The concentrations of N, P and C in the above-ground biomass of 14 dominant macrophyte species (including Chara globularis and Nitella translucens) in seven shallow lakes of NW Spain were measured by Fernandez-Alaez et al. (1999) that found significant differences for the three nutrients among the species and among the groups of macrophytes. The charophytes showed the lowest P (0.053% dry weight) and C (35.24% dry weight) content. Also, only the charophytes exhibited a strong association between N and P (r = 0.734, p < 0.0001), reflecting an important biochemical connection in these species.

Phosphorus was established as a limiting factor of all the macrophytes (N:P = 35:1), especially charophytes, in which it was below the critical minimum. Siong et al. (2006) used sequential P fractionation to study the nutrient speciation in three submersed macrophytes species, Chara fibrosa, Najas marina and Vallisneria gigantea, and the implications for P nutrient cycling in the Myall Lake, New South Wales, Australia. The mean TP of both Najas marina and Vallisneria gigantea was significantly higher than that of Chara fibrosa, even when the comparison made was based on the ash-free dry weight (AFDW). However, P co­precipitation with calcite (CaCO3) induced during intense periods of photosynthesis occurs in hard water lakes, and this indirect mechanism of reducing P bioavailability in the water column may have been underestimated in assessing Chara beds acting as nutrient sink in shallow lakes. According to their results, besides the indirect mechanism above, P in the water column was also directly co-precipitated with encrusted calcite along the charophyte intermodal cell, and such a calcification should be regarded as a positive feedback in stabilizing Chara dominance in lakes. Siong & Asaeda (2009) studied the effect of Mg on the charophyte calcite encrustation, and assessed whether charophytes growing on the non­calcareous sediments of the Myall Lake could function as an effective nutrient sink for P in a

similar manner to charophytes growing on the calcareous sediments of freshwater calcium — rich hard water systems. According to the last authors, calcification of Chara fibrosa was significantly inhibited by Mg in the water column and, consequently, reduced the formation of Ca-bound P that has a potential sink for P. However, a large percentage of non­bioavailable forms of P in the lake sediments suggested that P sink was through burial of dead organic matter and subsequent mineralization process.

The inorganic phosphorus concentration was not yet significantly related to the charophyte biomass. Palma-Silva et al. (2002) observed that the charophyte community (Chara angolensis and Chara fibrosa) sometimes occupied the entire benthic region in the Imboacica coastal lagoon in Brazil, and presented a large variation in C:N:P ratio. Results of their investigation (samples taken in March, April, May, July and October 1997) indicated that the charophytes fast growth may have absorbed a great amount of the nutrients entering the lagoon. Values of nutrient concentrations in the charophytes biomass were, according to those authors, within the expected range for the group, with the most eutrophic sampling station in the lake showing the highest N and P values. C:N:P ratios presented high values, and the biomass values were higher in the less eutrophic areas. The biomass reached maximum values of between 400 and 600 g DW m-2, and the C:N:P ratio varied from 51:7:1 to 1603:87:1, indicating that the two Chara species may grow in a wide range of nutrient concentration. The same authors concluded that the charophyte community would be responsible by the nutrient decrease in the water column and keeping the water clear after drawdowns (Palma — Silva et al. 2002).

Several authors concluded that the nutrient kinetics favor the phytoplankton growth over Chara, thus assuming a P-limited condition. Therefore, although nutrient concentration may influence the charophyte phenology and abundance, light appeared to be a stronger regulator in the Okeechobee Lake. Schwarz & Hawes (1997) also observed the influence of the water transparency on the variation of the charophyte biomass in the Coleridge Lake, New Zealand. In the latter lake, total algal biomass did not surpass 180 g DW m-2 between 5 and 10 m depth. Pereyra-Ramos (1981) worked with seven charophyte species collected from Polish lakes and observed an increase of their fresh dry weight during the summer (July): Chara rudis 2.07 kg m-2, Chara vulgaris 1.61 kg m-2, Chara contraria 0.54 kg m-2, Chara fragilis 0.39 kg m-2, Chara jubata 0.37 kg m-2, Chara tomentosa 0.28 kg m-2 and Nitellopsis obtusa 0.24 kg m-2. Together, the charophytes represented 53% of the total submersed macrophytes biomass, 28% of Elodea sp. and 8% of Ceratophyllum demersum, two submersed macrophytes. According to Howard-Williams et al. (1995), Chara corallina biomass in deep (average 90 m depth) New Zealand lakes ranged around 300 g DW m-2. Bakker et al. (2010) registered a strong decline of the Chara sp. biomass under the nutrient enriched condition of Lake Loenderveen, Norway. Similar situation was already detected by Blindow et al. (1993) and van de Bund & van Donk (2004) for other water bodies.

