Category Archives: BIOETHANOL

Calculations

The amount of ethanol produced in the SSF fermentations is calculated based on the weight loss as a result of CO2 formation in the reaction shown below.

C6H1206 ^2C2H50H + 2C02 (1)

image129
The resulting formula is derived as below, using the molar masses of ethanol and CO2 of 46.07 g/mol and 44.01 g/mol, respectively:

(С6Я1005)пЯ20 + (n — 1)Я20 ^ nC6H1206 (4)

Where n is in the range 2000 — 10000. The highest ethanol yield is obtained when the cellulose is completely hydrolyzed into glucose and fermented into ethanol as shown in Equation 1 and 4. We now consider the number of monomers (n) in one gram of cellulose. Under complete hydrolysis this amount of matter becomes the amount of glucose. From equation 1 it can be seen that the amount of ethanol produced is 0.512 g per g of glucose fermented. The potential weight of ethanol from 1 g of cellulose is 0.569 g (Equation 5).

= 2М*Ш 2 x 46.1 = ncrj" (5)

iiel&ethanoifromceiiuiose = Mw = 162 =0m579l9 ( )

W(C6H10O5)

Steam reforming

As mentioned earlier in this chapter, hydrogen production can be maximized per fed ethanol through pure steam reforming. However, the highly endothermic feature of this reaction limits its widely industrial application for hydrogen production. In order to lessen its heavy dependence on external energy supply, part of ethanol is sacrificed to provide required energy for steam reforming through the introduction of oxygen, which is named as oxidative steam reforming (Reaction 5). Depending on the value of 8, the enthalpy change of

Reaction (5) will become less positive, indicating less energy requirement from surroundings. The reaction will finally become autothermal at the point where little or no energy is needed from external sources (e. g., if 8=0.6, AHr,298K =4.4 kJ/mol).

C2H5OH(l) + 8 O2 + (3-2 8) H2O(l) (6-2 8) H2 + 2 CO2 (5)

Although the products from the desired reactions are only CO2 and H2, in reality, depending on the reaction conditions and catalysts used, the product distribution can be governed by a very complex reaction network. Possible reactions involved can be as follows.

CHsCH2OH CH4+CO+H2 (ethanol decomposition) (6)

CHsCH2OH CH3CHO+H2 (ethanol dehydrogenation) (7)

CHsCH2OH C2H4+H2O (ethanol dehydration) (8)

CH3CH2OH+H2O 2 CO+4H2 (ethanol incomplete reforming) (9)

2 CH3CH2OH (C2H5) 2O+H2O (ethanol dehydrative coupling) (10)

CH3CH2OH+H2O CH3COOH+2 H2 (acetic acid formation) (11)

CH3CHO CH4+CO (acetaldehyde decomposition) (12)

2CH3CHO CH3COCH3+CO+H2 (acetone formation) (13)

CO+3 H2 CH4+H2O (methanation) (14)

C2H4 coke (polymerization) (15)

CH4+2 H2O CO2+4 H2 (methane steam reforming)1 (16)

CH4 C+2 H2 (methane cracking) (17)

CO+H2O ^ CO2+H2 (water-gas shift) (18)

2 CO CO2+C (Boudouard reaction) (19)

There are many side reactions that might take place during ethanol steam reforming, complicating the product distribution. To get the highest possible H2 yield for industrial applications, it is essential to investigate the effects of temperature, reactants ratio, pressure, space velocity as well the catalytic parameters. A thermodynamic analysis was performed using the software HSC® Chemistry 5.1. All possible products, including solid carbon were included among the possible species that could exist in the equilibrium state. In the thermodynamic analysis, the following definitions are used.

H2 Yield % = moles of H2 produced x 100 6 x (moles of ethanol fed)

„ , mol of a certain product _ „„

Selectivity % = x 100

mol of total products

„ T moles of ethanol converted _ „„

EtOH Conv. % = x 100

moles of ethanol fed

The thermodynamic analysis in Fig.2 shows ethanol conversion, yield and selectivity of main products starting from a reactant composition similar to a bio-ethanol stream from biomass fermentation (ethanol-to-water ratio of 1:10). Ethanol conversion is not thermodynamically limited at any temperature. The methanation reaction, which is exothermic, is thermodynamically favored at lower temperatures (below 400 oC). At higher temperatures (above 500 oC) the reverse of this reaction, i. e., steam reforming of methane to CO2 and H2 becomes favorable. This would suggest that, if operated in a thermodynamically controlled regime, in order to minimize CH4 concentration in the product stream, the reaction temperature should be kept as high as possible. However, as shown in Fig.2, once the temperature is increased above 550 oC, the reverse-water-gas shift reaction takes off, i. e., CO formation becomes significant and hydrogen yield decreases. At this ethanol-to-water ratio, there is no solid carbon at the equilibrium state.

image167

Fig. 2. Product distribution from ethanol steam reforming at thermodynamic equilibrium with EtOH:Water=1:10 (molar), CEtOH=2.8%, and atmospheric pressure

Fig.3 shows the effect of ethanol-to-water molar ratio on H2 yield. Lower molar ratios of ethanol-to-water can increase the hydrogen yield, since both water gas shift reaction and CH4 reforming reactions would shift to the left with increased water concentration. In Fig.3, solid carbon selectivities for the lowest water concentrations are also included. At high ethanol-to-water ratios, solid carbon deposition becomes thermodynamically favorable, especially at lower temperatures.

