The limitations of the bioprocess control systems do not concern only the measurements or models, but at the same time much valuable human knowledge is only available in a qualitative heuristic form.

Hence, it has been found that the knowledge-based control structures using the human decisional factor (i. e. a subjectively element) offer sometimes better results. Moreover, the computer performances are developed in the detriment of the general knowledge concerning life phenomena and do not promote advanced comprehension upon the metabolic routes of bioprocesses. Consequently, the intelligent techniques (i. e. neural nets, fuzzy structures, genetic algorithms or expert systems) are capable of simulating human expert-like reasoning and decision making, dealing with uncertainties and imprecise information [24].

As the human perception about the bioprocess is commonly altered by the psychological factors, the intelligent control systems founded (only) on the human subjective knowledge is less valuable than the control systems who utilize the objective information fitted by a conceptual model. Hence, the literature recommends the intelligent control techniques utilization only if the control structure based on quantitative models fails.

Frequently, different process parameters are controlled in order to follow predefined transitory trajectories. Such control strategies can be designed by a trial-and-error approach in combination with operator’s experience and statistical analysis of historic data.

a) One method for automatic bioprocesses control using AI is based on expert systems (ES) that reproduce the human operator’ rules of action. The literature presents several examples how to transfer the knowledge from operators into knowledge-rule bases [38]. An ES conceptual architecture is presented in Figure 4.

The most used ES systems in bioprocess control are applied for supervisory control, or process monitoring and diagnosis.

Moreover, the ES logic is used to translate human language into a mathematical description. The parameters tuning is then regulated by phase detection based on if…then rules, conditional statements representing heuristic reasoning in which if expresses the condition to be applied and then-the action to be done. Of course, at the same time, it is not possible to calculate optimal parameter’ values with this method. For example [39] an ES was developed in order to supervise a conventional control system applied to fed-batch baker’s yeast cultivation and to surmount its limitation. Expert system BIOGENES can execute standard process control tasks, but also advanced control tasks: process data classification;

qualitative process state identification (metabolic state, process phase, substrate feeding); supervisory control through corrective actions.

SUPERVISOR

One of the main limits in developing ES is the knowledge acquisition process due to: (1) the linguistic rules formulation by human experts, i. e. the analytical description of their actions during the bioprocess (manual) control; (2) the loss of information during the transfer between the bioprocess expert and the IT specialist; (3) the subjectivity of human expert regarding own decision / rules.

b) Another AI technique, the fuzzy approach is based on fuzzy sets and fuzzy reasoning. In actual AI systems, fuzzy rules are often applied together with different types of models / parameter / state estimators. These fuzzy rules can be regarded as problem specific basis function system [40]. Any variable can be a fuzzy variable, particularly recommended when it is not possible to define its value in a given situation. One define fuzzy sets in the form of membership functions (between 0 and 1) in order to express what is likely to be considered as degree / level for a certain characteristic (high, medium, low). Relationships between fuzzy variables can be formulated with fuzzy logic operators (and, or, not) and processed by fuzzy logic. Fuzzy rules reflect the rules of thumb used in everyday practice and can be processed as if…then expressions. With a set of fuzzy rules, considered as universal process approximates, the behavior of a system can be described quite accurately. There are many applications: (a) hierarchical fuzzy models within the framework of orthonormal basis functions41 (Laguerre and Kautz functions); (b) several important use of fuzzy control in the Japanese bioindustry by the companies Ajinomoto, Sankyo or Nippon Roche [42]; (c) the control of the a-amylase fed batch bioprocess with the recombinant E. coli to maintain glucose and ethanol at low concentrations with 2 fuzzy controllers for feed rate control: feed forward and feedback [43] (see Figure 5 below):

c) One can use artificial neural networks (ANN) to get predictions about biosystem behavior. The traditionally used format of ANN is the feed forward. Given a set of process measurements, the output of ANN can be estimated parameters or process variables. The weights applied to the process measurements as inputs are determined through the "training process" of the ANN [44]. To train the ANN it is to get complete process information, corresponding to the NN inputs and outputs, from the data gathered in a set of fermentation runs. This set defines "an experimental space" and the ANN will predict outputs accurately only within this range and not beyond it.

