Category Archives: BIOGAS 1

Response model

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)


x3x5+0.65x4x5 (3-2)


+1.67x2x4+1.41x4x5 (3-3)

3.2.2 Analysis of importance of various factors on response functions

Importance of various factors on response functions was shown in table 3-3.



Dry tensile strength (N)

Wet tensile strength (N)

Degradation period


Beating degree
















Wet strength agent




Table 3-3. Importance of each factor

Characteristics of the respondents by wealth ranks

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.

Wealth Category

Slightly Well-off

Less Poor

The Poor

Family size (persons)




Married respondents (%)




Female respondents (%)




Respondent’s age (years)





No formal education (%)




Completed primary education (%)




Completed secondary education (%)




Instrumentation and control

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.

Limit viscosity

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).

Anaerobic filters

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 steps

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).

Bioethanol production by Kluyveromyces marxianus

1.1.2 Materials and methods 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.

The effect of different treatments on soil temperature

4.1.1 The effect of soil depth on soil temperature among treatments in different growth stages

The soil temperatures that varied with soil depth of the six treatments were shown in Fig.4-2.

Figure 4-2 showed that the mulching temperature was higher than the control during the whole growth period, the temperature of plastic film was the highest, the temperature of black film was higher than the biogas residue fibre film, and the temperature of A, B, C of the biogas residue fibre film was slightly higher than the control. It could be seen from the temperature curve of am7:00 and pm 14:00, soil temperature gradually decreased with the soil deepening, but, the temperature of soil surface decreased at pm 19:00, the soil temperature slightly increased with soil deepening in the stage of revival and flowering, and the soil temperature was constant in maturity; mulching had a certain warming effect in the stage of revival and flowering, and had not warming effect in maturity, the reason was that the film had been degraded.

Effect of chitosan on the performance of UASB treating fruit-processing wastewater

According to Kaseamchochoung et al. (2006), chitosan with 85%DD and MW of 3.5x10s Da yielded the highest flocculation efficiency and versatility to changes in environmental pH and ionic strength.

Lertsittichai et al. (2007) studied the efficiency of chitosan in a UASB reactor treating tropical fruit-processing industry wastewater. The details of their study were as follows. The fruit canning factory wastewater consisted mainly of sugar. The wastewater characteristics were: COD 5,130 to 5,520 mg/L, volatile fatty acid (VFA) 703 to 1,834 mg/L, pH 5 to 6 and ionic strength of 0.028 to 0.036 M.

Two identical UASB reactors with a working volume of 30 L were employed for the comparative study. The startup period was operated at a hydraulic retention time (HRT) of 85 hours, corresponding to an organic loading rate (OLR) of 1.45 g COD/L-d. Chitosan at a concentration of 2 mg/ g suspended solids was added to the reactor on the second day and the same amount was added on the 37th operating day. The HRT of both reactors were reduced in a stepwise fashion, at 85, 65, 45, and 35 hours, when the COD removal was higher than 80% for at least 3 times the HRT.

Throughout the operation of the process, the OLR values ranged from approximately 1 to 4 g COD/L-d. Lertsittichai et al. (2007) found that the UASB with chitosan addition gave 9 to 59% lower COD effluent and had a 4 to 10% higher removal efficiency than the control UASB. The low VFA values corresponded to high biogas production because VFA is an intermediate for methane production. The UASB with chitosan addition gave a lower VFA value and a 35% higher biogas production rate than the control (Fig. 5).

Effluent VSS refers to biomass washout. Lertsittichai et al. (2007) found that the biomass washout increased during the initial operation period of both reactors. After 35 days, the biomass washout decreased due to granule formation. The biomass washout from the UASB with chitosan addition was 16 to 68% lower than that from the control. The UASB with chitosan addition was found to consistently have 24 to 37% higher average particle sizes than the control, corresponding to the lower biomass washout.


Fig. 5. Biogas production against time (from Lertsittichai et al., 2007). R1 is the control UASB reactor and R2 is the reactor with chitosan addition. Reprinted with permission from Water Environment Research. Volume 79, No. 7, pp. 802 to 806, Copyright © 2007 Water Environment Federation, Alexandria, Virginia.

In addition, Lertsittichai et al. (2007) found that the UASB with chitosan addition consistently had a 6 to 41% longer solids retention time (SRT) than the control corresponding to a lower effluent VSS and a higher average particle size. The VSS from the bottom sampling ports of the UASB with chitosan addition was higher than that of control, leading to greater overall sludge density. From their observations, Lertsittichai et al. (2007) concluded that chitosan helped sludge pellet development. They gave the possible explanation that the cell surfaces of bacteria carry negative charges, and the electrostatic interactions between them are repulsive. Therefore, a cationic polymer, such as chitosan, assists the flocculation of the bacteria leading to faster sludge formation and a higher density of sludge retained in the reactor.

Overall, Lertsittichai et al. (2007) used only small amounts of chitosan (two injections with 2 mg chitosan/ g suspended solids at each injection). They saw no sign of inhibition to biomass activity. Throughout the course of their experiment at a mesophilic temperature (35oC), the UASB with chitosan addition clearly showed superior performance to the reactor without chitosan, with 9 to 59% lower effluent COD, 4 to 10% higher COD removal, up to 35% higher biogas production rate, and decreased washout of biomass and increased granular size.

Target properties: Holland II L gas

For the production of compliant, high calorific L gas, conditioning by the addition of air and LPG is described in the following section.

Please note the following when interpreting the diagrams below: The field of admixtures includes a range of 0 — 20 Vol -% for presentational purposes. In practice, for technical and economic reasons, it is desirable to make the least possible admixtures with a "target" Wobbe Index of 12.4 kWh / m3 for example (setting of the gas appliances). In this context it should be noted that according to G 486 appendix B, the mole fractions of propane are not to exceed 3.5 mol% (6 mol% at p <100 bar) and butane max.1.5 mol% in natural gas, in order make a conversion of standard and operating conditions using the AGA8-DC92 equation of state.

The "field" of the possible mixtures is bounded by the Wobbe Index of 13 kWh / m3, the given calorific value limits, the max. oxygen volume fraction of 3%, and the maximum propane/butane or air admixture. For each value of air addition, there is always a value for the propane / butane addition.

The following figures apply only to the four initial properties of the biogas used.

Figures 9 and 10 show the calorific values and the Wobbe index for an air and LPG admixture of 0 to 20 vol -% to a biogas with an initial methane content of 94 vol -% and 96 vol -%.

H in kWh/m3

S, n

H in kWh/m3


S, n

HS in kWh/m3

S, n

Figures 11 and 12 show the calorific values and the Wobbe index for an air and LPG admixture of 0 to 20 vol -% with an initial methane content of 98 vol -% and 99,5 vol -%.


Fig. 12. Possible highly calorific L gas mixtures by admixing air and LPG to an initial concentration of 99,5 Vol. -% methane

The red area represents the required calorific value range from 9.97 to 10.4 kWh / m3. The green dots show the possible, compliant mixtures that lie within all the conditions to be fulfilled.