я-Butanol has favorable properties as a gasoline blending agent and provides a valu­able target to validate R. eutropha as a host for synthetic biology [14]. The PHB synthesis pathway in R. eutropha proceeds through 3-hydroxybutyryl-CoA, which is also an intermediate in the я-butanol synthesis pathway. Our strategy will be to divert the flux from 3-hydroxybutyryl-CoA to PHB and to redirect it to я-butanol. Recent work has demonstrated that high titers of я-butanol can be produced in E. coli by choosing heterologous genes judiciously and maximizing reducing equiv­alents available for я-butanol production [2]. Therefore, a detailed understanding of the expression of heterologous genes for я-butanol production in R. eutropha and the metabolic flux in R. eutropha PHB — mutants will be essential in achieving high titers of я-butanol.

Although я-butanol can be used directly as a gasoline replacement due to its higher energy content and lower water solubility and corrosivity relative to ethanol, it would only address short-haul ground transportation, since it could not be used to power aircraft or long-range rail and trucks. Dehydration of butanol to butylenes (C4) and oligomerization affords C8, C12 and C16 olefins with some disproportion­ation to non-oligomer C9 , C10, C11, C13, C14, and C15 olefins. These olefins can undergo double-bond isomerization, skeletal isomerization, cyclization and/or aro — matization, forming isoalkenes, cycloalkenes, and/or aromatic products. Hydrogenation of this mixture may provide hydrocarbons suitable for use as jet fuels. We will transform the я-butanol obtained from H2/CO2 cultivation of engi­neered R. eutropha to these hydrocarbon mixtures and evaluate them as replacement for jet fuel. We are currently exploring novel catalysts for the dehydration and oli­gomerization of butanol to hydrocarbon mixtures that resemble jet fuel.

Depending on the characteristics of the fatty waste materials, normally three main steps are performed:

• Extraction of the oil or fat from the fatty feedstock.

• Filtration and removal of contaminants.

• Neutralization or esteri fi cation of the FFA.

The extraction process is relevant in the meat and fish processing industry, as most types of fatty waste materials are normally associated with other materials, for example meat residues and bones or fish’s heads and viscera. Thus, it is necessary to separate the oil or fat from the remaining materials. Depending on the fatty residues to separate, the process varies, involving for example heating or solvent extraction.

At the laboratory scale, the waste animal fats (e. g. tallow, lard, or poultry fat) collected from slaughterhouses or food processing companies can be melted and filtered in order to obtain the fat and remove gums, protein residues, and suspended particles [67]. For extracting the fish oil from the fish’s residues, firstly fish’s viscera and heads can be cooked thoroughly in boiling water. The supernatant oil is taken from the top of the boiling vessel and placed in a separatory funnel, where the oil is washed (with distilled water at about 60°C) and separated from the water and solid residues. The fish soapstock is squeezed and as a result the crude fish oil containing some solid impurities is separated from the cake of fish dregs. The resulting crude oil is centrifuged and placed in a separatory funnel where it is washed. Finally, the oil is vacuum-filtered to remove any remaining impurities.

Industrially, the bulk of the material to be rendered consists of the leftover parts of a slaughtered animal (fats, bones, and other parts). The first step in the rendering
process is the milling and grinding of a mixture of materials to generate a mass that is screw-conveyed to a batch digester where it remains for 4-5 h to be cooked with saturated vapour at about 110°C, until it loses about 70% of its moisture content. Then the digester is opened and its contents are discharged into a percolator tank, heated by steam, where the liquid fat separates from solids by percolation and siev­ing. After percolation, the fat is centrifuged and/or filtered and sent to a decanter tank for storage and eventual final separation from the aqueous phase present. The solid material removed from the fat in this operation is added to the solid material from percolation. The solid material is hot-pressed generating more fat that is added to the one percolated for purification. The pressed material is milled in a hammer mill, and then sent for screening to obtain the particle size of flour. The material retained in screening returns to the mill. Passing through the screening the meat/ bone meal is bagged and stored for shipping and using in pet food [25].

