Limit viscosity (qlim) corresponds to the viscosity of a fluid at the maximum dispersion of the aggregates under the effect of the shear rate (Tixier & Guibad, 2003). The limit viscosity is estimated through the rheogram, when the dynamic viscosity becomes linear and constant. This parameter has been shown to be of great value when studying the rheological characteristics of sludge, since it determines the level of influence of important factors such as the total solids fraction (TS; Lotito et al., 1997). TS (%) and volatile solids (VS, % of TS) are parameters measured in the biogas process in order to control the amount of solids that may be transformed to methane. Also, Pevere and Guibad (2005) reported that the limit viscosity was sensitive to the physicochemical characteristics of granular sludge, i. e. it was influenced by changes in the particle size or the zeta potential.

1.1 Dynamic yield stress

Yield stress (to) is defined as the force a fluid must be exposed to in order to start flowing. It reflects the resistance of the fluid structure to deformation or breakdown. Rheograms from rotational viscometer measurements are used as a means to calculate yield stress. It can also be obtained by applying rheological mathematical models (section 2.6; Spinosa & Loito 2003). Yield stress is important to consider when mixing reactor materials, since the yield stress is affecting the physico-chemical characteristics of the fluid and impede flow even at relative low stresses. This might lead to problems like bulking or uneven distribution of material in a reactor (Foster, 2002).

fact clogging by biosolids, influent suspended solids, and precipitated minerals is the main problem for this system. Applications of both upflow and downflow packed bed processes can be observed. Prevention of methanogens found at the lower levels of the reactor from the toxicity of hydrogen sulfide by stripping sulfide in the upper part of the column and solids removal from the top by gas recirculation can easily be achieved in downflow systems in comparison to upflow systems. However, there is a higher risk of losing biosolids to the effluent in the downflow systems. Design OLR is often in the range of 8-16 kg COD/m3-d which is more than tenfold higher than the design loading rates for aerobic processes (Rittmann and McCarty, 2001).

Anaerobic digestion is a biological process, which is used for the treatment and valorisation of organic waste. Generally It goes through the four steps, as mentionned above, and which are hydrolysis, acidogenesis, acetogenesis and methanogenesis. In the case of co-digestion biodegradable solid waste is added at the head of the process. A preliminary stage of disintegration of the substrate, which is in general a nonbiological step for the transformation of the complex polysaccharide, lipids and proteins, is considered (Thiele, 1991).

2.2.1 Hydrolysis

The hydrolysis is an extracellular process in which complex particulate organic substances (proteins, polysaccharides, lipids, cellulose… etc) are broken up into simple soluble compounds (acid amino, simple, acid sugars fatty, glycerol. etc). It is a significant stage before the process of fermentation, because the fermentative bacteria cannot absorb complex organic polymers directly in their cells. The hydrolytic enzymes include the cellulase, the cellobiase, the xylanase and amylase for the decomposition of sugar polysaccharides, the protease for the degradation of the protein in amino acids, and lipase for the degradation of the glycerol lipids and the fatty acids with long chain (LCFA) (Batstone & al., 2002 and Kaseng & al., 1992).

1.1.2 Materials and methods

1.1.2.1 Microorganisms

Kluyveromyces marxianus 499 obtained from Institute of Agricultural and Food Biotechnology Warsaw, Poland, in lyophilized form was used in all experiments. The yeast strain was cultivated on plates prepared with Wort Agar growth media from Merck Company Darmstadt (Germany) with the addition of 3% lactose using an incubator shaker under sterile conditions at pH 4.5 and a temperature 25°C for 48 h. The yeast was aseptically transferred from the plates into 300 ml cultivation flasks containing 100 ml of Wort Agar medium from Merck Company Darmstadt (Germany) supplemented with 3% lactose, and cultivated at 25°C for 24 h on a rotatory shaker. The yeast culture was immobilized and suspended in 2% w/ v sodium alginate and then added drop-wise to 1.5% w/ v CaCl2 solution. The CaCl2 was decanted. The beads were used for inoculation of experimental reactors.

