A new comminution device for high-quality chip production

Raffaele Spinelli

a

,

⁎

, Eugenio Cavallo

b

, Alessio Facello

b

a

CNR IVALSA, Via Madonna del Piano 10, Sesto Fiorentino (FI), Italy

b

CNR IMAMOTER, Strada delle Cacce 73, Torino, Italy

a b s t r a c t

a r t i c l e i n f o

Article history:
Received 22 December 2011
Received in revised form 24 January 2012
Accepted 25 January 2012
Available online xxxx

Keywords:
Biomass
Fuel
Energy
Pellets

The authors tested a chipper prototype adopting a new comminution device, designed to produce high qual-
ity chips when processing delimbed logs. The machine was

fitted with innovative tubular blades, mounted on

a

flywheel. The prototype was powered by a 55 kW farm tractor through the standard power take-off. The

machine appeared as ef

ficient as most conventional disc or drum chippers in the same size class, but offered

a much better chip quality. Chips were free from any particles longer than 45 mm, and with a very limited
content of

fine particles (max. 2.5%). Of course, this was achieved when using premium wood raw material,

such as delimbed small logs. Performance varied with tree species: poplar was the softest and easiest to chip,
whereas robinia was the hardest and required a much larger effort. Diesel fuel consumption varied between
3.4 and 4.3 dm

3

per oven-dry tonne.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

The European greenhouse gas mitigation strategy includes a mas-

sive use of wood biomass for energy conversion

[1]

. This resource is

very abundant in all European countries and is largely unexploited

[2]

. Wood biomass is particularly suited to heat generation, for cost-

effective substitution of fuel oil, electricity or natural gas (

Table 1

)

[3]

. Modern wood-

fired heating systems are as user-friendly as con-

ventional boilers, fed with fossil fuels: they are generally

fired with

wood-chips and designed for automatic operation

[4]

. However,

these new systems have a much higher investment cost than their
conventional counterparts, which prevents widespread adoption by
small-scale users

[5]

. Hence the overwhelming success of small-

scale pellet boilers, which offer the same bene

fits at a much lower in-

vestment cost

[6]

. For this reason, pellet boilers can be installed by

very small users, for heating few rooms or single

flats

[7]

. Here they

can replace older polluting technologies

[8]

and may also be used

for air conditioning

[9]

. The lower investment cost is justi

fied by ex-

treme simpli

fication, compared to chip-fed boilers. The latter are

designed to handle a relatively wet and heterogeneous fuel, and are
necessarily more complex. On the contrary, pellet boilers only need
to handle a very dry, homogeneous fuel, which is totally free from
oversize particles or contaminants

[10,11]

. In fact, pellets are the arti-

ficial product of an industrial process, more complex than chip pro-
duction

[12]

. As a result, they are much more expensive than chips,

and their production is outside the reach of local small-scale opera-
tors: that reduces the bene

ficial impact of energy biomass on rural

development, which is one of the main goals of the new energy strat-
egy

[13]

. A possible solution would be to produce top-quality chips

that can be fed to very simple boilers, or by the same boilers designed
for

firing pellets. Moisture and ash content could be reduced within

acceptable limits by prolonged storage and careful raw material se-
lection, respectively. Yet, achieving the right particle-size distribution
might prove the hardest challenge. No chipper type currently on the
market seems able to produce a small, even-size chip, free of oversize
particles

[14]

. In fact, industrial chippers offer a rather coarse product,

which often needs to be screened before feeding to small-scale resi-
dential chip-

fired plants

[15]

. That may be enough to rule out feeding

this material to even simpler pellet boilers. However, the large price
difference between wood chips and wood pellets has motivated sev-
eral entrepreneurs to try and produce very high quality chips for use
in pellet boilers

[16]

. Within this context, the Italian

firm CRM has de-

veloped an entirely new chipper, designed to produce pellet-size
chips from hardwood logs. This machine adopts a totally new commi-
nution principle, different from any of the systems known to date

[17]

. The goal of this study was to determine with scienti

fical

methods the performance of the new machine, especially for what
concerns: power requirements, productivity, fuel consumption and
product quality. The results were compared with the same data pub-
lished for conventional units.