The industrial process for extracting oil from fish by-products (e. g. heads, viscera, fish bones, and skin) operates in a continuous mode. Thus, after milling and grinding the fish, by-products are screw-conveyed to a continuous steam cooker with a resi­dence time of about 15 min. After cooking, the coagulated mass is pre-strained in a strainer conveyor before entering a screw press that separates the press cake from the press liquor. The press cake is disintegrated in a tearing machine (a wet mill) and dried in an indirect steam dryer with internal rotating blades. The meal passes through a vibrating screen furnished with a magnet to remove extraneous matter-like pieces of wood and metal (e. g. fish hooks) before entering the hammer mill. The ground meal is automatically weighed out in bags that are closed and stored. The press liquor then passes through a buffer tank before separation into oil, “stick” water, and fine sludge in a centrifuge. The oil passes through a buffer tank before water and sludge impurities are removed (polishing) in the oil separator. After polishing, the oil often passes through an inspection tank before storage in the oil tank [35].

Another possibility for extracting lipids from fatty waste materials is by using an organic solvent, such as я-hexane. For example, Nebel and Mittelbach [72] tested nine solvents for extracting fat from meat and bone meal, obtaining about 15% fat with all solvents, but я-hexane was found to be the most suitable solvent to perform the extrac­tion, because it is relatively cheap and has a low boiling point. The fat was then con­verted to methyl esters via a two-step process, whose quality was according to the European specification for biodiesel (EN 14214) except the cold-temperature behav­iour and the oxidation stability. Oliveira and Bechtel [74] described a solvent extraction procedure using a 2:3 solution of isopropyl alcohol/hexane (99.9% purity) for extract­ing lipids from salmon’s by-products including heads, viscera, frames, and skin.

The sizes of both photobioreactors (HTR and ELR) and the raceway pond are esti­mated based on the capacity to produce 50,000 tonnes of dry weight biomass annu­ally. The volume and area of the horizontal photobioreactor were scaled up from the data presented by Chisti [6], the external loop bioreactor was scaled up from the design by Acien Fernandez et al. [1] , and the raceway pond was scaled up from the Outdoor Test Facility design in Roswell, NM [31]. Table 1 contains the annual production of biomass for each cultivation system and the biomass produced and harvested per batch.

1.5 Dewatering

Methods such as centrifugation, pressure filtration, vacuum filtration and tangential flow filtration (TFF) are used unaided to dewater microalgal biomass. This section explores the use of different dewatering methods either alone or as a preceding step to the aforementioned unit operations for microalgae dewatering.

The cost of the extraction and transesterification stages was based on one biomass production process technology, with the results shown in Fig. 13. The extraction stage was found to be significantly more expensive than the transesterification stage. The major contributors to the extraction costs were the large fixed capital costs and the cost of large quantities of solvents, respectively. The main components of the fixed capital costs were the costs of large mixing tanks and the pumping capacity required in filling and emptying the tanks. The small cost incurred during transesterification resulted from the significantly reduced volume of materials that required processing, with only 7.8 tonnes of saponifiable lipid estimated to pass to

■ Cultivation □ Dewatering О Extraction ■ Esterification

Fig. 13 Extraction and transesterification costs for algal lipids the transesterification stage daily from the original 151 tonnes of biomass processed in the extraction stage. The electricity costs for both processes were minor, as shown in Fig. 13.

1.7.2 Overall Production Costs

Overall, considering the economic outcome, a raceway pond coupled with a dual­stage dewatering process would be the preferred method to produce biodiesel. Considering biodiesel as the only saleable product, production costs were estimated as approximately $74/L of biodiesel. The calculations are based on the assumptions that glycerol is allowed to be sold, residue from the process also sold as animal feed and carbon credits received as discussed in Sect. 7. However, including these in the model only reduced biodiesel production costs to $72.60/L. With petroleum-based diesel currently retailing at ~ $1.10/L, though this analysis incurred a -50% error, biodiesel from microalgae still remains far too expensive, as compared with tradi­tional fuel.