Various applications were studied: (a) biomass and recombinant protein concentration estimation via feed forward NN for a fed batch bioprocess with a recombinant E. coli [45, 46]; (b) two types of NN (input/output and continuous externally recurrent) can control the batch and fed batch piruvat production from glucose and acetate with a recombinant strain of E. coli [47]; (c) two NN also to control the submerged bioprocess of Monascus anka fungus cultivation (the temperature and the dissolved oxygen are the inputs and the controlled outputs are the glucoamylase activity and the concentration of the red pigment [48]; (d) NN based soft sensor for online biomass estimation in fed bioprocess for polyhydroxibutirate production [49]; (e) media formulation optimization with genetic algorithm evaluated by ANN [50].

d) Because all types of information must be used in order to improve the bioprocess control: mathematical / deterministic models, heuristic knowledge, rule-based reasoning, a new control structure is developed in the last years — i. e. hybrid control system (HCS). HCS acts on both parts of bioprocess control: conventional control systems (i. e. based on a priori model) merge with AI techniques, in a complementary way: if a priori (mathematical) model exists, it will be preferred; else the linguistic rules (i. e. expert systems / fuzzy techniques) will be used.

This is an Intelligent Control Structure (ICS) based on Hybrid Control Techniques (HCT). The most widely used hybrid structure combines balance equations with ANN: (a) balance equations for substrate and cell concentrations coupled with ANN for growth rate model in case of bakers ‘yeast fed batch cultivation [51]; (b) ANN is responsible for modeling the unknown kinetics in applications with the yeast Saccharomyces cerevisiae or activated sludge urban wastewaters bio treatment [52]; (c) batch bioprocess of animal cells [53].

The shear force imposed in the development of granules in this experiment, in terms of superficial upflow air velocity (i. e. 1.6 cm/s), resulted in the development of biogranules with an average diameter of 2.25 mm. The strong shearing force produced by aeration during the aerobic phase prevents the development of bigger aerobic granules. However, reduction in famine period may also lead to the formation of bigger aerobic granular sizes (Liu and Tay, 2006).

The average settling velocity of the sludge and anaerobic granular sludge used as the seeding were 9.9 ± 0.7 m/h and 42 ± 8 m/h respectively. The settling velocity of the biogranules increased from 17.8 ± 2.6 m/h to 83.6 ± 2.6 m/h at the end of experiment. The average settling velocity of the mature biogranules reached almost 80 ± 7.6 m/h, which was nearly three times greater than the settling velocity of the aerobic granules reported by Zheng et al. (2005).

The increase in settling velocity has given significant impact on the biomass concentration in the reactor. The relationship between the concentration of the MLSS and settling velocity of the granules is shown in Figure 7. Despite the short settling time (5 min), the high settling velocity possessed by the developed biogranules enabled the biogranules to escape from being flushed out during the decanting phase. Such conditions have caused more biogranules to retain in the reactor and resulted in the increase of biomass concentration.

100

90

80

70

60

50

40

30

20

10

0

The SVI value has also improved from 277 mL/g at the initial stage to 69 mL/g at the mature development of biogranules. This indicates good settling properties of the biogranules, which is favorable in wastewater treatment plant operation. Figure 8 demonstrates the SVI profile along with the settling velocity. As the SVI value improved, the granular settling properties increased from 50 m/h to about 80 m/h. The SVI of biogranules seems to vary depending on the settling time of the reactor system. McSwain et al. (2004) reported the SVI of biogranules improved from 115 ± 36 ml/g to 47 ± 6 ml/g when the settling time decreased from 2 to 10 min. Biogranules developed with anaerobic seeding, showed higher settling velocity and improved SVI.

100

80

60

40

20

0

The granular strength of the biogranules was measured based on the integrity coefficient (IC) defined earlier. The smaller the value of IC, the higher the strength and ability of the biogranules to clump together and being prevented to break due to shear force of the aeration. Figure 9 shows the profile of IC of the developed biogranules as a function of time. The IC reduced as the biogranules developed. The initial value of IC was 30. Then the IC was reduced to about 9 as it reached a mature stage. According to Ghangrekar et al. (2005), biogranules with integrity coefficient of less than 20 were considered high strength granules. The reduction in IC value indicates the increase in the strength of the bond that holds the microorganisms together within the developed biogranules.