Bioethanol production from agave juice continuous fermentation process is shown below. In continuous fermentation process, the effects of dilution rate, nitrogen and phosphorus source addition and micro-aeration on growth, and synthesis of ethanol of two native Saccharomyces cerevisiae S1 and S2 strains were studied.

Continuous cultures were carried out in a 3 L bioreactor (Applikon, The Netherlands) with a 2 L working volume. Cultures were started in a batch mode, by inoculating fermentation medium with 3.5 x 106 cells/mL (97±2 %. initial viability) and incubating at 30 °C and 250 rpm for 12 h. Afterwards, the culture was fed with fermentation medium (12 °Brix = 95 ± 5 g/L reducing sugar and 1 g/L of ammonium sulfate). Culture media were sterilized at 121 °C for 15 min.

To reach the steady state in each studied condition, the culture was maintained during five residence times and samples were taken every 6 h. A steady state was reached, when the variation in the concentrations of biomass, residual sugars and ethanol were less than 5%. Data presented on tables and figures are the mean ± standard deviation of three assays at the steady state.

Effect of the dilution rate on S. cerevisiae strains fermentative capability in continuous cultures

Both yeast strains (S1 and S2) were used and fermentation medium was fed at different D (0.04, 0.08, 0.12 and 0.16 h-1) for studying the effect of dilution rate (D) on the kinetic parameters and concentrations of biomass, residual reducing sugar and ethanol at a steady state of agave juice continuous fermentation process (Table 5 and Figure 5).

Concentrations of biomass and ethanol decreased as D increased for both strains cultures while residual reducing sugars increased parallel with the increase of D (Figure 5).

Although, S. cerevisiae S2 consumed more reducing sugars than S1 for each D, ethanol yields reached by S1 were higher than those obtained by S2, which were near the theoretical value (0.51) with no significant differences among the different D tested (p>0.05) (Table 5).

At D = 0.04 h-1, S1 and S2 strains reached the highest ethanol productions (43.92 and 38.71 g/L, respectively) and sugar consumptions (96.06 and 94.07 g/L, respectively) which were similar to those obtained using batch fermentations (see Batch fermentation process section). The low fermentative capacities displayed by both strains at higher D than 0.04 h-1 could be due to a low content of nutrients and/or toxic compounds in agave juice cooked [15].

Both strain cultures reached maximal ethanol production rates at 0.12 h-1 (2.37 and 2.53 g/L-h, respectively for S1 and S2), maximal growth rates were achieved at 0.16 h-1 (0.44 and 0.38 g/L-h, respectively for S1 and S2) and maximal sugar consumption rates were obtained at 0.08 h-1 (5.08 g/L-h) for S1 and at 0.12 h-1 (9.96 g/L-h) for S2 (Table 5 and Figure 6).

Effect of the pH value on the fermentative capacity of S1 and S2 strains — The effect of pH was observed, switching from a controlled pH (at 4) to an uncontrolled pH (naturally set at 2.5±0.3). Figure 7 shows biomass and ethanol productions for strain S1, in non-aerated or aerated (0.01 vvm) systems fed with sterilized medium. Results did not show significant differences on the biomass or ethanol productions (P > 0.05) between the fermentations with control (4) and with no control (2.5) of pH. Conversely, biomass and ethanol productions increased on aerated culture compared to that non aerated, for both pH levels studied. These results agreed with those reported by Diaz-Montano et al. [20]. These results are important, since the operation of a continuous culture naturally adjusted to a low pH would limit the growth of other yeasts [21, 22] or bacteria [23, 24], indicating the feasibility of working with non-sterilized media on an industrial scale. Another advantage of not controlling the pH is that instrumentation for this operation is not required, thus removing it from the initial investment [25].