There are at least three types of uncertainty associated with most LC studies. The first has to do with modeling inputs. Although many LCA practitioners utilize a single average value for modeling inputs, all parameters generally exhibit a range of values in the real world and all measurements are subject to some unknown error. Key examples of algae modeling parameters that may have wide ranges of values or unknown measurement errors include algae yield; algae lipid content; conversion efficiency; and even life cycle impact factors (energy use, GWP, etc.) for material inputs such as electricity from the US grid, etc. The second type of uncertainty is associated with spatial and temporal differences in systems operation. These sys­tematic differences in time and location can have important effects on LCA results; (e. g., it is reasonable to expect higher algae yields in sunnier parts of the country). The third and final type of uncertainty arises from extrapolation of bench-scale data to hypothetical full-scale systems. This type of uncertainty is largely unavoidable at present, in the absence of many full-scale algae-to-energy systems that have been in operation for any appreciable length of time.

Stochastic tools have become increasingly important for bounding uncertainty in LCA over the last few years (Fig. 4). Monte Carlo analysis is one of the common stochastic tools used by practitioners [30]. This method is useful for quantifying a range of probable output values from a series of input variables which have been assigned empirical or theoretical distributions. These distributions make it possible to encapsulate the three types of uncertainty referenced in the previous paragraph. Repeated sampling from the input distributions creates distributions of output val­ues, which can be parameterized to give empirical estimates of mean or median. Empirical uncertainty for output parameters can also be quantified using standard deviations, standard errors, or percentiles [10]. Most life cycle software (e. g., SimaPro and GaBi) now include stochastic toolkits to perform Monte Carlo and related analyses. For LC practitioners using spreadsheet-based models, a number of commercial add-ins (e. g., Crystal Ball® and @Risk®) allow for flexible management of input and output distributions in models. It should be noted that few of the life

cycle studies published to date have included uncertainty, largely because data availability is a limiting factor and the computational complexities are nontrivial. Moving forward, it will be necessary for algae life cycle models to address this uncertainty in a systematic fashion.

Although the biochemical composition of algae is very different among algal groups, cellulose is a common material among many algal species. The process of cellulose biological degradation has been extensively studied in recent years. The mechanism of cellulose enzymatic hydrolysis by anaerobic bacteria is quite different from the mechanism of aerobic organisms. Anaerobic bacteria have a large multienzyme complex—cellulosome, which is attached to the cell envelope and consists of up to 11 different catalytic enzymes carried by scaffold-proteins [113, 114]. The enzy­matic hydrolysis of algal cellulose is relatively slow and can be inhibited by the close association with other structural materials, such as polyphenols, fucoidan, protein, and alginate. Therefore, other specie-specific sulphonated, methylated or carboxylated polysaccharides, mannitol, proteins, and lipids usually determine the more readily biodegradable fraction of algal biomass.

Archaea obtain a limited amount of energy from methanogenesis and possess the slowest growth rate among the anaerobic digester microorganisms. Maintaining valid environmental and operational parameters for archaea is one of the key factors for effective methane production. The main environmental factors are temperature, pH, alkalinity, and redox potential. Operational parameters, such as C:N:P ratio, the presence of essential micronutrients, organic loading rate (OLR), hydraulic (HRT) and solids retention time (SRT), and incoming salts and toxicants concentration are subject to tight control and regulation. The accumulation of certain intermediates or byproducts, such as VFAs, ammonia, and hydrogen sulfide, can lead to inhibition of methane production [62].

1.1.1 Temperature

Temperature is one of the most important environmental factors for methanogene — sis. The methanogenic archaea can be classified according to the temperature ranges for maximum growth and substrate utilization rates. The optimal temperature for growth of psychrophilic organisms is between 10 and 15°C, mesophilic between 35 and 40°C, and thermophilic between 58 and 68°C [63, 64]. The rate of methane generation by psychrophilic microorganisms is significantly slower compared to mesophiles and thermophiles, and therefore the psychrophilic regime is rarely used for large-scale methane production. The methanogens are the most sensitive organ­isms to temperature variation. A sudden temperature change as small as 2-3°C causes an accumulation of VFAs and a decreasing methane generation rate espe­cially at thermophilic conditions [65]. A significant temperature drop affects the activity of all anaerobic microorganisms and ceases methane production, but the microorganisms are able to recover after temperature stabilization [65-67].