During the initial development, the microbes within the biogranules were loosely bounded to each other. At this stage, the biogranules may consist of more cavities causing the biogranules become less dense, as manifested by low settling velocity. As more microbes are linked together, the biogranules increase in size. Under certain selective pressures (i. e. short

settling time, hydrodynamic shear force, starvation of the microbial cell), microbes may produce more extrapolysaccarides (EPS) (Lin et al., 2003; Qin et al., 2004). As reported by Zhang et al. (2007) and Adav and Lee, (2008), the EPS contribute greatly to the strength and the stability of aerobic granules. When microbial cells produce more EPS, they form a cross — linked network and further strengthen the structural integrity of the granules. The cavities within the biogranules will be filled with EPS as it is a major component of the biogranules matrix material. This caused the biogranules to become denser and stronger as shown by their high settling velocity and lower IC value. The physical characteristics of the seed sludge and the matured biogranules are summarized in Table 4. The developed biogranules possess desirable biomass characteristics in the biological wastewater treatment system.

The profile of the biomass concentration (i. e. MLSS) after seeding with the anaerobic granules is shown in Figure 10. During the first few days, almost half of the sludge was washed out from the reactor causing a rapid decrease in the biomass concentration. The MLSS reduced from initial concentrations of 5.5 g/L to 2.9 g/L mainly due to the short settling time

used in the cycle (i. e 5 min). During this initial stage, the anaerobic granules were also observed to disintegrate into smaller fragmented biogranules and debris resulted from shear force caused by aeration. These small fragments have poor settling ability and were washed out from the reactor causing an increase of suspended solids concentration in the effluent.

9

8

As the experiment continued, the concentration of the biomass increased and reached 7.3 g MLSS/L on the 66th day. The profile of MLVSS follows the same trend of MLSS, ranging from 1.9 g/L to 5.6 g/L. The mean cell residence time (SRT) also increased from 1.4 days at the initial stage to 8.3 days on the 66th day, indicating the accumulation of the biomass in the reactor. As less biomass was washed out during the decanting period, the increase in SRT is

another manifestation of good settling characteristics resulting from the high settling velocity. Nonetheless, the benefit of high SRT will depend on the goal of the treatment process (Tchobanoglous et al., 2004). The SRT is affected by the settling velocity. The profiles of the settling velocity and the SRT as functions of time are given in Figure 11.

Intensified livestock industry and increased consumption of meat and animal products are contributing to a surplus of animal by-products in Europe and other developed countries. In Europe more than 1500 million tons of animal slurry is produced every year [12]. Traditionally, slurry has been recycled as fertilizer, providing nitrogen (N) and phosphorous (P) source for plants and crops. However accumulation of carbon and leaching of N and P causes a serious and negative environmental impact (water, air and soil contamination). Thus, pathogens from improperly treated animal wastes often threaten public health. The emission of GHG during livestock slurry management has been widely ignored compared to the local environmental problem, as the impact itself is global and therefore indirect. It is not long ago that the climate changes became an important global issue, and animal slurry has been identified as a major source of GHG emissions in the agricultural sector.

determines the carbon flow. The principal of the conversion of organic materials is its oxidation either by oxygen in aerobic conditions or by transferring electrons when oxygen is not available (anaerobic condition). Degradation of organic materials in animal slurry in nature mostly occurs under anaerobic conditions that produce GHG, which breaks the carbon flow balance. To balance carbon flow, aerobic degradation must occur to bring the organic materials back to water and CO2 which was spent for photosynthesis, however the oxygen in animal slurry is critical due to high contents of organic materials which consume

Aerobic degradation may occur in the surface due to diffusion of oxygen but the amount is still insignificant. Hence, aerobic treatment of animal slurry often shows less environmental impact such as oxygen depletion of aquatic systems. The representative GHG in the agricultural sector are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). In Denmark, animal manure accounts for about 40% of total CH4 and 20% of total N2O emissions [14]. CH4 originates mainly from enteric fermentation in ruminant animals like cattle, whereas for pig production, slurry management is the primary source for CH4 emission. Another important greenhouse gas is N2O which is emitted from turnover of nitrogen in manures and in agricultural soils [15]. In comparison to CO2, it is reported that the emission from CH4 and N2O is low [14], however their global warming potentials are 23 and 296 times higher than that of CO2, respectively [2]. The distribution of GHG in total emissions is given in Figure 1, showing that the agricultural share of global emissions is 13.5% [13], while that of national emissions in Denmark is considerably higher at 18% [15].

