Category Archives: Advanced Biofuels and Bioproducts

Solvent Extraction

Molina Grima et al. [24] have shown that lipid extraction from P tricornutum can be achieved on wet biomass. The study achieved 90% fatty acid yield on wet bio­mass in comparison to a 96% fatty acid yield from lyophilised biomass. The wet biomass was extracted at approximately 20% (w/v). The wet biomass leaving the dewatering stage from this study is at 50% (w/v). Therefore, the extraction method described by Molina Grima et al. [24] should provide enhanced interaction between the solvent and the cells. However, this extraction method involved three steps: (1) direct saponification of biomass oil, (2) extraction of unsaponifiable lipids and (3) extraction of purified free fatty acids (FFA) [24]; hence, it was compared to a simi­lar method conducted by Ramirez Fajardo et al. [27], which was a two-step solvent extraction process. The two-step method involved lipid extraction using ethanol and lipid purification using a two-phase system of hexane and water. The crude lipid extraction involved a reaction between ethanol (96% v/v) and the biomass in an agitated tank for ~20 h. The crude lipid contains both saponifiable and unsaponifiable lipids. The saponifiable lipids include glycolipids and acylglycerols which are use­ful for biodiesel, whereas unsaponifiable lipids contain components such as amino acids and chlorophyll pigments which must be removed from the extract [27]. Water is added to the ethanol/crude-extract mixture to create a hydroalcoholic solution, and hexane is added to create a biphasic extraction system that separates the exploit­able and unusable lipids thus purifying the extract. The majority of the saponifiable lipid will be found in the hexane phase, whereas the more polar components will remain in the hydroalcoholic phase [ 24] . It is assumed that hexane purification reaches 80% recovery after four continuous extractions.

Carbon Audit and Discussion for Biodiesel Production

For biodiesel production, a transesterification process is used to convert the extracted and purified lipids into FAME. The process involves different unit operations. However, the emission sources can be categorised based on either steam or electric­ity usage. Both emissions are Scope 2. For the modelled facility, 1,834 MJ/year of steam and 470,716.3 kWh/year electricity are required. For this study, it was assumed that a natural gas boiler is used to produce the required steam and the emis­sions are calculated based on the amount of natural gas needed to produce the required energy. Net emissions from the production of biodiesel are 668.42 tonnes of CO2-e/year. 85% of the emissions are due to power requirements in running the pumps and the remaining 15% is due to the production of steam. The extraction design chosen has a significant amount of pumping and filtration units, all of which are energy-intensive processes.

Medical and Pharmaceutical Applications

Chlorophyll and its derivatives have found wide applications in the medical and pharmaceutical industries. Chlorophyll stimulates tissue growth through the facili­tation of a rapid carbon dioxide and oxygen interchange between the tissue and the blood stream [8, 19, 50]. Because of this tissue-growth stimulating propensity, chlo­rophyll is able to prevent bacterial advancement in a wound and speeds up the wound-healing process [8, 50]. Some studies have found that chlorophyll can accel­erate the rate of wound healing by more than 25%. Chlorophyll is used as a wound­healing accelerator in the treatment of ulcers and oral sepsis. Chronic ulcer is a significant health problem in society, with lengthy periods required for its treatment. The application of ointments containing chlorophyll derivatives on the ulcer was found not only to rapidly eliminate pain but also to improve the appearance of the affected tissues. The ulcer discharge and characteristic odour also decreased significantly after a few days of chlorophyll treatment [7]. The antibacterial prop­erty and deodorising nature of chlorophyll were found to be very helpful in the treatment of oral sepsis [15].

Chlorophyll and its derivatives have recently been identified as a powerful thera­peutic for cancer chemoprevention due to their high displays of antioxidant and anti­mutagenic activities [12, 25] . Increasing consumption of fruits and vegetables that contains high levels of chlorophyll has been associated with reducing cancer risks.

Toxicological Tests

It is well known that despite the bioactive (beneficial) compounds, several toxic compounds can be accumulated in algae and microalgae. Compounds like alka­loids, domoic acid, azaspiracid, brevetoxin, okadaic acid, pectenotoxin, or micro — cystins have been described.