The effect of dilution with an inert gas on the equilibrium H2 yield is shown in Fig.4. The addition of inert gas increases the equilibrium hydrogen yield at low temperatures and has no effect at high temperatures. At low temperatures, the dominant reaction is the methanation/ methane steam reforming. Diluting the system favors the methane steam reforming, and hence we see a difference at low temperatures. At high temperatures, the main reaction is the reverse water gas shift reaction, which is not affected by dilution, since there is no change in the number of moles with the extent of this reaction. Increased pressure has a negative influence on hydrogen yield at lower temperatures and no effect at higher temperatures (Fig.5).

image168

Fig. 3. Effect of EtOH-to-water molar ratio on equilibrium H2 yield and C selectivity at (no dilution)

image169

Fig. 4. Effect of dilution on equilibrium hydrogen yield (Dilution ratio used: Inert:EtOH:H2O = 25:1:10)

Although it is important to be aware of the thermodynamic limitations, these analyses do not provide any information about the product distribution that would be obtained under kinetically controlled regimes. However, the study is still meaningful for guiding the choice of the desirable reaction parameters such that reaction is always controlled by kinetics under thermodynamically favorable conditions.

Due to its simplicity, flexibility, maturity, and high hydrogen yield, thermal bioethanol steam reforming has been extensively studied and a variety of technical improvements and researches directions have been proposed and implemented over the past several decades. The discussions of the following sections will focus on this technique.

image170

Fig. 5. Effect of pressure on equilibrium hydrogen yield (EtOH:Water=1:10 (molar ratio), no dilution)

Use of cassava for bioethanol production

1.2 Bioethanol production

Instead of chemical synthesis, the bioprocess, i. e. fermentation of simple sugars by microorganism is nowadays used extensively to produce ethanol from renewable sugar- containing biomass. Important ones are sugar crops, starch crops, and lignocellulosic materials derived from agricultural residues. The two former ones are recognized as the first generation feedstock for bioethanol production while the last one is the second generation feedstock. When ethanol is produced by yeast fermentation of sugar feedstock such as sugar cane, molasses, sugar beet and sweet sorghum, yeast can directly consume simple sugars and convert them to ethanol. However, starch and cellulose feedstock are a polymer of glucose and cannot directly be utilized by yeast. They have to be converted or depolymerized to glucose prior to yeast fermentation. Depolymerization or hydrolysis of starch is much simpler and more cost effective than that of cellulosic materials and can be achieved by acid or enzyme or a combination of both.

Starch is a polysaccharide comprising solely of glucose monomers which are linked together by glycosidic bonds. It is composed of two types of glucan namely amylose, a linear glucose polymer having only a-1,4 glycosidic linkage and amylopectin, a branched glucose polymer containing mainly a-1,4 glycosidic linkage in a linear part and a few a-1,6 at a branch structure. Most starches contain approximately 20-30% amylose and the rest are amylopectin. Some starches contain no amylose such as waxy corn starch, waxy rice starch, amylose-free potato, amylose-free cassava and some have very high amylose contents upto 50-70% as in high amylose maize starches. These two polymers organize themselves into semi-crystalline structure and form into minute granules, which are water insoluble. Starch granules are less susceptible to enzyme hydrolysis. Upon cooking in excess water, the granular structure of starch is disrupted, making glucose polymers become solubilized and more susceptible to enzyme attacks. At the same time, the starch slurry becomes more viscous. This process is known as gelatinization and the temperature at which starch properties are changed is named as gelatinization temperatures. Different starches have different gelatinization temperatures, implying different ease of cooking. Cassava starch has a lower cooking temperature, relatively to cereal starches; the pasting temperatures for cassava, corn, wheat and rice are 60-65, 75-80, 80-85 and 73-75°C (Swinkels, 1998; Thirathumthavorn & Charoenrein, 2005).

The starch hydrolysis by enzymes is a two-stage process involving liquefaction and saccharification. Liquefaction is a step that starch is degraded by an endo-acting enzyme namely a-amylase, which hydrolyzes only a-1,4 and causes dramatically drop in viscosity of cooked starch. Typically, liquefying enzymes can have an activity at a high temperature (> 85°C) so that the enzyme can help reduce paste viscosity of starch during cooking. The dextrins, i. e. products obtained after liquefaction, is further hydrolzyed ultimately to glucose by glucoamylase enzyme which can hydrolyze both a-1,4 and a-1,6 glycosidic linkage. Glucose is then subsequentially converted to ethanol by yeast. By the end of fermentation, the obtained beer with approximately 10%v/v ethanol, depending on solid loading during fermentation, is subjected to distillation and dehydration to remove water and other impurities, yielding anhydrous ethanol (Figure 1).

Physico-mechanical technologies to Improve ethanol yield

Several approaches to increase ethanol yield from sorghum involve physical or mechanical treatments, v. gr: reduction of particle size, decortication or steam flaking. The aim of these treatments is to reduce physical barriers to hydrolytic enzymes in order to yield more fermentable sugars in shorter reaction times.

4.1.2 Particle size

Particle size of ground sorghum meals also plays an important role in the starch-to-ethanol conversion process. Wang et al. (2008) observed that fermentation efficiencies of finely ground samples were approximately 5% higher compared to coarsely ground counterparts. This effect is a consequence of differences in gelatinization temperature and accessibility of starch to hydrolyzing enzymes. Wang et al. (2008) reported that gelatinization temperatures of larger or coarser particles are 5-10°C higher compared to finer particles.

The conversion of meals with smaller particles enhanced digestibility due to an improvement in the relative surface-contact area. Mahasukhonthachat et al. (2010) indicate that starch digestion proceeded by diffusion mechanisms is based on an inverse square dependence of rate coefficient on average particle size.

