The anaerobic reactor was initially charged with 300 mL of distilled water, 500 mL of the inoculum and 200 mL of a nutrient-trace element solution. The composition of this nutrient — trace element solution is given in detail elsewhere (Borja et al., 2001).

The start-up of the reactor involved stepped increases in COD loading using an influent substrate concentration of 17.2 g COD/L. During this period the organic loading rate (OLR) was gradually increased from 0.25 to 0.50 g COD/ (L d) between 1 and 15 d, 0.75 g COD/ (L d) between 16 and 30 d, 1.00 g COD/(L d) between 31 and 45 d and finally 1.25 g COD/(L d) between 46 and 60 d.

After the preliminary step, the reactor was fed in series of semicontinuous experiments using OLRs of 0.9, 1.2, 1.4, 1.7, 2.1, 2.8, 3.5, 4.1 L COD/(L d) for the OWSW1, which correspond to hydraulic retention times (HRTs) of 40.0, 28.6, 25.0, 20.0, 16.6, 12.5, 10.0 and 8.3 d, respectively. After these experiments with OMSW 1 five different OLRs were assessed for the OMSW 2, 3.0, 6.0, 9.05, 12.0 and 15.0 g COD/ (L d), these OLRs corresponded to HRTs of 50.0, 25.0, 16.6, 12.5 and 10.0 d, respectively.

Once steady-state conditions were achieved at each feed flow-rate, the daily volume of methane produced, and total and soluble COD, pH, total volatile fatty acids (TVFA) and volatile solids (VS) of the different effluents were determined. The samples were collected and analysed for at least 5 consecutive days. The steady-state value of a given parameter was taken as the average of these consecutive measurements for that parameter when the deviations between the observed values were less than 3% in all cases. Each experiment had a duration of 2-3 times the corresponding HRT.

The organic loadings applied in this work were increased in a stepwise fashion in order to minimise the transient impact on the reactor that might be induced by a sudden increase in loadings.

1.3 Gas parameters, gas quality figures (G 260, G 685)

The gas used in the networks for the final customer has to fulfill quality and composition requirements. According to the standard defined by DVGW G 260 working sheet two main types of natural gas (gas families) are distinguished which stem from different sources and production locations:

• H-Gas, high calorific value (Russian source, typically)

• L-Gas, low calorific value (North Sea source, mainly)

The calorific value is generally used for the billing, as the final consumer/customer must receive his bill with the energy value included, meaning in the unit of kWh in a period (i. e. a year, a month). The energy value yields from multiplication of accumulated flow and calorific value, e. g. 3000 m3/a * 10.1 kWh/m3 equal to 30300 kWh/a.

Nora Niemetz1, Karl-Heinz Kettl1, Manfred Szerencsits2 and Michael Narodoslawsky1

1Graz University of Technology, 2Okocluster, Austria

1. Introduction

Biogas production is discussed controversially, because biogas plants with substantial production capacity and considerable demand for feedstock were built in recent years. As a consequence, in most cases corn becomes the dominating crop in the surrounding and the competition on arable land is intensified. Therefore biogas production is blamed to raise environmental risks (e. g. erosion, nitrate leaching, etc.). Furthermore it is still discussed, that a significant increase of biogas production could threaten the security of food supply. The way out of this dilemma is simply straight forward but also challenging: to use preferably biogenous feedstock for biogas production which is not in competition with food or feed production (e. g. intercrops, manure, feedstock from unused grassland, agro-wastes, etc.). However, the use of intercrops for biogas production is not that attractive since current biogas technology from harvest up to the digestion is optimized for corn. Additionally current reimbursement schemes do neither take the physiological advantages and higher competitiveness of corn into account nor compensate lower yield potentials of intercrops which are growing in late summer or early spring. Higher feed-in tariffs for biogas from intercrop feedstock, as they are provided for the use of manure in smaller biogas systems, would not only be justified, as shown below, but also stimulating. Beyond that, the plant species used as intercrops as well as the agronomic measures and machinery used for their growing seem to provide lots of opportunities for optimization to increase achievable yields. Moreover, adaptations of biogas production systems, as discussed in this chapter, facilitate biogas production from intercrops.

Further advantages of intercrops growing are that they contribute to a better soil quality as well as humus content and reduce the risk of nitrous oxide emissions. Simultaneously intercrops allow a decrease of the amount of chemical fertilizer input, because the risk of nitrate leaching is reduced and if leguminosae are integrated in intercrop-mixtures, atmospheric nitrogen is fixed. This is important, because conventional agriculture for food and feed production utilizes considerable amounts of mineral fertilizers. Due to the fact that the production of mineral nitrogen fertilizers is based on fossil resources, it makes economically and ecologically sense to reduce the fertilizers demand.

