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)

Based on the economic results of the PNS optimization and previous SPI evaluation of different intercrops, a footprint for the PNS results was calculated. The evaluation includes every substrate, transport, net electricity and infrastructure for fermenters and CHP units. SPIonExcel already provides a huge database of LCIA datasets which can be used for modeling the scenarios. In case of intercrops substrate the SPI value for conservation tillage + self-loading trailer from Table 4 was used.

The overall footprint points out the environmental impact for one year of production. In case of the optimum solution it would need 93.08 km2 of area which has to be reserved to embed the production sustainably into nature. The overall footprint is shared between both products according the amount of output and the price per MWh (electricity: 205 €/MWh; heat: 22.5 €/MWh). Price allocation of the footprint leads to a higher footprint for the higher valued product.

Scenario 1 has a benefit from the ecological point of view and almost equal revenue according to Table 9. For scenario 2 there is only a slightly difference to the optimum solution because of two small CHP units instead one.

Main impact categories are in every case ‘fossil carbon’, ’emissions to water’ and ‘air’. This mainly derives from the utilization of net electricity which contributes around 45 % to the whole footprint. Main contribution to this categories stemming from net electricity and
machinery input in agriculture which are still mainly fossil based. This is also the main optimization potential for a further decrease of the footprint.

5PI for emissions to soil

■ SPI foremissions to water

■ SPI for emission to air

■ SPI for renewable

■ SPI for fossil C

SPI for non renewable SPI for area

Scenario 2

Compared to other electricity provision system the optimum solution from the PNS has an ecological benefit in footprint ranging from 61 to 96 % which is pointed out in Figure 7. Although the footprint of the optimum solution could be optimized by using the produced electricity for itself and not selling to the grid (which has economic reasons because of high feed-in tariffs) the ecological benefit compared to other sources is obvious. Every contribution to a greener net infects simultaneously all net participants.

5. Conclusion

The three pillar principle of sustainability serves as conceptual framework to conclude this study. Not only economic and ecological factors are important to implement innovative structures. Often we forget about the social component, the third pillar of sustainability. Not to do so farmers’ opinion about intercrops where taken into account. It turned out that intercrops production also abuts on farmers’ psychological barriers and the need of intensive cooperation among farmers in the surrounding of a biogas plant. In conjunction with economic risk and high investments, determining farm management for at least 15 years it becomes obvious, that well-considered decisions are to be made. Therefore, it is not astonishing that farmers hesitate, if economic benefits do not clearly compensate social an managerial risks of biogas production from intercrops. Furthermore, the situation that biogas production from corn is favorable regarding practicability in comparison to biogas production from intercrops, reduces farmers motivation to decide for the latter. But even the growing and harvesting of intercrops requires additional work and the strict time frame to cultivate fields, the risk of soil compaction through harvest and potential lower yields of main crops after winter intercrops are counter­arguments to cooperate with farmers already running biogas plants. Higher feed-in tariffs for biogas from intercrops seem to be inevitable and sensitization of decision makers and farmers is needed to emphasize that the planting of intercrops holds many advantages and that intercrops reduce the ecological footprint decisively. Although a higher energy input for agricultural machines is required because of the additional workload for intercrops. In summary the energy balance per hectare including biogas production points out a benefit. In times of green taxes a reduction of CO2 emissions can diminish production costs. More biogas output per hectare raises the income beside minimized mineral fertilizer demand reduces costs and lowers the ecological footprint. Furthermore, biogas production from intercrops contributes to a reduction of nitrate leaching and nitrous oxide emissions from agriculture. With the transport optimization in-between the network the ecological footprint decreases caused by intelligent fermenter set-up going along with less transport kilometers and fuel demand. A farmer association running an optimal network described before lowers the investment risk and ensures continuous operation and stable substrate availability. On the other hand an association has the potential to strengthen the community and the social cohesion of regions. Some of the advantages mentioned before effect the regional value added positively. On closer examination it could be shown that intercrops can play an important role in sustainable agriculture for the future by running a social and ecological acceptable network and still being lucrative for the operators and the region. Finally biogas production from intercrops does not affect the security of food supply. On the contrary it may even increase productivity in the case of stockless organic farming.

6. Acknowledgment

The research presented here was carried out under the project "Syn-Energy" funded by the Austrian Climate and Energy Fund and carried out within the program "NEUE ENERGIEN 2020" (grant Number 819034).