Therefore, sometimes it is required to perform some toxicological tests mainly based in the mouse bioassay. Article 5 of a European Commission Decision dated 15 March 2002, laying down rules related to maximum permitted levels of certain biotoxins and methods of analysis for marine bivalve molluscs and other seafood states: “When the results of the analyses performed demonstrate discrepancies between the different methods, the mouse bioassay should be considered as the reference method.” The basic procedure involves i. p. injection of an extract of the sample containing the toxin and observing the symptoms. A deeper review on toxi­cological analysis can be read in the book edited by Gilbert and §enyuva “Bioactive compounds in Foods” [42] .

Microalgae

Microalgae are a very diverse group of organisms. The predominant organic con­stituent can vary from carbohydrates to proteins or lipids. The green microalgae were predominantly tested as possible substrate for biogas production (Table 20)

Substrate

Reactor

T (°С)

HRT

(days)

OLR

(gVS/L-day)

VS red.

(%)

Conv. eff. (%)

CH4

(L/L-day)

CH4

(L/gTVS)

CH4 (%)

References

Unknown cyanobacteria (Lake

BMP assay

16-26

35

0.366

60-65

[401]

Dian)

(1L)

A. platesis

Batch (0.25 L)

38

35

0.293

61

[157]

A. maxima

Batch (0.12 L)

35

105

0.33

[158]

Arthrospira

Batch (11 L)

35

28

0.91

0.28-0.29“

0.31-0.32

[156]

A. maxima (frozen, control)

Semi-

35

20

2

26

0.38

0.19

72

[233]

A. maxima (live)

continuous

35

20

2

28.6

0.40

0.2

70

A. maxima (ultrasonic)

(1.5 L)

35

20

2

23

0.34

0.17

75

A. maxima (50°C, pHl)

35

20

2

0

0.00

0

0

A. maxima (50°C, pH3)

35

20

2

16.6

0.22

0.11

64

A. maxima (50°C)

35

20

2

28.3

0.40

0.2

70

A. maxima (50°C, pHll)

35

20

2

26.6

0.42

0.21

73

A. maxima (50°C, pH13)

35

20

2

12.9

0.18

0.09

68

A. maxima (100°C, pHl)

35

20

2

10.1

0.12

0.06

60

A. maxima (100°C, pH3)

35

20

2

20.1

0.28

0.14

67

A. maxima (100°C)

35

20

2

25.8

0.36

0.18

71

A. maxima (100°C, pHll)

35

20

2

27.8

0.44

0.22

74

A. maxima (100°C, pH13)

35

20

2

21.4

0.28

0.14

73

A. maxima (150°C. pHl)

35

20

2

4.1

0.04

0.02

53

A. maxima (150°C, pH2)

35

20

2

22.2

0.32

0.16

70

A. maxima (150°C)

35

20

2

24.2

0.36

0.18

73

A. maxima (150°C, pHll)

35

20

2

31.9

0.48

0.24

76

A. maxima (150°C. pH13)

35

20

2

8.1

0.16

0.08

68

Table 19 Characteristics of anaerobic digestion of selected cyanobacteria

36 Biogas Production from Algae and Cyanobacteria Through Anaerobic Digestion…

40

u>

Table 19 (continued)

HRT OLR

Substrate

Reactor

T (°С)

(days)

(gVS/L-

A. maxima

SCSTR

15

20

2

A. maxima

(1.5 L)

25

20

2

A. maxima

35

20

2

A. maxima

52

20

2

A. maxima

Semi-

35

20

continuous

35

20

1.95а

(1.5 L)

35

20

35

20

3.88а

90.7% A. maxima+ 9.3%

35

20

2.22а

sewage sludge

67.3% A. maxima +32.7%

35

20

2.93а

sewage sludge

50.6% А. нл7,а7нл7+49.4%

35

20

3.92а

sewage sludge

Sewage sludge (homogenized)

35

20

1.94а

90.4% A. maxima+ 9.6% peat

35

20

2.2а

extract

65% A. maxima+ 35% peat

35

20

extract

48% A. maxima + 52% peat

35

20

4.13а

extract

Peat extract

35

20

1.95а

чо

£

VS red.

