration of CO2 in air (0.04%) is suboptimal for plant growth; therefore, for optimal productivity, CO2-enriched air must be supplied (Pulz, 2001). CO2 may be available from flue gas or other waste gas streams, but the cost of gas compression and extensive sparging systems for arrays of PBRs is significant. The location of large algal plants sufficiently near the source, along with the safety concerns of large-scale distribution of flue gas (low in O2 and high in CO2, NOX, and SOX) at ground level, could present its own challenges (Scott et al., 2010).

In cases of carbon limitation, the efficiency of mass transfer of CO2 from gas to liquid form in the culture medium becomes critical to productivity. Certain algal species can grow heterotrophically or mixotrophically, in which case all or some of the carbon and energy requirements can be supplied from an organic carbon source such as glucose or acetate (Lee, 2001). The use of heterotrophy can reduce the dependence of productivity on light and CO2 supply, which releases some of the key constraints on reactor design (Pulz, 2001). Heterotrophic cultivation of micro­algae in sterilizable fermenters has achieved some commercial success, although biomass productivity has yet to match that of yeast and other heterotrophic organisms (Lee, 2001).

Mixing and nutrient concentrations are also linked to pH control. Mixing promotes reactions of CO2 with H+, OH-, H2O, and NH3 in the medium, which affect the pH and hence CO2 uptake rates (Kumar et al., 2010). The pH increases along the length of tubular reactors due to consumption of CO2. In long reactors, CO2 injection points may be necessary to prevent a rise in pH above optimal levels (Chisti, 2007). CO2 addition is commonly controlled by feedback from a pH meter (Carvalho et al., 2006).

The removal of toxic metabolites is also critical to the efficiency of growth and photosynthesis. Under high irradiance, oxygen generation in closed PBRs can be up to 10 g O2.m-3min-1. Maximum dissolved oxygen levels should not exceed 400% saturation (with respect to air-saturated culture) (Chisti, 2007). A build-up of O2 in the reactor can cause the key carbon-fixing enzyme RuBisCO to bind oxygen instead of carbon dioxide, leading to photorespiration instead of photosynthesis (Dennis et al., 1998). High oxygen concentrations, in addition to intense light, lead to the formation of oxygen radicals that have toxic effects on cells due to membrane dam­age (Molina Grima et al., 2001; Pulz, 2001). Many algal strains cannot survive in O2 over-saturated conditions for more than 2 to 3 hours. High temperatures and light intensify the damage (Pulz, 2001). Oxygen build-up limits the maximum length of a closed tubular reactor. Typically, a continuous tube should not exceed 80 m (Molina Grima et al., 2001), although the exact length depends on biomass concentration, light intensity, liquid velocity, and initial O2 concentration. In a closed reactor, culture must continuously return to a degassing zone, where it is bubbled with air to strip the O2. The degassing zone is typically optically deep compared with the solar collector, and hence poorly illuminated; thus its volume should be small relative to the solar collector (Chisti, 2007).

In high-density algal cultures, the key challenges in nutrient provision are in mass transfer of CO2 to cells and O2 away from cells. Efficient mixing and aeration, with­out inducing shear stress and requiring excessive energy input, are important param­eters. Bubbling of gas through cultures can be used to simultaneously introduce CO2, strip O2, and mix the culture broth (e. g., bubble columns and airlift reactors). The overall mass transfer coefficient (kLa) of the reactor is an important parameter in determining the carbon supply. The kLa depends on reactor geometry, agitation rate, sparger type, temperature, mixing time, liquid velocity, gas bubble velocity, and gas holdup (Ugwu et al., 2008).

Susan T. L. Harrison, Christine Richardson, and Melinda J. Griffiths

Centre for Bioprocess Engineering Research Department of Chemical Engineering University of Cape Town, South Africa