In the case study, a spa town in Upper Austria, the set-up of the supply chain is seen as key parameter. An important issue in this case are more decentralized networks for biogas production. This can be achieved e. g. with several separated decentralized biogas fermenters which are linked by biogas pipelines to a centralized combined heat and power plant.

Genetic engineering is one of the methods for improvement of activity in hydrogen generation by microorganisms. Although the yields of generated hydrogen can be performed by optimization of the reaction conditions, genetic modifications seems to be the appropriate solution at the moment. The main idea of modification rely on implantation of other genes into the bacteria strains containing hydrogenase.

decomposition of formic acid in presence of formate-hydrogen liaze (FHL) representing the set of enzymes localized in the inner cell membrane. Hydrogenase 3 coded as hycA and formate dehydrogenase known as fdhF are the main components of the FHL. The presence of hycA gene limits the synthesis of fhlA, responsible for better activity of FHL towards hydrogen. Therefore the removal of hycA increases the fhlA gene expression and in consequence hydrogen production by 5-10%. (Hallenback, 2009). The research of the FHL genes expression were performed by Bisaillon et al. and other authors (Bisaillon, 2006, Turcot, 2008, Penfold, 2003) and they found almost two times higher rate of hydrogen generation for modified strain of E. coli HD701. Genes responsible for nickel-iron hydrogenases (hydrogenase I and II) coded by hya and hyb operons were found in the E. coli genom as well. It was found that elimination of these enzymes by genetic modification can result with almost 35% higher production of hydrogen (Hallenback, 2009, Bisaillon, 2006, Turcot, 2008). Other profits originating from genetic engineering are related to deactivation of enzymes responsible for transformations of glucose into lactic, succinic and fumaric acids. The removal of ldhA (lactic acid) and frdBC (succinic and fumaric acids) genes results in increase of hydrogen formation. The 1.4 fold higher amount of hydrogen were found by Yoshida et al. (Yoshida, 2006) in this situation. The new mutant strain of SR 15 can produce 1.82 mol H2/ mol glucose what is close to the theoretical value (2 mol H2/mol glucose). Studies performed by Maeda et al. (Maeda, 2007) showed that bacteria BW2513 with seven modified genes (hyaB, hybC, hycA, fdoG, frdC, ldhA and aceE) generate 4.6 fold more hydrogen than wild-type strain.

The nitrogenase and uptake hydrogenase play an important role in the photofermentation process of hydrogen generation by PNS bacteria. The engineering of the mutants free of uptake hydrogenase is the basic task of gene modifications. Genes coding hydrogenase (hup) can be modified by resistance gene insertion into the hup genes or by deletion of hup genes (Kars, 2009, Kars, 2008, Kim, 2006). Appropriately modified Rhodobacter spheroids can generate hydrogen also in the absence of light (Kim, 2008).

Production of polyhydroxybutyrate (PHB) accompany hydrogen generation by PNS bacteria what applies the excess of reducing equivalents in other metabolic pathway. The PHB is the storage material stored in cytoplasm. This compound is formed in the environment rich in carbon compounds but lean in nitrogen (Kemavongse, 2007). The PHB is unwanted competition product accompanying hydrogen generation. The removal of genes responsible for formation of PHB syntase effectively stops generation of the polymer (Kim, 2006). Low activity in PHB formation not always results in an increase of hydrogen yield. Whereas in presence of lactate, malate or malate the amount of photogenerated hydrogen is not influenced by PHB (Hustede, 1993) the presence of acetate can increase photofermentation towards hydrogen. However, the importance of PHB as biodegradable polymer significantly increased in recent years. Therefore, simultaneous photogeneration of hydrogen and PHB gained economic dimension (Yigit, 1999).

There are genetic modifications influencing changes in the amount of LHC (light harvesting complexes). The reduction of pigment present in LHC diminish the self-shadow effect and therefore better access of light into deeper located cells. The decrease of amount of LH1 (Vasilyeva, 1999) complexes with maximum of absorption at 875 nm or those with absorption maximum at 800 and 850 nm (LH2) (Kim, 2006) can increase the amount of photo generated hydrogen. Genetic manipulations cannot lead to total elimination of the pigments (Kim, 2006).

The negative influence of ammonium ions on nitrogenase is well recognized. Therefore, genetic modifications of nonsensitive to NH+ ions should be the subject for considerations. Among many methods reducing the role of ammonium ions in photofermentation is blockage of Calvin cycle via mutation of genes coding the RuBisCO enzyme. This way the excess electron stream is directed to nitrogenase even in the presence of NH4+ ions. Another modification can be achieved by disruption of proteins transporting NH4+ ions through cytoplasmic membrane. Strains of this type ( e. g. Rhodobacter capsulatus) loose their ability to regulate nitrogenase in presence of ammonium ions. (Qian, 1996). Such modifications allow to perform photofermentation even in the presence of molecular nitrogen. Although the amount of generated hydrogen is lower than in nitrogen free atmosphere but economically much more favorable (Yakunin, 2002).