Table 5. Kinetic parameters at the steady state of continuous cultures of two strains of S. cerevisiae (S1 and S2) fed with agave juice at different dilution rates (D).

phosphorus supplementation on S. cerevisiae S1 sugar consumption

Since both S. cerevisiae strains were unable to consume sugars efficiently in cultures fed at D higher than 0.04 h-1, a nutritional limitation and/or some inhibitory substances formed in the agave cooking step (Maillard compounds), which can act on S. cerevisiae strain activity. In fact, Agave tequilana juice is deficient in nitrogen sources (Table 3). Amino acids are the most important nitrogen source in agave juice; however, their natural concentrations (0.02 mg N/L) are not enough to support balanced yeast growth and the complete fermentation of sugars [26]. Therefore, agave juice supplemented with ammonium sulfate at 1 g/L could be insufficient. Several authors point out the importance of nitrogen sources (type and
concentration) for achieving a complete fermentation, since they improve cell viability, yeast growth rate, sugar consumption and ethanol production (11; 20). It is worth noting that ammonium phosphate (AP) was chosen as a nitrogen source, since the two macronutrientes frequently implied in the causes of stuck fermentation when present in small quantities are nitrogen and phosphate (see the reviews by Bisson [11]).

Therefore, the effect of the ammonium phosphate (AP) addition on S. cerevisiae S1 sugar consumption was studied in a continuous culture (Figure 8). To study the effect of nitrogen and phosphorus source addition on the agave juice fermentation by S. cerevisiae, S1 strain was used and fermentation medium was fed at D of 0.08 h-1, while after the steady state was reached, the ammonium phosphate (AP) concentration was gradually increased, as follows: 1g/L (first addition), 2 g/L (second addition), 3 g/L (third addition) and 4 g/L (fourth addition).

The fermentation was started in batch mode using the fermentation medium. After 12 h, the culture was fed using medium supplemented with 1 g/L of AP (first addition). At the steady state, residual concentrations of sugars and ammonium nitrogen were 29.42 and 0.08 g/L, respectively. These results were not significantly different (p>0.05) from the condition previously tested for the same strain (at D = 0.08 h-1), feeding an unsupplemented fermentation medium (Figure 5).

Those residual concentrations of reducing sugars (high) and ammonium nitrogen (low) indicate the necessity of adding more AP. At the steady states of the second (2 g/L), third (3 g/L) and fourth (4 g/L) additions of AP, the residual sugars concentrations were 25.96, 21.25 and 17.60 g/L, respectively. This indicates that the residual ammonium nitrogen concentrations were 0.31, 0.36 and 1.29 g/L, respectively; indicating that the AP addition improved S. cerevisiae S1 fermentative capability, but other nutritional deficiencies still existed [27].

Effect of the micro-aeration rate on S. cerevisiae S1 fermentative capability — Lack of oxygen has proved to be a main limiting factor to fermentation [11], since yeasts require low amounts of oxygen for synthesizing some essential lipids to assure cell membrane integrity [28]. Because S. cerevisiae is Crabtree-positive, alcoholic fermentation is privileged in culture media containing high sugars concentrations, even in the presence of oxygen [29]. The effect of the micro-aeration rate (0, 0.01 and 0.02 vvm) on the fermentative capacity of S. cerevisiae S1 (at D = 0.08 h-1) was studied for investigating the yeast oxygen requirement during the continuous fermentation, using the last fermentation medium supplemented with 4 g/L of AP for feeding at D of 0.08 h-1. Biomass and ethanol concentrations increased as air flow increased, reaching at the steady state, 5.66, 7.18 and 8.04 g/L, and 40.08, 44.00 and 45.91 g/L, respectively for 0, 0.01 and 0.02 vvm (Figure 9).

Figure 9. Concentration of Residual reducing sugar, Ethanol and Biomass at the steady state of continuous culture of two strains of S. cerevisiae S1 fed with agave juice (D = 0.08 h-1) at different micro-aeration rates. Data are presented as mean ± standard deviation of four assays at the steady state.