1.2.3 Response model

Response models of dry tensile strength, wet tensile strength and degradation period at a=0.05, were significant and the models were shown as equation 3-1, 3-2 and 3-3.

yi=30.38+9.135×10-3xi+5.4×2-1.52×3+0.067×4+0.94×5-1.02xi2-0.64×42-1.15×52+ 1.14x1x4+ 1.24x2x3+0.96x3x4-1.11x4x5 (3-1)

y2=13.81-0.36×1+2.47×2-0.35×3-0.36×4+1.43×5-0.62×42-0.42×52+0.64x1x3+0.46x2x5-0.44

x3x5+0.65x4x5 (3-2)

y3=34.95+0.22×1+3.59×2+0.017×3+0.5×4+2.33×5-1.12×12-1.07×22-1.32×32-0.87×42-0.87×52

+1.67x2x4+1.41x4x5 (3-3)

The empirical evidence suggest that the probability of a household adopting biogas technology increases with decreasing age of the head of household, increasing household income, increasing number of cattle owned, increasing household size, male head of household and increasing cost of traditional fuels (Walekhwa et al., 2009). Also economics, material shortage, operation, and the people’s acceptance are considered to be the main factors preventing the diffusion of biogas technology (Ta§demiroglu 1988).

Findings on education show the slightly well-off respondents to had relatively good education than other categories although the post secondary education was generally low across the three categories. Post secondary education such as vocational and other training is important as it creates professionals and experts including biogas experts in rural areas. The extremely poor spend very little in education hovering around 2% of household budgets (Banerjee (2007). The reason for low spending in education is that children in poor households typically attend public schools or other schools that do not charge a fee even if the education quality is poor. Poor parents are not reacting to the low quality of these schools, either by sending their children to better and more expensive schools or by putting pressure on the government to do something about quality in government schools. This partly occurs because quite often they are illiterate themselves and therefore may have a hard time recognizing that their children are not learning much (Banerjee, 2007).

Regarding family size respondents from slightly well off had small family size (3.3 persons) compared to the less poor (4.6 persons) and the poor (5.9 persons) (Table 3). This could be explained partly by the low levels of education of the poor. The less educated are more likely to start family life early than educated ones and therefore have high chances of having several children in their reproductive life time. These findings are consistent with Banerjee (2007) observation that family size is large for the extremely poor respondents.

A biogas plant with an annual operating capacity of 15,000 tons a year requires 3-5 hours’ work daily in order to keep the amount of work gone to minimum with a particularly recommended use of an effective measurement of control technology. For safe exchange of data it is also possible for someone who is not on-site to monitor and control the unit, i. e. the unit can be remotely controlled. For example, the agitators can be switched on and off, and all the solid supply equipment can be monitored. Information about malfunctions can be registered on computer service or on the operator’s mobile phone, these guarantees a short reaction time when anything unexpected happens.

power of the liquid manure and its ammoniacal content have increased, due to fermentation the unpleasant smell of liquid manure and organic wastes have disappeared, as the organic acids have been decomposed. The biogas plant is therefore useful for both the operators and their neighbors.

7. Conclusion

There is flexibility in the types of digester designs available. Digester types and designs are selected for the types of feedstocks to be digested. There are general approaches to tank design, mixing systems, and electrical generation systems. There are different construction quality approaches for household and farm-based in comparison to commercial digesters.

As mentioned above, the economic prosperity and quality of life of a country are closely linked to the level of its per capita energy consumption and the strategy adopted to use energy as a fundamental tool to achieve the same (Amigun et al. 2008; Singh & Sooch 2004). This is illustrated in Figure 1.

Renewable energy could provide the much desired sustainable rural revitalization in most developing countries. It is an ideal alternative because it could be a less expensive option for low income communities. An ideal renewable energy source is one which is locally available, affordable and can be easily used and managed by local communities. Anaerobic digestion is one of a number of technologies that offers the technical possibility of decentralized approaches to the provision of modern energy services using resources such as; cow dung, human waste and agricultural residues to produce energy. Anaerobic digestion of the large quantities of municipal, industrial and agricultural solid waste in Africa can provide biogas that can be used for heat and electricity production and the digester residue can be recycled to agriculture as a secondary fertilizer. Anaerobic digestion systems are relatively simple, economical, and can operate from small to large scales in urban and rural locations (Amigun & von Blottnitz, 2009). In this regard, many African governments have realised that renewable energies could play a very important role in supplementing other existing energy sources.

Anaerobic digestion describes the natural breakdown of organic matter in the absence of oxygen into a methane rich gas (biogas) via the complex and synergistic interactions of various micro-organisms types including hydrolytic, fermentative, acidogenic, and methanogenic bacteria (Lusk et al. 1996, Parawira, 2004b). The first group of microorganism secretes enzymes, which hydrolyses polymeric materials such as proteins and polysaccharides to monomers such as glucose and amino acids. The fermentative bacteria convert these monomers to organic acids, primarily propionic and acetic acid. The acidogenic bacteria convert these acids to hydrogen, carbon dioxide, and acetate, which the methanogens utilize via two major pathways to produce methane and carbon dioxide (Lusk et al. 1996; Verma 2002). The potential for organic matter decomposition to generate a flammable gas has been recognized for more than 400 years. In 1808, it was determined that methane was present in the gases produced during the anaerobic digestion of cattle manure. In 1868, Bechamp, a student of Pasteur attempted to isolate the microorganism responsible for the anaerobic bioconversion of ethanol to methane.