Genetic modifications can be very effective but also troublesome and very expensive. Therefore other methods of process improvement are under investigations. Optimum value of pH equals 7. Photofermentation with Rhodobacter sphaeroides starting at pH=6.8 and ending at pH=7.5 results in significant drop of activity ( 7 times) but PHB concentration is tripled (Jamil, 2009).

Photofermentative bacteria belongs to mesophilic microorganisms and operate between 30 and 35 oC. Therefore, any critical temperatures act against high yield of hydrogen. For example Rhodobacter capsulatus operating at temperatures varying from 15-40 oC produce 50% less hydrogen than the same bacteria kept at constant temperature of 30 oC (Ozgura, 2010).

The access of photobacteria to the light with appropriate length and intensity play a crucial role m hydrogen photogeneration. Better access of light induce better phosphorylation and in consequence more effective synthesis of ATP and better yield of photofermentation (Kars, 2010).

Although the PNS bacteria absorb light in wide spectrum 400-950 nm the range of 750-950 nm is the most important (Eroglu, 2009, Ko, 2002). The light intensity is as well important as their wavelength. For Rhodobacter sphaeroides the amount of generated hydrogen grows linearly from 270 W/m2 (4klx) to 600 W/m2 (~ 10 klx). Below 270 W/m2 no activity of bacteria is observed (Miyake, 1999, Uyar, 2007).

Application of illumination with wavelength longer than 900 nm results in overheating of the system. This require additional cooling systems because of decrease the amount of generated hydrogen. An application of appropriate filters cutting the unwanted range of spectrum seems to be the only solution in this situation (Ko, 2002). Considering natural irradiation one should remember about day-night periodicity. It was found, however, that amount of generated hydrogen is even higher under periodic irradiation than under the continuous one (Eroglu, 2010, Koku, 2003). The day-night illumination induces better activity of nitrogenase what results from better adjustment of PNS bacteria to live in natural conditions (Meyer, 1978).

The presence of organic compounds, also those containing nitrogen (except NH4+ ions) is the key issue for the photofermentation. However, presence of macro and microelements at appropriate concentration can influence the hydrogen productivity. Iron belongs to the most important ones. This element exists mainly as the cofactor of proteins engaged in metabolism. Process of photofermentation, strictly related to the transport of electrons.

There are many electrons carriers such as cytochromes (proteins containing Fe) or ferredoxin. Moreover, the main enzyme in photofermentation — nitrogenase contains 24 atoms of iron in each molecule. The presence of iron ions in medium containing PNS bacteria is one of the very important factors influencing hydrogen productivity. At concentrations of Fe2+ ions lower than 2.4 mg/l there is no hydrogen in products. At concentrations higher than 3.2 mg/l the gradual decrease of evolved hydrogen is observed. It was assumed that non physiological coagulation of the cells can occurs (Zhu, 2007). Molybdenum is the second microelement playing an important role in photofermentaive hydrogeneration. The optimal concentration of molybdenum is 16.5 pmol/l (Kars, 2006).

The substrate yield in hydrogen production can be significantly improved by adding other strains of bacteria into the liquid medium. Improvement in photofermentation was achieved by adding halofilic archeons of Halobacterium salinarum type. The integral membrane protein — bacteriorhodopsin as the pump for the light excited electrons. The H+ ions are pumped out from cytoplasm outside the cell. The proton gradient is then engaged in ATP synthesis by Rhodobacter sphaeroides and this way increasing hydrogen generation. In this case, it is advised to use strains of PNS bacteria tolerating high concentrations of salts (Zabut, 2006) because of the high activity of bacteriorodopsyne in aqueous solution with high ionic strength.

For protection of the environment, the recycling of organic materials has essential role. The anaerobic digestion (AD) is an important method to decrease the quantity of organic wastes by utilization them for energy and heat production. The by-product of this process is the digestate.

In an AD process, different organic materials could be used alone or in mixture of animal slurries and stable wastes, offal from slaughterhouse, energy crops, cover crops and other field residues, organic fraction of municipal solid wastes (OFMSW), sewage sludge. The quality of digestate as a fertilizer or amendment depends not only on the ingestates but also on the retention time. The longer retention time results in less organic material content of the digestate because of the more effective methanogenesis (Szucs et al., 2006).