Meanwhile, residual sugars decreased as micro-aeration increased, reaching 17.67, 10.71 and 4.48 g/L, respectively for 0, 0.01 and 0.02 vvm; showing an improvement in the fermentation process due the dissolved oxygen in the must. However, statistical differences were not found in biomass and ethanol yields at the different tested aeration rates (p>.05) (Table 6). In addition, sugars consumption rates and ethanol and biomass productions increased as micro-aeration increased, achieving a faster fermentation (Table 6). These results were in accordance to those reported by Diaz-Montano [20]. Viability of the S1 strain was 100% in aeration experiments.

Glycerol is a metabolite providing yeast metabolic activity information. In fact, yeasts produce glycerol mainly for reoxidating the NADH generated by glycolysis. Since the citric acid cycle and the respiratory chain are slightly activated by micro-aeration, NAD might be partially regenerated, and consequently, glycerol concentration decreases [30]. However, in this work, glycerol concentration increased as aeration increased (Table 6). Given that
biomass concentration and fermentation efficiency also increase as aeration increases, glycerol production could contribute to faster NAD regeneration.

It is very important to increase the thermal efficiency of coal conversion for not only protecting the limited coal resources but also reducing CO2 and air pollutant emission. Steam gasification of coal is one of the most promising energy conversion technologies for producing hydrogen.

At present, much focus is on the development of methods to produce higher recovery yield bioethanol from lignocellulosic biomass. This can be done through two methods; (1) use of pre-treatment to increase the readiness of lignocellulosic biomass for hydrolysis. (2) increase the conversion yield of lignocellulosic biomass into bioethanol through simultaneous fermentation of glucose and xylose into bioethanol.

As mentioned, one barrier to the production of bioethanol from biomass is that the sugars necessary for fermentation are trapped inside the lignocellulosic biomass. Lignocellulosic biomass has evolved to resist degradation and to confer hydrolytic stability and structural robustness to the cell walls of the plants. This robustness is attributable to the crosslinking between the polysaccharides (cellulose and hemicellulose) and the lignin via ester and ether linkages. Ester linkages arise between oxidized sugars, the uronic acids, and the phenols and phenylpropanols functionalities of the lignin. The cellulose fraction can be only hydrolysed to glucose after a pre-treatment aiming at hydrolytic cleavage of its partially crystalline structure. A number of pre-treatment methods are now available — steam explosion, dilute acid pre-treatment [31] and hydrothermal treatment [32]. Hydrothermal treatment prevent the degradation of cellulose content inside the lignocellulosic biomass during pre-treatment because hydrothermal can be performed without addition of chemicals and oxygen to the lignocellulosic biomass. Hydrothermal treatment involves two process where during the first process, lignocelluosic biomass was soaked in water at 80 °C to soften it before being treated in the second process with higher temperature at 190-200°C.

Another way to increase the recovery yield of bioethanol from lignocellulosic biomass is to convert every bit of biomass into bioethanol. This means using all the available sugars from cellulose and hemicelluose and fermented into bioethanol. Lignocellulosic biomass have high percentage of pentoses in the hemicellulose, such as xylose, or wood sugar, arabinose, mannose, glucose and galactose with majority sugar in hemicelluloses is xylose which account more than 90% present. Unlike glucose, xylose is difficult to ferment. This meant that as much as 25% of the sugars in biomass were out of bounds as far as ethanol production was concerned. At the moment, research shows that steam explosion or mild acid treatment performed under adequate temperature and time of incubation, render soluble the biomass hemicellulose part with the formation of oligomers and C5 sugars that are easily extracted from the biomass. The C5 sugar stream can be individually fermented to ethanol by microorganisms such as E. coli, Pichia stipitis and Pachysolen, that are able to metabolise xylose, or be used as carbon source in a variety of other fermentative processes [33].

Butanol is another attention attracted alternative fuel to gasoline besides ethanol because of its properties with respect to gasoline blending, distribution and refuelling, and end use in existing vehicles. For instance, butanol has relatively high energy content which is 30% higher than ethanol and is closer to gasoline. Additionally, butanol has low vapor pressure, low sensitivity to water and it is less volatile, and less flammable when compared with other liquid fuels [63]. Therefore butanol can be handled conventionally in the existing petroleum infrastructure, including transport via pipeline. It also can be blended, at any ratio, with either gasoline or diesel fuel at existing refineries, thus avoiding the capital investment associated with plant revamps and the need for major operational, etc.