CONTENTS

9.1 Microalgae for Bioenergy…………………………………………………………………………. 114

9.2 Analytical Tools for Assessing Environmental Sustainability…………………… 115

9.3 Environmental Sustainability of Microalgal Processes…………………………….. 117

9.3.1 Overview of Environmental Assessment of Algal Biodiesel…………. 117

9.3.2 Algal Bioreactors………………………………………………………………………….. 123

9.3.3 Nutrient Provision for Microalgal Culture…………………………………….. 125

9.3.4 Biomass Recovery……………………………………………………………………….. 126

9.3.5 Biomass Drying and Conversion………………………………………………….. 126

9.3.6 Impact of Species Selection…………………………………………………………. 127

9.3.7 The Biorefinery…………………………………………………………………………….. 127

9.4 Economic Considerations of Microalgal Production………………………………… 128

9.4.1 Cost of Algal Biomass and Algal Oil…………………………………………….. 128

9.4.2 Key Process Components Contributing to Cost……………………………. 132

9.4.3 Potential Earnings from By-Products…………………………………………… 132

9.5 Key Focal Areas for Improving Environmental and Economic

Sustainability……………………………………………………………………………………………. 133

References…………………………………………………………………………………………………………. 134

Microalgae appear to be the only source of biodiesel that have the potential to com­pletely replace fossil diesel (Table 11.2). Unlike other oil crops, microalgae grow rapidly and many of them are exceedingly rich in oil (Griffith and Harrison, 2009). Microalgae commonly double their biomass within 18 to 24 h (Sheehan et al., 1998).

OS 4^

TABLE 11.1 С02 Biofixation and Biomass Productivity of Various Microalgae in Different Reactor Configurations C02 Fixation Rate (g m_3h_’) or Specific Growth Rate11 (h_1) or Microalgal Species C02 Feed Gas (%) Removal Efficiency (%) Biomass Productivity0 (g m_3h_’) Reactor Type Chlamydomonas reinhardtii 30* NA 0.08-0.01d Batch hotobioreactors Chlorella pyrenoidosa too* NA 0.09-0.09d Chlorogleopsis sp. 5 0.8-1.9a 0.0007-0.00604 Scenedesmus obliquus 60* NA 0.06-0.04d Spirulina platensis 0.03 NA 0.0082-0.002d C. kessleri 18 NA 0.84d Open photobioreactor Chlorella sp. 6-8 10-50% NA Chlorella sp. 10 46% 0.09f N. salina 5 NA 1.25е N. salina 15 NA 4.1е Spirulina LEB18 6 0.218 0.2475f Spirulina platensis 10 39% 0.1164f Closed photobioreactor Botryococcus braunii 2-3 3-18a NA Chlorella sp. 0.03 1.3784h 0.75 llf Chlorella sp. 0.03 NA 0.099f 3 NA 0.212f 10 NA 0.045f 15 NA 0.030f

Source: Table is updated version of published work of Kumar, A. et al. (2010); Fulke, A. et al. (2010); Ramanan, R. et al. (2009); Zhao, B. et al. (2011); Ho, S. H. et al.

(2010); Cheng, L. et al. (2006).

Note: Abbreviation: NA, not available, з, ь, c prom Kumar A. et al., 2010. d Specific growth rate (h_1). e Biomass productivity (g mr3/d_1). f Biomass productivity, calculated (g L-1d-1). g Calculated C02 fixation rate (g g_1d_1). h C02 fixation rate (g L-1d-1).

1 C02 fixation rate (mg L-1d-1). j Biomass productivity (mg L-1d-1).

*mM in medium.

TABLE 11.2

Comparison of Some Biodiesel Sources

Oil Yield (L ha-1y-1)

172

446

1,190

1,892

5,950

Microalgaea

Microalgaeb a 70% oil (by wt.) in biomass. b 30% oil (by wt.) in biomass.

Source: Adapted from Chisti (2007) and Mata et al. (2010).

The oil content in microalgae can exceed 80% by weight of dry biomass (Spolaore et al., 2006). The biofuel production potentials of various algal strains reported are summarized in Table 11.3. Depending on the species, microalgae produce many different kinds of lipids, hydrocarbons, and other complex oils. Hexadecanoic acid methyl ester (16:0), palmitoleic acid methyl ester (16:1), octadecanoic acid methyl ester (18:1), and stearic acid methyl ester (18:0) are some of the major FAMEs found to be suitable for biodiesel production derived from microalgal lipids (Dayananda et al., 2007; Francisco et al., 2010; Fulke et al., 2010). Using microalgae to produce biodiesel will not compromise the production of food, fodder, and other products derived from crops (Griffith and Harrison, 2009). The strain Botryococcus braunii, however, grows slowly and produces about 30% to 73% hydrocarbons under labora­tory conditions (Dayananda et al., 2006; 2007; 2010).