Biogas technology is known to destroy pathogens. The thermophilic AD increases the rate of elimination of pathogenic bacteria, therefore the amounts of fecal coliforms and enterococcus fulfilled the requirements of EU for hygienic indicators (Paavola & Rintala, 2008). Mesophilic digestion alone may not be adequate for correct hygienization, it needs a separate treatment (70 oC, 60 min., particle size<12 mm) before or after digestion, especially in the case of animal by-products (Bendixen, 1999; Sahlstrom, 2003).

Two types of digestate are the liquid and the solid ones which are distinguished on the bases of their dry matter (DM) content. The liquid digestate contains less than 15% DM content, while the solid digestate contains more than 15% DM. Solid digestate can be used similar to the composts or could be composted with other organic residues and can be more economically transported over grater distances than the liquid material (Moller et al., 2000).

Khemkhao et al. (2011) found that all methanogen DGGE bands observed in the control were also detected in UASB with chitosan addition. The observed acetotrophic methanogens were the family Methanosarcinaceae and the species Methanosaeta soehngenii. The observed hydrogenotrophic methanogens were the order Methanomicrobiales, the genus Methanolinea sp. and the species Methanoculleus marisnigri. On the other hand, some of the acetotrophic methanogens were observed in the UASB with chitosan addition, but not in the control. These acetotrophic methanogens were the order Methanosarcinales, the species Methanosaeta thermophila and Methanosaeta harundinacea.

Methanogenic archaea are oxygen-sensitive anaerobes (Bitton, 1994). They can grow into individual cells, filamentous chains, cubes and/or sarcina. Methanogens are subdivided into two subcategories: (i) hydrogenotrophic methanogens and (ii) acetotrophic

methanogens.

Hydrogenotrophic methanogens convert H2 and CO2 into CH4. Acetotrophic methanogens convert acetate into CH4 and CO2. The acetotrophic methanogens grow slower than the acid-forming bacteria. About two-thirds of CH4 is derived from acetate conversion by acetotrophic methanogens. The other third is the result of H2 and CO2 reduction by hydrogenotrophic methanogens.

As stated above (Khemkhao et al., 2011), lower biomass washout was observed from the UASB with chitosan addition than from the control, especially at higher biogas production rates. The DGGE analysis shows that UASB with chitosan addition contains higher populations of Methanosaeta species than the control. It can be concluded that the chitosan helped to retain these methanogens, thus resulting in higher populations of acetotrophic methanogens.

Tiwari et al. (2005) and Tiwari et al. (2006) have reported that acetotrophic methanogens significantly accelerate granule development. Higher population of acetotrophic methanogens may in turn lead to higher methane production in the reactors with chitosan addition.

Chitosan has been reported to act like an ECP in enhancing the aggregation of acidogens. As shown in Fig. 7, the aggregated acidogens then form granules with highly elastic outer

hydrophilic layers around a core of methanogens. According to Hulshoff Pol et al. (2004) and Thaveesri et al. (1995), the acidogens (round and rod cells) aggregate by forming ECP. Dispersed cells are washed out, while some methanogens (rectangular cells) are enclosed inside, becoming the nucleus of a granule with an outer elastic hydrophilic layer formed by ECP-rich acidogens and an inner core of hydrophobic methanogens. Chitosan has been thought to act like ECP in aggregating anaerobic sludge (El-Mamouni et al., 1998). Therefore it may increase the elasticity of outer hydrophilic layers of the granular samples. In UASB with chitosan addition, the growing methanogens are better protected inside an acidogenic layer and may become less susceptible to adhesion to gas bubbles (filled circles) and consequently may be less washed out from the reactor than those in the control.

The polymer additives appear to play a similar role to naturally secreted ECP in aggregating anaerobic sludge. The addition of polymers to anaerobic systems changes the surface properties of bacteria to promote association of individual cells. Polymer may form a solid and stable three-dimensional matrix within which bacteria multiply and daughter cells are then confined (Liu et al., 2002; Show et al., 2006a; Uyanik et al., 2002).

In addition, Show et al. (2006b) have reported that adding an appropriate dosage of polymer in the seeding stage accelerates the start-up time by approximately 50% and the granule formation by approximately 30%. In addition, granules developed in polymer-assisted reactors exhibited better settleability, strength and methanogenic activity at all OLRs tested. Positively charged polymer forms bridges among the negatively charged bacterial cells through electrostatic charge attraction. The bridging effect would enable greater interaction between biosolids resulting in preferential development and enhancement of biogranulation in UASB reactors (Show et al., 2006a).

In the experiments of Khemkhao et al. (2011), the UASB reactor with chitosan addition was treated with a one-time chitosan dose of 2 mg chitosan/g VSS on the first operating day. The performance of the UASB reactor may be further enhanced by more injections of the chitosan solution. However, the evidence from the one-time chitosan dose of 2 mg chitosan/g VSS on the first operating day was that the initial stage of granulation was very important for forming high quality granules.

