Moridis and Reagan [132] indicated that production at a constant bottom-hole pres­sure is the most promising strategy in Class 3 hydrate deposits because it is appli­cable to a wide range of formation k, allows continuous and automatic rate increases to match the increasing keff (the result of the dissociation-caused reduction in SH), and it eliminates the possibility of ice formation through the selection of a bottom­hole pressure above that at the quadruple point of hydrates (Fig. 14).

Figure 15 shows the gas production rates from a Class 3 deposit with the proper­ties of the 18 m-thick Tigershark accumulation (see [132]) when using constant bottomhole-P depressurization. QP followed a cyclical pattern that includes long rising segments, followed by short precipitous drops (a behavior caused by the self — controlled formation and destruction of secondary hydrates around the well). It reached a maximum QP=4.3 x 105 ST m44day of CH4 (=15 MMSCFD), with an average Qavg = 2.3 x 105 ST m3/day (=8.10 MMSCFD) over the 6,000-day production period, and with manageable water production. These results indicated that gas can

be produced from Class 3 deposits at high rates over long times using conventional well technology.

Water is a scarce resource in most of America, especially outside of the Southeast and Great Lakes states. The USA faces two macroscale drivers of water scarcity in the coming century: a growing domestic population and the continuing depletion of fossil fresh water ground reserves. Between 2005 and 2050, the US Census Bureau predicts a population increase of greater than 40% [43] , increasing demands on water for domestic and commercial applications. At the same time, the US Geological Survey estimates that over 40% of agricultural fresh water and more than 30% of nonagricultural fresh water is drawn from deep aquifers that do not refresh over meaningful timeframes [ 26] . The magnitude of this resource, accumulated over geological time scales and currently being depleted faster than natural recharge rates, is poorly quantified nationwide.

Terrestrial energy crops grow as an open system, incurring water losses from irrigation inefficiencies [22], soil evaporation [27], and transpiration for leaf cool­ing and motive force for nutrient uptake; it is estimated that only 0.2-0.4% of water used in agriculture is fixed as plant matter [11, 17, 19] (Table 2). Agriculture is by far the largest source of water utilization in the USA, accounting for fully 80% of consumption from all sources [51]. Whether water is supplied by irrigation or natu­ral rainfall, agricultural water use limits availability for other applications [ 34] . Similarly, open-pond algal systems suffer from evaporative losses [44], although such losses are ameliorated through the use of closed systems or salt-tolerant spe­cies [9, 24].

Several advanced biofuel systems offer the opportunity to diminish concerns sur­rounding water withdrawals by creating closed bioreactors that obviate water losses from evaporation and transpiration [30, 31, 49]. Closed systems facilitate complete

nBOE barrel of oil equivalent on an energy basis bSupp Calc 1 cSupp Calc 2 dPETRO FOA targets

eSupp Calc 5 with El Paso, Texas for reference fSupp Calc 6 with North Dakota as reference gGGE gasoline gallon equivalent on an energy basis

hBiomass growth on saline or brackish water: 0 g/GGE [9, 24]; cellulosic ethanol from switchgrass (no irrigation): 1.9 g/GGE (thermochemical). 5.8-9.8 g/GGE (biochemical) [68]; open-pond batch algae systems could consume 223-1,421 g/GGE fresh water [44, 62]

‘Supp Calc 3. Upper limit includes water use from dry mill com processing Calculations: O-biocatalyst/woody biomass; 1.2-2.4-switchgrass; 58-algae [44]

Calculations: O-biocatalyst/woody biomass; 1-8-algae [44, 45]

Calculations: O-biocatalyst/woody biomass; 3-algae [45]

water recycle: in the limit, water is required during biofuel production only as the ultimate source of electrons during water oxidation, providing two electrons (as hydride) and molecular oxygen during CO2 reduction. This requirement amounts to roughly 1 gal of water/gal of fuel (Supp Calc 3), plus whatever water is consumed by growing cultures. While these values are somewhat imprecise at this early stage, they will almost certainly represent a vast savings over terrestrial plants and over at least some forms of photosynthetic organisms.