Similarly to bioethanol, butanol can be biochemically produced from both agricultural crops and lignocellulosic biomass using Clostridium acetobutylicum or C. beijerinckii to ferment lignocellulosic hydrolysate sugars (hexoses and pentoses) to butanol. Traditionally, sugar — rich agricultural crops such as corn, cane molasses and whey permeate have been successfully used as feedstocks in the commercial production of butanol for decades. However, the cost for these food crops rises significantly nowadays; therefore, lignocellulosic biomass becomes more popular as substrates for butanol production. In similar ways of producing bioethanol, pre-treatments are required prior to enzymatic hydrolysis (using cellulase and cellobiose). However, one of technology challenges is the inhibition caused by by-products in pre-treatments such as furfural, HMF, acetic acid, and ferulic acid generated in dilute acid pre-treatments etc. Among these by-products, ferulic and p-coumaric acids were found can significantly inhibit fermentation but furfural and HMW were surprisingly stimulating to the cell culture [64].

The resulted lignocellulosic hydrolysate is then fermented by microorganisms via Acetone — butanol-ethanol (ABE) fermentation (Figure 4). The main challenge in the ABE fermentation is the product butanol itself is toxic to the fermenting microorganisms. In order to overcome this drawback, focused research efforts are to (1) improve the fermentation strategies to minimise the level of inhibitors accumulated such as simultaneously removing butanol and (2) to develop or genetically improve butanol — producing cultures.

However, biobutanol has several potential shortcomings. It is more toxic to humans and animals in the short term than ethanol or gasoline (although some components of gasoline, such as benzene, are more toxic and/or carcinogenic). And it is not clear whether butanol will degrade the materials commonly used in automobiles that can come into contact with motor fuels; building evidence suggests that it will not cause problems, but there has been no definitive testing on the wide range of potentially affected polymers and metals [65].

Additionally, butanol is reported cannot deliver a better economic feasibility and a more sustainable environmental performance when compared with bioethanol under the current level of technology [66]. The relatively low yield of solvents out of glucose (mixture of acetone, ethanol and butanol), which is in the range of 33% — 45% (wt), is the main cause for the high cost of butanol. This economic study argued that butanol perhaps can be sold as chemicals rather than transport fuel unless the technology would be improved to make butanol production economically competitive with bioethanol.

The efficient separation of bio-oil establishes a solid foundation for its upgrading. Currently, conventional methods for bio-oil separation include column chromatography, solvent extraction, and distillation.

1.2.1. Solvent extraction

The solvents for extraction include water, ethyl acetate, paraffins, ethers, ketones, and alkaline solutions. In recent years, some special solvents, such as supercritical CO2, have also been used for extraction or other research. By selecting appropriate solvents for extraction of the desired products, good separation of bio-oil can be achieved.

Some researchers have used non-polar solvents for the primary separation of bio-oil, such as toluene and n-hexane, and then proceeded to extract the solvent-insoluble fraction with water; finally, the water-soluble and water-insoluble fractions were further extracted with diethyl ether and dichloromethane, respectively (Garcia-Perez et al., 2007; Oasmaa et al., 2003). A lot of organic solvents are consumed during the process. Considering the cost of these solvents and the difficulty of the recovery process, the operating costs are unacceptable, which hinders its industrialization.

Supercritical fluid extraction is based on the different dissolving abilities of supercritical solvents under different conditions. Supercritical fluid extraction at low temperatures contributes to preventing undesirable reactions of thermally sensitive components. Researchers usually use CO2 as the supercritical solvent. In a supercritical CO2 extraction, compounds of low polarity (aldehydes, ketones, phenols, etc.) are selectively extracted, while acids and water remain in the residue phase (Cui et al., 2010).