For cost-effective commercial biodiesel production, appropriate strain selection according to the suitability for site of cultivation and local environmental conditions is imperative (Sheehan et al., 1998; Griffith and Harrison, 2009; Chanakya et al., 2012). The key challenge for microalgal biodiesel production is the screening and selection of microalgal species that can maintain a high growth rate with high lipid content in addition to a high metabolic rate (Griffith and Harrison, 2009). The spe­cies that are metabolically rigorous can tolerate high concentrations of salt, CO2, high alkalinity, and high temperature; and have the ability to grow and replicate under nutritional stress by altering their metabolic pathways—these are the species that are found to be most promising in this regard (Verma et al., 2010). Nitrogen limi­tations have been found to enhance lipid accumulation in the microalgae (Griffith and Harrison, 2008). Yeesang and Cheirsilp (2011) studied the effect of nitrogen deprivation and iron (Fe3+) enhancement with higher light intensity on lipid con­tent. They observed an increase in lipid content from 25.8% to 35.9% (Yeesang and Cheirsilp, 2011). The findings of Liu et al. (2008) also confirmed that lipid content in Chlorella vulgaris increased by three — to sevenfold when the growth medium was supplemented with 0.012 mM Fe3+.

Enhancement of CO2 sequestration and lipid accumulation is one of the major challenges that can be duly addressed by an extensive search for the new genes involved in the process (bio-prospecting) or targeted genetic engineering, both of which are promising approaches (Kumar et al., 2010).

Genetic and metabolic engineering transformations in microalgae are lim­ited to very few microalgal species. The use of molecular biology techniques as a toolkit to engineer microalgae for biodiesel production is a demanding strategy. Understanding, incorporation, and expression of the gene encoding rate-limiting enzyme of inorganic carbon uptake and lipid biosynthetic pathways are of more importance (Badger and Price, 2003; Verma et al., 2010). With the advancements in genome sequencing with sequence availability of Anabaena, Ostreococcus tauri, Thalassiosira pseudonana, and other algal species (Beer et al., 2009; Verma et al., 2010), the genetic transformation of microalgal species for various purposes is now promising. Cyclotella cryptica and Navicula saprophila were genetically trans­formed with the acetyl-CoA carboxylase (acc) gene isolated from Cyclotella cryptica for enhanced lipid synthesis. Such efforts could successfully enhance the activity of the acc gene; however, no significant lipid content was found to increase in trans­genic species, indicating that acc activity by itself cannot increase lipid biosynthesis and accumulation (Dunahay et al., 1996). More holistic approaches were forwarded for lipid enhancement through genetic engineering. Studies on the insights of various regulatory steps of the lipid biosynthetic pathway (Courchesne et al., 2009), expres­sion, and regulatory analysis of genes and enzymes (such as fatty acid synthase, acetyl-CoA carboxylase, acyl-CoA, diacylglycerol acyltransferase) involved in triac — ylglycerol (TG) formation have been carried out (Bouvier-Nave et al., 2000; Dehesh et al., 2001; Jako et al., 2001).

Genetic transformations, which influence TG biosynthesis, may enhance bio­diesel production in transgenic microalgae (Verma et al., 2010). There have been considerable enhancements in the genetic engineering aspects of algae to improve the performance of transgenic microalgae, including (1) the efficient expression of transgenes, (2) riboswitches for gene regulation in algae, (3) inducible nuclear promoters and reporter genes (luciferase), as well as (4) inducible chloroplast gene expression (Beer et al., 2010).