Characteristics of the commercial hydrolytic enzymes

Most cellulase enzymes are relatively unstable at high temperatures. The maximum activity for most fungal cellulases and |3-glucosidase occurs at 50±5°C and a pH 4.5- 5 (Taherzadeh & Karimi, 2007; Galbe & Zacchi, 2002). Usually, they lose about 60% of their activity in the temperature range 50-60 °C and almost completely lose activity at 80°C (Gautam et al., 2010). However, the enzymes activity depends on the hydrolysis duration and on the source of the enzymes (Tengborg et al., 2001). In general, cellulases are quite difficult to use for prolonged operations.

As mentioned before, the enzyme production costs mainly depend on the productivity of the enzymes-producing microbial strain. Filamentous fungi are the major source of cellulases and mutant strains of Trichoderma (T. viride, T. reesei, T. longibrachiatum) have long
been considered to be the most productive (Gusakov et al., 2007; Galbe & Zacchi, 2002). Preparations of cellulases from a single organism may not be highly efficient for the hydrolysis of different feedstocks. For example, Thrichoderma reesei produces endoglucanases and exoglucanases in large quantities, but its P-glucosidase activity is low, resulting in an inefficient biomass hydrolysis. For this reason, the goal of the enzymes producing companies has been to form cellulases cocktails by enzymes assembly (multienzyme mixtures) or to construct engineered microrganisms to express the desired mixtures (Mathew et al., 2008). Enzyme mixtures often derive from the co-fermentation of several micro-organisms (Ahamed & Vermette, 2008; Kabel et al., 2005; Berlin et al., 2007), (Table 4). All the commercial cellulases listed in table 4 have an optimal condition at 50°C and pH of 4.0-5.0. More recently, some enzymes producers have marked new mixtures able to work in a higher temperature ranging from 50 to 60°C (Table5).

In 2010, new enzymes were produced by two leading companies, Novozymes and Genencor, supported by the USA Department of Energy (DOE). Genencor has launched four new blends: Accelerase®1500, Accelerase®XP, Accelerase®XC and Accelerase®BG.

Accelerase®1500 is a cellulases complex (exoglucanase, endoglucanase, hemi-cellulase and P-glucosidase) produced from a genetically modified strain of T. reesei. All the other Accelerase are accessory enzymes complexes: Accelerase®XP enhances both xylan and glucan conversion; Accelerase®XC contains hemicellulose and cellulase activities; Accelerase® BG is a P-glucosidase enzyme. In February 2010, Genencor has developed an enzyme complex known as Accellerase®Duet which is produced with a genetically modified strain of T. reesei and that contains not only exoglucanase, endoglucanase, P- glucosidase, but includes also xylanase. This product is capable of hydrolyzing lignocellulosic biomass into fermentable monosaccharides such as glucose and xylose (Genencos, 2010)[3]. Similarly, Novozymes has produced and commercialized two new enzymatic mixtures: cellic Ctec, and cellic Htec. Cellic CTec is used in combination with Cellic HTec and this mixture is capable to work with a wide variety of pretreated feedstocks, such as sugarcane bagasse, corn cob, corn fiber, and wood pulp, for the conversion of the carbohydrates in these materials into simple sugars (Novozyme, 2010)[4].

In order to meet the future challenges, innovative bioprocesses for the production of new generation of enzymes are needed. As already described, conventional cellulases work within a range of temperature around 50°C and they are typically inactivated at temperatures above 60-70 °C due to disorganization of their three dimensional structures followed by an irreversible denaturation (Viikari et al., 2007). Some opportunities of process improvement derive from the use of thermostable enzymes.

EM decomposition and reduction

Using METATOOL v5.0 (von Kamp & Schuster, 2006), the network is decomposed into 201 EMs, which are too many to be incorporated in the model. In general, as the network size increases, the number of EMs undergoes combinatorial explosion (Klamt & Stelling, 2002), leading to overparameterization (which implies an excessive number of parameters relative to the measurements available to determine them). This problem can be avoided using the Metabolic Yield Analysis (MYA) developed by Song and Ramkrishna (2009) by which an original set of EMs is condensed to a much smaller subset. As a result, 201 EMs are reduced to 12 EMs which can be classified into three groups depending on the substrate associated with them (Table 2.2).

Подпись: GLYCOLYSIS 1 GLC + ATP ^ G6P + ADP 6 GOL ^ GOLx 2 G6P 0 F6P 7 GAP + NAD + ADP 0 PG3 + NADH + ATP 3 F6P + ATP 0 DHAP + GAP + ADP 8 PG3 0 PEP 4 DHAP 0 GAP 9 PEP + ADP 0 PYR + ATP 5 DHAP + NADH ^ GOL + NAD PYRUVATE METABOLISM 10 PYR ^ ACD + CO2 14 ACT ^ ACTx 11 ACD + NADH ^ ETH + NAD 15 ACT + CoA + 2ATP ^ AcCoA + 2ADP 12 ACD + NADHm ^ ETH + NADm 16 PYR + ATP + CO2 ^ OAA + ADP 13 ACD + NADP ^ ACT + NADPH PENTOSE PHOSPHATE PATHWAY 17 G6P + 2NADP ^ Ru5P + CO2 + 2NADPH 20 R5P + X5P 0 S7P + GAP 18 Ru5P 0 X5P 21 X5P + E4P 0 F6P + GAP 19 Ru5P 0 R5P 22 S7P + GAP 0 F6P + E4P CITRIC ACID CYCLE

EM Net reaction

1 Подпись: SubstrateПодпись: GlucoseПодпись: XyloseПодпись: MixtureGLC ^ 2 CO2 + 2 ETH + 2 MAINT