Aliphatic hydrocarbons are excellent biofuel targets, as they are already predomi­nant components of petroleum-based gasoline and diesel fuels and would be com­patible with existing engines and fuel distribution systems. Recently, genes for alkene biosynthesis (oleABCD) have been identified in multiple bacteria and the functions of these genes elucidated in in vivo and in vitro studies (e. g., [1]).

The OleABCD proteins catalyze the condensation of fatty acids to long-chain alkenes and this production is enhanced in E. coli by the overproduction of fatty acids. To produce long-chain alkenes in R. eutropha, fatty acid overproduction will be engineered into R. eutropha using techniques that have been used successfully in other proteobacteria [1, 21].

2.2.1 Farnesene

The isoprenoid pathway represents an important source of advanced biofuel precur­sors such as farnesene. Chemical hydrogenation of farnesene produces farnesane, which can serve as a diesel fuel. All terpenoids originate from the same universal precursors (isopentenyl pyrophosphate [IPP] and its isomer dimethylallyl pyrophos­phate [DMAPP]), which can be generated through two known biosynthetic path — ways—the mevalonate-dependent (MEV) isoprenoid pathway mostly found in eukaryotes and the deoxyxylulose 5-phosphate (DXP) pathway found in most prokaryotes. The R. eutropha genome encodes the DXP pathway, which generates the precursor molecules IPP and DMAPP demonstrated to be essential in prokary­otes for the prenylation of tRNAs and the synthesis of farnesyl pyrophosphate (FPP), which is used for quinone and cell wall biosynthesis [15]. While farnesene may be produced in R. eutropha through the manipulation of its native DXP path­way, the tight regulation of essential metabolites produced through this route may pose a significant challenge in achieving reasonable titers. For optimal production of isoprenoid-based fuel molecules, we therefore propose the incorporation of the MEV pathway, which we expect to be unregulated by this host. We will synthesize and express genes originating from distinct prokaryotic and eukaryotic sources to enable production of a-farnesene in the R. eutropha strains.

As mentioned in Sect. 3, the production of bioethanol from microalgae involves four main stages: (1) pre-treatment of the biomass to remove hemicellulose and to improve access to available cellulose, (2) hydrolysis of the cellulose (and potentially

the hemicelluloses) to form simple sugars, (3) fermentation of the simple sugars such as glucose (and potentially xylose) to form bioethanol, (4) product recovery where the bioethanol is purified for commercial applications. The overall process for bioethanol production is shown in Fig. 12.

When dealing with high acidity feedstocks, in particular waste frying oils or animal fats from the meat or fish processing industry, one needs to perform a pre-treatment to guarantee that the transesterification reaction is performed in an efficient way and that the quality of the biodiesel obtained follows all the applicable norms such as the EN 14214. Also, it is important to know their characteristics and the presence of contaminants that reduce the efficiency and effectiveness of the alkali-catalyzed transesterification.

For the removal of contaminants, of special concern is the presence of moisture that has a strong negative influence in the transesterification reaction. Water content of waste oils and animal fats may vary considerably depending on the origin. Rice et al. [81] reported a range of 1-5% (w/w) of water contents in waste frying oils. The presence of water inhibits the esterification and transesterification reactions, favours the hydrolysis of triglycerides and FFA, lowers the esters yield, and renders the ester and glycerol separation difficult [7, 18]. If the water concentration is greater than 0.5%, the ester conversion rate may drop below 90% [19]. Water also promotes soap formation in the presence of the alkali catalysts, increasing catalyst consump­tion and diminishing its efficiency. The water content in the feedstock should be lower than 0.06% (w/w) [64, 81]. Heating the waste frying oil or tallow over 100°C, to about 120°C, can boil off any excess water present in the feedstock. For other contaminants, other strategies should be employed in a case-by-case scenario.

The waste frying oils may have other impurities such as solid particles resulting from the food frying and sodium chloride that is added to the fried food. Depending on the feedstock characteristics, the separation of these solid particles may be accomplished by filtration, pressing, or centrifugation. The presence of chlorides may cause corrosion problems in the process equipment and piping system.

