Determination results of the fibre length to width ratio of each group were measured and analysed by Motic Images Plus and shown in table 2-3.

Table 2-3 showed that average length distribution range was 0.52 mm to 4.80 mm, which of 0.25 mm to 0.5 mm was minimum, which of more than 5 mm was maximum, average width distribution range was 0.03 mm to 0.26 mm, which of 0.25 mm to 0.5 mm was maximum, which of 0.5 mm to 1 mm was maximum; average length to width ratio distribution range was 3.92 to 32.14, which of 0.5 mm to 1 mm was minimum, which of more than 5 mm was maximum. These showed that biogas residue length to width ratio had a greater dispersion. Although the average length to width ratio distribution range was smaller than straw fibre, which showed that it had a certain available value.

Agnes Godfrey Mwakaje

Institute of Resource Assessment, University of Dar es Salaam,

Tanzania

1. Introduction

Dairy farming plays a key role in the lives of poor, rural people in developing countries, providing a major proportion of their cash income, capital assets, draught power, fuel and fertilizer. Small-scale dairying produces valuable food products and provides a regular income and work. Dairying also provides much of the cash needed to perform other socio­economic activities. Milk production generates reliable incomes to meet household livelihoods (Somda et al., 2005). Possession of dairy animals means also financial security, status, self-confidence and an opportunity to have some control over their live (Ramkumar,

2004) . It is also more labour intensive and supports substantial employment in production, processing and marketing. This is partly because dairy production often require the introduction of specialised dairy breeds and increased levels of inputs (nutrition and health care) and good linkages to markets, both for milk sales and input acquisition. In Kenya dairy farming has become a very significant source of income and food for an estimated 625,000 smallholder producer households and for those involved in the marketing of milk, in total some 25% of all households in Kenya benefit from dairy farming (Muriuki et al., 2001). In Tanzania about 700 000 dairy cattle are available under smallholder farmers, with an average of 4 cows per household, there might be 175 households keeping indoor fed dairy cattle in Tanzania. Dairy farming in Tanzania is estimated to grow at a rate of 6% per year and there are about 190,000 registered farmers currently (Swai and Kurimuribo, 2011). Most of these cattle are kept in the highland and relatively cold regions of Arusha, Mbeya, Kagera, Iringa and Morogoro. Smallholder dairy farming in Tanzania has had a significant impact on poverty alleviation in terms of income, education, food security and stabilizing farm incomes (Kisusu et al., 2000).

On the other hand, dairy manure is potential for biogas generation. Dairy manure biogas digester technology has proven to be technically and economically feasible and successful in many applications (Schwengels, 2009). Technology pathways involving biogas, natural gas or electricity are advantageous (Hedegaard et al 2008) for rural development. Empirical evidence suggests that each household can realise up to US$ 724 by replacing wood use with biogas, apart from other positive impacts to the environment (Langeni et al., 2010). A study by the Institute of Resource Assessment (IRA), University of Dar es Salaam, in 2005, shows a reduction of firewood consumption from 700 to 145m3 for Lomwe Secondary

School following the adoption of biogas technology which meant a reduction Energy saved annually is approximately 6.7 Terra Joules (T. J) (a reduction of 78.9%) of CO2 annually (IRA,

2005) .

The solids feeder (Fig. 23) is a device that feeds the digester by the solid biowastes, i. e. dry organic matter. The solids feeder consists of a dosing container, weighing cells, identification/weighing system, and a mechanical system. The mechanical system of the solids feeder consists of a hydraulic drive system for the pushing floor, cross feeder auger drive, transport auger drive, transport auger, tamping auger drive, and tamping auger.

(b)

The flow curves for reactor fluids A-E (Figures 2-3) indicated different flow behaviour according to the definitions by Schramm (2000). A Newtonian behaviour of reactors A and D, fed with slaughter house waste and wheat stillage, respectively, was illustrated where the exerted shear stress was almost proportional to the induced shear rate. However, a small yield stress of 0.2 Pa and 0.3 Pa were detected, indicating a pseudo­Newtonian behaviour.

Fluids from reactor B, receiving biosludge from paper mill industry 1 as substrate, indicated an unusual performance at the beginning of the rheogram with decreasing shear stress, thereafter a linear increase in shear stress. A yield stress of 14 Pa was detected. A space between the curves was noticeable when the shear rate increased and afterwards decreased for reactor B (Fig. 2). This area describes the degree of thixotropy of this fluid, which means that the increase of this area is related to the amount of energy required to breaking down the thixotropic structure. Thus, the flow curves obtained with the three-step protocol indicated a thixotropic behaviour of reactor fluid B.

