Optimisation of biogas yields from anaerobic digestion by feedstock type - The Biogas: Science, Production and Applications - page 151

and improved the volumetric methane production from 0.9 to
2.3 kg VSm 3 digester day 1 (Fig. 6.5(c)). This, however, is probably the
limit of metabolic capacity for this digester, as can be seen in Fig. 6.5(d).

6.7.2 Case study 2. Optimising methane production from
agricultural crops and residues

Renewable energy production using agricultural crops represents the largest
expansion in AD technology in Europe (Weiland, 2010; Murphy et al.,
2011), with around 6000 plants currently in operation in Germany alone.
Optimising methane production from these requires a whole systems
analysis involving the types of crops that are grown, production costs,
harvest time, the storage and pre-treatment of the crop, the inclusion and
type of co-digestate, and resource recovery from the digestate product. The
concept of optimisation can be taken still further to consider not just the
methane yield for the biomass produced, but the energy balance for this,
including direct and indirect energy usage as shown in Fig. 6.6 and further
again in the form of a life cycle assessment.
Although it is useful to have a crop that has a high specific methane yield,
it is more important that the methane yield per hectare of land under
cultivation is maximised and that this yield is achieved using environmen-
tally friendly crop rotations (Amon et al., 2007a). One of the most common
crops grown for digestion in central Europe is maize (Zea mays L.), but
cereals such as wheat and triticale or grasses and legumes may be better
suited to colder and wetter climatic conditions (Smyth et al., 2009; Rinco´ n
et al., 2010). Amon et al. (2007a) developed the methane energy value
system for estimating the biogas production potential of crop materials.
This is based on a compositional analysis of crude protein (XP), crude fat
(XL) crude fibre (XF), cellulose (cel), hemi-cellulose (hem) and starch. The
data are processed by regression analysis against experimental data from
specific methane yield tests and coefficients are established that can then be
used to calculate methane yields based on chemical composition. This
technique has been used to evaluate a number of different crop varieties and
also the impact of time of harvest on yield (Amon et al., 2007b). The latter is
particularly important as the specific methane yield of the biomass material
may decline in late harvest even though the biomass yield per hectare
increases: the crop is therefore optimally harvested when the product of
specific methane yield and VS yield per hectare reaches a maximum. Amon
et al. (2007a) suggested that the concept of the methane energy value model
could be further extended for optimising methane yields from versatile crop
rotations that integrate the production of food, feed, raw materials and
energy.

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