The acid value of oil is another important parameter to be determined, since it allows one to evaluate which is the most adequate method to produce biodiesel. For example, depending on the oil acidity, one — or two-step process can be used, where in a first step, the level of FFA is reduced to below 3% by acid-catalyzed esterification with methanol as reagent and sulphuric acid as catalyst and, in a second step, trig­lycerides in product from the first step are transesterified with methanol by using an alkaline catalyst to produce methyl esters and glycerol [94] .

The FFA content of waste frying oil and animal fats vary widely. Waste oils typi­cally contain 2-7% (w/w) of FFA [95], while animal fats may contain 15% FFA but can be as high as 40% [18, 93, 94]. In order to maximize the methyl esters yield, Freedman et al. [39] proposed to use vegetable oils with a FFA content lower than

0. 5% (w/w) in order to not affect the yield of transesterification reaction. Rice et al. [81] reported that a reduction of FFA from 3.6to0.5% increased yields from 73to 87%. Canakci and Van Gerpen [19] referred that a FFA level above 5% can lower the ester conversion rate below 90%. A study from the Sustainable Community Enterprises [85] concluded that due to its high acidity, salmon oil requires an esterification pre-treatment to be possible to perform the transesterification

In the presence of FFA and moisture, saponification reactions occur because the fatty acids react with the catalyst to produce soaps, decreasing the methyl esters yield, or even inhibiting the transesterification reaction. Even in small amounts, these contaminants can reduce the reaction rate by orders of magnitude [18] . Moreover, the formation of soap consumes catalyst and causes emulsions to be formed, which limits the mass transfer between phases, significantly reducing the chemical reaction rate and the selectivity to biodiesel. This further complicates the separation of phases after the reaction completion and makes it difficult to recover and purify biodiesel [7] .

The equations (1) and (2) represent, respectively, the saponification of FFA and esters.

R — COOH + NaOH heat > R — COONa + H2O

FFA Metalic alkoxide Salt Water (1)

R — COOR’ + NaOH water > R — COONa + OH — R’

Ester Metalic alkoxide Salt Alcohol (2)

Aryee et al. [9] used FTIR and titrimetric analytical methods for FFA determina­tion in fish oils extracted from salmon skin, concluding that the FFA content of Atlantic salmon skin lipids increased linearly from 0.6 to 4.5% within the 120 days it was stored at 20°C, as a result of auto-oxidation. Wu and Bechtel [96] also found that the FFA level in salmon heads and viscera increases with the storage time and temperature. From a practical point of view, this results show that at least the fish oils should be used immediately after their extraction, limiting somehow the utiliza­tion at a local scale or when the logistical networks are efficient.

Refined vegetable oils normally do not need a pre-treatment in order to produce biodiesel. However, the waste frying oils and the animal fats with high acidity (more than 2.5% w/w of FFA) need a pre-treatment to reduce their FFA content. This is nor­mally done by acid-catalyzed esterification, using H2SO4 as catalyst and methanol as reagent in the proportions of 2.25 g of methanol and 0.05 g of sulphuric acid per each gram of FFA in oil. From the several approaches proposed in literature such as esterification and distillation refining method [99], Bianchi et al. [14] concluded that esterification is the most attractive to lower the FFA content of waste animal fat to 0.5% (from a typical range of 10 to 25%) using a solid acid ion-exchange resin as catalyst.

During esterification, the FFA are converted to methyl esters, but the triglycer­ides remain essentially unconverted to esters for low methanol to oil molar ratios and short reactor residence times [7, 29, 51, 60]. The esterification reaction can be represented as follows

R — COOH + CH3OH acid catalyst > r — COOCH3 + H2O

FFA methanol esters water (3)

Since water is formed as a by-product during esterification, it needs to be removed or the reaction will be quenched prematurely. One possible approach is to remove water while the reaction occurs, for example, using a membrane reactor. Another approach is to perform the reaction in two rounds with the removal of methanol, sulphuric acid, and water phase in between, followed by the addition of more fresh reactant to perform a second-round reaction driving it closer to completion [19-21, 94]. Zhang et al. [100] suggested the addition of glycerine after the second-round reac­tion to remove all the water from the oil stream, having the advantage of removing the acid catalyst which may cause neutralization of the alkali-catalyst during the transesteri fi cation reaction.