2. Materials

The CRM chipper is a trailer-mounted device, weighing 1500 kg

and designed for operation by a small-size farm tractor. The single-
axle trailer carries a 4 m

3

container and the chipping device. This

resembles a conventional disc chipper, and is run by the tractor
power-takeoff (PTO). However, the classic radial knives mounted on

Fuel Processing Technology 99 (2012) 69

–74

⁎ Corresponding author. Tel.: [phone]; fax: [phone].

E-mail address:

[e-mail]

(R. Spinelli).

0378-3820/$

– see front matter © 2012 Elsevier B.V. All rights reserved.

doi:

10.1016/j.fuproc.2012.01.034

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Fuel Processing Technology

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / f u p r o c

conventional disc chippers are replaced by tubular knives, which
carve the wood rather than slice it (

Fig. 1

). As a result, no oversize

particles (sticks) are produced or allowed through the disc and into
the chip storage. Furthermore, the product looks somewhere be-
tween chips and planer curls, rather than classic chips (

Fig. 2

). In

fact, the curvilinear shape of the CRM chips has a vague resemblance
to the shape of pellets, which may further facilitate replacement. In its
current version, the new chipper is designed for manual feeding,
obtained by gravity through a downward sloping feeding funnel
(

Fig. 3

). The machine is a commercial prototype and has been run

by CRM for almost two years, while tending to their primary energy
contracting business. The test was conducted in September 2011 at
the FORLENER forest machinery fair in Biella, Northern Italy. CRM
was exhibiting at the fair and made the machine available on site,
together with the farm tractor normally used to power it. The tractor
had maximum rated power of 51.5 kW at 2350 rpm, and a maximum
engine torque of 265 Nm at 1400 rpm. The PTO was set on the
540 rpm standard speed.

3. Methods

The study was conducted on 1 to 2 m long logs, of three different

species, and namely: hybrid poplar (Populus × euroamericana), black
locust (Robinia pseudoacacia L.) and sweet chestnut (Castanea sativa
L.).

Table 2

shows the main technological characteristics of the three

wood species. In Italy, hardwoods represent about 70% of the annual
harvest and are especially appreciated as fuelwood, due to their
higher density and lower moisture content compared to softwoods.

The machine was fed one log at a time, each log representing one

repetition. The test included 35 repetitions per species, for a total of
105 repetitions. All logs were weighed with a portable load cell. Indi-
vidual log volume was determined by measuring the total length and
the diameter at mid-length with a tape and a calliper, respectively.
Logs were fed to the chipper in a random sequence, in order to spread
the effect of blade wear randomly on all three species

[18]

. The aver-

age moisture content of the logs was 46%, 22% and 22% on wet base
respectively for hybrid poplar, black locust and sweet chestnut. Aver-
age log length, diameter and weight were 1.7 m, 0.088 m and 7.9 kg,
respectively. Log weight varied between 2 and 16 kg.

Diesel fuel consumption was determined by installing a volumet-

ric

flow metre on the fuel supply line of the engine. The flow metre

had a frequency output of 2000 pulses per dm

3

. Fuel consumption

was recorded at one second intervals. A torque and rotational speed
transducer was placed between the cardan shaft and the tractor
power take-off (PTO), making it possible to calculate rpm and deliv-
ered power at the PTO. All measurement devices were checked and
calibrated before starting the test. Sensors

fitted on the tractor were

connected to a data acquisition system integrated with a personal
computer, making it possible to record torque and speed at the PTO,
as well as diesel fuel consumption. A visual description of the data
collection set up is shown in

Fig. 4

. All parameters were recorded at

the 10 Hz frequency. Before starting the test, the engine was run for
about 30 min in order to reach a steady temperature. Each replicate
lasted between 7 and 13 s, with an average of 9 s.

Effective time consumption was determined on the fuel consump-

tion graphs (

Fig. 5

), rather than by timing the actual work

[19]

. When

the machine is processing small batches, it is very dif

ficult for an ex-

ternal observer to accurately determine when the machine is working
and when it is running idle. In fact, the machine evacuation system
will keep spitting small amounts of chips for many seconds after the
disc has

finished its job. During this time the engine work load is

dropping again. Under real work conditions, a new load would be en-
gaging the chipper at this stage, and the engine work load would not
be decreasing so sharply and for so long. To determine the beginning

Table 1
Comparison of different heating systems for residential use.