Fig. 2. Rheogram — flow curves illustrating shear stress (t; Pa) vs shear rate (y; s-1) for fluids from reactor A (▲), B (▲) and C (▲) with a three-step protocol.

Reactors C and E revealed viscoplastic behaviours, i. e. a pseudoplastic behaviour with yield stress. Reactor C, fed with biosludge from paper mill industry 2, showed a yield stress of 4 Pa (Fig. 2), and reactor E, receiving cereal residues, a yield point of 4.5 Pa (Fig. 3). The yield stress is defined as the force that a fluid must overcome in order to start flowing (Spinosa & Lotito, 2003). Also for reactor E, a small space between the curves was noticeable when the shear rate increased and afterwards decreased (Fig. 3). This area difference might indicate some degree of thixotropy.

Shear Rate у

Fig. 3. Rheogram — flow and viscosity curves for reactors D (▲; •) and E (▲; •) with a three — step protocol. Flow curves illustrating shear stress (t; Pa) vs shear rate (у; s-1) and viscosity curves illustrating dynamic viscosity (q; Pa*s) vs shear stress (у; s-1).

three-step protocol. Flow curves illustrating shear stress (t; Pa) vs shear rate (у; s-1) and viscosity curves illustrating dynamic viscosity (q; Pa*s) vs shear stress (у; s-1).

The viscosity curves (Figures 3-4) did almost correspond to the flow curve behaviour for the investigated biogas reactor fluids (Table 2). Using the scheme by Schramm (2000) the viscosity curves for reactor A indicated a viscoplastic liquid and for reactor D a viscoplastic or pseudoplastic liquid. The viscosity initially dropped very quickly for reactor A, specifically indicating Bingham viscoplastic fluids with pseudo-Newtonian behaviour. Generally for reactors A-E, the viscosity decreased with increasing shear rate, until it reached its limit viscosities (Table 3).

The limit viscosities ranged 6-36 mPa*s with the highest value for reactor E (Table 3). However, the limit viscosity was similar for reactor C and E despite a difference in TS (%). The dynamic viscosity ranged 18-443 mPa*s for the reactor fluids (Table 3). The reactors A and D showed lower dynamic viscosity values compared to reactor B, C and E, possibly due to their pseudo-Newtonian behaviour. Also, there was a difference in dynamic viscosity between reactor fluids B and C both receiving biosludge with similar TS (%) but coming from two different paper mill industries in Sweden. Thus, the results demonstrated that similar TS values do not necessarily correspond to similar dynamic or limit viscosity values. This contradicts the results presented by Tixier and Guibad (2003), who reported that an increase in TS for activated sludge corresponded to a higher limit viscosity and higher yield stress. Nor, did biosludge from two different Swedish pulp — and paper mill industries with similar TS give similar viscosity values.

Samples from reactor B showed different rheological behaviour depending on when they were measured. The yield stress and viscosity increased when the reactor fluids had been stored and resting compared to when analyzed immediately after sampling, indicating thixotropic behaviour. Figure 5 illustrates how the B reactor fluid after been resting for 48 hours showed a resistance to flow, known as static yield stress. This value (24 Pa) decreased until it reached the dynamic yield stress (7 Pa), which was the value needed in order to become liquid and start flowing. When the analysis was done right after the first measurement (Second measurement), the fluid had already been stirred, so the two different structures that form the resistance to flow were now mixed and no static yield stress was detected. The static yield stress might be initiated by several factors, e. g. weakness of the fluid structure, low mixed liquid solid suspension (MLSS) concentration, small size of particles and poor dewater ability (Pevere et al. 2006).

DNA samples of the microbial consortium along different lengths of the biofilter were extracted using an Easy-DNATM Kit (Invitrogen, USA) following the manufacturer’s instructions. The 16S rRNA gene was amplified using universal bacterial primers. Polymerase chain reaction (PCR) was conducted as previously reported (Garcia-Pena et al., 2011). The PCR amplification products were purified using a QIAquick PCR Purification Kit (Qiagen, UK). The PCR-amplified DNA products were separated by DGGE on 8% polyacrylamide gels with a linear gradient of 5-30% denaturant (100% denaturant was 40% [v/v] formamide plus 42% [w/v] urea) using the DCodeTM System (Bio-Rad, Hercules, CA, USA). After purification, the PCR products were sent to a sequencing service (Macrogen Inc., Korea). The nucleotide sequences of each 16S rRNA gene were aligned using the Clustal X software (Higgins et al., 1996). Identification of the sequences was made after performing BLAST searches of the NCBI database.

