The bio-oil and biomass fuel (methane) be reported as:

Pbo Pgm ‘ tyharv ‘ tycellys ‘ tysepgc

and:

Pbmf Pgm ‘ tyharv ‘ tycellys ‘ tysepBs ‘ Фте/bmf ^

where is the productivity (of bio-oil (BO), biomass fuel (BMF), and grown mass (GM)) and ф represents the efficiency of harvesting (harv), cell lysing (cellys), separations (sep) (of biocrude (BC) and biomass in the post-extraction slurry (BS)), and refining (ref). Each efficiency is defined
as the mass of the output divided by the mass of the input for that step (cf. [6]). For example, the biocrude separations efficiency, 9sepBC, is defined as the mass of biocrude recovered divided by the lysed algal biomass, and the neutral lipid fraction is embedded in this efficiency [6].

The macroscopic ordering displayed in living systems is an “emergent” property arising from a collective behavior of the elementary microscopic components [93]. The low-frequency internal motions in protein molecules play a key role in biological functions where it is suggested that there is a direct relationship between low-frequency motions and enzymatic activity [94]. The symmetry of protein molecules is also a very important factor in understanding its structure and function, which depends on stability, num­ber of subunits, and folding efficiencies that limits the functionality of the protein. The functionality requirements of symmetry and asymmetry can drive the evolution of proteins to have any of the crystallographic point groups [95]. The breathing motions demonstrated by protein molecules are oscillations of the protein’s symmetry emanating from the center of symmetry of the molecule. These vibrations could potentially be a source and receiver of multipole EMF. Symmetrical and oscillatory nature of pro­teins, which constitute enzymes, exhibits unique features that have the potential for interaction via external multiple EMF coupling.

At larger scale, two popular cultivation systems have been used for mi­croalgal biomass and lipid production: open raceway ponds and closed photobioreactors. At present, open pond systems, especially large raceway ponds, are much more widely used, but bear the risk of attracting compet­ing algae, grazers or viruses [38]. Although minimizing the cost of algae farming is one of highest priorities to achieve commercial algal biodiesel production, both systems require optimization of complex factors that sat­isfy high level production cultivation.

For example, these include irradiation, nutrients, temperature, dis­solved O2 and CO2 contents, pH, salinity, water quality, mixing efficiency and harvesting ability. Culturing and environmental conditions affect algae productivity, lipid yield and fatty acid compositions. For example, a pilot study showed that high growth rates and lipid accumulation of Chlorella sp. could be achieved primarily by increasing nitrogen concentrations and nitrogen starvation, respectively [39]. Similarly, growth of hydrocarbon- producing Botryococcus braunii was strongly dependent on light, tem­perature, salinity [40], nutrient quantity and composition [41]. Total lipid, carotene and chlorophyll contents of Navicula sp. increased with increas­ing salinity of the medium from 0.5 to 1.7 M NaCl [42]. In some cases, wastewater (e. g., municipal wastewater) can be used as a nutrient source of microalgae growth. Pulz suggested that productivity of 60-100 mg dry weight L-1 d-1 or a biomass concentration of 1 g L-1 is achievable in open pond systems [43]. Table 2 lists some of the desirable traits for the selec­tion process of microalgae with potential for biodiesel production.

FIGURE 2: Suggested 5-step protocol for rapid selection of microalgae for biodiesel production. Step 1: Local sampling sites should be chosen where microalgae frequently undergo adverse conditions; Step 2: Dilution series in growth medium provide the simplest, most cost-effective and fastest method; Step 3: Nile red staining of near stationary cultures followed by visual inspection provides a simple and rapid screening for algae with high lipid accumulation ability; Step 4: A standardized growth assay in the laboratory can provide comparative data on lipid productivity; Step 5: Parameters can be directly optimized under outdoor conditions using mid-scale cultures as these are often very different to small-scale laboratory conditions.