2 25.31 GLC ^ BIOM + 41.43 CO2 + 33.21 ETH

3 40.41 GLC ^_______________________________________________ BIOM + 56.52 CO2 + 48.31 ETH + 15.10 GOLx

4 XYL ^ 1.833 CO2 + 1.583 ETH + 1.583 MAINT

5 2 XYL ^ 2 CO2 + 1.5 ETH + 1.5 MAINT + XOLx

6 31.97 XYL ^ BIOM + 49.42 CO2 + 33.21 ETH

7 138.5 XYL ^ BIOM + 160.4 CO2 + 117.6 ETH + 84.37 GOLx_____________

8 GLC + 4 XYL ^ 2 ACTx + 2 CO2 + 2 MAINT + 4 XOLx

9 GLC + 4 XYL ^ 9.333 CO2 + 8.333 ETH + 8.333 MAINT

10 2.39 GLC + 25.99 XYL ^ 22.19 ACTx + BIOM + 37.82 CO2 + 9.037 ETH

11 5.333 GLC + 2 XYL ^ ACTx + 8.5 CO2 + 4.5 ETH + 7.5 GOLx

12 81.62 GLC + XYL ^ 12.03 ACTx + 1.754 BIOM + 105.9 CO2 + 85.25 ETH + 39.01 GOLx

Table 2.2. EMs represented in terms of extracellular metabolites. Acronyms for metabolites: ACTx = acetate, BIOM = biomass, CO2 = carbon dioxide, ETH = ethanol, GLC = glucose, GOL = glycerol, MAINT = Dissipated ATP for maintenance, XOLx = xylitol, XYL = xylose.

Genome level studies

After the completion of the project of the backer’s yeast Saccharomyces cerevisiae genome sequencing in 1996, genomes of some fungi have been decrypted. Many reasons drive the decision to sequence one genome and not the other one: some fungi being for a long time scientific models, others displaying industrial relevance, and others acting as saprophytes or pathogens. Backer et al. (2008) propose an interesting concept: let’s change our way of thinking and let’s consider microorganisms (especially fungi) as reservoirs for sustainable answers to environmental concerns. This point of view fits well with the directing idea of this chapter. To improve the "biomass to ethanol" process, we have to consider many options and enlarge the fields to be prospected rather than being focalized on a single system. As an example for biomass degradation, many efforts have been directed though Trichoderma reesei due to historical reasons (discovered during World War II because it degraded uniforms and cotton tents) and to its capacity to produce cell wall degrading enzymes, specially cellulases. But, as it will be described later in this section, this fungus is not — by far for some categories of enzymes — the most equipped in CWDE. Other fungi have to be considered then.

Genome sequence availability offers the scientific community the opportunity to analyze them, deciphering their metabolism, in order to find response to fundamental or applied questions. As valorization of plant biomass arise as an important question to be addressed, several studies attempt to describe the fungal polysaccharide degradation potential. An extensive and complete work leads to a comparison of the genome of 13 fungi (Martinez et al., 2008). The first observation is that the yeast model Saccharomyces cerevisiae is poorer in CWDE than filamentous fungi (Fig. 2). This is not surprising regarding their respective lifestyles; all the filamentous fungi shown in Fig. 2 are saprotrophs or pathogens in the opposite of S. cerevisiae. This is a first argument for considering the natural habitat of a fungus when examining it for a peculiar application. Here, clearly, fungi living in plant environment displayed many more genes encoding CWDE or associated activities. Note that the model for plant polysaccharide degradation, Trichoderma reesei, displays fewer putative glycosyl hydrolases (200) than the pathogens Magnaporthe grisea (231) and Fusarium graminearum (243). Perhaps even more important is the number of cellulose binding modules (CBM), allowing a better enzyme-substrate binding and then a better efficiency in natural cellulose hydrolysis. T. reesei was predicted to have half CBM than the two pathogens (Fig. 2). In the same study, T. reesei is shown to be poorer than M. grisea and F. graminearum in cellulases, hemicellulases and pectinases (Martinez et al., 2008).

image141

Fig. 2. Number of predicted glycosyl hydrolases (GH), glucosyl transferase (GT), cellulose binding modules (CBM), carbohydrate esterase (CE) and polysaccharide lyases (PL) in the genome of Saccharomyces cerevisiae, Aspergillus oryzae, Neurospora crassa, Trichoderma reesei, : Magnaporthe grisea and Fusarium graminearum (data from Martinez et al., 2008).

The main idea driving genome study is that evolution leads to genomes remodeling: i. e. leading to CWDE diversity for fungi dealing with plants. But through the example of T. reesei, it could be concluded that genome study — if available — is useful but not sufficient. Furthermore, obviously, a gene is not a protein; it has to be transcribed and mRNA has to be translated and modified to yield to mature and functional proteins.

Regulation and pricing

To establish the local market for bioethanol demand in transportation sector, Thailand has released the regulation of denatured ethanol for gasohol uses (Table 10) to ensure high quality fuel for automobile use. No regulation for biofuel uses is announced by the government. In stead, the utilization of bioethanol as liquid fuel has been promoted by price incentive system. The retail price of gasohol E10 (for octane 95) is cheaper than gasoline around 0.33 USD/liter by exemption and reduction of excise & municipal tax and Oil Fund charge.

At the initial phase of trading ethanol locally, the price of ethanol for domestic market is referred to the price of imported ethanol from Brazil (FOB price of Brazilian Commodity Exchange Sao Paulo) with the additional cost of freight, insurance, loss, survey and currency exchange rate. Thai cassava ethanol industry has used two feedstock, i. e. molasses and cassava; the former one being utilized at a higher production capacity. This leads to shortage

No.