3. Conclusion

Chitosan is a biopolymer which can be used to enhance the sludge granulation process and UASB performance. Flocculation efficiency of chitosan was sensitive to its characteristics as well as to the pH and ionic strength of the environment. An increase in the deacetylation of the chitosan from 70 to 85% led to a two-fold reduction in the chitosan concentration necessary to achieve 90% flocculation at pH 7 (Kaseamchochoung et al., 2006).

Chitosan, with a degree of deacetylation of 85% and molecular weight of 3.48x10s Da, yielding high flocculation efficiency (85 to 100% flocculation) and broad flocculation region (2 to 45 mg/ g suspended solids), was shown to accelerate granulation in a 30-L pilot-scale UASB used to treat wastewater from a tropical fruit-processing industry (Lertsittichai et al., 2007).

For the same amount of chitosan, chitosan in the solution form was shown to be significantly better at enhancing the granulation process and the UASB performance than chitosan in bead or powder forms (Nuntakumjorn et al., 2008).

For POME treatment, the biogas production rate and the COD removal of the UASB with chitosan addition was on an average 16% and 5%, respectively, higher than that of the control. A DGGE analysis indicates that the chitosan helped to retain the methanogens in the genus Methanosaeta, thus resulting in higher populations of acetotrophic methanogens. Further investigations are required to determine optimal chitosan dosages and the optimal times to add chitosan under thermophilic conditions (Khemkhao et al., 2011).

4. Acknowledgment

The authors are grateful to the King Prajadhipok and Queen Rambhai Barni Memorial Foundation for financial support to S. Lertsittichai, to Thailand Research Fund (TRF-Master Research Grant, Grant No. MRG-OSMEP505E225) for the financial support to B. Nuntakumjorn and to Thailand Graduate Institute of Science and Technology (TGIST) and the Joint Graduate School of Energy and Environment (JGSEE) for the financial supports to M. Khemkhao. We also would like to acknowledge Faculty of Engineering, King Mongkut’s University of Technology North Bangkok for supporting the publication fee. The authors would like to thank Ngaung-Khaem water quality control plant for providing sludge, Suksomboon Palm Oil Co., Ltd. for wastewater samples and Taming Enterprises Co., Ltd. for providing chitosan samples. Special thanks to Dr. Elvin Moore for his critical reading of the manuscript.

Norazwina Zainol

Universiti Malaysia Pahang, Malaysia

1. Introduction

Biogas produced in anaerobic digesters consists of methane (50%-80%), carbon dioxide (20%-50%), and trace levels of other gases such as hydrogen, carbon monoxide, nitrogen, oxygen, and hydrogen sulfide. Anaerobic digestion is a biological process in which organic material is decomposed by bacteria in the absence of air. The general technology of anaerobic digestion of complex organic matter is well known and has been applied for over 60 years as part of domestic sewage treatment to stabilize organic wastes. Bal & Dhagat (2001) points out that the anaerobic process is more advantageous than the aerobic process in organic waste treatment because of the high degree of waste stabilization, low production of excess biological sludge, low nutrient requirement and production of methane gas as a useful byproduct. Several studies have been carried out for evaluating kinetic parameters and model equations for anaerobic digestion by Siles et al. (2010), Borja et al. (2005), Jimenez et al. (2004), Raposo et al. (2009), Rincon et al. (2009) and Hu et al. (2002); these are all based on the Monod kinetic model (Monod 1950) and on the revised kinetic model developed by Chen et al. (1980) and Hashimoto et al. (1981).

In the microbiology of methanogenic process four different bacterial groups are identified as being responsible for carrying out the anaerobic digestion of complex organic matter. The first group of bacteria is hydrolytic bacteria which catabolizes carbohydrate, protein, lipid and other minor components of organic matter to fatty acids, H2 and CO2. The second group of bacteria is hydrogen producing acetogenic bacteria which catabolizes certain fatty acids and neutral end products to acetate, CO2 and H2. The third group of bacteria is homo acetogenic which synthesizes acetate using H2, CO2 and formate, and hydrolyzes multicarbon compound to acetic acid. Finally, the fourth group of bacteria i. e. methanogenic bacteria utilizes acetate, carbon dioxide and hydrogen to produce methane. The concerted action of these four bacterial groups ensures process stability during anaerobic digestion of the complex organic matter.

The reactions involved in these steps are given below:

• Phase-I. Solubilization of carbohydrate via hydrolysis

[CeH10O5]n + nH2O = nCeH^Oe • Phase-II. Acidogenesis fermentation of glucose to acetate

Where

pmax is the maximum specific microbial growth rate в is a dimensionless kinetic parameter

_s^ _ к

So Цтахв_1+К

Where

K is an dimensionless kinetic parameter.