(%)

Conv. eff. (%)

ch4

(L/L-day)

сн4

(L/gTVS)

сн, (%)

References

5

5.6

0.06

0.03

54.1

[160]

20

17.6

0.24

0.12

69

40.8

39.2

0.4

0.2

72

6.7

6.6

0.07

0.04

53.3

42.3

0.31a

0.31

74

[411]

26.2

0.37а

0.19

72

24.7

0.51а

0.17

69

24.2

0.62а

0.16

65

44.2

0.69а

0.31

73

38.3

0.82а

0.28

74

48.1

1.41а

0.36

76

45.8

_

0.64а

0.33

73

28.7

0.44а

0.2

71

40.3

0.84а

0.28

70

37.1

0.91а

0.22

60

38.5

0.43а

0.22

56

P. Bohutskyi and E. Bouwer

90.7% A. maxima+ 93% spent

35

20

2.2“

sulfite liquor

66.1 % A. maxima + 33.9%

35

20

3“

spent sulfite liquor

50.6% A. maxima+A9A%

35

20

4“

spent sulfite liquor Spent sulfite liquor

35

20

1.95“

A. maxima

SCSTR

55

8

2.82“

(0.2 L)

55

12

1.88“

55

16

1.41“

35

8

2.82“

35

12

1.88“

35

16

1.41“

A. maxima

SCSTR (12 L)

35

33

0.97

BMP biomethane potential; SCSTR semi-continuous stirred-tank reactor

“Estimated value from data presented in the paper

bEstimated from data given in L CH4/g COD using a COD/VS ratio of 1.5

Table 20 Characteristics of AD of green microalgae

Substrate

Reactor

T (°С)

ChloreUa sp.

BMP assay

37

ChloreUa (residues, butanol extraction)

(0.4 L)

37

Same + glycerol (4% of mass)

37

ChloreUa (residues chlorof./methanol extraction)

37

ChloreUa (residues after ACIST)

37

Same + glycerol (4% of mass)

37

C. vulgaris

Batch (5 L)

28-31

Chlamydomonas reinhardtii

Batch (0.25 L)

38

ChloreUa kessleri

38

D. salina

38

S. obliquus

38

Dunaliella

Batch (11 L)

35

ChloreUa + unknown specie + C. reinhardtu

Batch (0.5 L)

34

and Pseudokirchneriella subcapitata at

34

low level

34

41

41

Same species, pretreated, T= 80 C

Batch (0.5 L)

41

41

Same species

Plug flow

34

Scenedesmus sp. 80% and ChloreUa sp. 20%

Semi-continuous 35

(algae from stabilization pond)

(11 L)

50

Scenedesmus sp. 80% and ChloreUa sp.

Semi-

50

20% + aluminum sulfate 90-120 mg/L

continuous

53

(algae from stabilization pond)

(11 L)

50

53

53

HRT

(days)

OLR

(gVS/L-day)

VS red.

(%)

сн4

(L/L-day)

CH4 (L/gVS)

CH4 (%)

References

38а

0.47ь

[436]

34а

0.32ь

30а

0.34ь

30а

inhib.

22а

0.26ь

22а

0.28ь

64

0.32-0.38с

68-76

[612]

35

0.387

66

[157]

35

0.218

65

35

0.323

64

35

0.178

62

28

0.91

0.4-0.41d

0.44-0.45

[156]

14

0.22

65

[237]

25

0.28

65

45

0.39

65

14

0.22

65

25

0.31

65

14

0.18

65

25

0.25

65

0.01

0.32

65

[237]

ЗО

1.44

43.1

0.23-0.27d

61d

[110]

ЗО

1.44

54

0.31d

62d

зо

1.44

0.3-0.31d

61d

зо

1.44

0.315-0.326d

22

1.44

0.303d

11

1.44

0.315d

7

1.44

0.207d

Mix of algae (major: Scenedesmus sp..