Flocculation is used to amass microalgae cells from the broth. Flocculation can be used as an initial dewatering step that will significantly enhance the ease of further processing. Microalgae carry a negative charge which prevents them from self aggregation within suspension. The surface charge on the algae can be countered by the addition of chemicals known as flocculants. These cationic moieties flocculate the algae without affecting the composition and toxicity of the product. Types of fl occulants include Al2(SO4)3 (aluminium sulphate), FeCl3 (ferric chloride) and Fe2(SO4)3 (ferric sulphate). These multivalent salts are commonly used and vary in effectiveness, which is directly related to the ionic charge of the flocculant.

The other types of flocculants used are polyelectrolytes, which are cationic poly­mers. Polymer flocculants have the advantage of physically linking cells together. The extent of aggregation by the polyelectrolyte depends on the specific properties of the polymer. Key polymer characteristics include charge, molecular weight and concentration. Increasing the molecular weight and charge on the polymers has been shown to increase their binding capabilities.

Lubian [18] showed that at a pH of approximately 4.5 and 6.5, the algal species Rhodomonas baltica achieved flocculation efficiencies of 68 and 77% at chitosan concentrations of 80 and 160 mg/L respectively. Tetraselmis suecica attained 42% efficiency at 80 mg dosage. When the pH of the cultures was pre-adjusted to 8, the algal species displayed efficiencies above 75%. At the moment, there is no reliable correlation between algal taxonomic groups and the concentration of chitosan required for effective flocculation [14]. Lubian [18] observed that pH control is very critical to the performance of microalgal flocculation.

Climate change is a significant issue in today’s world. As accepted by the wider scientific, political and social communities, climate change is unequivocally—a greater than 90% chance—due to global warming caused by the activities of humans since the 1750s (IPCC 2007). Thus a project which absorbs CO2, a major contributor
to global warming, would play a significant role in combating climate change. The importance of the microalgal biodiesel production is underlined by the ability of microalgae to absorb carbon dioxide. Carbon capturing occurs in the cultivation phase of the algae biomass, where CO2 fixation occurs through the biological photo­synthesis reaction. This CO2 bio-sequestration has attracted attention due to the pos­sibility of converting this harmful waste into a valuable product.

The carbon and energy audits are focused on Australia (but applicable else­where), and used as a basis for all the discussions in this section. To reach the CO2 reduction targets, the Australian Federal Government has implemented two primary drivers: The National Greenhouse and Emission Reporting Act (NGER Act) which regulates and sets guidelines on how both Scope 1 (activity direct) and Scope 2 (activity dependent) emissions should be reported; and the Carbon Pollution Reduction Scheme (CPRS) which is the “cap-and-trade” scheme where emitters are required to purchase permits for their emissions.

Under CPRS, emitters who exceed certain limits are required to obtain permits for their Scope 1 emissions. For example, if Company A emits 10 tonnes of CO2-e above a certain limit, the company is required to possess 10 permits (each permit is equivalent to 1 tonne of CO2-e) (NGER Guidelines, 2008). The permit is either allo­cated to mitigate costs of the scheme to some key industries or auctioned to the highest bidder. There is a fixed amount of permits sold in line with the national emis­sion cap, with an initial selling price of $25 per permit [37]. Companies would pur­chase permits if their internal costs of abatement are higher than the price of permits, and would directly reduce their emissions if their internal costs of abatement are lower than the price of permits. It is expected that permit prices might rise to between $35 and 50 per permit by 2020 [37]. The microalgal cultivation process which cap­tures CO2 will reduce the overall Scope 1 emissions of an industry and convert this harmful waste into valuable products. By undertaking a complete audit on the pro­cess, the exact capturing ability of the process will be ascertained and analysed.