222. Column chromatography

The principle of column chromatography is that substances are separated based on their different adsorption capabilities on a stationary phase. In general, highly polar molecules are easily adsorbed on a stationary phase, while weakly polar molecules are not. Thus, the process of column chromatography involves adsorption, desorption, re-adsorption, and re­desorption. Silica gel is commonly used as the stationary phase, and an eluent is selected according to the polarity of the components. Paraffin eluents, such as hexane and pentane, are used to separate aliphatic compounds. Aromatic compounds are usually eluted with benzene or toluene. Some other polar compounds are obtained by elution with methanol or other polar solvents (Ertas & Alma, 2010; Onay et al., 2006; Putun et al., 1999).

Among the biological processes used for the construction of bioreactors, there are two fundamental types of processes: the aerobic and anaerobic.

Aerobic processes are those that need oxygen. There are strict aerobic processes, which are those that can only work if there is oxygen, and facultative aerobic processes, which are those that can switch to anaerobic, according to the concentration of oxygen available.

In general, the aerobic processes have the following reaction:

organic materia + O2 ^ CO2 + H2O + new cells (9)

As can be seen in the above reaction, essentially, aerobic metabolism is responsible for catalyzing larger molecules into carbon dioxide, water and new cells. It is noteworthy that the different groups of microorganisms have different metabolisms, and therefore are able to catalyze a wide range of substances, although sometimes other secondary products are obtained in addition to those mentioned.

Aerobic processes are very efficient, operate at a wide range of possible substances to degrade, and in relatively simple cycles are stable; there is rapid conversion of organic pollutants in microbial cells and their operation relatively free of odors [38].

From nutritional standpoint, artemia is non — selective filter feeder, and eats algae, bacteria, protozoa and yeasts, as long as feed particles diameter is not over 50-70 pm. Artemia feed can be alive or dead in artificial culture system. Artemia can uses bacteria and protozoa as feed sources, which grow in artemia culture medium. This protozoa (such as; Candida, Rhodotorula) can also be directly swallowed by artemia. The best algae’s for artemia nutrition includes: Dunaliella salina, Spirulina and Scenedesmus. For artemia culture, agricultural products can be utilized such as; rice, corn, wheat, barley flours and their bran.

4. Reproduction

All bisexual species holds 42 chromosomes (2n = 42). A. persimilis holds 44 chromosomes (2n = 44) and Artemia partenogenic is diploid, triploid, tetraploid and even pantaploid. As a general rule, artemia populations are defined on the basis of the number of their chromosomes. However, contrary to mammalian, female artemia is heterogametic [3]. Artemia is produced by two ways: sexual and parthenogenesis (development of a new individual from an unfertilized egg). The mature female ovulates each 140 hours.

According to strain of artemia or method of living, it selects one of the following conditions; oviparous or ovoviviparous. In suitable situation of rising, reproduction trend is as larvae production (ovoviviparous) and in unsuitable situation of growth (salinity >50gm/lit and oxygen <5mg/lit) oviparous will occur. In the latter condition, growth of embryo will stop and enters diapauses. In suitable saline and nutritional conditions, females can produce 75 naplius each day and over its lift cycle (50 days), it reproduces 10-11 times.

In extreme hypoxia, due to increasing hemoglobin production, the color of artemia will change from light brown to yellow and then red.

Artemia cysts are spread by wind and birds. Earth pond or region of high salty water is suitable for culture and reproduction of artemia.

The most sophisticated modeling tool is that introduced by the metabolic engineering. This approach relies upon the concept of metabolic pathways as sequences of specific enzyme — catalyzed reaction steps converting substrates into cells’ products. The manipulation of metabolic pathways to improve the cellular properties and especially the yield or the productivity of some important metabolites is of interest. So the metabolic engineering is recently developed with the purpose of generating information for the oriented modification of the enzymatic, regulatory or transport activities of the cells. The information will be used to build upgraded cells by the further application of the recombinant DNA technology.

The determination and the correct interpretation of the structure and the control mechanisms of metabolic networks are the first critical tasks of the metabolic engineering in order to fulfill the goal of rational pathway manipulation [14]. The main accent is towards considering the metabolic network as a whole and not the individual reactions. Due to the increased complexity of these networks and of the corresponding regulatory mechanisms the physiological state (metabolic steps characteristics at specific genetic and environmental conditions) of the cells is determined by the in vivo metabolic fluxes and their control.