Semi-

35

10

2

0.18

0.09d

71.4d

[410]

ChloreUa sp.)

continuous

35

10

4

0.573

0.143d

69d

(4 L)

35

10

6

0.818

0.136d

68d

75% mix of algae + 25% waste paper

35

10

4

0.968

0.24d

64d

50% mix of algae+ 50% waste paper

35

10

4

1.17

0.29d

60d

25% mix of algae + 75% waste paper

35

10

4

0.317

0.085d

53d

67% mix of algae + 33% waste paper

35

10

3

0.823

0.274d

67d

40% mix of algae+ 60% waste paper

10

10

5

1.607

0.321d

60d

33% mix of algae+ 67% waste paper

35

10

6

0.856

0.142d

60d

Waste paper

35

10

4

0.452

0.324d

62d

Tertaselmis (fresh)

CSTR (2-5 L)

35

14

2

0.62d

0.31

72-74

[609]

Tertaselmis (dry)

35

14

2

0.52d

0.26

72-74

Tertasehms (dry) + NaCl 35 g/L

35

14

2

0.5d

0.25

72-74

ChloreUa sp. residues after ACIST (C/N 8.53)

CSTR (4 L)

25

15

5

0.94

0.188

64.5

[436]

30

15

5

1.135

0.227

68.3

35

15

5

1.51

0.302

67.9

40

15

5

1.54

0.308

69.2

ChloreUa sp. residues after ACIST+glycerol

CSTR (4 L)

25

15

5

0.96

0.192

62

(C/N 12.44)

30

15

5

1.04

0.208

61.7

35

15

5

1.475

0.295

65.3

40

15

5

1.325

0.265

63.1

ChloreUa sp. (75%). Scenedesmus sp. (23%)

CSTR

45

20

0.43d

71

[109]

BMP biomethane potential; CSTR continuous stirred-tank reactor

“Methane production reached asymptotic values earlier in samples with lower HRT

bEstimated from data given in L CH4/g TDS using a VS/TDS ratio of 0.9 for ChloreUa and 0.85 for residues “Estimated from data given in L CH4/g COD using a COD/VS ratio of 1.5 dEstimated from data given in the paper

because of their widespread, fast growth rate, and robustness. The BMP determined for Chlamydomonas reinhardtii, Chlorella kessleri, and Scenedesmus obliquus was 0.387, 0.218, and 0.178 L/gVS, respectively [157]. The amount of biogas produc­tion correlated well with the extent of algal degradation. C. reinhardtii exhibited a higher cell disintegration rate in comparison to C. kessleri and S. obliquus. Number of the Chlorella and Scenedesmus species as well as several other algae (e. g., Nannochloropsis) have resistant trilaminar membrane-like structure containing nonhydrolysable sporopollenin-like biopolymer—algaenan [161-164]. The overall cell wall structure has complex organization with three distinct layers: rigid internal microfibrillar, medial trilaminar, and external columnar (for green algae Coelastrum) [165]. The major algaenan functions are protection from parasites and desiccation [166]. Chlorella and Scenedesmus have internal rigid cell walls either glucose-mannose type or glucosamine-type [167-169]. In contrast, C. reinhardtii has a cell wall composed of proteins and glycoproteins [170-172]. Resistant cell wall retained S. obliquus cells undamaged after 6 months of digestion [157] . The average methane yield with different green microalgae from batch experiments is presented in Fig. 10 (error bars represents minimum and maximum values reported for each specie).

Optimal parameters reported in the literature for stable AD of a mixture of Chlorella and Scenedesmus were an OLR up to 4 gVS/L-day and an HRT greater than 11 days [110].

Fig. 11 Methane yield vs. OLR. (a, b) Gracilaria—diamonds, solid line; M. pyrifera (continuous stirred-tank reactor)—crosses, dash dot line; M. pyrifera (non-mixed vertical flow reactor)—boxes, long dash line; Ulva juices—triangles, dash dot dot line; Arthrospira—pluses, short dash line; Scenedesmus and Chlorella—circles, dots line. (c, d) Sargassum—boxes, solid line; Laminaria — circles, long dash line; Laminaria alginate extraction sludge—filled circles, dots line; Ulva— triangles, dash dot line; Ulva and manure— fi lled triangles, dash dot dot line; Enteromorpha intestinalis— crosses, short dash line

Biodiesel and Green Diesel Production Processes

The ADP is widely used for processing a variety of organic residues and wastes. The AD can be integrated into many potential pathways for algal biofuels production as shown in Fig. 19 [433]. Coupling biodiesel and green diesel production processes with the ADP (Fig. 20) can improve the overall efficiency of energy recovery and reduce the final cost of biofuel [434]. Based on the theoretical calculations, the ADP can recover approximately 55-85% of the biomass energy content in a coupled biodiesel-ADP, depending on algal lipid content [263]. Similar results were achieved by the calculation of theoretical energy output from conversion of T. suecica [435].