Plant type

Chips

Pellets

Nat. gas

Fuel oil

Power

kW

100

100

100

100

Purchase price

€

55 000

30 000

14 000

18 000

Service life

Years

15

15

20

20

Interest rate

%

5

5

5

5

Annual usage

Hours

1300

1300

1300

1300

Ef

ficiency

%

80

84

93

88

Actual ouptut

MWh year

− 1

104

109

121

114

Fuel consumption

units year

− 1

55

34

12 115

12 188

Fuel price

€unit

− 1

65

200

0.8

1.3

Depreciation

€year

− 1

3667

2000

700

900

Interest cost

€year

1467

800

368

473

Fuel

€year

3575

6809

9692

15 844

Maintenance

€year

564

439

165

192

Overheads

€year

478

353

115

142

Total cost

€year

9750

10 401

11 039

17 550

Energy cost

€MWh

− 1

94

95

91

153

Note: Fuel units: chips = tonne; pellets = tonne; nat. gas = m

3

; fuel oil = litre.

Fig. 1. Detail of the disc with the tubular knives.

Fig. 2. Chips obtained with the new machine.

70

R. Spinelli et al. / Fuel Processing Technology 99 (2012) 69

–74

and the end of process time, all graphs were analyzed in order to es-
timate a basal fuel consumption

figure, taken as a reference for the

running machine before its disc actually engaged the wood. This ref-
erence

figure was found to be 3.7 dm

3

h

− 1

, which was adopted as the

threshold for de

fining actual chipping time. All test time when fuel

consumption was above this level was counted as chipping time
and used for calculating net chipping productivity. Average fuel con-
sumption when chipping was calculated on the records above the
3.7 dm

3

h

− 1

threshold.

A single one-kg sample was collected from each repetition for de-

termining moisture content and particle size distribution. The former
was obtained with the gravimetric method, according to European
standard CEN/TS 14774-2; the latter with the oscillating screen meth-
od, using four sieves to separate the following

five chip length classes:

>45 mm, 45

–16 mm, 16–8 mm, 8–3 mm, b3 mm. Each fraction was

then weighed with a precision scale.

Data were analyzed with the Statview advanced statistics soft-

ware. In particular, the software was used for performing a typical
analysis of variance (ANOVA), especially suited to the factorial exper-
iment just described. ANOVA tables were drawn, in order to see how
the sum of squares was divided between main effects and residuals

[20]

. Analysis of variance for particle size distribution was conducted

after arcsine transformation, in order to satisfy the normality
assumption.

4. Results and discussion

The average power requirement varied between 14 and 17 kW,

and the peak requirement between 27 and 35 kW (

Table 3

). Chipping

poplar required about 20% less power than chipping chestnut or robi-
nia, and this difference was statistically signi

ficant (

Table 4

). Power

requirement showed the highest variability with robinia, possibly
hinting at a relatively dif

ficult wood — hard and stringy. The rotation-

al regime of the power take-off changed very little with feedstock
type. Its modest variations indicated that the power delivered by
the tractor was adequate to the work. In general, the speci

fic energy

consumption (kWh t

− 1

) measured in this study was lower than

reported by Spinelli et al.

[21]

for a converted grinder. This result

was consistent with the less ef

ficient comminution action exerted

by converted grinders. The ratio between peak and average power re-
quirements compared well with data reported by Bouchard and Sa-
voie

[22]

.

Average productivity varied between 2.6 and 4 fresh t h

− 1

, and

was 60% higher for poplar compared to chestnut and robinia
(

Table 5

). The higher productivity obtained for poplar was statistical-

ly signi

ficant, and it was mainly related to the higher moisture con-

tent, which made poplar logs signi

ficantly heavier than the rest.

When productivity was related to dry matter, then all values varied
around 2 oven-dry t h

− 1

without any signi

ficant differences between

tree species. The chipper required between 7 and 9 kWh to process
one oven-dry tonne of wood. Productivity

figures were in scale with

those reported by Spinelli and Hartsough

[23]

for manually-fed

tractor-powered chippers, after accounting for the different time ele-
ments included in the two studies. In this respect, readers must recall
that all

figures in the study refer to pure comminution work, and are

calculated for comminution time only, excluding all other work time
and delays

[24]

. In particular, delays can represent a signi

ficant pro-

portion of a chipper's scheduled work time, and may occupy up to
50% of the total work site time

[25]

. In actual operations, delays re-

duce machine productivity and decrease hourly fuel consumption.
In fact, our study focused on pure comminution work in order to min-
imize operator effect, because the machine was totally independent
from operator control during this work phase. Operator effect is a
main source of variability and may account for productivity differ-
ences up to 77%

[26]

.