Liquid hot water fractionation does not employ any catalyst or chemicals. Pressure is utilized to maintain water in the liquid state at elevated temperatures (160-240 °C) and provoke alterations in the structure of the lignocelluloses [131-133]. LCF in LHW undergoes high temperature cooking in water with high pressure. LHW pre-treatment has been reported to have the potential to enhance cellulose digestibility, sugar extraction, and pentose recovery, with the advantage of producing hydrolysates containing little or no inhibitor of sugar fermentation [134].

Water is an abundant, non-toxic, environmentally benign and inexpensive solvent. LHW is the part range of sub-critical water that near its critical point (374 °С, 22.1 MPa), Sub-critical water (SCW) possesses marvellous properties which are very different from that of ambient liquid water [135-138]. In SCW, dielectric constant, surface tension, and viscosity decrease dramatically with increasing temperature, which enhances the solubility of organic compounds. Sub-critical water is more like non-polar organic solvent (similar with acetone), thus it can substitute for some of organic solvents, and become a clean medium for chemical reactions. SCW is a tunable reaction medium for conducting ionic/free radical reactions, and an effective medium for energy and mass transfer. The ionic product of SCW is larger by three orders of magnitude than that of ambient water, which means concentrations of hydrogen and hydroxide ions are much higher. Therefore, in addition to the increase in kinetic rates with temperature, both acid and base catalyses by water are enhanced in SCW, which can be a solvent or reactant participated in chemical reaction. And without any pollution, hydrolysis in SCW is an environment-friendly technology

The objective of the liquid hot water is to solubilise mainly the hemicellulose to make the cellulose more accessible and to avoid the formation of inhibitors. By keeping the pH between 4 and 7 the autocatalytic formation of fermentation inhibitors are avoided during the fractionation [34, 139, 140]. If catalytic degradation of sugars occurs it results in a series of reactions that are difficult to control and result in undesirable side products.

The slurry generated after pre-treatment can be filtered to obtain two fractions: one solid cellulose-enriched fraction and a liquid fraction rich in hemicellulose derived sugars [34]. Lignin is partially depolymerised and solubilised as well during hot water fractionation but complete delignification is not possible using hot water alone, because of the re­condensation of soluble components originating from lignin.

Water under high pressure can penetrate into the LCF, hydrate cellulose, and remove hemicellulose and part of lignin. The major advantages are no addition of chemicals and no requirement of corrosion-resistant materials for hydrolysis reactors in this process. Liquid hot water pre-treatments are attractive from no catalyst requirement and low-corrosion potential. Liquid hot water has the major advantage that the solubilised hemicellulose and lignin products are present in lower concentrations, due to higher water input and subsequently concentration of degradation products like furfural and the condensation and precipitation of lignin compounds is reduced. However, water demanding in the process and energetic requirement are higher and it is not developed at commercial scale [141].

Several environmental factors can potentially influence a willow short-rotation coppice plantation and all should be evaluated prior to plantation establishment to maximize success. Ecologically, the majority of willow species are common in cold temperate regions and are adapted to mesic-hydric habitats. However, most riparian species require well-aerated substrate and flowing moisture, whereas non-riparian species have less exacting soil aeration requirements [23]. Moisture availability is an important factor determining native distribution in natural environments, successful plant establishment and high biomass yield. On average, willow coppice requires more water for growth than conventional agricultural crops [24] and consequently highly moisture retentive soil is an essential prerequisite. The lower St. Lawrence Valley, where most willow plantations in Quebec have been successfully established over the past two decades, is characterized by a temperate and humid climate with an annual average temperature of 6.4°C, average growing season (May-October) temperature of 15.8°C and a mean total annual precipitation of 970 mm. The period without freezing is on average 182 days and the total number of growing degree-days (above 5°C) is 2100.

