Stepwise Models

This modelling approach allows for the formation of intermediate components and their subsequent conversion to final products, as shown in equation M.5.

biomass —> product component j M.5

where the rate of formation of component j with a yield Vj at a given time t is given by equation M.6:

koj exp (- Ep (Vj* — V) M.6

eft RT

Table 2.2 Experimental Kinetic Parameters for Overall

Reaction

Rate Expressions

Data source

Sample

TemDerature

Activation

Frequency

Range PCI

Energy fkJ/тоП

Factor la;1)

Akita & Kase (65)

a Cellulose

250-330

224.0

1.0 x 1017

Modified cellulose

250-330

134.0

1.7 x Ю10

Bilbao et al.(2-6)

cellulose

230-300

54.3

5 x1017

53.8

1.6 x 1017

xylan

< 280

10.2

9.8

lignin

<325

17.8

2.6 x 103

Pinaster pine

290-325

16.4

6x 102

> 325

52.9

1.7 x 1016

barley straw

240-270

12.5

42

>270°C

25.7

8.2 x 106

Broido (66)

cellulose

226-328

221.6

1.7 x 1015

Brown and Tang (67)

Ponderosa

149.9

Chatterjee (68)

Cotton

227.3

138.1

Kanuary (69)

a cellulose

100-700

79.5

1.7 x 105

Lewellen et al, (70)

Cellulose

139.8

6.8 x 109

Maa(71)

Birch wood

400-1200

31.4

0.10 cm/s

Douglas fir

400-1200

14.7

0.03 cnVs

Simmons & Lee (72)

cellulose

36

1.6 x 1010

Stamm (73)

Douglas fir sawdust

110-220

104.7

2.4 x 105

a Cellulose

110-220

108.8

6.0 x 105

Hemicellulose

110-220

111.8

7.1 x 106

Lignin

110-220

96.3

1.1 x 104

Coniferous wood

95-250

123.5

6.2 x 107

Thumer& Mann (74)

oak sawdust

300-400

106.5

2.5 x 106

Tran & Rai (43)

Douglas fir bark

100-850

101.7+142.7X*

2.1 x 108

Catalysed bark **

100-850

102.6+86.2X*

2.3 x 10s

Salazar et al.{75)

eucalyptus regnans Ic hemicellulose

450-600

54

165.9

cellulose

166

1.1 x 1012

sc hemicellulose

83.6

1.5 Х104

cellulose

417.6

2.4 x 1033

Samolada et al. (76)

fir wood

400-500

56.5v

136

94.5 g

2.4 x 104

Varhegyi et al. (55)

Avicel cellulose

205 #,

1.26 x 1015

_

222 §

6.3 x 1016

234 ~

4x 1017

** bark with 15%K2C03

*

X denotes fractional conversion

# 10C7min heating rate

g

total gases

§ 80C7min heating rate

V

total volatiles

« preheated, then 10C7min heating rate

Ic

large cylinder

sc

small cylinder

where:

Vj* ultimate attainable yield of product j, i. e, the yield at high temperature and long residence times (77).

The constants k0j, Ej and Vj* cannot be predicted beforehand and must be

estimated from experimental data, a problem that increases as the number of reactions postulated increases. The model provides a simple scheme that can be used to predict product yields, This method has been used by Krieger et al. (77).

Some models have taken into account the competitive nature of some of the pyrolysis reactions which have been postulated to account for the variations in product yield. This is done by Bradbury et al. (44) for cellulose as shown in Figure 2.7.

Figure 2.7 Proposed Pyrolysis Model for Pure Cellulose (44)

Bradbury et al. (45) used this approach for their kinetic model as shown above. This model was based on the pyrolysis of pure cellulose. Theoretical and experimental results for weight loss agreed to within ± 5%. As a consequence this mode) has been used to account for char yields in the models of large particle pyrolysis (77, 78, 79, 80).

Koufopanos et al. (41,42) and Nunn et al. (76) proposed that the biomass pyrolysis rate could be related to the individual pyrolysis rate of the biomass components i. e.

biomass = a (cellulose) + b {hemicellulose) + c (lignin)

where:

• bracketed terms () represent the fractions of the biomass components not transformed into gases or volatiles

• a, b, c are the weight fractions of the corresponding biomass components in the virgin biomass.

The reaction scheme of the individual components then followed a similar reaction scheme as shown in Figure 8. In both cases, theoretical and experimental results for the weight loss agreed to within ± 10%. However, for the model of Koufopanos et al. (41,42) there was no indication that it could be used to predict product yields. Nunn et a!. (76) found that, in general, the calculated values fitted laboratory data within ± 7% for temperatures up to about 950-1000°C. Similar approaches have been adopted by Simmons (65), Salazar (68), Samolada (69) and Varhegyi (55).

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