Description/Details

Value

Analytical method

1

Ethanol plus higher saturated alcohols, %vol.

> 99.0

EN 2870 Appendix 2 Method B

2

Higher saturated (C3-C5) mono alcohols, %vol.

< 2.0

EN 2870 Method III

3

Methanol, %vol.

< 0.5

EN 2870 Method III

4

Solvent washed gum, mg/100 mL

< 5.0

ASTM D 381

5

Water, %wt.

< 0.3

ASTM E 203

6

Inorganic chloride, mg/L

< 20

ASTM D 512

7

Copper, mg/kg

< 0.07

ASTM D 1688

8

Acidity as acetic acid, mg/L

< 30

ASTM D 1613

9

ph

> 6.5 and < 9.0

ASTM D 6423

10

Electrical conductivity, pS/m

< 500

ASTM D 1125

11

Appearance

Clear liquid, not cloudy, homogenous, and no colloidal particles

12

Additive (if contains)

Agree with consideration of Department of Energy Business

Source: Modified from Department of Energy Business, Ministry of Energy, Thailand (2005).

Table 10. The Thai standard of denatured ethanol for gasohol use as announced by the Department of Energy Business.

of molasses and price increase. As a result, the reference price based on Sao Paulo does not reflect the real ethanol situation of the country, both in term of production and uses. Subsequently, the pricing formula of biofuel ethanol has been revised. The reference price of bioethanol for fuel uses, as approved by The National Energy Policy Committee, Ministry of Energy, has taken into account for the cost of raw materials and produced quantities of fuel ethanol from both feedstocks, i. e. molasses and cassava, using the conversion ratios of 4.17 kg molasses (at 50°Brix) and 2.63 kg cassava chips (with starch content > 65%) for 1L of anhydrous ethanol. In addition, the structure of ethanol reference prices includes the production costs of each feedstock, which are 6.125 and 7.107 Baht/L for molasses and cassava, respectively. This monthly-announced ethanol reference price reflects the actual cost of local ethanol producers.

PEth = (PMol x QMol) + (PCas x QCas)

QTotal

Where

PEth = Monthly reference price of ethanol (Baht/L)

PMol = Price of molasses-based ethanol (Baht/L)

PCas = Price of cassava-based ethanol (Baht/L)

QMol = Quantity of molasses-based ethanol (million L/day)

Qcas = Quantity of cassava-based ethanol (million L/day)

Qiotai = Total ethanol quantity (million L/day)

(For QMol, QCas and QTotal using the value of one month previously, e. g. for the 5th month reference price, use the value of 3th month)

P Mol = RMol + CMol

Where

PMol = Price of molasses-based ethanol (Baht/L)

RMol = Raw material cost of molasses, using a previous 3-month average export price announced by Thai Customs Department and the conversion ratios of 4.17 kg molasses (at 50°Brix) for 1L of anhydrous ethanol, e. g. using the average export price of 1st, 2nd, and 3rd month to calculate the price of 5th month

CMol = Production cost of molasses-based ethanol (6.125 Baht/L)

P Cas = RCas + CCas

Where

PCas = Price of cassava-based ethanol (Baht/L)

RCas = Raw material cost of cassava, using the root price of one month previously, the conversion of 2.38 kg (25% starch) fresh roots for 1kg of chips with the production cost of 300 Baht/ ton chips, and the conversion ratios of 2.63 kg cassava chips (with starch content > 65%) for 1L of anhydrous ethanol

CCas = Production cost of cassava-based ethanol (7.107 Baht/L)

(Note: 1 USD = 30 Baht)

3. Conclusion

Cassava is not only a traditional subsistence food crop in many developing countries, it is also considered as an industrial crop, serving as a significant raw material base for a plenitude of processed products. Important ones are starches, modified starches and sweeteners for application in food, feed, paper, textile, adhesive, cosmetics, pharmaceutical, building and biomaterial. Consequently, the demand of cassava has been rising continuously and thereby contributes to agricultural transformation and economic growth in developing countries. Recently, in some countries such as Thailand, China and Vietnam, cassava is also used as the energy crop for producing bioethanol, an environmentally friendly, renewable alternative fuel for automobile uses. The promise of using cassava for bioethanol use is supported by many reasons including distinct plant agronomic traits for high tolerance to drought and soil infertility, low input requirement relatively to other commercial crop, and potential improvement of root yields. In addition, roots are rich in starch and contain low impurities. Although, fresh roots contain high moisture contents and are perishable, simple conversion to dried chip can be achieved by farmers at a low cost. Chips as corn analog are less costly to transport, store and process. High energy input for ethanol production from starch materials become less concerned as low energy consumption processes are developed including SSF, SLSF for uncooked process and VHG for a higher ethanol concentration. Improved waste treatment and utilization is also significant in order to minimize overall production cost. With those development, the use of cassava as an energy crop raises more concerns for food and fuel security. Both are critical to agricultural countries that mainly import fossil oil fuel and have lost their economic growth. To overcome that concern, the development of sufficient feedstock supply is considered as the first priority. A short-term and long term plans for root yield and productivity improvement by good cultivation practice and varietal improvement have been presently implemented in some regions. By that with a combination of zero-waste process concept, effective policies and market mechanism, the use of cassava as a food crop, industrial crop and energy crop become sustainable and beneficial to mankind.

Fermentation

Hydrolyzates obtained from sorghum fiber are solutions rich in both hexoses and pentoses (Kurian et al., 2010). Production of ethanol from these mashes is possible only with the use of osmotolerant and pentose fermenting yeast or bacterial strains (Table 2).