Soil composition is another important factor for ensuring willow crop establishment and yield. In general, willow can be grown on many types of agricultural land. However, since this species is more water-dependent than other crops, particularly dry land should be avoided. On the other hand, although willow has been shown to be a rather flood-tolerant species compared to other woody energy crops [25], permanently submerged soils also constitute unsuitable sites. Ideally, willows should be grown on a medium textured soil that is aerated but still retains a good supply of moisture. Most willows grow best in loamy soils, with a pH ranging from 5.5 -7.0, although to a certain extent suitable soil types may range from fine sands to more compact clay soils. Several studies have shown that heavy clay soils are not very suitable for willows [26]. Most abandoned agricultural lands in Quebec are thus highly suited to growing willows, being situated in temperate regions and often adequately fertile. Other pre-establishment considerations are linked to the location of the plantation. Economical (and ecological) benefits can be maximized when high production levels of willows are achieved in combination with low input requirements, which result in high-energy efficiency and low environmental impact. For this reason, choosing the right location is crucial for achieving a sustainable energy production system. Normally, the plantation should be situated as close as possible to the end utilisation point (e. g. within 50-100 km from a power plant or transformation industry, etc.) and in any case should be established in proximity to main roads, highways or railroads. For the same reasons, the shape of willow fields should be as regular as possible to avoid loss of time and energy during management and harvest operations. For practical reasons (mainly linked to tillage and harvest) land with an elevated slope (>15%) should be avoided. Ideal sites are flat or with a slope not exceeding 7-8%.

The name of activated charcoal is applied to a series of artificially prepared porous carbons to exhibit a high degree of porosity and a high inner surface. These characteristics are responsible for their adsorptive properties, which are used widely in many applications in gas phase and liquid phase. Chemically it is composed of carbon, oxygen, hydrogen and ash. The activated carbon adsorbent is a very versatile, because the size and distribution of pores in the carbonaceous structure can be controlled to meet the current and future technology. The pore sizes ranging from smaller called micropores (2.0 nm) until the mesopores (2 — 50 nm) and macropore (<50 nm). It should be borne in mind that most adsorption occurs in the micropores (greater than 90% of the surface area) the mesopores and macropores are extremely important because in the activated charcoal are those which facilitate access of the species will adsorb to the interior of the particle and of the micropores [58].

One of the materials that have been studied as biomass support is activated charcoal. Its high porosity and high surface area activated charcoal make it an ideal material to be carried out the process of adsorption of heavy metals. Another reason why activated charcoal is used for the adsorption is its low cost, since it is an abundant product is obtained as a byproduct of the production of oil from coconut, olive and processing of sugar cane [55].

These regions hardly attain a yield of 14 M mt at the MSY level, being the Indian Ocean the most productive of this group with 12 M mt caught in the whole area. The stock biomass approaches to 24 M mt at the MSY level but at the current exploitation level, this variable implies a reduction of almost 2 M mt, with almost 22 M mt.

The three regions in which it is divided show remarkable differences implying important characteristics in the fishing intensity applied; for instance, the Antarctic zone seems to have been completely overexploited and probably collapsed since 1992 (more recent catch data are not available) and the maximum catch was attained by the early eighties with nearly 100,000 mt as mean trend (Fig. 4A). The same as the Pacific western central, in the Indian ocean eastern the yield describes an increasing trend, with nearly 7 M mt in the last three years, with no signs of stabilization in the near future, which is also encouraging (Fig. 4B). The catch in the western region is also growing, but it seems to be stabilizing currently; the catch is 4.5 M mt corresponding to a maximum stock biomass of 9 M mt (Fig. 4C).

The catch at the Antarctic shows a declining trend, with a maximum yield of nearly 40,000 mt recorded in the middle fifties (Fig. 4D) and a stock biomass of 80,000 mt. Same as the Antarctic Indian Ocean, these fisheries seem to be completely collapsed since the late eighties.

The Mediterranean and Black sea display a quite stable catch trend through the last 30 years and the MSY was attained by the year 1990 with 1.6 M mt, from a biomass of 3.2 M mt. After that year there has been a slow declining trend, such that the current stock biomass is no higher than 3 M mt (Fig. 4E).

Figure 4. Trend of total catches extracted from several regions of the Indian Ocean, Antarctic, and Mediterranean-Black Sea in the period 1950 — 2010. A. Antarctic Indian ocean; the maximum yield was obtained in the year 1980 and the fisheries collapsed by the early nineties. B. Indian Ocean eastern; the fisheries are in a eumetric stage of growth nowadays. C. Indian Ocean western; after a sustained growth since the fifties, these fisheries appear to be approaching their MSY level nowadays. D. Antarctic ocean; all information related to this ocean confirms a collapse of its fisheries, as occurred in this case. E. Mediterranean-Black sea; after a period of slow but consistent growth, the MSY appears to have attained the MSY level in the late nineties, followed but a slow decline nowadays. F. The areas outside the Antarctic show the same trend as the Antarctic itself, with an apparent collapse nowadays.