In 1996, biodegradability of plant fibre paper was studied, which lower mechanical properties under certain environmental conditions, and eventually fragmented or completely degraded. Weight reduction and observation methods were employed by Gao Yujie (1996), Zhang Wenqun (1994), Wang Weigang (2003), Li Zhiming (2004) and Wang

Wei to study the biodegradability (2009), observation method only can describe the film degradation process and the weight reduction method can quantitatively explain the degradation process of biodegradable film.

In this chapter, the physical properties and chemical composition of the biogas residue produced by anaerobic fermentation using ruminant feces were determined and analyzed; manufacturing technology and performance of biogas residue film was studied by the methods of central composite quadratic rotatable orthogonal experiment; biogas residue fibre mulching for planting eggplant was studied with the method of random plot experiment.

Sustainable recycling of organic wastes demands clear regulations of recycled wastes, the used recycling methods and the controlling of products. These regulation processes for the digestate are different in certain countries, respected the elaboration and the used limits.

In Hungary, the digestate is regarded as other non-hazardous waste if the ingestate does not contain sewage or sewage sludge, while in the presence of these materials the conditions of the digestate utilisation depend on the quality of the given material.

In Scotland the BSI PAS110:2010 digestate quality assurance scheme is applied. If a digestate complies with the standards for the quality, the usage criteria and the certification system stated in the worked scheme, the Scottish Environment Protection Agency (SEPA) does not apply the waste regulatory control for it.

In Swiss the digestate which suits the limits, can be used as soil conditioner and fertilizer in "bio"-agriculture.

In Germany the origin of the input materials determines the quality label of digestate product by biowaste and renewable energy crops. Digestates have to fulfil the minimum quality criteria for liquid and solid types which determine the minimum of nutrients and the maximum of pollutions in the digestate. Pollutions mean toxic elements, physical contaminants and pathogen organisms. The quality of digestate products is regularly controlled by "Bundesgutegemeinschaft Kompost e. V." (BGK) (Siebert et al., 2008).

Chandrasekar (2006) demonstrated the gas processing unit (GPU). In stage one biogas from the digester will be cleaned of moisture droplets, particulates and hydrogen sulfide. The cleaned gas mixture, which consists primarily of methane (CH4) and carbon dioxide (CO2), will be then converted in stage 2 to ultra-high purity hydrogen in a steam reformer. As a first step to realize this vision, a GPU was installed (Fig. 19) which has been successfully removing over 99% of hydrogen sulfide (H2S) along with most of the water droplets and particulates. A steam reformer has been also installed.

In the GPU biogas from the digester is pressurized to over 3 inches water column by a blower. It then passes through a coalescing filter to remove most of the particulates and water droplets. Water collected in the coalescing filter gets automatically drained out once it reaches a certain level. The biogas is then heated to about 85 oF in a heater before it passes through two successive activated carbon beds where H2S is converted into elemental sulfur. The process has been optimized so that bed replacement is needed only once every six months. The configuration of dual beds allows for continuous operation even when one bed is being replaced. The bed manufacturer should be contracted to replace the used beds, thereby obviating the need for the farmer to handle the sulfur. The design requires minimum operation and maintenance and has been set up to be controlled through a computer that will also monitor the incoming gas pressure, control and monitor the blower as well as monitor the exit H2S concentration and shut the blower/GPU if the exit concentration is greater than the set point. If the GPU shuts down, biogas will automatically feed the engine generator like before to produce electricity. A simple schematic of the GPU is shown in Figure 20.

When considering the rheology for biogas reactors their viscosity is estimated to correspond to a given TS of the reactor fluid. This is mainly based on historically rheological data from sewage sludge with known TS values. However, problems may arise when using these TS relationships for other types of substrates which may impose other rheological characteristics of the reactor fluids. Furthermore, often low consideration is given to possible viscosity changes due to variation in feedstock composition etc.

Shift in the viscosity and elasticity properties of the reactor material related to substrate composition changes can alter the prerequisites for the process regarding mixing (dimension of stirrers, pumps etc. or reactor liquid circulation) and likely also foaming problems (Nordberg & Edstrom, 2005; Menendez et al., 2006). It may also call for changes in the post treatment requirements and end use quality of the organic residue e. g. dewatering ability, pumping and spreading on arable land (Baudez & Coussot, 2001). The additions of enzymes can be used to reduce the viscosity of the substrate mixture in the digester significantly and avoid the formation of floating layers (Weiland, 2010; Morgavi et al., 2001). All these factors affect the total economy for a biogas plant.