Average fuel consumption was compatible with the small engine,

and varied between 7 and 7.8 dm

3

per hour. There was no statistically

Fig. 3. Test set-up. In the foreground the data acquisition system. Researchers are collecting chip samples for determining m.c. and particle-size distribution.

Table 2
Wood characteristics of the species used for the test.

Common name

Poplar

Chestnut

Locust

Latin name

Populus × euroamericana

Castanea
sativa L.

Robinia
pseudoacacia L.

Wood density at

15% m.c.

kg m

− 3

340

580

750

Compression

strength

MPa

31

51

73

Shear strength

MPa

3.4

7.3

10.5

Ultimate bending

strength

MPa

55

106

138

Modulus of

elasticity

MPa

7850

11 380

15 000

From: Giordano G. 1986

[31]

.

71

R. Spinelli et al. / Fuel Processing Technology 99 (2012) 69

–74

signi

ficant difference between species (

Table 6

). Peak fuel consump-

tion varied from 8.3 to 9.7 dm

3

per hour, and was signi

ficantly

lower for poplar. Speci

fic fuel consumption varied between 1.8 and

3.4 dm

3

of diesel per fresh tonne, or between 3.4 and 4.3 dm

3

of die-

sel per oven-dry tonne. Chipping poplar incurred a signi

ficantly lower

speci

fic fuel consumption, compared to the other species. Even when

removing the effect of moisture content, speci

fic fuel consumption

was 20% lower for poplar. These

figures were very near to those indi-

cated in a similar study by Spinelli et al.

[19]

on a larger industrial

chipper. In that study, fuel consumption varied between 2.9 and
3.4 dm

3

per oven-dry tonne (odt), whereas the machine in this

study used between 3.4 and 4.3 dm

3

odt

− 1

. It was impossible to de-

termine if that depended on the economy of scale obtained with a
larger machine, or on a supposed lower ef

ficiency of the new commi-

nution system.

Table 7

shows the results for particle size distribution. The chips

were exceptionally even. They were free of any particles longer than
45 mm, and contained between 1.7% and 2.5%

fines. In the worst

case,

fines did not account for more than 5.4% of the total sample

weight. Fine chips in the 16

–8 mm and 8–3 mm classes represented

between 92% and 97% of sample mass. Poplar logs produced over
three times less large chips than chestnut or robinia logs, and the dif-
ference was statistically signi

ficant (

Table 8

). Compared to the other

species, robinia produced signi

ficantly less chips in the 16–8 mm

class and signi

ficantly more chips in the 8–3 mm and b3 mm classes.

The differences were

−8%, 35% and 44% respectively. Poplar offered

the most even product and robinia the most uneven product, always
within the narrow limits of a very good chip. Furthermore, robinia
tended to produce smaller chips than the other species. In general,
the new machine offered a much better product than described in
any other studies. At worst,

fine particles represented only 2.5% of

the total chip weight, which compared very favourably with the 6
to 10% range reported for commercial chips produced in Italy

[27]

,

or with the 3 to 6% range found for stems chipped with a full-length
knife drum chipper

[19]

. What is more, the

fine particles were

obtained through knife-action

– non impact – and were therefore

less prone to cause bridging or other similar problems

[28]

. Oversize

particles were totally absent from the chips obtained with the new

Fig. 4. Schematics of the data acquisition system:

❶ tractor; ❷ torque and rotational speed transducer; ❸ chipper; ❹ volumetric flow metre; ❺ data acquisition system; ❻ PC.

0

5

10

15

20

25

30

35

40

45

0

2

4

6

8

10

12

14

16

18

20

Time (s)

PTO power (kW)

0

2

4

6

8

10

12

14

16

18

Consumption (l h

-1

)

Consumption (l h

-1

)

PTO Power

Fuel consumption

Fig. 5. Fuel and power consumption graph. Example from test Robinia 28.

Table 3
Data for power consumption and rotational regime.