The first practical application of anaerobic digestion for energy production took place in England in 1896 when biogas from sewage sludge digestion was used to fuel street lamps. As is the case for many other renewable technologies, interests in anaerobic digestion suffered with the rise of the dependence of petroleum. However some developing countries, mainly in Asia, embraced the technology for the small scale provision of energy and sanitation services (Monnet 2003). Since that time, anaerobic digestion has received considerable interest to harness its waste disposal and energy producing capabilities, with municipal sewage disposal attracting the widest application (Lusk et al. 1996).

The anaerobic digestion process will occur at most temperatures below 70°C, but in the commercial operation of digesters two main temperature ranges are typically employed; the mesophilic range (30-44°C ) and thermophilic range (45-60°C). In addition to sewage sludge, organic farm wastes, municipal solid waste, green botanical waste and organic industrial waste have also been used as feedstock in various small to large scale digesters across the world. Current commercial anaerobic digestion processes generally involve the following steps; pre-treatment (including size reduction and the separation of non-biodegradable substances), digestion, biogas cleaning and conditioning (to remove CO2, water vapour and other undesirables), and subsequently biogas utilization (via internal combustion engines, or the more efficient combined heat and power plant (CHP)). The solid residue from the digestion process (called digestate) can be used as compost.

Various types of small to medium scale biogas digesters have been developed including the floating drum, fixed dome, and plastic bag design (Amigun & Blottnitz 2007). The amount of biogas produced from a specific digester depends on factors such as the amount of material fed, the type of material, the carbon/nitrogen ratio, and digestion time and temperature (Omer & Fadalla 2003; Schwart et al. 2005; Chynoweth et al. 2001). Depending on the context, any type may be used. However, most of the small to medium scale biogas plants built so far are of the fixed dome type (Amigun & von Blottnitz 2009). The technology is gradually gaining popularity in developing countries, especially in Africa where the lack of clean and sustainable energy source represents damage to the environment and its people (Amigun & von Blottnitz 2009). In addition, Sub-Saharan Africa with its warm climates is well-suited for the biogas digester technology (Aboyade 2004).

In the subsequent sections of this chapter, the current state of status of biogas technology in sub-Saharan Africa will be presented, along with a discussion of opportunities and challenges faced. The socio-economic benefits of biogas digesters is also been investigated through the use of case studies of commercial and demonstration plants on the continent. The economics of biogas technology in terms of investment and maintenance in the rural African context is discussed.

The characteristics of two-phase OMSW are obviously very different from the characteristics of olive cake resulting from three-phase centrifuge systems. Two-phase OMSW is a thick sludge that contains pieces of stone and pulp of the olive fruit as well as vegetation water. It has a moisture content in the range of 60-70% while olive cake from a three-phase extraction process has only around 40-45% moisture. It also contains some residual olive oil (2-4%), 2% ash with a 30% potassium content (Alba et al., 2001).

The average composition of the two-phase OMSW is: water (60-70%), lignine (13-15%), cellulose and hemicellulose (18-20%), olive oil retained in the pulp (2.5-3%), mineral solids (2.5%). Among their organic components, the major ingredients are as follows: sugars (3%), volatile fatty acids (C2-C7) (1%), poly-alcohols (0.2%), proteins (1.5%), poly-phenols (0.2%) and other pigments (0.5%) (Borja et al., 2002).

As it can be seen, the two-phase OMSW has a high organic matter concentration giving an elevated polluting load. The high polluting power and large volumes of solid waste generated (around 2 millions of tons per year in Spain) can pose large-scale environmental problems, taking into account the 2000 Spanish olive oil factories, most of them located in the Andalusia Community (Borja et al., 2002).

Samples from three different positions of the biofiltration system were taken to evaluate the spatial distribution of the microbial population. Figure 3 shows a picture of the biofiltration system and the positions where the samples were collected. The samples at the top of the reactor "a" correspond to the inlet of the biogas stream mixed with different air fluxes, samples "b" and "c" correspond to the middle part of the reactor and sample "d" was located at the bottom of the biofilter (outlet of the biogas stream).