Advanced gene manipulation tools are essential for an efficient application of genetic engineering technology, but they are still in the development stage. These gene manipulation tools include:

• Homologous gene replacement or nuclear gene targeting [346, 347]

• Inducible nuclear promoters [348]

• Gene silencing approaches [349-351]

• Gene expression regulation by riboswitches [352, 353]

• New protein tagging approaches [354]

Chloroplast Transformation Technologies

Commonly, wild-type green algae have heavily stacked thylacoids and large light­harvesting antenna complexes (LHC) acclimated for low light conditions that cause photoinhibition under high light conditions [355, 356]. To eliminate the formation of reactive oxygen species, the absorbed photons need to be released as fluorescence and waste heat. This release of energy reduces conversion efficiency of light energy to biomass [357]. One solution to increase the conversion efficiency is to reduce the LHC size and enhance light penetration to the growth media [357-360]. For exam­ple, RNA interference technology was applied to downregulate the entire LHC gene family in C. reinhardtii [361].

Metabolic Network Reconstruction and Simulation

A metabolic network model is a prospectively powerful tool for selection of the most suitable wild-type organism, as well as to provide direction for the genetic engineering of a more efficient mutant [362]. Combinations of knockout and added genes can be optimized for target product and/or biomass yield [363, 364]. A genome scale reconstructed metabolic network improved bioethanol production for Saccharomyces cerevisiae though genetic engineering and evolutionary adapta­tion [365, 366]. The optimal parameters of the designed bioprocess are the growth media composition, the level of irradiation, the temperature, the product yield, the physical dimensions, and the cost efficiency of the overall process [367] .

Knowledge of the occurrence of in situ GH is very incomplete, and is either based on limited direct evidence (hydrate samples) or inferred from other data. In perma­frost regions, direct evidence of gas hydrate is provided by ongoing R&D programs (discussed below), and by analysis of industry 3-D seismic data and data obtained during the drilling and logging of conventional oil and gas wells. In marine environ­ments, most of the inferences of GH occurrence are based on indirect indicators involving interpretation of relatively low-quality 2-D seismic data. Direct GH detec­tion and characterization from marine 3-D seismic data have recently been reported by Dai et al. [32]. The use of four-component ocean bottom seismic surveys has also shown great promise [4, 14].

GH dissociation takes place in the reservoir. The transformation of the solid GH into gas and free water begins next to the well, and moves outward over time as dissociation continues. The well design must allow for the production of natural gas with variable amounts of free water. GH wells will be more complex than most conventional and unconventional gas wells because of a number of technical challenges, including:

• Maintaining commercial gas flows with high water production rates. The water production from a GH reservoir could be highly variable, and water-to-gas ratios in excess of 1,000 bbls/MMSCF (i. e., 100 times larger than what is expected in conventional gas wells) are possible. This requires some form of artificial lift.

• Operating with low temperatures and low pressures in the wellbore to prevent hydrate formation or freezing in the wellbore and flowlines—this is critical for onshore developments producing from below thick permafrost layers. Coupled with the high water production, this requires larger wellbore, tubing, and flowlines in order to minimize friction losses.

• Controlling formation sand production into the wellbore.

• Ensuring well-structural integrity with subsidence in the reservoir and GH dis­sociation around the wellbore.

Technologies exist to address all of these issues, but add to development costs, especially compared to other nonconventional sources of natural gas. GH develop­ment also has one distinct challenge compared to other unconventional resources, and that is the high cost of transportation to market. Additionally, GH developments cannot be effectively drilled at the close well spacing that is used in heavy oil because of the much lower value of gas.