Tomita [21, 22] has reported low temperature gasification of Yallourn coal catalysed by nickel. By the different way of prepared catalyst as usual like conventional impregnation methods, coal was mixed with an aqueous solution of hexamine nickel carbonate. This mixture gave a perfect homogeneous, catalyst bearing coal. Analysis product gas he found that the gases in the rapid stage of char gasification at 723 K presented mainly hydrogen and carbon dioxide. The study demonstrates the first time that nickel catalyst can enhance gasification reactivity in a similar manner as observed for activate carbon.

temperatures. The good results exhibited carbon conversion reached 85%, with 30 min for steam gasification at low temperature as 723 K.

Ohtsuka [25] reported calcium catalysed steam gasification of Yallourn brown coal with the same way of preparation catalyst as Tomita have done. The results also showed that high activity for calcium catalyst steam gasification of brown coal.

Takarada [26] investigated catalyst steam gasification of coal by mixing of K-exchange brown coal. The results gave evident that rate of enhancement of K-exchange Yallourn coal by physical mixing method is independent of the caking property of higher rank coal; Potassium is a highly suitable catalyst for catalytic gasification.

Recently, Miki [27] also carried out pyrolysis and gasification of coal which was loaded Ni by ion — exchanged method, and again proved nickel loaded brown coal has a high activity in coal gasification.

Optimum cellulose conversion to glucose with the hydrolysis efficiency of 82%, 67% and 66% for oil palm trunk, rubberwood and mixed hardwood, respectively obtained using two- stage concentrated sulfuric acid hydrolysis at elevated temperature using 60% sulfuric acid treated in a water bath with a temperature of 60°C for 30 min at the first stage hydrolysis and subsequently subjected to 30% sulfuric acid at 80°C for 60 min at the second stage [36]. As stated in the study by [35], optimum fermentation parameters for lignocellulosic hydrolysates using Saccharomyces cerevisae was obtained using 33.2°C and pH 5.3 with the fermentation efficiency of 80%, 85% and 90% for oil palm trunk, rubberwood and mixed hardwood, respectively. The optimum cellulose conversion and fermentation efficiency were used to calculate the actual ethanol yield per tonne (L/t) and the conversion efficiency of lignocellulosic biomass. The conversion efficiency was calculated in percentage of actual yield over the theoretical yield. The theoretical yield was calculated in assumptions that all the cellulose was converted to glucose and further converted to ethanol theoretical yield (51%) in 100% conversion rate. Using the cellulose conversion and fermentation efficiencies, the actual ethanol yields per tonne lignocellulosic biomass can be calculated for lignocellulosic biomass as the equation below:

Ethanol yield in [1000 (kg) x cellulose content x actual hydrolysis efficiency liter per tonne of = x ethanol theoretical yield (0.51) x actual fermentation feedstock (L/t) efficiency] / 0.789

(Note: Ethanol has a density of 0.789 kg/L)

The results of bioethanol yield per tonne for oil palm trunk, rubberwood and mixed hardwood and their conversion efficiencies were presented in Table 2.

As shown from the Table 2, using the same amount of feedstock, mixed hardwood produced slightly higher in volume of bioethanol (215 L/t) compared to oil palm trunk and rubberwood with the ethanol yield per tonne of 204 L/t and 206L/t, respectively. The volume of bioethanol produced using oil palm trunk, rubberwood and mixed hardwood per metric tones of dry weight basically were higher than those reported by [20] as shown in Table 1. The highest conversion efficiency was obtained from oil palm trunk (66%), followed by mixed hardwood (60%) and rubberwood (57%).

If bioethanol yield per tonne feedstock values are taken into consideration, the three lignocellulosic biomass studied was higher than most of the comparing feedstock. The three lignocellulosic biomass ethanol yields per tonne of feedstock were much higher than sugarcane, sugarbeet and cassava. This could be explain by the high moisture content of the sugarcane, sugarbeet and cassava implies the use of a greater amount of feedstock to reach the same sugar content that may released from the lignocellulosic material. However, lower bioethanol yield per tonne feedstock of the studied lignocellulosic biomass was found to be lowered than those wheat and corn feedstocks. This is due to the higher glucose convertible substance in the wheat and corn which contributed to higher ethanol yield. Overall, the conversion efficiency for the studied lignocellulosic biomass was lower than sugar containing material and starchy material. This showed how critical the hydrolysis and fermentation efficiency of the lignocellulosic biomass contributed to a higher ethanol yield to make it comparative with these commercial feedstocks. The three lignocellulosic biomass used in this study in terms of ethanol yield per tonne feedstock were found to be comparable with the results obtained from the lignocellulosic biomass obtained from other studies and conversion efficiency (Table 1). The studied lignocellulosic biomass contained higher amount of cellulose as the glucose convertible material. Therefore, this may contributed to higher ethanol yield per tonne of feedstock.