Eq. 12 shows that effluent substrate concentration depends on the influent substrate concentration.

The minimum retention time indicating when the washout of micro-organisms occurs is numerically equal to the reciprocal of the maximum growth rate:

(13)

There are two different approaches generally used to study the kinetics of biogas production of lignocellulosic waste: one approach is to find the rate-limiting substrate for the kinetic evaluation; another approach is using chemical oxygen demand or volatile solids concentration as an indicator of the substrate concentration (Chen & Hashimoto, 1978). There are difficulties in using COD or VS as the gross substrate since a portion of the COD or VS is not available to the microbes as substrate. The laboratory test for COD of high strength residues requires at least 100 times dilution which generally yields unreliable data. Also, some of the volatile acids in the effluent are volatilised during the VS determination. Because the volatile acids are precursors of biogas production, their volatilisation during the VS determinations causes errors in the calculated amount of substrate utilised.

Biogas production is directly correlated with COD reduction. Since no oxidising agent is added, the only way COD reduction can occur is through the removal of organic material from the waste, such as through the evolution of methane and carbon dioxide. The other avenues of COD reduction through hydrogen sulpsulphide and hydrogen gas evolution are insignificant (Chen & Hashimoto, 1978). A reduction of 1 g COD is equivalent to the production of 0.35 l of methane at STP. Knowing the COD loading to the reactor and the volume of methane produced, the remaining COD in the digester can be calculated.

The biodegradable COD in the reactor will be directly proportional to (Bo — B) where B denotes the volume (in litres) of methane produced under normal conditions of pressure and temperature per gram of substrate (COD) added to the digester and B0 is the volume of methane produced under normal conditions of pressure and temperature per gram of substrate added at infinite retention time or for complete utilization of substrate and B0 will be directly proportional to the biodegradable COD loading (Chen & Hashimoto, 1978). Therefore, from Eq. (12) one obtains:

b0-b _ к

Bp Цтах0_1+К

From Eq. (14) one obtains:

К В

Цтах (Во_В)

Thus, by first calculating the value of Bo, the graph of 0 versus B/ (Bo-B) produces a straight line with an intercept of 1/pmax and with a slope of K/ pmax. To obtain the parameter B0 one uses the following equation, which is easily derived from Eq. (14):

Since B is the methane production per gram of added COD, the volumetric methane production rate (8) equals B multiplied by the loading rate:

c bs0 B0S0 |i__________ К___ I

0 0 І цтах0—1+КІ

Where

8 has the dimensions of volume methane per volume digester per unit time.

The objective of the present study is to develop kinetic parameters for two-stage biogas production using banana stem waste as substrate.

Philippi biogas project is funded by the working for energy programme of South Africa, and it is situated in horticultural area zoned as agricultural land[8]. Two digesters have been constructed on site and each of them is 10 m3. The total plant capacity could 12000 — 15000 litres of biogas per batch load. This is equivalent to 25kw per batch or more than 100 hours of cooking time. This project is still in its early stages of implementation.

1.1.4 Tanzania

Tanzania has a population of 42.5 million people of which 75% live in the rural area (United Nations, 2007). This is one of the countries that has progressed well in terms of biogas development and has several case studies. The first one is in the region of Tanga, which is known for sisal production as a cash crop. The sisal is sold to a number of sisal processing companies to produce fibre. Using the available production methods, only 4 % of the sisal biomass is recovered as fibre and the rest is waste, which is either burnt, producing carbon dioxide or left to decompose, producing Methane (The Bioenergy Site, 2009). Utilising sisal waste for bioenergy can thus be environmentally beneficial since 80% of the plant mass is suitable for biogas production, and can also increase profit to the sisal growing farmers (The Bioenergy Site, 2009). With this opportunity in place, UNIDO, through its initiative on "Rural Energy for productive use" established a biogas pilot demonstration project, with the support from Common Fund for Commodities (CFC)[9]. The plant situated at the Katani Sisal estate in Hale, and utilises the sisal waste generated from the sisal processing plant. The biogas power plant has installed capacity of 300 kW, and was inaugurated by the Tanzanian President in 2008 (UNIDO, 2008). The electricity generated from this plant is used for lighting and running small-scale industries. The company, Katani Limited, also provides energy services to local schools and hospitals in the area (PISCES and FAO, 2009). The company currently plans to expand the capacity to 7000 kW that will be connected to the grid (The East African, 2011).

A Tanzanian Domestic Biogas programme was also initiated in 2007, following a feasibility study by the GTZ. The programme set an ambitious goal of developing 3500 to 4000 units per annum. However, it was estimated that the current construction rate is only 200 to 400 per year (Sika, 2010).