Transcription-level regulations by transcription factors can also be used as a strat­egy to control the overall metabolite flux. The effect of transcription regulatory pro­teins has also been studied with respect to their expression levels to increase the production of secondary metabolites of interest in plants (Verma et al., 2010). In addi­tion to the approaches discussed above, further genome sequencing efforts need time. Advancements in existing tools and the development of new genetic transformation tools and screening methods will add further rigor to the efforts to optimize the accumulation of lipid and/or other metabolites alongside improving the economics of its production (Beer et al., 2010; Verma et al., 2010). Looking at the current interest in microalgae-based biofuels and microalgae/prototrophs, fundamental research will indisputably provide further advances in the near future (Beer et al., 2010). However, with respect to the utilization of genetically modified crops in India, that country has already accepted the release of Bacillus thuringiensis (Bt) cotton, which is suc­cessfully growing without causing any environmental problems. We (the authors) are of the opinion that in the near future, the scientific community will be exploring genetically modified microalgae in both open ponds as well as closed photobioreac­tors. But prior to doing that, several scientific issues should be addressed, and risk assessment (to ecosystem) studies must be performed to determine the legitimacy of using genetically modified microalgal strains to produce biodiesel.

Manjinder Singh, Rekha Shukla, and Keshav Das

Biorefining and Carbon Cycling Program College of Engineering The University of Georgia Athens, Georgia

CONTENTS

5.6 Introduction……………………………………………………………………………………………….. 77

5.7 Harvesting Processes………………………………………………………………………………….. 79

5.8 Gravity Sedimentation……………………………………………………………………………….. 79

5.9 Centrifugation…………………………………………………………………………………………….. 80

5.10 Filtration……………………………………………………………………………………………………… 80

5.11 Flotation……………………………………………………………………………………………………… 81

5.12 Flocculation………………………………………………………………………………………………… 81

5.13 Electrolytic Coagulation…………………………………………………………………………… 82

5.14 Energy Efficiencies of Harvesting Processes………………………………………………. 83

5.15 Conclusion…………………………………………………………………………………………………. 85

References…………………………………………………………………………………………………………… 86

6.1 INTRODUCTION

Microalgae have been identified as a potential alternative resource for biofuel production. Significant drawbacks to algaculture include dilute culture density and the small size of microalgae, which translates into the need to handle large volumes of culture during harvesting. This energy-intensive process is therefore considered a major challenge for the commercial-scale production of algal biofuels. Most of the currently used harvesting techniques have several drawbacks, such as high cost, flocculant toxicity, or nonfeasibility of scale-up, which impact the cost and quality of products. As harvesting cost may itself contribute up to one-third of the biomass pro­duction cost, substantial amounts of research and development initiatives are needed to develop a cost — and energy-effective process for the dewatering of algae. Several factors, such as algae species, ionic strength of culture media, recycling of filtrate, and final products, should be considered when selecting a suitable harvesting tech­nique. Harvesting cost and energy requirements must be reduced by a factor of at least 2 if algal biomass production is to be viable for very low-cost products such as biofuels. There could be considerable cost and energy savings in custom-designed, multi-stage harvesting techniques for algae farms. In such systems, a variety of har­vesting technologies are arranged in a sequence based on culture chemistry, specific characteristics of each technique, and its energy requirements to dewater pond water to either 5% or 10-20% solids.

Techniques and processes of microalgae cultivation, harvesting, and dewatering have been reviewed extensively in the literature (Lee et al., 1998; Spolaore et al., 2006; Khan et al., 2009; Harun et al., 2010; Uduman et al., 2010). Due to the very dilute culture (<1.0 g of solids L-1) and typically small size of microalgae with a diameter of 3 to 30 pm, large volumes must be handled to harvest algal biomass, which is an energy-intensive process. Therefore, harvesting microalgal biomass is considered a challenging issue for commercial-scale production of algal biofuels. Conventional processes used to harvest microalgae include concentration through centrifugation, foam fractionation, chemical flocculation, electro-flocculation, membrane filtra­tion, and ultrasonic separation. The resulting high cost of biofuel production is a major bottleneck to its commercial application. The cost of harvesting may itself contribute to approximately 20% to 30% of the total cost of algal biomass, and the above methods would be viable only if the biomass harvested is used for extract­ing high-value products such as nutraceuticals (Girma et al., 2003). Harvesting, in general, can be defined as a series of processes for removing water from the algal growth culture and increasing the solids content from <1.0% to a consistency of up to 20% solids, depending on the downstream processing requirements for conversion to fuel. Thermal drying is generally discouraged (except when sufficient waste heat is available), because the amount of thermal energy needed to dry the algae would be a major fraction, if not all, of the energy content of the algal biomass.