To significantly improve photoautotrophic cultivation, new microalgae strains and process conditions must be developed to enable fast growth and high lipid accumulation at high CO2 levels [64,65]. If CO2 is from flue gas, algae strains’ high tolerance to SOx, NOx, and high temperature is desirable [66]. Morais et al. [67-69] isolated several microalgae from the waste treatment ponds of a coal fired thermoelectric power plant, and in­vestigated their growth characteristics when exposed to different concen­trations of CO2. When cultivated with 6% and 12% CO2, Chlorella kessleri showed a maximum biomass productivity at 6% CO2 while Scenedesmus obliquus showed a maximum biomass productivity at 12% CO2. They also found that Spirulina sp., Scenedesmus obliquus, and Chlorella vulgaris grew well when the culture medium contained up to 18% CO2, and Spiru­lina sp. exhibited the highest rate among them. Chang et al. [70] found that some strains of Chlorella could grow in an atmosphere containing CO2 up to 40%. Base on these studies, we can try to use some means to promote microalgae growth stimulated by CO2 addition [71]. Such as pump CO2 emission from power plants, industrial processes, or soluble carbonate through a sparger system of orifices evenly distributed over the bottom of the ponds when cultivating microalgae in large open-ponds.

The bioenergy supply must use sustainable sources and create high greenhouse gas emission savings compared to fossil references in order for the Ecofys Energy Scenario, to which this study is related, to be considered sustainable. To ensure this we have derived a set of sustain­ability criteria to assess the sustainable bioenergy potentials from resi­dues, wastes, complementary fellings, energy crops and algae that we apply in this study. An overview of these criteria is presented in Table 2. For reader convenience, the details and application of these criteria are described together with their results in the Section 3 as they are closely related to each other.

3.3 SUSTAINABLE BIOMASS AVAILABILITY

3.3.1 SUSTAINABILITY OF BIOENERGY: LAND USE AND FOOD SECURITY

Our work explicitly prioritises a number of land uses over land use for bioenergy cropping. In addition, we restrict bioenergy cropping to land suitable for rain-fed cultivation of energy crops in order not to require ir­rigation as an agricultural input.

Therefore the following land is not used for bioenergy cropping in this study:

• Land used for supplying food, feed and fibre; taking into account future population growth and a diet change scenario

• Land used for protection of biodiversity and high carbon stock forest eco­systems

• Land used for human development by expanding the built environment

• Land not or marginally suitable for rain-fed cultivation of energy crops

We performed an assessment of land potential for rain-fed cultivation of energy crops based on this land use prioritisation. This assessment was based on data from a recent IIASA study [15]. Section 2.7 in that report provides an assessment of production potential for different bioenergy crops. We used the source data of this study and additional own analyses to perform the assessment using a stepwise approach. In this stepwise ap­proach we started with the total global land mass excluding Antarctica and applied a number of exclusions to account for the sustainability criteria for land use in our work. This resulted in a 6,730,000 km2 (673 million hect­ares) potential for energy crops in this study as shown in Fig. 3.

The assessed potential is located on grassland and non-densely vege­tated woodland. Most land of these types is currently used as low-intensity grazing lands for livestock. It can be made available for other purposes through a combination of limiting future demand for livestock products and intensifying livestock systems with a low current intensity. As a reference,

the total land currently used to support livestock is included in Fig. 3. The value of 39,200,000 km2 (3920 million hectares) is based on estimates of the agricultural cropland used to grow animal feed crops [15] and the land used as permanent meadow or pasture [16].

The applied exclusions were as follows:

1. Exclusion of all current protected land, barren land, urban land and inland water bodies as they cannot be used for agriculture. Accord­ing to the IIASA data, this totals 54,230,000 km2 (5423 million hectares).

2. Exclusion of current agricultural cropland to safeguard current food production. According to the IIASA data, this totals 15,630,000 km2 (1563 million hectares).

3. Exclusion of conversion of all current unprotected forested land to protect forest biodiversity and forest carbon stocks. According to the IIASA data, this totals 28,060,000 km2 (2806 million hectares).