The control of the anaerobic digesters is necessary to ensure a good operating of the digester. Since anaerobic digestion is a complex process implying several groups of micro­organisms which are sensitive to several factors of operation, it is significant to be able to detect the non balance of the process at the beginning to take an action in time to prevent its failure. As with other biological processes, anaerobic digestion can be controlled by measuring the substrate convertion (COD or removed VS), the accumulation of intermediaries (VFA, pH, alkalinity, H2, CO), the formation of product (gas production rate, CH4, CO2).

2.3 Methane and carbon dioxide

Biogas is composed mainly of CH4 and CO2. The ratio CH4 to CO2 is normally stable in the digester and any change may be due to the process instability. However, the ratio also depends on the composition of the substrate, the temperature, the pH and the pressure (Hickey & Switzenbaum, 1991). Since the dissolution of CO2 strongly depends on the pH, the fluctuation of the pH can also change the gas composition. A better indicator is thus the production of methane, rather than its composition in the gas (Anderson & Yang, 1992).

The production of methane combines the production of biogas with the measurement of percentage of methane. The production rate of methane (L-CH4/ days) was used successfully like an on line indicator to control a CSTR digester (Feitkenhauer & al., 2002).

of hydrogen as a fuel. However, a major doubt on hydrogen as a clean energy alternative is that most of the hydrogen gas is currently generated from fossil fuels by thermochemical processes, such as hydrocarbon reforming, coal gasification and partial oxidation of heavier hydrocarbons (Castello et al., 2009; Mohan et al., 2007). These methods are considered to be energy intensive and not environmental friendly. It is well known that only biological hydrogen production processes from the fermentation of renewable substrates, such as organic wastewater or other wastes are the promising alternative for hydrogen generation. Several strategies for the production of biohydrogen by fermentation in lab-scale have been found in the literature: photo-fermentation (Gadhamshetty et al., 2008), dark-fermentation (Krupp & Widmann, 2009) and combined-fermentation, which refers to the two fermentations combined (Nath & Das, 2009). However, no strategies for industrial scale productions have been found. In order to define the industrial scale biohydrogen production, more information from laboratory scale experiments are needed, especially related to design and optimization process, and operating parameters. Moreover, generation of biohydrogen by acidogenic phase of anaerobic process (dark-fermentation) is connected with incomplete degradation of organic material into organic acids, so there is a need to utilize by-products of the fermentation process.

As a result, the fermentative hydrogen production could be coupled with subsequent anaerobic digestion step with the conversion of remaining organic content to biogas. A two — stage fermentation process, in which acidogenesis and methanogenesis occur in the separate reactors may offer several advantages, such as improved total wastewater degradation and enhancing biohydrogen and methane production (Venetsaneas et al., 2009).

The dairy industry produces highly concentrated, carbohydrate-rich wastewaters, but their potential for biohydrogen generation has not been extensively studied. There were some experiences working with cheese whey as the substrate for biohydrogen production (Azbar et al. 2009; Castello et al., 2009; Venetsaneas et al., 2009). The objectives of this work were: (1) to check the ability to produce biohydrogen using raw, unsterilized UF whey permeate, (2) to combine biohydrogen dark-fermentation process with methane fermentation of biohydrogen production by-products (mainly organic acids) in two-stage continuous fermentation process.

Fig.3-12 showed the effects of bauxite and wet strength agent on degradation period when other factors were held at 0 level. Degradation period gradually increased with the increase of bauxite and wet strength agent, the maximum occurred when bauxite was held at 6%, and wet strength agent was held at 3%, wet strength agent at a suitable amount could improve the wet tensile strength of film, and make degradation period grow.

Chantaraporn Phalakornkule12 and Maneerat Khemkhao2

1Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, 2The Joint Graduate School of Energy and Environment, King Mongkut’s University of Technology Thonburi,

Thailand

1. Introduction

Anaerobic digesters have been applied for the treatment of wastewater yielding biogas as a value by-product. The biogas from the treatment plant can be utilized for generating heat and electricity. Anaerobic bacteria form granules through cell self-immobilization which then settle out as floc aggregates. These granules are dense microbial consortia packed with different bacterial species and contain millions of organisms per gram of biomass (Liu & Tay, 2002; Liu et al., 2003; Sheng et al., 2010). Granules in anaerobic digestion are important for enhancing process efficiency by increasing biomass hold-up. An anaerobic digester with higher biomass hold-up will be better in terms of COD removal and biogas production.

Granular sludge is a prominent characteristic of upflow anaerobic sludge blanket (UASB) reactors. This type of reactor has a longitudinal structure with a gas/liquid/solid separator at the top, while microbial granules with high settling velocity are formed in a thick biomass blanket zone at the bottom (Lettinga et al., 1983). The performance of UASB systems depends upon the granulation process. Unfortunately, a long start-up period is required for the development of anaerobic granules in UASB reactors since anaerobes are slow-growing bacteria (Liu & Tay, 2002; Show et al., 2006a). When seed sludge is not granulated, the UASB start-up periods are relatively long and washout of finely dispersed sludge particles is a typical problem (Poh & Chong, 2009).