The OLR variation can be derived from either variation in influent chemical oxygen demand (COD) or variation in flow rate with constant COD. An increase in OLR beyond the optimum level is followed by a decrease in the main process parameters such as COD removal, specific methane production. In addition, high amount of suspended solids "known as biomass wash-out" are observed in the effluent, indicating that the reactor suffered a process imbalance and that biomass accumulated in the reactor (Converti et al., 1993; Fezzani and BenCheikh, 2007; Rincon et al., 2008). This could be ascribed to an increase in the concentrations of the VFA with a consequent decrease in pH (Tiwari et al., 2006) or to escalated levels of inhibitory or toxic compounds such as phenols, lignin and others.

Therefore, there is a maximal operational value for this parameter. For instance, Rizzi and coworkers in the year of 2006 reported a decrease in COD removal and specific methane production when OLR was increased from 10 to 15 kg COD/m3-d. With the OLR increase to 20 kg COD/m3-d the biomass excess started to wash out, followed by deterioration of the reactor performance. In a different study, stable reactor performance was observed when the OLR increased from 1.5 to 9.2 kg COD/m3-d with the maximum methane production rate achieved for an OLR of 9.2 kg COD/m3-d. However, a significant decrease in the pH value (from 7.5 to 5.3) was observed when OLR was further raised to 11.0 kg COD/m3-d. In addition, the increase in the effluent COD with increased OLR was paralleled to a sharp increase in the effluent total volatile fatty acids (TVFA, g acetic acid/L) by about 400% (Rincon et al., 2008). This indicates that, at higher OLR the effluent total COD and mainly soluble COD is largely composed of the unused volatile acids produced in the reactor due to the inhibition of methanogenesis.

Methanobacteriaceae and Methanosaeta were found the main methanogens in a laboratory scale up-flow anaerobic digester treating olive mill wastewater (Rizzi et al., 2006). However, the authors also reported an interesting population shift by OLR variation. At lower OLR i. e. 6 kg COD/m3-d, hydrogenotrophic Methanobacterium predominated in the reactor but the number of cells/g sludge showed a 1000 fold decrease from 1011 to 108 when the OLR was increased to 10 kg COD/m3-d. In contrast, phylotypes belonging to the acetoclastic Methanosaeta were not affected by OLR variation and at 10 kg COD/m3-d, dominated in the biofilm (109 cells/g sludge) (Rizzi et al., 2006).

Olive oil wastewater is characterized by high levels of inhibitory compounds such as tannins, and lipids. As a result, increased OLR leads to higher concentration of these substances and a consequent inhibition of methanogenic cells. However, acetoclastic Methanosaeta due to its high affinity for acetate is capable of occupying the deepest and thus more protected niches in the granule or biofilm with low concentrations of substrate (acetate) (Gonzales-Gil et al., 2001). Phylotypes belonging to the genus Methanosaeta were also dominant independent of different OLR in other anaerobic digesters (Rincon et al., 2008).

In a different study was investigated the microbial ecology of granules in UASB reactor fed by synthetic wastewater under various OLR. The authors showed that the predominant microbial biomass was Methanosaeta. However, increasing the OLR led to a substantial increase of Methanosarcina in the granules (Kalyuzhnyi et al., 1996). The increase of Methanosarcina in the studied synthetic wastewater (toxin-free) due to increasing OLR is explained by the low affinity of these methanogens for acetate in comparison with Methanosaeta. Hence, by increasing OLR and consequent VFA concentration, Methanosarcina is favored.

As reviewed earlier, under mesophlic conditions Methanosaeta plays a significant role in making cores of sludge granules (Sekiguchi et al., 2001) and thus their ratio seems to control the speed of granulation (Rincon et al., 2008). Higher OLR, result in consequent higher concentration of substrates (i. e. acetate) in the reactor. Morvai and coworkers in 1990
investigate the influence of organic load ranging from 0.5-3.0 g/L on granular sludge development in an acetate-fed system. They argued that in the range of feed acetate levels examined, higher concentrations of acetate caused faster granulation of the sludge bed and, presumably of the microbial population, and resulted in better sludge structure and improved sludge settleability.

Low OLR has been reported to cause acute mass transfer limitation leading to disintegration of the larger granules (Ahn et al., 2002). The disintegration begins at the core of the granules due to substrate limitation with a consequent loss of granules strength and stability. However, this was not in agreement with the studies reported, which low OLR (<1.5 kg COD/m3-d) did not lead to disintegration of the granules in UASB reactors (Tiwari et al., 2005). This could be ascribed to the different experimental settings and wastewaters used in these studies. Teo and coworker (2000), treat a high iron bearing wastewater in a UASB reactor. Evidence shows that the presence of divalent and trivalent cations ions, such as Fe2+ and Fe3+, helps bind negatively charged cells together to form microbial nuclei that promote further granulation.