Additionally, the changes in the bacterial community were also determined by taking samples during long-term operation of the biofiltration system (Samples from 1a to 9a). The analysis was performed by a DDGE system using 16S rRNA as a bacteria-specific target for PCR amplification. Figure 4 shows an example of denaturing gradient gel DDGE (15% to 60%) from samples of different times of cultivation compared with the initial bacterial community. In summary, around 13 bands for the total bacterial community were systematically detected over long-term operation of the biofiltration system. (Samples 1a to 3a correspond to days 5, 10 and 20th of operation. Sample 4a was obtained at day 45th of operation, when the inlet load was increased to 3000 ppmv. Samples from 5a to 8a were obtained in days 90, 110 130 and 150 of operation, respectively.) In view of the total bacterial community, the bands remained constant until variations in intensity appeared. In lane 4a, lower intensity bands revealed a weakening pattern, which suggest a decrease in certain types of bacteria, when the H2S concentration increased from 1500 ppm to 3000 ppm. Both the decrease in removal efficiency and the decrease in the microbial population could be explained by the toxicity of the extremely high H2S concentration. This factor was assumed to be responsible for the disappearance of some of the microbial species. Increased intensity

in some bands in the gel (boxes A and B) demonstrated intensifications of specific band patterns. These data suggested the eventual dominance of H2S-oxidizing bacteria (SOB) and bacteria able to consume VFA.

To compare bacterial community between different samples and to determine possible changes in composition, the presence or absence of a band in a DDGE gel was analyzed using a binary system. A 0 value was assigned when the band was absent (i. e., different band is considered a different microorganism) and 1 when the band in two or more samples was present (i. e., same microorganism) at similar positions in the gel. Jaccard’s index and the Sorensen-Dice index could then be calculated. Table 2 shows the matrix constructed using the DDGE gel containing different band patterns obtained at different times of operation (lanes 1a to 9a) and at different lengths along the biofilter (9a, 9b, 9c and 9d). Nineteen different bands (arbitrarily named A to S) were found in the samples analyzed by gradient DDGE.

Once the number of bands that were similar or different between the two samples was determined, the similarity of the different samples was determined by calculating the Jaccard and Sorensen-Dice indexes. Two different aspects were analyzed: the similarity of the samples during the time of cultivation (lanes 1a to 9a) and the similarity at a different position in the reactor (lane 9a compared to 9b, 9c and 9d).

Regarding time of cultivation, the bands A, B and E to J constantly appeared in the microbial community, suggesting little change in the microbial populations during the operation of the biofilter. The most similar microbial communities were found in lanes 6a and 7a, with a Jaccard index of 0.875 and a Sorensen-Dice index of 0.933, which corresponded to the steady state of the biofiltration system at an average H2S inlet concentration of 1500 ppmv and a removal efficiency of 95%. In contrast, the least similarity was found between lanes 4a and 5a, 6a and 7a (Jaccar’s indexes of 0.438, 0.47 and 0.56, respectively). In lane 4a, the microbial community sample was exposed to an increased H2S concentration of 3000 ppmv. These data differed from those found by Maestre et al., 2010. These authors reported a wide phylogenetic diversity and showed that the initial populations became more specific, being the SOB the dominant community.

The similarity between the microbial communities along the biofilter was also calculated. For this purpose, the bands in lane 9a were compared with the bands in lanes 9b, 9c and 9d. Significant differences in the microbial population were observed at different lengths along
the biofilter. The highest divergence was found between lanes 9d and 9b, with a Jaccard index of 0.333 and a Sorensen-Dice index of 0.5. These data could be partially explained by the H2S concentration gradient: a higher concentration at the inlet of the biofilter and a lower concentration at the outlet (sample 9d). The accumulation of metabolic products could also explain the divergence. The highest similarity was found in samples of lanes 9b and 9c, which corresponded to the middle of the reactor, where apparently the environmental conditions were more homogeneous (Jaccard index of 0.833 and Sorensen-Dice index of 0.909). These data are in agreement with the results obtained by Maestre et al., 2010 and Omri et al., 2011 about the divergence in microbial populations along the reactor.

Sequence analysis of DNA extracted from single bands representing specific species were then used as an approach for further community characterization. Sequence analyses of bands (Table 3) revealed the predominant bacteria in the biofiltration system. The structure of the bacterial community sequenced was associated with microbial activity in the system as a function of the pollutant eliminated in the biofiltration system.

Table 3. Sequence analysis and species identification of the major (7) DGGE bands for the biofilter samples.