Industrial production of algal biofuel is still in its infancy and therefore uncer­tainty in all stages of production and unpredictability of economy has been highly debated. Some very optimistic estimates on algae biofuels propose that the cost of algal oil production must be reduced by 5 to 6 times, in addition to the tax and envi­ronmental subsidies, to make them competitive with petroleum fuels (Chisti, 2007). For economical production of algal biomass, the selection of harvesting technol­ogies is so crucial. Several factors, such as algal strain, ionic strength of culture media, recycling of filtrate, and final fate of harvested biomass, must be consid­ered when selecting the harvesting technique. For example, the filamentous alga Cladophora with very long thread-like filaments (several centimeters long) lends itself to relatively cost-effective harvesting using membrane filtration. In contrast, chemical flocculation is not recommended if the harvested biomass must be pro­cessed for nutraceutical and pharmaceutical products because of the residual con­tamination caused by the flocculants. In general, it is quite difficult to recommend a single technique as the best for harvesting and recovery without consideration of specific process conditions and downstream product use. Scientists all over the world have developed several techniques for harvesting and recovery processes that rely on facts to simplify this overall process. Judicious exploitation of the different harvesting technologies is therefore necessary to reduce the harvesting cost and energy requirements by the desired factor of 2 if algal biomass production is tar­geted for very low-cost products such as biofuel. Advances in different methods of algal harvesting and dewatering to resolve our energy crisis, along with energy

utilization by various techniques with respective constraints and drawbacks of each method, are discussed in this chapter. In addition, this chapter discusses the pros and cons of different algae harvesting techniques along with their energy requirements. The potential advantages of multi-stage hybrid harvesting systems involving more than one technique deployed in a specific sequence for efficient and energy-effective biomass recovery are discussed.

It is well recognized that the production phase is most significantly affected by its energy requirements. This is most marked for photobioreactors and contributes significantly to costs. Norsker et al. (2011) explored these interactions. In the hori­zontal tubular reactor, liquid circulation contributes to both capital costs through the pump required and to energy costs. By reducing the linear velocity from 0.5 m s-1 to the minimum value predicted of 0.3 m s-1, a reduction in cost per kilogram of 25% was achieved. Similarly, the aeration of the flat-plate reactor (or vertical tubular reac­tor) contributes significantly to capital and operating costs. In the former, a reduction in the aeration rate from 1 vvm (volume per volume per minute) to 0.3 vvm resulted in a 48% reduction in cost. The contributions of mixing and mass transfer in the flat — plate and tubular reactors were on the order of 52% and 30%, respectively.

Productivity significantly influences the cost of production. This is well demon­strated by the increased productivity with increasing light intensity, as correlated by Williams and Laurens (2010). Areal productivity increased from some 10 g m-2d-1 at an irradiance of 15 MJ m-2d-1 to 30 g m-2d-1 on doubling the irradiance to 30 MJ m-2 d-1. The impact of improved productivity under improved irradiance is illustrated by comparing the cost for production in Eindhoven (the Netherlands) and Bonaire (Dutch Antilles). In the three cases analyzed (Norsker et al., 2011), the cost decreased by 40% to 45% under conditions of increased illumination (sum­mer high of 7,000 and 5,000 W-h m-2d-1, respectively; winter low of 4,500 and <1,000 W-h m-2d-1, respectively, in Bonaire and Eindhoven).

The provision of CO2 can significantly impact the costing, based on whether the CO2 is provided “free” as a by-product of an adjacent process or requiring its purchase as a compressed gas (Williams and Laurens, 2010). Stephenson et al. (2010) further considered the compression energy, with associated costs, based on the CO2 concentration in the gas stream.

Williams and Laurens (2010) noted that their energy costs (typical of the US envi­ronment) were some sixfold higher than those estimated by an analysis conducted in British Columbia, Canada, where hydroelectric power was used. This highlights the potential for the use of renewable energy resources in conjunction with algal production.