Tiwari et al. (2006) tried to enhance the granulation process by using natural ionic polymer additives. These may thus reduce the effect of low OLR (i. e. substrate limitation) on the granules and delayed the disintegration. Meanwhile was reported that COD removal rate, the COD specific removal rate (rs) and methane production rate were not suppressed by increasing OLR when treating wine wastewater and sewage mixture (Converti et al., 1990). That indicated that no inhibition factor related to the organic content of the effluent was present in both wine wastewater and sewage mixture studied.

This was further supported by the cell mass concentration varied very little with increasing the OLR. However as completely noticed by the authors, even at the absence of inhibitory compounds in the initial part, the removal rate increased with the OLR, following a first order kinetic. In the second part, instead the removal rate tended to a constant maximum value, following a zero order kinetic. Afterwards, the removal efficiencies as well as the methane production yield gradually decreased with increasing influent COD due to increasing the OLR, which evidently showed a substrate inhibition occurrence (Converti et al., 1990).

This supports the idea that even at the absence of the inhibitory compounds in the wastewater, increasing influent COD by the means of increasing OLR could lead to substrate inhibition and consequent reduced removal efficiencies. In other study is described the dependence of the removal rate on the OLR by an empirical equation similar to Monod’s model (Eq. 9) to compare the degradability of different effluents (Converti et al., 1990):

rs(max)OLR

(k+OLR)

where rs(max) (kg COD/kg of vss d) is the maximum value of rs, and k is a constant which physically is expressed in units of OLR, an increase of k indicates increased treatment ability of the studied effluent. The desired OLR is the function of the favorable effect of OLR on stimulating the growth of methanogens in the bioreactor by providing them with higher substrate concentrations, its reverse effect on elevating the concentration of inhibitory compounds and the buffering capacity of methanogenic community. In the other words, the maximal operational value of OLR is translated into the highest methane production
(indicating the highest conversion efficiency of the system) that the buffering capacity of methanogenic community is still capable of compensating for elevated concentrations of inhibitory compounds (Tabatabaei et al., 2011).

Static simulation means to execute a simulation for a fixed time; it is therefore good and often used for checking a set of scenarios or planning alternatives. Dynamic simulation reflects the changes of variables over time. This can be applied for historical data or for the future (based on forecasts). In case of reconstruction and tracking of certain values in the network (e. g. gas quality, calorific value) dynamic simulation is required. The typical time scale for simulations are hours which correspond well to most of the measurement cycles which are read from the field devices.

2.6 Tracking of gas quality parameters in the network

Online-simulation is used for tracking of gas quality parameter in the network. This method saves measuring equipment (gas chromatographs) in the field giving a much more detailed view of the way, value and distribution the gas quality parameters (see figure 6). Most often it will be applied for tracking the calorific value for all nodes on an hourly base (if used with historic data it will be called a reconstruction run). If the computed results shall be applied for billing purposes, then an official acknowledgement/permit of the technical authority (board of weights and measures) is required and a permanent surveyance system has to be installed.

Situation old — new

nput/ Feed-in

Cabrific Va ue

Registered Load Measurement (consumer)

SLR = Standard Load Profile (consumer)

Fig. 6. Schematic view of areas of influence from different input of calorific values at feeding points

2.7 Examples

The following example demonstrates the results of a simulation which tracked the calorific value with historic data for 24 hours (reconstruction simulation) in a smaller city with a medium sized distribution network with some trunk lines (see figure 7).

There are six feeder points in total; in the north and the west are equal calorific values, in the south there is one point with different calorific value. (The different calorific values are made visible by different colors on the pipe segments, arrows indicate flow directions). In the middle of the network there is a mixing area (blue and pink) while near the southern feeder point the initial value dominates (dark yellow); the eastern branch of the grid shows a moderate mixed value (red). The small diagrams aside the network lines show the variable flow at the feeder points.

Figure 8 shows a detailed view of the distribution of the calorific value in the middle area of the network, figure 9 demonstrates the area of influence from the different feeder points in the same network by different colors in the background.

Fig. 9. Areas of influence of the feeder points (calorific value distribution distinct by colored borders in the background: yellow/green)

The next example shows the result of the tracking of the calorific value for high pressure network (see figure 10). Here two biogas plants feed into the network at different points; the three feeder points from other upstream transportation systems (not shown here) are equipped with process gas chromatographs ensuring complete information of the incoming gas qualities. The pipelines are color coded according to their calorific value. As the calorific value changes with time there are different values which are transported downstream in the pipes (visible in simulation only, not in this snapshot at a distinct time). Apart from the transport of different calorific values the areas of influence are discernable (e. g. blue colored pipes).

Fig. 10. Tracking oft he calorific value in a high pressure transport network and biogas feedings (A, B)