Band sequencing results showed that the dominant members of SOB consisted of Bosea thiooxidans and Thiobacillus sp (Table 3). Das et al., 1996 reported that Bosea thiooxidans was a new gram-negative bacterium isolated from agricultural soil and capable of oxidizing reduced inorganic sulfur compounds. Data showed that this microorganism was strictly aerobic. Experiments conducted to evaluate thiosulfate oxidation showed that the growth yield varied with the concentration of this compound; the greatest growth was observed at a
concentration of 5 g/L. Under these conditions, conversion of thiosulfate to sulfate was stoichiometric, and the pH of the medium decreased from 8.0 to 6.6. The distance matrix phylogenetic tree based on the level of difference between Bosea thiooxidans and 19 reference strains of the alpha subclass of the Proteobacteria indicated that strain BI-42 belonged to a new lineage located between the methylotrophs, the genus Beijerinckia, and the Rhodopseudomonas palustris group. No close relationship was found between the strain and other sulfur-oxidizing bacteria, such as Thiobacillus acidophilus and Acidiphilium species (Das et al., 1996). Microorganisms utilize sulfur compounds for the biosynthesis of cellular material or transform these compounds as part of a respiratory energy-generating process. Most of the known sulfur-oxidizing bacteria belong to the genera Thiobacillus, Thiothrix, Beggiatoa, Thiomicrospira, Achromatium, Desulfovibrio, Desulfomonas, Desulfococcus, and Desulfuromonas. Furthermore, members of the genus Thiobacillus have been studied extensively to increase understanding of the coupling of oxidation of reduced inorganic sulfur compounds to energy biosynthesis and assimilation of carbon dioxide.

The presence of Arcobacter butzleri, belonging to the Phylum Proteobacteria (e — Proteobacteria), could be associated with VFA degradation (these compounds were introduced into the biofilter through the biogas stream). This microorganism is able to grow under both aerobic and anaerobic conditions over a wide temperature range (15-42 °C). However, optimal growth occurs under microaerobic conditions (3-10% O2). Arcobacter

butzleri was also recently found as a member of the microbial population in a microbial fuel cell (MFC) used to produce electricity from synthetic domestic wastewater that contained a mixture of VFAs as electron donors (Freguia et al., 2010). Similar results were obtained by Nien et al., 2011, where an Arcobacter butzleri strain, ED-1, was also determined to be part of the microbial community of a MFC fed with acetate. Although aerobic species were predominant because the metabolic activity determined (sulfate as the main product), the DGGE showed that profile some facultative anaerobes were as part of the microbial population, which could be related to the trophic properties of the community, and the different substrates in the biogas stream (H2S and VFAs).

Some of the species found in the present study agreed with those previously reported in the literature for biofiltration systems used in the removal of reduced sulfur compounds. For example, Ding et al., 2006 studied a packed compost biofilter for the treatment of a mixture of H2S and methanol using 16S rRNA sequencing analysis. The authors established that the microbial community was composed of strains of Thiobacillus, Sulfobacillus, and Alicyclobacillus hesperidensis. In a biofilter packed with compost, activated carbon and sludge used for the removal of H2S, Chung, 2007 determined a microbial population composed of Pseudomonas citronellolis, P. fluorescens, P. putida, S. capitis, Bacillus subtilis and Paracoccus denitrificans. In a recently published work (Omri et al., 2011), it was reported that most bacteria in the operation samples were of the genera Pseudomonas sp., Moraxellacea, Acinetobacter and Exiguobacterium, which belong to the phyla Pseudomonadaceae, gamma — Proteobacteria and Firmicutes.

A neighbor-joining tree (Fig 5.) of partial 16S rRNA sequences (approximately 750 bp) was constructed in MEGA4 (Tamura et al., 2007) by considering sequences obtained and comparing them with others in the data bank.

4. Conclusion

The feasibility of CH4 was demonstrated. High VS removal, the increased methane yield, and the natural pH control during the stable period of the ADS was obtained by codigestion of VFW and MR, due to an adequate ratio of nutrients and the availability of proteins for new cell synthesis. However, the increasing MR concentration in the ADS increased the H2S concentration in the gas stream. The elimination of H2S and VFAs by a biofiltration system was successfully determined, reaching high removal efficiencies of both compounds (95% and 99%, respectively). This approach could allow the potential use of the biogas maintaining the methane (CH4) content throughout the process. The microbial population characterization of the bioifltration system showed that dominant members of SOB were Bosea thiooxidans and Thiobacillus sp. Some facultative anaerobes were also determined in the system, which could be explained by the composition of the biogas stream and the conditions at different length of the reactor.

5. Acknowledgment