4. Exclusion of all land that is not or marginally suitable for rain-fed agriculture to ensure only rain-fed bioenergy cropping. We derived from the IIASA data that this totals 25,150,000 km2 (2515 million hectares). This is based on a conservative assumption where we used the highest per-crop value for availability of moderately suit­able, suitable and very suitable land among the different crops ana­lysed in the IIASA study as the value for all crops combined. This means that we assume that there might be a 100% spatial overlap of the given per-crop potentials. The used availability of moder­ately suitable, suitable and very suitable land is therefore the mini­mum availability, meaning the applied exclusion of not or margin­ally suitable land is the maximum exclusion possible based on the data set.

5. Exclusion of additional land for future requirements for biodiversity protection, human development and food demand. These amounted to an additional total of 2,200,000 km2 (220 million hectares). This number was based on Ecofys analyses discussed in more detail in Sections 3.1.1 through 3.1.3.

As shown above, the 2nd O EROI and PFROI are less than 1 for algal bio­fuels produced in this production system, even for the Highly Productive Case, which assumes efficient growth and processing methods. Includ­ing additional expenses that were omitted by this analysis (i. e., capital, labor, externalities, etc.) would further reduce profitability. Additionally, transportation using algal biofuels produced in these cases is more water intensive and resource intensive than conventional fuels. The challenge for achieving energy-positive, profitable biofuel production from algae is rooted in the thermodynamic challenges associated with converting ma­terials with low energy density (such as dispersed photons, CO2, and nu­trients) to energy-dense fuels [29]. This conversion requires a significant reduction in specific entropy, which thereby requires a significant amount of work input (i. e., energy expense). This body of work demonstrates that producing petroleum fuel substitutes from algae without using discounted electricity, nutrients, and/or CO2 is not energetically favorable with the existing technologies considered. Although improving algal biomass pro­ductivity by optimizing physical conditions, biochemical conditions, and by genetic engineering might improve the overall biofuel yield, the re­quired amounts of carbon, nitrogen, and phosphorus are dictated by stoi­chiometry. While some variation in algal stoichiometry exists (evidenced, for example, by the change in chemical composition that can occur under different growth conditions, such as nitrogen starvation), any algal spe­cies will inevitably be constrained by stoichiometric conditions. Thus, producing more algae (e. g., by increasing photosynthetic efficiency) also increases the nutrient requirement and the associated energy embedded in the nutrients. As shown in this study, there can be more energy and cost embedded in the nutrients consumed than produced in the resulting algal biomass. As a result, low-energy and low-cost sources of carbon, nitrogen, phosphorus, and water (for example, from waste streams) would likely be needed.

Researchers have two options for conducting analyses for non-com­mercial algal energy production processes: (1) use data from experimental processes (which are devoid of the efficiencies that accompany large-scale production) or (2) use data from models of future commercial-scale sys­tems. The Experimental Case and the Highly Productive Case represent these two approaches, respectively. The presentation of results for both measured lab and up-scaled estimates is important because it enables more informed modeling of the innovation process from lab to production. It is unclear how well a lab-scale experiment needs to perform before moving to the next stage of development. We see this simultaneous presentation of multiple metrics (EROI, FROI, water intensity, nutrient constraints, and CO2 constraints) as part of a critical due diligence process for inventors and investors. Ongoing, it can be possible to have standard experimental test conditions that enable consistent comparison and tracking progress as new technologies are incorporated into the process chain. This tracking of progress can mimic that of the photovoltaic cell industry.