Actually, there are known many different techniques used for biomass extraction: liquid — solid extraction, liquid-liquid extraction, partitioning, acid-base extractions, ultrasound extraction (UE), microwave assisted extraction (MAE). The capability of a number of extraction techniques have been investigated, such as solvent extraction (J. A. Saunders, D. E. Blume, 1981) and enzyme-assisted extraction (B. B. Li, B. Smith, M. M. Hossain, 2006). However, these extraction methods have drawbacks to some degree.

The choice of extraction procedure depends on the nature of the natural material and the components to be isolated. The main conventional extraction procedures are liquid-liquid extraction and liquid-solid extraction. For liquid-liquid extraction is using two different solvents, one of which is always water, (water-dichloromethane, water-hexane, and so). Some of the disadvantages of this method are: cost, toxicity and flammability (Kaufmann 2002; McCabe, 1956; Perry, 1988; Sarker et. al., 2006).

Solid-phase extraction (SPE) can be used to isolate analytes dissolved or suspended in a liquid mixture are separated from a wide variety of matrices according to their physical and chemical properties. Conventional methods include: soxhlet extraction, maceration, percolation, extraction under reflux and steam distillation, turbo-extraction (high speed mixing) and sonication. Although these techniques are widely used, have several shortcomings: are very often time-consuming and require relatively large quantities of polluting solvents, the influence of temperature which can lead to the degradation of thermo labile metabolites (Kaufmann 2002; McCabe, 1956; Sarker et. al., 2006; Routray, 2012).

Supercritical fluid extraction (SFE), microwave-assisted extraction (MAE) and pressurised solvent extraction (PSE) are fast and efficient unconventional extraction methods developed for extracting analytes from solid matrixes.

Onofre Monge Amaya, Maria Teresa Certucha Barragan and Francisco Javier Almendariz Tapia

Additional information is available at the end of the chapter http://dx. doi. org/10.5772/55303

1. Introduction

Microorganism is a microscopic organism, commonly used term to describe a cell or more, including viruses [1]. This definition includes all prokaryotes, as the eukaryotic unicellular: protozoa, algae and fungi. Bacteria are an extremely diverse group of organisms with extensive variation of morphological, ecological and physiological, which due to their diversity, are regularly found in heterogeneous communities [2].

The microorganisms are widely used in the biological treatment of waste solids and liquids [3]. Importantly, the microorganisms used in biological treatments as a partnership a community or consortium [2].

Nowadays there is a current increasingly important in the sense of using microorganisms, especially bacteria, algae and fungi, for decontamination and to help recovery from natural environments and for treating municipal or industrial effluents. It is estimated that the best microorganisms for the removal of toxins present in a place, are initially isolated in their own area where they have been naturally selected, although in a second genetic manipulation of these can significantly strengthen the capacity of microorganisms culture collections and isolates from the different environments of interest. This is supported by the observation that microorganisms capable of living in a polluted environment, and thus perform vital functions, in cellular metabolism have highly effective devices for decontamination. To know the details of these mechanisms could be exploited to purify the water, such as using bacteria capable of capturing heavy metals on their cell wall [4].

Thus, this rapid advance of science and technological development has allowed decontamination using microorganisms in the water. Using the metabolism of microorganisms has enabled the construction of biological reactors at much lower cost than

© 2013 Almendariz et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

physicochemical, and also the construction of treatment plants mixed system for greater efficiency [5]. Developed countries currently use these biomining processes through the involvement of bacteria such as Acidithiobacillus ferrooxidans.

Some species of Klebsiella and Pseudomonas are capable of degradation of reactive pollutants. It also recognizes the ability of some microorganisms or their enzymes to degrade under certain conditions to cyanide reagent, employed in the leaching of gold and silver recovery [6]. Several studies have shown that the biomass of different species of bacteria, fungi and algae are capable of concentrating metal ions in their structures that are found in aquatic environments [7].