Finally, the areas outside the Antarctic, apart from a peak of the catch in the middle sixties, display low yields that currently are above 11,000 mt from a stock biomass of 40,000 mt. This fishing region appears to be collapsed too.

There is an ever-growing demand for new and improved bioethanol production microorganism strains. Desirable characteristics of bioethanol production microorganisms are listed in Table 1.

Ethanol production microorganisms, mainly Zymomonas mobilis and Saccharomyces cerevisiae, are potential candidates for bioethanol productions because they showed many of the characteristics presented in the table 1. However, Zymomonas mobilis strains have attracted much attention because their growth rate is higher than that of Saccharomyces cerevisiae,

conventionally used microorganisms for commercial bioethanol production. Zymomonas mobilis has been used in tropical areas for making alcoholic beverages from plant sap [2], but its narrow spectrum of fermentable carbohydrates has hampered its industrial exploitation [3]. Several researchers have taken on the challenger on developing recombinant organisms, including: S. cerevisiae, Z. mobilis, Escherichia coli, Klebsiella oxytoca and Erwinia herbicola [4-5], but the bioethanol production from biomass materials by genetically engineered strains has not yet reached a sufficient level for commercial application [6]. Zymomonas cells are gram­negative rods; a minority of the strains are motile, with 1 to 4 polar flagella. These organisms need glucose, fructose, or (for some strains) sucrose in the growth medium. They are very unusual microorganisms since they ferment these sugars anaerobically by way of the Entner-Doudoroff mechanism, followed by pyruvate decarboxylation. The oxidation — reduction balance between G6P dehydrogenase and triosephosphate dehydrogenase on one hand and ethanol dehydrogenase on the other, is mediated through NAD+. Sugar fermentation is accompanied by formation of a small amount of lactic acid, with traces of acetaldehyde and acetoin [2].

The simplified fermentation process is:

C6H12O6 + (carbon source) ^ 1.8 CH3CH2OH + 1.8 CO2 +

+ 0.2CH3CH (OH)COOH + 0.22 CH2O + ATP + 32.7 kcal (1)

The molar growth yield indicates that Zymomonas is only about 50% efficient in converting its carbon and energy sources. Growth is partially uncoupled. About 2% of the glucose substrate is the source of about half of the cellular carbon. Several amino acids also serve as carbon sources. Some strains grow only anaerobically; others display various degrees of microaerophily. Apparently, the main effect of oxygen is the oxidation of part of the ethanol which converts into acetic acid. Most strains are alcohol tolerant (10%) and grow in up to 40% glucose. The wide pH for growth range from 3.5 to 7.5, and acid tolerance are quite typical. This bacterium has been isolated from fermenting agave sap in Mexico, from fermenting palm saps in Zaire, Nigeria, and Indonesia, from fermenting sugarcane juice in Northeastern Brazil. Undoubtedly, they are important contributors to the fermentation of plant saps in many tropical areas of the America, the Africa, and Asia.

cerevisiae cells measure 3-7 microns wide and 5-12 microns long. It has elliptic, round and oval shapes and reproduces is by a division process known as budding [7]. It is believed that S. cerevisiae was originally isolated from the skin of grapes [8]. Its optimum temperature growth range is 30° C [9]. S. cerevisiae is tolerant of a wide pH range (2.4-8.2), being the optimum pH for growth between values of 3.5 to 3.8 [10]. In addition, S. cerevisiae is high growth rate (0.5 h-1) in the yeast group. With respect to S. cerevisiae nutritional requirements, all strains can grow aerobically on glucose, fructose, sucrose, and maltose and fail to grow on lactose and cellobiose. Also, all strains of S. cerevisiae can use ammonia and urea as the sole nitrogen source, but cannot use nitrate since they lack the ability to reduce them to ammonium ions. They can also use most amino acids, small peptides and nitrogen bases as a nitrogen sources [11]. S. cerevisiae have a phosphorus requirement, assimilated as a dihydrogen phosphate ion, and sulfur, which can be assimilated as a sulfate ion or as organic sulfur compounds, such as the amino acids: methionine and cysteine. Some metals, such as magnesium, iron, calcium and zinc are also required for good growth of this yeast.