The long history of research into algae-based wastewater treatment, pioneered by algologists Oswald and co-workers (1953), was designed as a technology to carry out the dual role of microalgae used for wastewater treatment and protein production. It began with Golueke and Oswald (1965), who gained insight into the economic aspects of microalgae-based pond wastewater treatment technology and its poten­tial alternative sources of renovated effluent and protein production. Microalgae have been used extensively as appropriate treatment technologies in pond wastewa­ter treatment since the early 1950s (Oswald et al., 1953; Oswald and Gotaas, 1957; Fallowfield and Garrett, 1985; Lincoln and Earle, 1990; Ghosh, 1991; Oswald, 1991; Borowitzka, 1999; Oswald, 2003; Hanumantha Rao et al., 2011). Phycoremediation can provide a more sustainable long-term solution than any other type of wastewa­ter treatment in which a biological method is employed because microalgae have the greater capacity to fix CO2 by photosynthesis and efficiently remove nutrients from overloaded wastewaters at minimal cost (Hirata et al., 1996; Murakami and Ikenouchi, 1997). The most efficient nutrient removal from wastewater has been investigated using algal strains with special attributes such as extreme temperature tolerance, chemical composition of high-value by-products, heavy metal accumula­tion, and mixotrophic growth inter alia. The microalgae strain Phormidium was isolated from a polar environment below 10°C, and the capability of this strain to remove inorganic nutrients in wastewater during spring and autumn of cold climates was studied by Tang et al. (1997). Common microalgae in wastewater treatment include Chlorella, Oscillatoria, Scenedesmus, Synechocystis, Lyngbya, Gloeocapsa, Spirulina, Chroococcus, Anabaena, and others. Among these, the universally grown Chlorella species (vulgaris) has been used for wastewater treatment throughout the world. They are microalgae that can grow in nitrogen (N) and phosphorous (P) nutrient-enriched municipal wastewater and convert wastewater containing N and P into algal biomass (Green et al., 1995; Benemann and Oswald, 1996; Olguin, 2003; Orpez et al., 2009). Other efficient microalgal species used to remove N and P in var­ious industrial effluents include Botryococcus braunii, which was used for primary treated sewage waste (Sawayama et al., 1995); Scenedesmus obliquus, which was used in the treatment of urban wastewater (Martinez et al., 2000); and artificial wastewater (Gomez Villa et al., 2005). The pollutants are recovered from the system by harvesting biomass (Adey et al., 1996). Aside from microalgal biomass build­up, luxury reserved materials in the form of pigments, protein, antioxidants, amino acids, and other bioactive compounds make them ideal for stripping nutrients. High — rate wastewater treatment of hazardous or organic pollutants has been carried out by microalgae with special attributes. The most widely studied microalgal strains are Chlorella, Scenedsmus, and Ankistrodesmus species, in which various industry effluents were used, such as paper industry wastewaters, olive oil production waste­water, and mill wastewaters (Ghasemi et al., 2011; Rawat et al., 2011). Microalgal strain selection plays an important role in HRAP wastewater treatment. Microalgal collections house only a few thousand different microalgal strains that can efficiently support wastewater treatment and biomass production for value-added by-products and meet near-future demands for alternate biofuels. Therefore, we need to concen­trate on effective microalgal strains in combination with recent advances in genetic engineering and material science to fix the problem.

It is immensely difficult to sample and accurately count microalgal cells that grow attached to the substratum or culture vessel. The cells are scraped from the sur­face using a spatula to dislodge them into a suspension. The cells are then care­fully homogenized so as to separate the cell clumps, and this can result in the death of some cells. The cells that are clumped are indistinguishable from single cells, and therefore cell enumeration may be inaccurate (Guillard and Sieracki, 2005). Colonies of Microcystis aeruginosa were separated to a homogenous cell suspension by alkaline hydrolysis with 0.01 M KOH at 80°C for about 30 minutes, but higher molarities of KOH resulted in cell loss (Box, 1981). In addition, complete separation of colonies to single cells was achieved by heating at 80°C for 30 minutes followed by 30 seconds of vortex-mixing (Box, 1981). Furthermore, in these studies, sonica — tion (20 KHz, c. 50W) did not completely reduce the colonies to single cells, and this procedure resulted in cell death on some occasions (Box, 1981).

It has been reported that the efficiency of the method for separating the colonies into a single-cell suspension relies on the microalgal cells used (Box, 1981). The accuracy of enumerating microalgal cells displaying colonial growth is hindered if there is no uniformity in the number of cells per colony. In addition, the cells cannot be sufficiently distinguished from each other, thus compromising accuracy. The enu­meration of dividing microalgal cells is subject to inherent inaccuracies.