Bioremediation is defined as a natural process, during in which different microorganisms are capable of removing organic and inorganic contaminants in a given environment [8]. In the bioremediation process, there are different objectives to assess; mainly seeks to avoid a long term noxious effects to other organisms as well as natural resources; it seeks to recover the ecological balance that exists in the environment; and finally, it seeks to achieve that the contaminated area, with a subsequent treatment by biological processes, can be reused for recreation or productive purposes [9].

Different types of biological treatment systems are used in the field of environmental engineering. The biochemical reactions leading to the oxidation of organic matter are conducted in reactors that can be classified as aerobic or anaerobic, suspended growth or biofilm, with mechanical or without mechanical mixing, etc. In order to design an appropriate reactor for a given wastewater treatment system, both the microbial kinetics of substrate removal and the fundamental properties of different reactors have to be understood [10].

The use of microorganisms as tools of decontamination is fairly recent. The biggest advances in the field were made after the oil spill of the Exxon Valdez on the coast of Prince William, in Alaska (1990). Since this ecological disaster lot of oil left in the water, sought alternative ways of dealing with pollution.

The scientists who developed the first successful experiences of bioremediation of oil a large scale in Alaska, were based on the premise that all natural ecosystems have organisms capable of metabolizing toxic compounds and xenobiotics, although these are often found in proportions less than 1% microbial community. This premise was fulfilled in Alaska and in almost all cases studied later [5].

Several studies have shown that the biomass of different species of bacteria, fungi and algae are capable of concentrating metal ions in their structures that are found in aquatic environments [7]. Bacteria as the genus Pseudomonas of mining environments have been identified that are resistant to heavy metals such as cadmium (Cd), copper (Cu) and lead (Pb) [11]. Some species of marine microalgae, Staphylococcus saprophyticus and fungi have been reported the biosorption of cadmium, chromium, lead and copper from wastewater [7, 12, 13].

In other studies it is known that microbial strains are able to bioremediate contaminated soils with different metals and organic compounds. It is known that Escherichia coli is able to bioaccumulate cadmium concentrations of 5 mg/L as well as copper and zinc which are taken from the culture medium by a process in which occurs a binding peptides secreted by the bacteria [14]. Other studies reported bacterial consortia isolated from mining effluents that adsorbed copper [15], as well as anaerobic consortia that in continuous system showed high percentages of copper and iron removal [16, 17].

It is well-known that the use of the water drainage basins are the primary sources for anthropogenic unloads; it represents a risk for the human health, particularly the pollution caused by the high concentrations of some heavy metals, such as, zinc (Zn), nickel (Ni), chromium (Cr), lead (Pb) and copper (Cu) [18, 19, 20]. Those metals go through the aquatic environment principally by direct loads of industrial sources, soils and sediments and are distributed in the water, biota, being the mining industry one of the most important source [21, 22, 23]. An example of contamination by heavy metals in water and sediment, it is the San Pedro River basin, one of the most important rivers of the north of Sonora, Mexico. This river has been severely contaminated by the wastewater discharges with high concentration of copper generated during the mining activity of the region. In addition, wastewaters discharges untreated raw sewage coming from the city of Cananea, Sonora [24]; also contribute with the pollution of this source of water. Considering the importance and impact of this problem on the community of Cananea city, different kinds of researches had been developing. In reports the sampling stations in the San Pedro River and the propagation and isolation of the bacteria and the copper biosorption in an aerobic bioreactor. However, it is necessary to make more studies that allow suggesting other alternatives for the treatment of acid mine drainages (AMD), and consequently, reducing the concentration of the heavy metals in the San Pedro River, until acceptable levels according to Mexican regulations (NOM-001])[15).