Alcoholic fermentation by yeast consists of three main stages: (1) transporting sugars within the cell, (2) transforming sugars into pyruvate through glycolysis pathway and finally (3) converting acetaldehyde to ethanol.

The simplified fermentation process is:

C6H12O6 + (carbon source) ^ 2CH3CH2OH + 2 CO2 + 2 ATP + 25.5 kcal (2)

Xylitol is a five-carbon sugar alcohol of high value added as a sweetener for high power, anticariogenic properties and insulin metabolism independent that guarantee its application in food and pharmaceutical industries. The power as a sweetener is similar to sucrose, and higher than ordinary polyols in addition to reduce caloric value, can be tolerated by diabetics. It is an anticariogenic and cariostatic compound that is not metabolized by microorganisms of the oral microbiota; thus, it is used in the manufactures of sweets. Can be used clinically for the prevention of otitis media because it inhibits the growth and adhesion of Pneumococcus spp and Haemophilus influenzae in nasopharynx cells, and it has skin smoothing properties [9, 10, 11, 12, 13, 14]. Owing to all these characteristics, xylitol is a feedstock of particular interest to the food, odontological and pharmaceutical industries.

Xylose is a sugar widely distributed in nature. Plants and fruits contain relatively low concentration, and extraction from natural sources is usually not profitable [10]. Nowadays, xylitol is derived industrially via a chemical process from hydrolyzates of lignocellulosic wastes by either chemical reduction or microbial fermentation [9]. However, due to a requirement for several chemical purification steps, such a process is very expensive. Therefore, this conversion could be alternatively performed by bacteria, filamentous fungi, yeasts or purified enzymes from these microorganisms which are capable of reducing xylose to xylitol as a first step in D-xylose metabolism [15]. Nevertheless, to make this process exploitable and economical at an industrial level, the bioconversion must be rapid, offer high yield, employ an alternative and cheap culture media and allow for results comparable to those of the present technology.

Lignocelluloses are the most abundant organic mass in the biosphere, which accounts for approximately 50% of the biomass. In nature the annual production of biomass is estimated to 10 to 50 x 109 tons [16] Their major components, cellulose, hemicellulose and lignin, vary with plant species. The pentose fraction, composed of D-xylose (usually not less than 95%) and L-arabinose is much higher in hardwoods (19 to 33%) than in softwoods (10 to 12%)[17]

Hemicellulose is a branched polymer, which is composed of both linear and branched heteropolymers of D-xylose, L-arabinose, D-mannose, D-glucose, D-galactose and D — glucuronic acid with a high content of xylans, that consist essentially in p-1,4 links with branching variables; due to it heterogeneous structure and low degree of polymerization, it is easily hydrolyzed to xylose [15, 16]. Xylan accounts for 11-35% (dry weight basis) of lignocellulosic materials such as hardwoods and agricultural residues, such as sugarcane bagasse [14, 18], rice straw [19], and soy hulls [20, 21] which are xylan-rich substrates and have been satisfactorily used as alternative media for xylitol production through different treatments [22] and cultivation conditions [23], aimed at increasing process yield and productivity.

D-xilose, also can be converted into a range of substances of industrial interest such as fuels and solvents (ethanol, butanol, 2,3-butanediol, acetone and 2-propanol), alditols (xylitol and glycerol) and organic acids (latic, acetic and butyric acid). It can also be used as a substrate for production of glucose isomerase [14].

For this process of xylitol production, pure xylose is necessary. The process starts with the production of xylose from xylan after acid-catalysed hydrolysis from hard-wood; however, the chemical process requires several purification steps, because only pure xylose can be used for chemical reduction. Therefore, overall xylitol yield is relatively low (50 — 60 %) from the total xylan content of the wood hemicelluloses [24, 11, 15].

Furthermore, the choice of cultivation and/or conversion system is another crucial point for the success of this bioprocess. Different bench-scale cultivation systems were investigated, utilizing batch, fed-batch and continuous processes [15]. Another important factors which affect the xylitol production is the quantity of inocula, substrate, media, temperature, pH and aeration [17, 25]

On the other hand, the biotechnological procedures are based on the utilization of microorganisms and/or enzymes. These procedures are interesting because they do not require a pure xylose solution as is the case when xylitol is produced by the chemical pathway. The bioconversion process would hold more promises of both hexoses and pentose sugars from lignocellulosic materials. The promising yeast species include the generous Candida, Pichia, Debaryomyces and Pachysolen [9, 26, 14, 16] by NADPH-dependent xylose reductase, enzyme which can ferment hemicelluloses hydrolysate from woody plant materials (Figure 3).