The flux can be defined as the rate of material processing through a whole metabolic pathway. The value of the flux does not introduce information about the activity of the enzymes from the considered pathway, but it represents only their contribution regarding the substrate conversion into the final metabolite of this pathway.

The quantification of the metabolic fluxes is the principal objective realized by the techniques of Metabolic Flux Analysis (MFA) [15]. Metabolite balancing is the first operation in the determination of fluxes, done with the major hypothesis that the intracellular fluxes can be evaluated by measuring the extracellular fluxes. The metabolite balancing is performed by using a stoichiometric model for the intracellular reactions and by applying a mass balance around each intracellular metabolite, without any enzyme kinetic information. The general defining relationship is of matrix form:

S ■ v = r (29)

where: S=stoichiometric matrix of the metabolic network

v=vector of unknown fluxes

r=vector of measured metabolite extracellular concentrations, whereas the metabolite intracellular concentrations is 0.

The rows number is representing the number of metabolites in the pathway and the number of columns is equal to the unknown number of fluxes at steady state condition. The resulting system is normally underdetermined as the number of reactions is normally greater than the metabolite number. There are also various network structure characteristics (metabolic branch, reversible reactions, and metabolic cycles) that can increase the system degree of freedom.

So beside the metabolic balancing constraints additional constraints are needed to solve the equations system. If finally there are more constraints than the freedom degree, the system becomes over determined and redundant equations are to be used to test the consistency of the overall balances. The supplementary constraints can be obtained by using other information regarding the intracellular biochemistry and/or by applying others techniques

[15].

So, another tool to perform MFA is the Linear Programming (LP) [14, 16]. Conforming to this method the metabolic fluxes are determined by simultaneously accomplishing 2 conditions: to be in line with the metabolic balances constraints and to optimize a certain objective function. So it is to formulate the mathematical problem:

Minimize c ■ v = ^ cjvi

Subject to S ■ v = r (30)

where: c= vector of the weight factors of fluxes in the objective function.

The objective functions can be: maximize the metabolite production rate or cells’ growth rate; minimize the ATP production rate or substrate uptake rate; maximize growth rate for a given metabolite formation rate.

Another source of additional constraints is usually the introduction of certain types of supplementary measurements. The most useful tool of this type is the application of isotopic tracer methods. In isotopic tracer techniques there is a substrate in the cells labeled with an easily detectable isotope of a specific atom, normally 14C, but especially 13C , stable and non radioactive isotopes, to be detected by Nuclear Magnetic Resonance (NMR).

The isotope distribution among the metabolites from a network for a certain labeled substrate and known biochemistry is a function of the in vivo metabolic fluxes. This distribution can be obtained by studying the NMR spectra or by measuring the mass isotopomer (the molecules of the same metabolite, but with different labeling characteristics) distribution by Gas / Liquid Chromatography coupled with Mass Spectrometry (GC / LC-MS).

The general model for the determination of metabolic flux distribution is presented in the Fig. 2. The implementation of such flux quantification methods seems simple, but due to the high integrated networks complexity is rather an intensive computer application. There are now important studies of metabolic modeling used to improve the metabolite production in aerobic bioprocesses [17-22].

During the start-up period, 2 L of mixed sludge and 2 L of synthetic textile wastewater were added into the reactor system giving the working volume of 4 L with 5.5 g/L of sludge concentration after inoculation. The system was supplied with external carbon sources consisting of glucose, sodium acetate and ethanol with substrate loading rate of 2.4 kg COD/m3-d. The operation of the system started with 5 min filling of wastewater entering from the bottom of the reactor. The operation then continued with the react phase followed by 5 min settling, 5 min decanting and 5 min of idle time. The react time varies depending on the hydraulic retention time set for the system. Figure 3 shows the steps involved in one complete cycle of the hybrid biogranular system. During the biogranules development, the HRT of the reactor was set for 6 hours for one complete cycle. This will give a react time of 340 minutes. The react phase is divided into equal anaerobic and aerobic react periods. Table 2 shows the successive phase for one complete cycle of the reactor system.