The UASB start-up period can be shortened by enhancing sludge granulation. The development of well-settleable granular sludge is the key factor for successful UASB operation (Show et al., 2006b). Both synthetic and natural polymers are known to promote particle agglomeration and have been used to enhance the formation of anaerobic granules (El-Mamouni et al., 1998; Show et al., 2006a; Show et al., 2006b). Chitosan is a natural flocculant that has been used for the solid-liquid separation treatment of livestock wastewater (Garcia et al., 2009). Recently, chitosan in the form of freely moving polymeric chains has been found to enhance sludge granulation and shorten the start-up period of UASB systems (El-Mamouni et al., 1998; Lertsittichai et al., 2007; Liu et al., 2002; Thaveesri et al., 1995).

2. Chitosan as flocculants

Chitosan has been largely employed in many areas, such as photography, biotechnology, cosmetics, food processing, biomedical products (artificial skin, wound dressing, contact lens, etc.) and in a system for controlled liberation of medicines (capsules and microcapsules). In addition, chitosan has been used as a flocculant for the removal of metallic and colouring ions from industrial effluents by bonding the micro-floc particles together to form larger, denser flakes that are easier to separate (de Alvarenga et al., 2010; Renault et al., 2009).

Chitosan is a natural polysaccharide whose structure is similar to extracellular polymeric substances (ECP). ECP are widely known to assist anaerobic cell aggregation. Polymeric chains of ECP enhance flocculation by bridging microbial cells to form an initial microbial nucleus which is the first step in microbial granulation. There are many hypotheses to explain adhesion and aggregation processes by ECP. For example, in one hypothesis, ECP production is thought to occur prior to adhesion and the appearance of polymer materials at the initial site of contact between microbial cells is believed to be caused by the migration of polymer molecules onto the cell surface. In another hypothesis, ECP production is thought to occur after adhesion. In this case, it is believed that bacterial adhesion provides a favorable physiological condition for ECP excretion (El-Mamouni et al., 1998; Liu et al., 2002; Show et al., 2006a).

Chitosan is obtained by partial deacetylation of chitin (de Alvarenga et al., 2010). Chitin is a P-(1^4)-linked polymer of 2-acetamido-2-deoxy-d-glucose (N-acetyl-d-glucosamine) which exists in the exoskeletons of insects, crustaceans and the cell walls of fungi and algae. Basically, deacetylation involves the replacement of acetyl groups in the molecular chain of chitin by complete amino groups (NH2). Chitosan is a mixture of straight-chain copolymers of N-acetyl-D-glucosamine and D-glucosamine of varying degrees of deacetylation (DD), i. e., with varying average numbers of D-glucosamine units per 100 monomers (Khan et al., 2002; Sabnis & Block, 1997). Chitosan also has the advantage that it is naturally biodegradable and therefore should have little adverse affect on human health.

Chitosan is insoluble in water, organic solvents and aqueous bases, but it is soluble after stirring in acids such as acetic, nitric, hydrochloric, perchloric and phosphoric acids (de Alvarenga et al., 2010). The glucosamine moieties in chitosan carry free amine groups that are protonated in an acidic environment. The amount and the positions of the glucosamine determine the charge and the charge distribution in the chitosan molecule. Changes in charge density have an effect on the dissolution and binding properties of chitosan (Domard, 1996). The degree of deacetylation also controls the degree of crystallinity and hydrophobicity of chitosan (Vander Lubben et al., 2003). Chitosan enhances the flocculation of sludge, and the flocculation efficiency depends on both DD and molecular weight (MW).

For the base gas data, gas properties taken from the DVGW worksheet G 260 appendix 1 and sample values from the GASCALC computer program from e. on Ruhrgas AG have been used. The data are summarized in Table 4. Based on the technical characteristics of combustion of the base gases, the calorific ranges for the processed biogas were determined. Here, the calorific values for the calculations were assumed to be quantity-weighted averages and a + / — 2 percent band was placed around these (Tab. 5). In this way, in the following sections admixtures whose corresponding calorific values lie in this interval will be determined.

3.2 Data on processed biogases

As a basis for further considerations, data for the biogas compositions as specified in Table 6 will be used. Further accompanying substances occurring in the biogas have not been taken into account, because they vary too greatly depending on the fermented substrates and type of processing.

A processing level of 99.5% vol. methane is attainable and is state of the art. The N2 and O2 levels are 0.3 and 0.1 vol -% and remain constant for the calculations at these initial methane levels. The CO2 content conforms to the relevant selected methane content within the specified range.