Ballesteros et al. (2003) obtained 16.2 g ethanol/L when hydrolyzates obtained from sweet sorghum bagasse were fermented with Kluyveromyces marxianus. On the other hand, Kurian et al. (2010) working with Pichia stipitis obtained 38.7 g ethanol/L with a theoretical conversion of 82.5%. In Fig. 3, a flowchart of ethanol production from sorghum bagasse is depicted. A yield of 158 L ethanol/ ton biomass (wet basis) can be obtained after a sulfuric acid hydrolysis. The process yielded 110 kg of lignin and other non-fermentable materials. Almodares & Hadi

(2009) and Gnansounou et al. (2005) reported that the cellulase used in Simultaneous

Microorganism

Characteristics

Clostridium acetobutilicum

Useful in fermentation of xylose to acetone and butanol; bioethanol

produced in low yield

Clostridium thermocellum

Capable of converting cellulose directly to ethanol and acetic acid. Bioethanol concentrations are generally less than 5 g/l. Cellulase is strong inhibition encountered by cellobiose accumulation

Escherichia coli

Native strains ferment xylose to a mixture of bioethanol, succinic, and acetic acids but lack ethanol tolerance; genetically engineered strains predominantly produce bioethanol

Klebsiella oxytoca

Native strains rapidly ferment xylose and cellobiose; engineered to ferment cellulose and produce bioethanol predominantly

Klebsiella planticola ATCC 33531

Carried gene from Zymomonas mobilis encoding pyruvate decarboxylase. Conjugated strain tolerated up to 4% ethanol

Lactobacillus pentoaceticus

Consumes xylose and arabinose. Slowly uses glucose and cellobiose. Acetic acid is produced along with lactic in 1:1 ratio

Lactobacillus casei

Ferments lactose, particularly useful for bioconversion of whey

Lactobacillus xylosus

Uses cellobiose if nutrients are supplied: uses glucose, D-xylose and L — arabinose

Lactobacillus pentosus

Homolactic fermentation. Some strains produce lactic acid from sulfite waste liquors

Lactobacillus plantarum

Consumes cellobiose more rapidly than glucose, xylose, or arabinose. Appears to depolymerize pectins; produces lactic acid from agricultural residues

Pachysolen tannophilus Saccharomyces cerevisiae ATCC 24 860

Co-culture of S. cerevisiae and strains resulted in the best ethanol yield

Pichia stipits NRRL Y-7124, Y — 11 544, Y-11 545

NRRL strain Y-7124 utilized over 95% xylose based on 150 g/L initial concentration. Produced 52 g/L of ethanol with a yield of 0.39 g ethanol per g xylose

Pichia stipits NRLL Y-7124 (floculating strain)

Maximum cell concentration of 50 g/L. Ethanol production rate of 10.7 g/L. h with more than 80% xylose conversion. Ethanol and xylitol yield of 0.4 and 0.03 g/ g xylose

Saccharomyces cerevisiae CBS 1200

Candida shehatae ATCC 24 860

Co-culture of two yeast strains utilized both glucose and xylose. Yields of 100 and 27% on glucose and xylose, respectively

Table 2. Native and engineered microorganisms capable of fermenting xylose to bioethanol1

1 With data from: Balat et al. (2008) and Lee (1997).

Saccharification and Fermentation (SSF) can be added directly or from material previously deviated from pretreatment and inoculated along with Trichoderma reesei or other fungi such as Neurospora crassa and Fusarium oxysporum. These microorganisms were capable of directly fermenting cellulose (Mamma et al., 1996). F. oxysporum was used in a SSF along with S. cerevisiae, yielding 5.2 to 8.4 g ethanol per 100 g of fresh sorghum. The efficiency was calculated based on soluble sugars and not in total polysaccharides (Mamma et al., 1996).

Microorganisms and their metabolism

The extension of substrate utilisation is critical to determine the economic viability of ethanol production from LCB. This presumes a complete conversion of sugars presented in feedstocks to ethanol under industrial conditions. Under an industrial context, the microorganism chosen should meet some requirements, which are discussed in relation to four benchmarks: (1) Process water economy; (2) Inhibitor tolerance; (3) Ethanol yield; (4) Specific ethanol productivity. Several species of bacteria, yeast and filamentous fungi naturally ferment sugars to ethanol. Each microorganism has its advantages and disadvantages, some can use only hexoses for producing ethanol and others can use both, hexoses and pentoses, but many times with low ethanol yields (Hahn-Hagerdal et al. 2007). The mixture of sugars obtained after LCB hydrolysis, besides glucose, also contains other sugars e. g. xylose, mannose, galactose, arabinose and also some oligosaccharides. Therefore, in the fermentation process, microorganisms ferment these sugars into bioethanol according to reactions presented below. The calculation of the theoretical maximum yield should follow equation 1 for pentoses or equation 2 for hexoses:

3C5H10O5 ^ 5C2H5OH + 5CO2 (1)

C6H12O6 ^ 2C2H5OH + 2CO2 (2)

According to these equations, the theoretical maximum yield is 0.51 g bioethanol and 0.49 g carbon dioxide per g of xylose and glucose.

In order to obtain an economically feasible conversion process of any biomass, it is imperative that the microorganisms chosen should be able to convert efficiently all the sugars present into the desired end product, in this case bioethanol (Chu et al. 2007; Hahn — Hagerdal et al. 2007; Matsushika et al. 2009). The ideal yeast for bioethanol production from LCB should consume the sugars present and provide high production yields as well as specific productivities. Moreover it should not suffer any inhibition from the other components of the raw material (Hahn-Hagerdal et al. 2007).