Fig. 22 Energy output of different types ofbiofuels with Tetraselmis suecica [435]

Chlorella residue from the biodiesel production process is a feasible substrate for methane production [436, 437]. Either 1-butanol as a solvent for lipid extraction or acid catalyzed in situ transesterification was recommended because application of the normal chloroform/methanol mixture inhibited methane production. The observed methane yield from algal residues was approximately 52-63% from fresh algae. Addition of glycerol as a co-substrate slightly increased the methane yield by

4- 7% possibly due to a more favorable N to C balance [437].

Code Availability

The ability to numerically simulate the behavior of geologic hydrate reservoirs has improved substantially over the past 5 years in terms of both code availability and capabilities [8]. There are currently several numerical models that can simulate the system behavior in hydrate-bearing geologic media (e. g., [30,68,94,130,143,152, 153, 188]). Several of these codes were calibrated against the Mallik 2002 produc­tion test data, and the data from the Mt. Elbert MDT test [2] . A code-comparison study [1, 214] indicated that most of the participating codes appear capable of simu­lating the behavior of hydrates and reservoir fluids during common dissociation scenarios. The current consensus is that the models generally account for the impor­tant physics of the problem, and that validation and calibration (rather than ade­quacy of the numerical code capabilities) will be a constraining factor in the assessment of hydrates as an energy resource [214, 2] . Additionally, while uncer­tainties exist in the description of properties and processes involved in numerical simulators (e. g., thermal properties of composite GH-bearing systems, relative per­meability and capillary pressure, geomechanical properties related to subsidence after the dissociation of the cementing GH from the porous media, etc.), these knowledge gaps are being addressed [125, 127].

State of Laboratory Studies

Laboratory studies on natural HBS provide results that are dependent on the coring method and all the sample changes that occur prior to the measurement. In spite of these drawbacks, sampling and analysis of natural HBS provides general informa­tion on HBS reservoirs and critical site-dependant information. Laboratory studies of laboratory-synthesized HBS suffer from nonuniform samples and samples that are not fully representative of the natural environment. Specialized equipment is needed to maintain and test the samples. Hydrate laboratory researchers are striving to meet these challenges, but further development is needed.

Several types of measurements have been made on HBS over a wide range of media and saturations, particularly with THF hydrate. Studies that can convincingly validate the THF hydrate studies using methane hydrate are needed, especially at the low SH of natural HBS. Additional studies are needed to quantify fluid flow parameters over the broad range of conditions where hydrates occur. Better under­standing of the effects of fines migration and sand production as a result of hydrate dissociation is needed. These and future hydrate studies must use well-characterized specimen that possess the fundamental characteristics of natural in situ HBS includ­ing porous medium type, mineralogy, hydrate habit, uniformity, chemistry, and confinement.

IBT Recovery

Like most alcohols, IBT is expected to be toxic to microbial biocatalysts. Removal of the IBT as it is formed will help avoid product inhibition and maintain high reac­tor productivity. Thus, in situ product recovery will be an integral part of the biore­actor design effort. Because the physical properties of IBT are similar to those of

gas inlet

Ho ow fibers with attached ce s

Shell-side gas outlet

Tube-side gas inlet

n-butanol, methods developed to remove n-butanol from fermentation broths are also likely to work for IBT. A variety of adsorbents have proven effective at recover­ing n-butanol from fermentation broths [92-94], including polymeric resins, which adsorb n-butanol through hydrophobic interactions [95]. Hydrophilic polymers, like polyamides, polyurethanes and polyesters showed weak n-butanol adsorption. In addition, low-alumina zeolites, such as silicalite, effectively adsorb alcohols from dilute solutions. After the butanol has been adsorbed, it can be recovered from the resin by heat desorption. This desorption technique is less energy intensive (~2,000 kcal/kg alcohol) than steam stripping (~6,000 kcal/kg alcohol) or gas strip­ping (~5,000 kcal/kg alcohol) [93].