Since the Experimental Case contains many lab-scale artifacts, the constraints on the Highly Productive Case are more representative of the challenges that will be faced by the algal biofuels industry. Most of the conclusions in this study are based on the Highly Productive Case and the targets provided in the following section for achieving profitable algal biofuel production rely on the Highly Productive Case for comparison. 2, 5 1967

8.2.1 ALGAE ARE HARVESTED AND CONCENTRATED ONTO AMBERLITE ANION EXCHANGE RESIN

Amberlite CG-400, a divinylbenzene-based resin containing quaternary ammonium groups (3.8 mmol/g [21]), has been previously shown to bind and concentrate two different species of green algae for removal from the water supply [22]. Here, we used Amberlite to concentrate and dewater biomass from two potentially high oil-producing green algal species, Neo — chloris oleoabundans [23] and KAS 603 [24]. For routine comparisons, algal suspension was passed through the column until saturation was achieved. Algae appeared to bind on contact, initially accumulating as a band at the top of the resin bed. As more algae were added, the upper green layer progressively expanded down to the bottom of the resin bed. Prior to the resin bed becoming solid green, the flow-through was clear and color­less. Once the resin bed became solid green, color quickly began to appear in the effluent and this was taken as the saturation point.

To determine the binding capacity of the resin for Neochloris and KAS 603, the OD680 of the algal suspension was measured before and after passing the algae through an Amberlite column. The concentration of al­gae in the initial suspension and the in flow-through were determined from algal OD680 absorbance versus dry cell weight (DCW) calibration curves generated for both types of algae. The flow-through included the solution from the algae algal suspension that went through the resin and the sub­sequent wash to remove unbound algae. The total amount of algae bound to the resin was then determined by subtracting the amount of algae in the flow-through from the total that was added to the column.

For all the experiments algal binding to the resin was tested at pH 7, although there was no significant difference in binding over the pH range of 6-9. Since for Amberlite, algal binding decreases with increasing ionic strength, binding studies for both algae were tested under freshwater con­ditions (salinity 5 psu). The algal suspensions were used at 0.4 g/L, the value that we typically obtain in our simple airlift photobioreactors. When more dilute algal suspensions were tested, saturation of the resin was still achieved but, as would be expected, the volume of algal suspension and time required to achieve saturation increased. Since algae can be entrained in the resin beads, our protocol included a washing step between binding and elution to ensure only algae that were actually bound to the resin were counted.

Figure 1(a) gives a schematic for resin-binding and elution experi­ments shown in Figures 1-3. Algal binding capacity was determined for each cycle of algal binding. Reusability of the resin was assessed in terms of loss of binding capacity over multiple cycles of algal binding and elu­tion. These experiments were carried out either using only fresh sulfuric acid/methanol reagent to elute the algae (Series A, “fresh”) or using sulfu­ric acid/methanol that was recycled after the first use (Series B, “used”).

Although the flow through approach in Figure 1(a) gave repeatable values for saturation and was used for all experiments except that shown in Figure 4, we later found that longer contact times, which involved stirring excess algae with resin for up to 15 min, gave somewhat larger binding capacities than those obtained using the flow through method. However, while the binding capacity numbers would be improved with longer con­tact times, the patterns remain the same.

The results showed that the binding capacity for Neochloris was 37.0 mg/g resin whereas that for KAS 603 was 12.8 mg/g resin [Figure 1(b)]. In terms of efficiency of removal of algae from 1 L of 0.4 g/L suspension, 10 g of Amberlite will sequester 93% of the Neochloris, whereas 30 g of Amberlite will sequester 96% of the KAS603 during a single pass through the column. Although the difference in binding capacity could possibly be attributed to differences in surface charge density, studies to be detailed elsewhere show that the nature of the resin backbone, especially its hy — drophobicity has a large impact on binding capacity [25]. In resins based on a methacrylate backbone, both Neochloris and KAS 603 showed similar

binding capacities. Furthermore, binding capacity was greatly increased with the inclusion of glycol groups, which do not alter the charge charac­teristics of the resin but make it more hydrophilic.