One of the most effective and well-known ethanol producing microorganisms from hexose sugars is the yeast S. cerevisiae. This yeast is successfully employed at industrial scale, allowing for high ethanol productivity, since it bears high tolerance to ethanol and to inhibitors normally present in lignocellulosic residues. However, this yeast is unable to ferment xylose to ethanol efficiently, though it can only ferment its isomer, xylulose (Jeppsson et al. 2006; Chu et al. 2007; Hahn-Hagerdal et al. 2007; Matsushika et al. 2009). Some yeasts were reported to be efficient in xylose conversion to ethanol, such as, P. stipitis, Candida shehatae and Pachysolen tannophilus (Huang et al. 2009). Among them, P. stipitis exhibits the best potential for industrial application due to the high ethanol yield obtained (Huang et al. 2009). Nevertheless, this yeast is sensitive to organic acids, including acetic acid, which are present in lignocellulosic residues. These compounds inhibit both cell growth and the bioethanol production (Bajwa et al. 2009; Huang et al. 2009). Although, wild type S. cerevisiae cannot ferment xylose to ethanol, several genetic engineered strains have been already developed (Hahn-Hagerdal et al. 2007; Mussatto et al. 2010). Other yeasts, like P. stipitis, can naturally utilize both types of sugars with high yields and its use for producing 2nd generation bioethanol from HSSL is being developed (Xavier et al. 2010). Hence, it is important to improve the yeast strain with the most promising characteristics in order to optimize ethanol production from LCB hydrolysates through genetic engineering and/or strain adaptation (Chu et al. 2007; Hahn-Hagerdal et al. 2007; Matsushika et al. 2009). Table 7 summarizes the fermentation performance of several yeasts in different media. Among bacteria, the most promising for industrial implementation are Escherichia coli, Klebsiella oxytoca and Zymomonas mobilis. Z. mobilis is the bacteria which has the lowest energy efficiency resulting in a higher ethanol yield (up to 97% of theoretical maximum). However, this bacterium is only able to ferment glucose, fructose and sucrose to ethanol. Another problem appears when the medium has sucrose, due to the formation of the polysaccharide levan (made up of fructose), which increases the viscosity of fermentation broth, and of sorbitol, a product of fructose reduction that decreases the efficiency of the conversion of sucrose into ethanol (Lee et al. 2000). K. oxytoca, an enteric bacterium, found in paper, pulp streams and different sources of wood, is able to grow at low pH (minimum 5.0) and temperatures up to 35 °С. This bacterium is able to grow either on hexoses or pentoses, as well as on cellobiose and cellotriose (Lee et al. 2000; Cardona et al. 2007; Chen et al. 2010b).

Подпись: 1xylose isomerase from Firomyces sp. 2not available Table 7. Fermentation performance of several yeasts in different media
Подпись: Second Generation Bioethanol

Yeast

Strain

Description

Type of medium

Detoxification

method

Fermentative

process

Xylinitial

(gL-1)

Ye

(gff1)

Reference

Pichia stipitis

BCRC21777

Wild type

Rice straw hydrolysate

Overliming

Batch

30°C;100rpm

21

0.37

(Huang et al. 2009)

BCRC21777

Adapted

Rice straw hydrolysate

Overliming

Batch

30°C;100rpm

21

0.45

(Huang et al. 2009)

NRRL Y -7124

Adapted

HSSL

Overliming

Batch

30°C

40

0.30

(Nigam

2001a)

NRRLY-7124

Wild type

HSSL

Ion-exchange

resins

Batch

29°C;180rpm

21

0.48

(Xavier et al. 2010)

Saccharomyces cerevisiae

ADAP8

XYLAi, XKSl/

SUT1

Complex

None

Batch

30°C;200rpm

20

0.35

(Madhavan et al. 2009)

MA-N5

XYL1/XYL2/X

KS1

Complex

None

Batch

45

0.36

(Matsushika et al. 2009)

MA-R4

XYL1/XYL2/X

KS1

Complex

None

Batch

45

0.35

(Matsushika et al. 2009)

MA-R5

XYL1/XYL2/X

KS1

Complex

None

Batch

45

0.37

(Matsushika et al. 2009)

TMB3400

n. a.2

Spruce

hydrolysate

n. a2.

Fed-batch

6

0.43

(Hahn — Hagerdal et al. 2004)

TMB 3006

n. a2.

Spruce

hydrolysate

n. a2.

Fed-batch

6

0.37

(Hahn — Hagerdal et al. 2004)

MT8-1

XYL1/XYL2/X

KS1

Lignocellulosic

Hydrolysate

Biological with enzymes

Batch

30°C;100rpm

9

0.41

(Katahira et al. 2006)

F12

XYL1/XYL2/X

KS1

Vinasse residue

Biological with enzymes

Batch

30°C;300rpm

6

0.27

(Olsson et al. 2006)

 

Cf)

T3

(D

 

Several metabolic engineering and genetic modification strategies to enhance an efficient fermentation of xylose to ethanol were studied for S. cerevisiae (Chu et al. 2007; Hahn — Hagerdal et al. 2007; Matsushika et al. 2009). Although the genes that allow for xylose utilization are present in S. cerevisiae, they are expressed in low levels resulting in production rates of ethanol from xylose ten times lower than the verified for glucose as substrate (Chu et al. 2007; Hahn-Hagerdal et al. 2007). In pentose-fermenting yeasts, xylose catabolism begins with its reduction to xylitol by a NADH — or NADPH-dependent xylose reductase (XR), as seen in Fig. 9. Then, xylitol is oxidized to xylulose by NAD-dependent xylitol dehydrogenase (XDH) (Chu et al. 2007; Hahn-Hagerdal et al. 2007; Bengtsson et al. 2009). Xylulose is phosphorylated by the enzyme xylulokinase (XK) to produce xylulose-5- phosphate (X5P). This enters in glycolytic pathway and then in the pentose phosphate pathway (PPP). The formed intermediates are converted to pyruvate in the Embden — Meyerhof-Parnas pathway. Under anaerobic conditions, fermentation of pyruvate occurs by decarboxylation promoted by pyruvate decarboxylase to acetaldehyde which is then reduced to ethanol by alcohol dehydrogenase (Chu et al. 2007; Hahn-Hagerdal et al. 2007).