Many willow species do have abroad ecological amplitude. However, to obtain a high productivity, willow has specific site requirements. Being a pioneer species, willow is light demanding, and a rapid establishment can only be achieved without competition by weeds for light. Once established, the leaf area index of a willow canopy will exceed 6 m2xm-2 [4, 38] and will suppress weed growth. Willow thrives on most agricultural soils, as long as the pH is in the range of 5 to 7 [39]. Water use efficiency of a willow crop is about 4 to 6 gxkg-1 [4]. This is a high value compared to values of other tree species, but given the potentially high biomass production of willow, water availability is conceived as a critical factor in willow SRF [40]. Consequently, lighter soils, especially in drier areas, should be avoided for willow growing. A low precipitation during the growing season can be compensated for if winter precipitation is abundant and soils have a good water holding capacity or do have access to groundwater. While many willow species have a boreal-arctic origin and are native to northern temperate regions, fast-growing hybrids may be susceptible to frost damage from bud-burst and onwards. If planted at frost-exposed sites, a single night frost may decrease a single year’s productivity by 50% [41] and will also impact negatively on the biomass production in the following years. Therefore, sites prone to late spring frost should be avoided and it is important to choose clones which have a site-adapted phenology with regard to timing of bud burst. Willow can be harvested with a reasonable cost-efficiency on sites which are 5 ha or larger, and even on slightly smaller sites if willow is harvested on adjacent sites. Planting and harvest equipment for willow requires a relatively widely spaced headland (10 to 12 m in width), which means that single willow fields should not be smaller than 2 ha, and easily could be reached by the harvest machines [42]. Larger stones also should be removed from the soil surface, as they may damage harvest equipment. As planting (see section 4.2) requires a well prepared seed bed, autumn plowing and early spring seedbed preparation are common measures prior to planting. Such preparation has to go along with adequate weed control (see section 4.3). Another selection criterion for willow growing sites is the proximity to a consumer, usually a combined heat and power plant. As moist willow chips do have low energy content per volume, transportation distances by road should be minimized [43]. Finally, willow growing is a form of land-use, and as such, it may interfere with a range of other interests than sheer biomass production. Short rotation forests may affect landscape views, the environment and biodiversity in a positive or negative way, depending on the functions that we require from a semi-natural landscape element, and on how we choose to integrate such functions in a single growing system [1, 44].

As it was mentioned before, literature on charophytes biomass is not rare neither profuse worldwide and dates mostly from 1980 on, when eutrophication was recognized to be one of the most important events of the century. Despite of not being profuse, literature available consents a pretty good overview on the subject.

In the temperate region of the World, aquatic macrophytes show very sharp annual variation, with a growth season of their aerial biomass during the spring and summer, and another season of the underground biomass and detritus accumulation during the fall and winter. Very little, however, was done up to now regarding the charophytes biomass seasonality in the tropics. The single paper published based on charophytes from the tropical region defined, however, deterministic of the aquatic macrophytes biomass seasonality the rainy and dry periods. Water temperature and rain precipitation are, nevertheless, somewhat connected to each other, since the rainy season in the tropics somewhat coincides with the high temperature season.

A climate gradient in South America was studied, indicating that in warmer climates, submersed macrophytes may tolerate more underwater shade than in cooler lakes. Moreover, in several lakes in the warm and intermediate regions, submersed macrophytes were met until relatively greater depths than in the cool regions, taking the available light at the sediments surface into account. According to a very detailed long term study, global warming has been causing winters in the Netherlands to become warmer and wetter during the last 50 years, consequently increasing flow of humic acids and P from land to water that, ultimately, has been enhancing instability of charophyte populations. Such studies conclusion is that in the first half of the 20th Century interannual variation in precipitation was not sufficient to cause large changes in the internal P flux, and submersed macrophytes population was stable.

The presence of charophytes has been associated with the maintenance of clear water, and changes from a state of clear to turbid water have been associated with the eutrophication of the environment. Original data from eight lakes in southern Quebec, Canada and literature data from other lakes throughout the World were used to predict the maximum depth of charophytes colonization and the irradiance over the growing season at the maximum colonization depth, concluding that the depth distribution of the aquatic macrophyte communities is quantitatively related to the Secchi depth. Regression models using the same information above, defined that natural distribution of aquatic macrophytes is restricted to depths of less than 12 m, whereas charophytes can colonize to great depths and up to a predicted 42 m in the very clearest lakes.

The inorganic phosphorus concentration was not yet significantly related to the charophyte biomass. Concentrations of N, P and C in the above-ground biomass of 14 dominant macrophyte species (Chara globularis and Nitella translucens included) in seven shallow lakes of NW Spain pointed to significant differences for the three nutrients among the species and among the macrophytes groups, the charophytes showing the lowest P and C content. Also, only the charophytes showed a strong association between N and P.

Only recently some research has been carried out to evaluate the metal accumulation in charophytes. Therefore, charophyte specimens were exposed in laboratory experiments to different Cd, Cr and Zn concentrations, showing that the heavy metals concentrations in the plant increased with the increasing metals concentrations in the cultivation mediums used. As a result, negative growth occurred and the internode elongation was reduced when exposed to these metals at any concentration, however, intracellular Nitella gracilliformis revealed a potential for accumulating Cd, Cr and Zn.