The first step in the metabolism of D-xylose is the transport of the sugar across the cell membrane. Once inside the yeast cell, D-xylose is reduced to xylitol by either NADH — or NADPH-dependent xylose reductase. Xylitol is either secreted from the cell or oxidized to xylulose by NAD — or NADP-dependent xylitol dehydrogenase. The first two reactions are considered to be limiting in D-xylose fermentation. The phosphorylation of xylulose to xylulose 5-phosphate is catalyzed by xylulokinase, which is a prerequisite for its utilization by the central catabolic pathways [23, 17, 16].

In most studies on xylitol production by fermentative processes, xylose of analytical grade is commonly the major substrate. The main problem in the fermentation of these hydrolysates is the presence of toxic compounds released from the lignocellulosic structure during the hydrolytic process, as well as those originated from the sugar degradation, which inhibit the microbial growth and the fermentative activity of the yeasts. In this way, several approaches have been assayed to minimize this effect. According to Silva et al, 1998 the maximum xylitol production (54 g/L) occurs when the hydrolysate is first treated with CaO until reaching pH 8.4 and then treated with H3PO4 until the pH decreases to 6.0. Thus, pH is an important factor to take into account for the xylitol fermentation. Its effect is related to the acetic acid concentration in the hydrolysate, which concentration, if it is higher than 3.0 g/L, can inhibit the yeasts capability to convert xylose into xylitol [27]. Nonionized acetic acid, which is found in the medium at pH < 7.0, has been found to be the main inhibitor compound in yeast metabolism [28]

The hemmicellulosic hydrolysates from agroforest residues can be efficiently utilized in fermentative processes for xylitol production after an initial treatment designed to remove or reduce the compounds known to be toxic to cell metabolism. This technology is still in its research and development stage, but the results attained points that it may be feasible to

About 75% of bioethanol produced in the world being used to power automobiles, though it may be used for gasoline additives and other industries such as paints and cosmetics. Ethanol fuel blends are widely sold in the United States, Brazil, Europe and China. The most common blend is 10% ethanol and 90% petrol (E10). Vehicle engines require no modifications to run on E10 and vehicle warranties are unaffected also. However, only flexible fuel vehicles can run on up to 85% ethanol and 15% petrol blends (E85). Since 1976 the Brazilian government has made it mandatory to blend ethanol with gasoline with 5% ethanol and 95% petrol, and in 2007 the legal blend is around 25% ethanol and 75% gasoline (E25). Today, bioethanol contribute around 3% of total road transport fuel globally (on an energy basis) and considerably higher shares are achieved in certain countries [3]. The usage of bioethanol as transport fuel will be even more as the recent European Commission energy roadmap has set a target to increase the use of biofuels for transport from 5.75% from 2010 to 10% by 2020 under the Directive 2003/30/EC.

Bioethanol is also used as primarily gasoline additive and extender due to its high-octane rating. Bioethanol replacing lead as an oxygenate additive for traditional petrols in the form of Ethyl tertiary butyl ether (ETBE). The ethanol is mixed with isobutene (a non-renewable petroleum derivative) to form ETBE. At a 10% mixture, ethanol reduces the likelihood of engine knock, by raising the octane rating.

Beside the usage of bioethanol in fuel industry, bioethanol also can serve a wide range of uses in the pharmaceuticals, cosmetics, beverages and medical sectors as well as for industrial uses. The market potential for bioethanol is therefore not just limited to transport fuel or energy production but has potential to supply the existing chemicals industry. These include for use in acetaldehyde (raw material for other chemicals e. g. binding agent for paints and dyes), acetic acid (raw material for plastics, bleaching agent, preservation), ethylacetate (paints, dyes, plastics, and rubber), detergents, thermol (cold medium for refrigeration units and heat pumps), solvent for spirits industry, cosmetics, print colours and varnish, isopropyl alcohol (IPA), ethyl acetate (EAC), WABCO-antifreeze (disinfectant, cleaning agent for electronic devices, solvents) and vinasse, potassium sulphate (feeding stuffs, fertilizer).