image065

The most straightforward metabolic engineering strategy was the expression of a bacterial xylose isomerase (XI) gene, so that xylose can directly be converted to xylulose (Jeppsson et al. 2006). The XI gene from the thermophilic bacterium Thermus thermophilus was successfully expressed in S. cerevisiae, generating xylose-fermenting recombinant strains (Karhumaa et al. 2005). Also, the genes of Piromyces sp. XI were also successfully expressed in S. cereviasiae (Kuyper et al. 2003). Another possible metabolic engineering strategy consisted in expressing fungal XR and XDH genes. Stable xylose-fermenting S. cerevisiae strains were obtained by integrating the P. stipitis XYL1 and XYL2 genes encoding XR and XDH, respectively, and over expressing the endogenous XKS1 gene encoding xylulokinase (XK) (Bengtsson et al. 2009; Matsushika et al. 2009). However, ethanol yield attained with these strains was far from the theoretical maximum of 0.51 g. g-1, as can be seen in Table 7 because the metabolic pathway stopped in xylitol. This situation was attributed to the fact that since XR is NAD(P)H-dependent and XDH is strictly NAD+-dependent the relation between the two cofactors sometimes becomes unbalanced (Jeppsson et al. 2006; Chu et al. 2007; Bengtsson et al. 2009).

Wahlbom and Hahn-Hagerdal (2002) found that the addition of electron acceptors such as acetoin, furfural and acetaldehyde re-oxidized NAD+ needed by XDH and decreased the amount of xylitol formed. Shifting the cofactor utilization in the XR step from NADPH to NADH was also a successful strategy for decreasing xylitol (Jeppsson et al. 2006). Since S. cerevisiae lacks the xylose-specific transporter, another common approach is to express in this microorganism the gene that encodes the transport of monosaccharides from P. stipitis (Van Vleet et al. 2009). Hence, xylose uptake occurs by facilitated diffusion mainly through non-specific hexose transporters, which have lower affinity for xylose (Matsushika et al. 2009). This approach enhanced xylose fermentation to ethanol by S. cerevisiae (Van Vleet et al. 2009).

In addition to metabolic engineering, natural selection of strains and random mutation are also alternatives to obtain improved xylose-fermentative yeasts. These evolutionary engineering approaches were successfully applied to several S. cerevisiae strains for effective xylose fermentation. These methods are particularly useful since they are non-invasive and can identify bottlenecks in the xylose metabolic pathway that can then be targeted to be overcome by genetic engineering (Chu et al. 2007; Matsushika et al. 2009). Chu and Lee (2007) suggested that an intense selection pressure will favour the presence of S. cerevisiae mutants able to grow slowly on xylose.

Recent studies have redirected their attention to the xylose-fermenting yeast, P. stipitis. In this case, the major issue is the inhibitors tolerance which can be critical when real raw materials are tested. Hence, an evolutionary strategy has been adopted. The strains adaptation was normally accomplished by sequential transfer of culture samples to different media composed by increasing concentrations of the residue in study (Mohandas et al. 1995; Bajwa et al. 2009; Huang et al. 2009). To accelerate the mutations, ultra violet radiation (UV) was also tested by Bajwa and co-workers (Bajwa et al. 2009).

Many challenges in ethanol production from xylose using metabolically engineered strains were being overcome. Several approaches were successfully employed to engineer xylose metabolism. Nevertheless, these approaches are insufficient for industrial bio-processes mainly due to the low fermentation rate of xylose when compared with glucose. Another bottleneck is the lack of tolerance to the major inhibitors present in lignocellulosic feedstocks. A successful fermentation of LCB hydrolysates requires not only a producing strain that consumes all the sugars present but with tolerance towards lignocellulose degradation products. Moreover, most of the methodologies tested were applied to defined synthetic media containing pure substrates and their applicability to real complex substrates should be validated. However, the composition of the inhibitors in raw materials as lignocellulosic wastes changes frequently and, consequently, the metabolic engineering method probably need some modifications to be applied (Hahn-Hagerdal et al. 2007; Matsushika et al. 2009). Metabolic engineering approaches to improve inhibitor tolerance were so far limited to the over expression of specific enzymes including laccase, phenylacrylic acid decarboxylase, glucose 6-phosphate dehydrogenase and alcohol dehydrogenase (Hahn-Hagerdal et al. 2007). These enzymes can transform some of the inhibitors (mainly the aromatic compounds) into products that microorganisms can assimilate.

In brief, the technical and economic issues related to the choice of fermenting microorganism are the conversion efficiency uniformity, the tolerance to inhibitors, the process requirements (aeration, temperature, pH, sterilization) and the bioprocess licensing (Lawford et al. 1993). Further intensive studies that combine functional genomics analysis with metabolic engineering are required for developing robust yeast strains, tolerant to several inhibitors and to the variability of the substrate and with the ability to ferment xylose from lignocellulosic feedstocks, in order to produce ethanol, at similar rates as those attained with glucose, to be applied at industrial level (Chu et al. 2007; Hahn-Hagerdal et al. 2007; Matsushika et al. 2009).