Summarizing, all research done up to now on the charophytes biomass is still very punctual, i. e. they most often focused one special environment under very specific conditions. There are very few studies focusing a larger time scale and comparing several localities. In the last cases, results are much more consistent. The scientific community needs much more studies, to be able to formulate generalizations. In other words, despite of producing some important information, study of charophytes biomass is far from being exhausted, on the contrary they have just started.

Author details

Carlos E. de M. Bicudo

Instituto de Botanica, Sao Paulo, SP, Brasil

Norma C. Bueno

Universidade Estadual do Oeste do Parana, Cascavel, PR, Brasil

Acknowledgement

CEMB is very much indebted to CNPq, Conselho Nacional de Desenvolvimento Cientifico e

Tecnologico for partial financial support (Grand n° 309474/2010-8).

[1] Corresponding Author

[2] Corresponding Author

[3] Corresponding Author

+ The two authors contribute equally to this study

[4] Corresponding Author

[5] Corresponding Author

© 2013 Chavez and Chavez-Hidalgo, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

[6] Corresponding Author

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.

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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.

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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.

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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].

Due to biodiesel produced from oil crops, waste cooking oil and animal fat cannot meet the high demand for renewable transport fuels, another biomass feedstock microalgae becomes attractive. This is because (1) microalgae are sunlight-driven cells, (2) grow rapidly with biomass double time of 24 hours, (3) require less high quality land used compared to other feedstock, (4) many are exceedingly rich in oil and (5) biodiesel produced from microalgae is ‘carbon neutral’ [76] (see Figure 6). However, several challenges need to be tackled in order to produce biodiesel from microalgae commercially. Scott et al. [77] provides a comprehensive review discussing these challenges and potential tackles.

There are estimated 300 000 species in algal strain. After screening, typical species including Botryococcus braunii, Nannochloropsis sp., Neochloris oleoabundans, Nitzschia sp., and Schizochytrium sp. have up to 77% (dry wt) oil content [76]. Microalgal biomass is produced with the presence of light, fed carbon dioxide and essential inorganic elements including nitrogen (N), phosphorus (P), iron and in some cases silicon. Biomass is then harvested and extracted to obtain oil for biodiesel production using transesterificaiton with methanol. Nutrients and spent biomass are recycled in the downstream process.

Factors involved in these phases are all important to be considered and optimized to maximize the biomass yield and minimize the production cost. First of all, the light level needs to be manipulated to deliver an optimal light to all of the algae cells within the culture. The excess light level not only can results in less efficient use of absorbed light energy but also can cause biochemical damage to the photosynthetic machinery [77]. Secondly, though minimal nutrients requirement can be estimated according to the approximate molecular formula of microalgae which is CO0.48 H1.83 N0.11 P0.0i[78], nutrients such as phosphorous must be supplied in excess. In order to minimise the nutrient cost, sea water supplement with commercial nitrate and phosphate fertilisers can be used for growing microalgae [76]. Thirdly, the choice of facility (open raceway ponds or closed photobioreactor) is important since the scale-up of biomass production is largely depending on the surface area rather than volume because light only penetrate a few centimeters [77]. The former raceway pond is an open-top close loop recirculation channel with a typical depth of 0.3 m. It is relatively cheap to build and has been operated with extensive experience for decades. However, the drawbacks for this type of facility are (1) it is difficult to avoid microbial contamination, (2) it requires for extensive areas of land for ht raceways and substantial cost regarding harvesting, and (3) it has poorly mixed therefore has optically dark zone [76, 77]. The photobioreactor a tubular reactors consists of an array of glass or plastic transparent tubes. It requires a large amount of energy for pumping and compressing air for sparging culture [77].

The biomass broth from production phase is harvested and processed to remove water and residual nutrients which are recycled. The concentrated biomass paste is then extracted to obtain oil and lipids using water and extraction solvent (e. g. hexane) [79]. It is difficult to release lipids from microalgae intracellular location using an energy-efficient way because of the large amount of solvent required. Also it is key to avoid significant contamination by other cellular components such as DNA [77].

The efforts in academic research and industrial commercialization of biodiesel production from microalgae include: (1) integration of production process such as energy integration, water and nutrient recycling; (2) improvement of microalgae biology via genetic and metabolic engineering such as enhancing their photosynthetic efficiency, increasing biomass yield and oil content and improving temperature tolerance to reduce cost associated with cooling; (3) improving photobioreactors regarding their capacity and operational ability [76].