Botryococcus braunii Characterization

Microalgae are in general characterized by a high content of three main groups of biomolecules, proteins, polysaccharides, and lipids, according to the species and to the environmental growth conditions. Figure 2 reports the chemical composition of B. braunii strain used in the present study [30, 31]; the protein concentration and the polysaccharides content were determined by using the Lowry procedure [32] and the Dubois method [33], respectively; the lipid content was determined by weight after a solvent extraction procedure (n-hexane and chloroform/methanol mixture) [34], the ashes were determined by 5 h calcination at 550°C, and the intracellular water amount of the samples was evaluated by Karl-Fischer titration.

Figure 2 shows that B. braunii is characterized by a high amount of lipids (29%) and carbohydrates (22%), and a minor but relevant amount of proteins (7%); this composition is typical of this alga in its late growth phase (stationary phase) when the accumulation of lipids becomes much more relevant than cellular duplication.

The amount of ashes (22%) is in line with the typical ash content of microal­gae [35].

In addition to hydrocarbons, B. braunii also produces classic non-polar lipids as fatty acids, triacylglycerols, sterols, and polar lipids, as polyaldehydes, polyacetals, and non-polysaccharide biopolymers of very high molecular weight [36]. Thus, the oil extracted with n-hexane and chloroform/methanol mixture was initially analysed by GC-MS to qualitatively and quantitatively determine free fatty acid (after silyla- tion), triacylglycerol (after transesterification), and hydrocarbon (Tables 1 and 2) contents; then it was fractionated on a chromatographic column, thus separating hydrocarbons, non-polar, and polar lipids (Fig. 3).

Table 1 Free fatty acid and bounded fatty acid yields on a dry weight basis in the lipid extract of Botryococcus braunii

Fatty acids

Free fatty acids yield (%)

Bounded fatty acids yield (%)

16:0

0.23 ± 0.1

0.69 ± 0.09

18:2

0.17 ± 0.06

18:1

0.16 ± 0.2

1.8 ± 0.1

18:0

0.24 ± 0.04

0.07 ± 0.05

Total

0.64 ± 0.3

2.7 ± 0.3

Hydrocarbons

Yield (%)

C27H52

0.65 ±0.2

C29H56

2.8± 1

C29H54

0.57 ± 0.3

C29H54

1.4 ± 0.7

C31H®

2.5 ± 1

Total

7.8 ± 3

Table 2 Hydrocarbon yields on a dry weight basis in the lipid extract of B. braunii

□ Hydrocarbons

□ Non-polar lipids

□ Polar lipids

According to the literature [19 ] , the lipid oil of the A strain of B. braunii is mainly composed by non-polar lipids (62%) and hydrocarbons (29%), whereas polar lipids represent only a minor part (9%) of the composition.

By comparing the total amount of hydrocarbons calculated by GC-MS (7.8% on dry weight basis) and the percentage of the first fraction of the lipid oil (8.4% on dry weight basis), it is clear that the first fraction is entirely composed of hydrocarbons. This correspondence is not true in the case of non-polar lipids; the amount of fatty acids calculated by GC-MS (3.3% on dry weight basis) is much lower than the percentage of the second fraction of the lipid oil (17.8% on dry weight basis), indicating that bounded fatty acids and free fatty acids represent only a very small fraction of all the non-polar lipids. As reported in the literature, the main part of the non-polar fraction is in fact composed by a class of high molecular weight ether lipids, not GC-MS detectable ]37, 38] . These compounds are closely related to hydrocarbons, differently from what observed among the other vegetable ether lipids mainly based on glycerol (Fig. 4) [14]. the structures identified in the past years include alkadienyl-O-alkatrienyl ethers with an oxygen bridge between two C.7 hydrocarbon chains ] 37 ] . ether lipids with alkenylresorcinol linked by phenoxy bonds to one or two unsaturated hydrocarbon chains [39], or botryals, a-branched aldehydes originating from aldol condensation [38] .

Thanks to their hydrocarbon nature, these non-polar ethers could be processed by cracking to obtain biofuels, analogously to hydrocarbons, increasing the exploit­able fraction of B. braunii oil for energy and fuel purposes.

OH

alkadienyl-O-alkatrienyl ether

phenoxy alkenylresorcinolic

Fig. 4 Typical B. braunii high molecular weight non-polar lipids