Chemical composition of biomass - Biomass as Energy Source: Resources, Systems and Applications - page 46

Table 2.4. Predicted gross calorific values ( P GCV ) for biomass from different plant groups.

Number of Predicted GCV * Standard deviation

Material observations P GCV [kJ/g] [kJ/g]

WN bark 18 21.04 1.42

WN wood 56 20.10 1.50

WB bark 9 19.68 1.82

WB fruit-residue 104 19.68 2.06

Bamboo 13 19.57 0.41

WN log-residuals 6 19.56 0.64

HD fruit-residue 27 19.23 1.71

HG bagasse 28 18.73 1.46

HG stems 3 18.61 0.37

HG cob 14 18.14 1.08

HD stems 66 17.88 1.87

HG straw 183 17.82 1.57

HG leaves 7 17.46 1.55

HG husk 37 16.06 1.33

*Note that these predicted energy values based onC, H, O, N, S and ash levelsmay be biased and overestimated
by 1.8% on average; W: woody; N: needle trees; B: broad-leaf; H: herbaceous; G: grass; D: dicot.

value may be biased and it is claimed that this expression overestimates the GCV by 1.8% on
average (Obernberger and Thek, 2010).
For woody biomass (including bark), the gross calorific value is generally around 20.0 kJ/g (dry
basis), with a slightly lower value for herbaceous biomass (about 18.8 kJ/g). Tao et al . (2012a)
studied literature data on analyzed biomass samples and found that the first two principal compo-
nents explained about 70% of the variation in the observed C, H, O, N, S, and ash concentrations.
The first component was spanned by the C and H group and ash; the second was spanned by O
on one side and the N and S group on the other. Groups on opposite sides of a component are
negatively correlated with one-another. However, the correlations between the concentrations of
elements within individual groups, such as the C and H group or the N and S group were strong
and positive.
A dataset consisting of 775 observations for various biomasses was collected by Tao et al .
(2012a). Of these observations, 571 provided enough information to predict the corresponding
gross calorific value according to Gaur and Reed (1995); see Table 2.4. The average predicted
gross calorific value ( P GCV ) for this dataset was 18.78 kJ/g, with a standard deviation of 1.89 kJ/g.

2.6 CHEMICAL COMPOUNDS AND BIOMASS PROCESSING

2.6.1 Drying
One drawback of many processes involving biomass relates to its water content. The water content
of freshly harvested green biomass is often as high as 50-60% water (wet basis). Drying is an
energy consuming process but can be done at low temperature, meaning that surplus low-value
and low-temperature ( < 95 ◦ C) heat can be used. At industrial facilities and combined heat and
power (CHP) plants, heat generated by the condensation of vapor produced during drying at high
temperatures ( > 100 ◦ C) is often reused to increase the energy efficiency of the drying processes.
Patzek and Pimentel (2005) illustrated the importance of moisture content in biomass. These
authors discussed a case in which raw biomass with a natural moisture content of around 55%was
upgraded by drying it to yield a fuel with a moisture content of 10%by weight. Because the heat of
condensation was not recycled during the drying process, the net gain in available energy was low.