Reaction Phase
Anaerobic Aerobic

The operation of the reactor system was designed with intermittent anaerobic and aerobic react phases. The reaction phase started with an anaerobic phase followed by an aerobic phase. The reaction phase was repeated twice. During the anaerobic react phase, the wastewater was allowed to circulate from the upper level of the reactor and returned back through a valve located at the bottom of the system. The circulation process was carried out using a peristaltic pump at a rate of 18 L/h. The circulation system was stopped at the end of the anaerobic phase. The circulation process is required to achieve a homogeneous

distribution of substrate as well as a uniform distribution of the granular biomass and restricts the concentration gradient. The DO concentrations remained low during the anaerobic condition (0.2 mg/L) and reached saturation during the aerobic phase. The superficial air velocity during the aerobic phase was 1.6 cm/s. The system was operated without pH control causing variation in the range of 6.0 to 7.8 during the react phase.

In order to observe the changes on the characteristics of the biogranules due to the variations of HRT during textile wastewater treatment, the development of biogranules with sizes in the range of 0.3-2.5 mm was inoculated into the bioreactor at a ratio of 1:4 of the working volume of the reactor system. 1 L of acclimated mixed sludge was also added into the reactor system. The MLSS and MLVSS concentrations during the start-up of the experiment were 23.2 g/L and 18.4 g/L respectively. The operation steps of one complete cycle of the reactor system are shown in Table 3.

The morphological and structural observations of the granules were carried out using a stereo microscope equipped with digital image management and analyzer (PAX-ITv6, ARC PAX — CAM). The microbial compositions of the biogranules were observed qualitatively with a scanning electronic microscope (FESEM-Zeiss Supra 35 VPFESEM). The biogranules were left dried at room temperature prior to gold sputter coating (Bio Rad Polaron Division SEM Coating System) with coating current of 20 mM for 45 s. The microbial activity of the biogranules was

determined by measuring the oxygen utilization rate (OUR) following Standard Methods (APHA, 2005). The physical characteristics of the biogranules including settling velocity, sludge volume index, granular strength were measured throughout the experiment.

An initial value of 15 mL influent sample was taken from the influent tank before a new cycle operation started, while another 15 mL of the effluent sample was taken from the effluent tank after the effluent was released during the decanting phase as the final values. Samples were centrifuged for 5 min at 4000 rpm at 40C in order to pellet down all of the suspended particles from the samples. The supernatant was used to measure the removal performance of the COD, color and ammonia removal. All of the measurements for COD, color and ammonia were performed according to Standard Method (APHA, 2005).

10 mL of sample was taken from the top portion of the reactor about 10 minutes after the filling stage ended and 10 mL of sample was taken from the effluent after the decanting stage for the measurement of the suspended particles in the influent and effluent. Another 10 mL of sample volume was taken during the aeration phase for the analysis of MLSS and MLVSS, which were measured according to Standard Methods (APHA, 2005).

The bed height of the biomass in the reactor was measured twice a week in order to estimate the SVI. The bed height was determined immediately after the settling time ended and before the wastewater was drained out during the decanting time. The SVI value can be calculated by measuring the bed volume of the sludge biomass in the reactor divided with the dry weight of the biomass in the reactor. The bed volume is the bed height of the sludge biomass that settled in the reactor 5 minutes after the aeration phase stopped. The bed volume is obtained by multiplying the bed height with the surface area of the bed column. The measurement of the SVI and the sludge retention time were calculated according to Beun et al. (1999). The settling velocity was determined by recording the average time taken for an individual granule to settle at a certain height in a glass column filled with tap water (Linlin et al., 2005).

Determination of the biogranules’ strength was based on Ghangrekar et al. (1996). Shear force on the biogranules was introduced through agitation using an orbital shaker at 200 rpm for 5 minutes. At a certain degree of the shear force, parts of the biogranules that are not strongly attached within the biogranules will detach. The ruptured biogranules were separated by allowing the fractions to settle for 1 minute in a 150 ml measuring cylinder. The dry weight of the settled biogranules and the residual biogranules in the supernatant were measured. The ratio of the solids in the supernatant to the total weight of the biogranular sludge used for biogranular strength measurement was expressed as the integrity coefficient (IC) in percent. This percentage indirectly represents the strength of the biogranules. The higher the IC values the lesser the strength of the biogranules and vice versa.