After binding, algae were eluted with 100 mL of 5% sulfuric acid in methanol. This treatment visibly removed algae from the resin and regenerated the resin. It appears that most of the algae was dissolved by the sulfuric acid/methanol reagent as shown by filtering an eluate of

FIGURE 2: FAME yield and characterization by resin-bound transesterification. (a) FAMEs from normal or stressed (nitrogen-starved) Neochloris and KAS 603 were generated either by elution of resin-bound algae with 5% sulfuric acid/methanol reagent or by subjecting dry algal pellets subjected to sequential base hydrolysis followed by treatment with sulfuric acid in methanol to esterify free fatty acids. The FAME yields were expressed as percent of dry cell weight. The weight of a crude lipid extract from parallel batches of algae and the TAG content those extracts (determined by HPLC) are given for comparison. The FAME prepared from resin-bound algae were analyzed by HPLC/MS to determine the abundance of (b) saturated C16:0, C18:0, and C20:0 and (c) unsaturated C18:3 and C21:4 acyl constituents relative to total FAME derived.

recovery method

FIGURE 3: Efficiency of FAME production using recycled reagent, and FAME removal. (a) Algal were loaded onto Amberlite and eluted with 5% sulfuric acid/methanol four times in succession using either fresh reagent or reusing the old reagent after the initial elution. Twelve h after each elution the FAME was extracted with hexane, solvent was removed and the amount of FAME determined as a percentage of algal dry cell weight; (b) The sulfuric acid/methanol reagent containing algal FAME was either extracted with hexane or, for comparison, passed over a hydrophobic resin column composed of 80% EGDMA and 20% HMA; (c) FAME relative to total dry algal weight obtained by hexane extraction or after eluting FAME bound to the hydrophobic column.

FIGURE 4: Functionalized resins for improved binding of KAS603. The relative binding capacity of Amberlite for KAS 603 is compared to anion exchange resins composed of either (a) EGDMA-IM-DEG (60:30:10) or (b) DVB-DMA (60:40); (c) The results show that the binding capacity of Amberlite is low compared to the other resins; (d) In addition to binding more algae, subsequent elution of either Amberlite or DVB-DMA shows that greater amounts of FAME are produced from the DVB:DMA resin.

Neochloris through a pre-weighed PTFE filter (Millipore Omnipore, pore size 0.1 pm). Only 6.1% of the original DCW was recovered on the filter.

In order to determine how completely the resin was regenerated, resin columns were loaded with Neochloris [Figure 1(c) “fresh”] or KAS 603 [Figure 1(d) “fresh”] and eluted with fresh portions of sulfuric acid/metha — nol four times in succession. For comparison, parallel experiments were carried out where, after the initial binding and elution using fresh sul­furic acid/methanol solution, the reagent was then recycled for the three remaining cycles of algal binding and elution for both Neochloris [Fig­ure 1(c) “used”] and KAS 603 [Figure 1(d) “used”]. With each cycle, the amount of algae bound to the resin was determined based on the OD680 as described above. Using fresh reagent, Neochloris after the initial bind­ing and elution, binding capacity dropped to 97% of its initial value and remained constant thereafter. Using fresh reagent with KAS 603, binding dropped to 39% of its initial value after the first cycle but remained con­stant thereafter. When the experiments were carried out using the same sulfuric acid/methanol reagent for each cycle, the results were comparable to those obtained using the fresh reagent [Figures 1(c) and 1(d) “used”).

The results show that the sulfuric acid/methanol reagent can be used for multiple cycles before it is consumed or rendered ineffective. Eventu­ally methanol will be consumed while products such as glycerol, FAME, and other molecules will build up in the recycled sulfuric acid/methanol reagent. Unused methanol, being quite volatile would be easy to remove from the mixture. FAME, as we detail below, can also be easily removed. It will be interesting to see if the recycled reagent can be further fraction­ated and if potentially high value compounds might also be isolated from the mixture. The drop in binding capacity of Amberlite for KAS 603 is not understood. It is not due to the action of the sulfuric acid/methanol reagent alone since pretreatment of the resin with sulfuric acid/methanol prior to initial binding of algae does not lower the binding capacity. A similar drop in binding capacity is not seen with the high capacity anion exchange res­ins based on a more polar methacrylate backbone [25]. Perhaps then the best explanation relates to the hydrophobicity of the divinylbenzene that interacts with hydrophobic components in the algae thereby either mask­ing some of the charges on the resin or sterically interfering with binding.