The final step in performing an LCA is interpretation of the results to highlight principal themes emerging from the study. In the process of conducting an LCA the analyst should develop a deep understand of the relationship between the model structure, assumptions, inputs, and the model outputs. The analyst should highlight the most important relationships for readers who lack the time or expertise to repro­duce the analysis. The analyst is also tasked with developing broad conclusions from the analysis. Clearly, this process lends itself to subjective interpretation of results and must be handled carefully to ensure the results are as transparent and useful as possible.

One of the most common methods for minimizing subjectivity in data interpreta­tion is to perform a sensitivity analysis in which the connection between modeling inputs and outputs is quantified. For example, Clarens et al., used a sensitivity anal­ysis to report the top five input parameters driving energy use and greenhouse gas emissions during algae cultivation [8] (Fig. 5). The results, shown in Fig. 5, illus­trate how the model outputs respond to a change of ±10% on the input parameters in turn. From these results it is clear that algae high heating value (i. e., lipid con­tent), fertilizer production and application, and CO2 production and application are driving the burdens.

An important element of data interpretation is understanding how errors in the model could propagate and impact final results. Errors can be introduced into the model in several ways, including inaccurate or poorly transcribed data sources, inaccurate relationships in the model, or unrealistic modeling assumptions. A com­mon source of error in LCA models is double counting in which one emission is

incorporated into multiple metrics. Some reactive nitrogen species, for example, can contribute to eutrophication of surface waters and global warming.

One of the most effective ways to interpret the results of an LCA and understand whether there are sources of error is to benchmark the results to related studies. In the case of algae, this step has been largely ignored, probably because there was little prior literature until recently. This does not mean that comparing data to analo­gous systems is not worthwhile. To illustrate this, Fig. 6 shows the energy use required to produce biodiesel from algae from four different studies (one paper has two cases). Following adjustment to a standardized functional unit of 1,000-L algae biodiesel (Fig. 6a), the values are compared to the results from conventional soy biodiesel, a thoroughly characterized process (dotted line) as reported by Hill et al. [12]. A preliminary comparison of the results (Fig. 6a) suggests that algae are either much better or much worse than conventional soy biodiesel. Based on this compari­son alone, it would be difficult to say anything definitive about how favorable algae biodiesel may be relative to soy biodiesel.

Figure 6b summarizes the same results following adjustment of functional unit and system boundaries. As expected, these adjustments make the results of the four algae LCA papers more consistent. This increase in uniformity among stud­ies can be quantified using coefficients of variation (CV), where CV is defined as the ratio or standard deviation to mean value. CV in Fig. 6a, reflecting only nor­malization of the functional unit, is 1.39. From Fig. 6b, we see that CV is dramati­cally reduced, to 0.46, following manual adjustment for system boundaries. This decrease emphasizes the substantial impact of systems boundaries selection, here standardization of upstream nutrient burdens and coproduct allocations, on the outcome of algae LCA studies. A third and final normalization can be carried out in which key model assumptions regarding algae attributes and separations/drying parameters are made uniform across all studies. These parameters have been identified by one or more authors as model inputs that are especially critical dur­ing LCA of energy production from algae. Results from this final step of the assimilation analysis are presented in Fig. 6c. This increase in uniformity among selected studies enables more meaningful comparison between algae biodiesel and an external benchmark, as shown visually in the figure. In Fig. 6c, the various estimates for algae biodiesel, derived independently, then normalized, are very close to the estimate for soy biodiesel.

During data interpretation, it is common to incorporate other elements that are exogenous to the LC model but which could inform analysis of the results. One common example of this is the incorporation of economic drivers into the model. Campbell et al., for example, performed a combined economic and environmental life cycle analysis of producing biodiesel from algae grown in near-shore salt-water ponds in Australia [5]. The results of this study suggest that, based on GHG emis­sions alone, algae perform favorably relative to conventional terrestrial crops. This study is noteworthy because it is the only one to consider growing the algae in salt water. Given the tremendous potential to grow salt water species on marginal, near coastal waters, this is an approach that has been experimentally proposed in several papers by Chisti but for which little life cycle modeling results exist [6] .