Following harvesting, the algal biomass requires conversion to fuel through a variety of techniques to extract and process the sought-after products within the cells. The necessary processing depends upon the de­sired fuel [71]. The three fuel types that will be covered here are those that are currently considered the most suitable for energy recovery from algae: being biodiesel, bioethanol and biogas. Each of these biofuel types require a different process stream and diagrams are presented in Figures 1-3.

We compare the land used for energy crops and the primary bioenergy use of bioenergy crops and algae in the Ecofys Energy Scenario with literature values on potentials ([2], [3], [6], [10] and [11]).

In this comparison we differentiate between studies that applied no or few sustainability criteria (generally on food security and biodiversity) and those that applied a set of sustainability criteria in the same range of types as in this study (including e. g. criteria on water use, soil protection, degradation of land and deforestation and forest carbon stocks).

Fig. 7 shows that the land used for bioenergy cropping in the Ecofys Energy Scenario is at the lower end of the range of potentials found in literature. It is important to note that the given land use for energy crops in the Ecofys Energy Scenario is the maximum amount used during the 2005-2050 timeframe.

Fig. 8 demonstrates that the primary bioenergy use from energy crops and algae in the Ecofys Energy Scenario is at the lower end of the range of potentials for bioenergy from energy crops found in literature. It is im­portant to note that the primary bioenergy use from energy crops in Fig. 8 is the maximum amount used during the 2005-2050 timeframe. This maximum use occurs in 2035 and use is lower in all other years.

Я 1.057

488 I

■ Full sustainability criteria

■ Full sustainability criteria ■ contingency

■ No or some sustainability criteria

No or some sustainability crttena — contingency

Scene no — primary onorgy use energy crops

Scenario • primary energy use algae

0 200 400 600 800 1.000 1.200 1.400

All values in EJ

FIGURE 8: Biocrop energy comparison. Comparison of global primary energy use from energy crops and algae in the Ecofys Energy Scenario with primary bioenergy potentials for energy crops from literature ([2], [3], [4], [5], [6], [7], [8], [9], [10] and [11]). Contingency indicates the author gave the potential as a range, e. g. due to uncertainty in future yields. As [4] and [5] give the same numbers and partially have the same authors, they are grouped together.

Some of the original pioneering work with the bioeffects from weak elec­tromagnetic radiation in the form of microwaves was performed in Russia and extended into Europe in the 1970’s. The work by Grundler et al. [39], investigated the use of very weak microwave irradiation of a few mW/ cm2 at a frequency around 42 GHz ±10 MHz on Saccharomyces cerevi — siae. The experiments demonstrated multiple resonance dependent effect of coherent millimeter electromagnetic waves in the frequency region of 41.83 to 41.96 GHz that increased growth rates up to 15% or decreased the growth rate by 29% depending on frequency.

Banik et al. [5] investigated the use of electromagnetic irradiation at the microwave frequency from 13.5 to 36.5 GHz on Methanosarcina barkeri DSM-804, a methanogenic archaebacterium used in anaerobic di­gestion for biogas production. The bacteria were exposed for 2 h duration for three days before inoculation into the anaerobic digesters. Significant increases in methane (CH4) concentration were observed that peaked at 76.3% CH4 at 31.5 GHz, compared to 52.3% CH4 in control. Furthermore, an increase in specific growth rate was observed for every frequency with a significant reduction in the lag phase. The irradiated cultures had higher cell numbers and the cell diameter was enlarged by 20%. It was concluded that the growth rate and biomethanation potential ofM. barkeri DSM-804 could favorably induce catalytic abilities via a thermal microwave irradia­tion at 31.5 GHz.

Tambiev and co-workers (cited in [28]) observed that exposure of high frequency microwaves for 30 min at 2.2 mW cm-2 and 7.1 mm wavelength enhanced the growth of the cyanobacterium Spirulina platensis by 50%. Belyaev et al. [56] suggested that there was frequency-specific resonant interaction between low-intensity microwave and chromosomal DNA in E. coli.