8 Conclusions

This chapter surveyed some of the key challenges associated with utilizing LCA methodologies for studying algae-to-energy technology. These challenges have emerged over the last two years as a large number of systems-level life cycle studies of proposed algae-based energy technologies have appeared in the academic literature. Before 2009, only a few algae-to-energy LCAs had been published and even these were only nominally LCAs [ 15]. The assumptions about cultivation and drying that were used in these studies were not highly representative of previ­ously published reports. The recent work better reflects the way that the industry expects algae-to-energy systems will be deployed in the field, but the results are difficult to compare directly because of the varied boundaries and assumption specified by the authors. This chapter has highlighted most of the normative judg­ments faced by LC practitioners and discussed each in the context of algae-to-energy systems in order to support future work in this area.

From the existing literature, several themes begin to emerge that will assist in designing future analyses. One of the most common is that recent LCAs echo many of the conclusions of the first-generation algae research conducted in the 1980s and 1990s. These studies suggested many of the system’s-level implications of large-scale algae deployment [35]. Benneman’s report to the United States Department of Energy concluded that open ponds would be the only economically viable way to grow algae for sequestering CO2 from power plant flue gases [2]. Similarly, Votolina and others have suggested that algae-based wastewater treatment would be a technically com­petitive approach for conducting tertiary treatment of wastewater [32]. In both cases, these conclusions are well aligned with the results of more recent LCA studies, even though these early reports never use the term “LCA.” A second important theme is that algae-to-energy systems have a long way to go technologically before they are viable from an environmental burden standpoint. In this regard, LCA is a powerful design tool because it allows for a focus of R&D on those processes that will have the most significant impact on reducing the burdens of the processes as a whole.

In light of the large amount of investment in the algae-to-energy field, it is likely that LC tools will continue to be used to understand and assess the impacts of these emerging technologies. In order for these studies to be more immediately compa­rable, it is important that the community develop nominal assumptions about how to handle algae systems. This chapter can serve as a first step toward developing these norms.

Acknowledgments The authors gratefully acknowledge funding for this study from a UVA Fund for Excellence in Science and Technology Grant.

Today, there is a wide range of classical or conventional extraction techniques that have been traditionally employed for the extraction of interesting compounds from natural matrices, such as algae. In this group, techniques such as Soxhlet, liquid-liquid extraction (LLE), solid-liquid extraction (SLE), and other techniques based on the use of organic solvents are included. Although these techniques are routinely used, they have several well-known drawbacks; they are time consuming, laborious, they lack of automation and therefore are more prone to present low reproducibility, have low selectivity and/or provide low extraction yields. These shortcomings can be partially or completely overcome by using the newly developed advanced extraction techniques. This new kind of extraction techniques are characterized by being faster, more selective towards the compounds to be extracted, and also very important nowadays, these techniques are more environmentally friendly. In fact, by using the considered advanced extraction techniques, the use of toxic solvents is highly lim­ited. In the next sections, the most important advanced extraction techniques that have been employed to extract bioactive compounds from algae are briefly described and commented.

The pH is another important environmental factor for the ADP. Different groups of methanogens have different ranges of optimum pH. The acidogens exhibit maxi­mum activity at pH 5.5-6.5 while the optimum for methanogens is pH 7.8-8.2 [68]. Since the methanogens are more sensitive to pH variation, the pH in anaerobic digesters is usually maintained in the range of 7-8. Rapid inhibition of methanogens at pH higher than 8 can be caused by dissociation of NH4+ to the neutral NH3 form [69]. The presence of alkalinity is an important marker of pH persistence in anaero­bic digesters. The bicarbonate alkalinity buffers the fluctuations in the generation of VFAs and carbon dioxide at pH close to neutral. A stable ADP is characterized by the bicarbonate alkalinity in the range from 1,000 to 5,000 mg/L as CaCO3 [70].

The ratio between VFAs to alkalinity should be in the range of 0.1-0.25. A further increase of the ratio of VFAs to alkalinity indicates possible process deterioration and requires the OLR to decrease in order to lower the VFA formation rate.