Species

Mean

SD

Min

Max

Average

Poplar

14.4

4.0

3.4

19.5

Power

Chestnut

17.6

3.4

10.8

24.7

kW

Robinia

17.1

7.3

5.6

32.7

Peak

Poplar

27.3

6.5

10.3

37.1

Power

Chestnut

34.9

4.6

28.9

47.4

kW

Robinia

32.4

8.9

16.8

47.7

Average

Poplar

520.6

29.4

502.0

594.0

Rotation

Chestnut

517.7

30.1

495.0

600.0

Rpm

Robinia

516.9

30.3

480.0

596.0

Peak

Poplar

542.1

30.8

525.0

618.0

Rotation

Chestnut

542.5

29.9

527.0

616.0

Rpm

Robinia

541.8

30.6

524.0

617.0

Table 4
ANOVA table for power consumption and rotational regime.

Effect

DF

SS

MS

F-value

P-value

Power

Average

Material

2

202.35

101.175

3.72

0.0276

0.668

Power kW

Residual

102

2774.09

27.197

Peak

Material

2

1066.89

533.45

11.21

b0.0001

1.00

Power kW

Residual

102

4854.80

47.60

Average

Material

2

267.45

133.72

0.15

0.8616

0.07

PTO rpm

Residual

102

91 445.94

896.53

Peak

Material

2

8.25

4.12

0.00

0.9956

0.05

PTO rpm

Residual

102

94 556.00

927.02

72

R. Spinelli et al. / Fuel Processing Technology 99 (2012) 69

–74

machine, while they would normally account for 1 to 2% of total prod-
uct weight with other machines

[15,19]

. However, readers must con-

sider that the new comminuter was fed with an ideal raw material,
which contributed to the excellent result. This same condition was
not veri

fied for all the other cases found in the literature. Hence, we

could only demonstrate that the new machine fed with clean logs
produced superior chips, without categorically excluding that con-
ventional chippers fed with the same material may offer a similarly
good result.

Wood species was already known to affect the power required

for comminution

[29]

. In our case, the lower strength of fresh poplar

wood could explain why less power and fuel were needed for com-
minuting this species, compared to chestnut and robinia. Softer pop-
lar wood cut more easily, which could also account for the lower
incidence of large particles in poplar chips. On the other hand, the
higher productivity obtained with poplar wood was related to its
higher moisture content: once expressed in dry mass, productivity
was not signi

ficantly higher for poplar than for the other species.

Robinia was more dif

ficult to comminute, which was consistent

with its very high resistance and corresponded to the empirical
knowledge reported by both operators. Chestnut appeared the
most promising, and in fact is still one of the favourite species for
pellet production

[30]

.

5. Conclusions

The new machine adopts an innovative comminution principle

capable of producing high quality chips, if fed with

first-class raw ma-

terial. This product could be used by simpli

fied boilers, without pre-

liminary screening.

Few studies have recorded the same work parameters for conven-

tional chippers, which reduces the accuracy of the eventual compari-
sons. In particular, available studies refer to much larger machines,
whose hourly consumption and productivity are inherently higher,
regardless of comminution principle.

However, the new device appears as ef

ficient as the discs or drums

installed on conventional chippers, at least in terms of power require-
ment, fuel consumption and productivity.

This machine could be ideal for small-scale users, due to its low in-

vestment cost and to the capacity of producing suitable chips for
small scale boilers. That makes it very attractive for farms interested
in achieving energy independence.

Acknowledgements

The authors gratefully thank CRM for making available the chip-

per, the tractor and the most competent operators. Special thanks
are also offered to Giuseppe Paletto (CNR IMAMOTER

— Turin,

Italy), Ruben Laina (UPM

— Madrid, Spain) and Henrique Venturinelli

(UNESP

— Botucatu, Brazil) for their assistance with data collection

and laboratory work. This study was also made possible thanks to
the funding obtained from the Regione Piemonte within the scope
of project DCU-Net (P.S.R. 2007

–2013 Misura 124.2), and from the

COST Action FP902 within the scope of its 4th STSM programme.

Table 5
Data for productivity and fuel consumption.

Species

Mean

SD

Min

Max

Productivity t h

− 1

Poplar

4.0

1.1

1.1

5.1

Chestnut

2.4

0.7

1.2

3.6

Robinia

2.6

1.2

0.6

4.7

Productivity odt h

− 1

Poplar

2.1

0.6

0.6

2.7

Chestnut

1.9

0.5

1.0

2.8

Robinia

2.0

1.0

0.5

3.7

Average consumption dm

3

h

− 1

Poplar

7.0

1.0

4.7

8.4

Chestnut

7.8

1.0

6.0

9.7

Robinia

7.7

2.0

5.0

12.5

Peak consumption dm

3

h

− 1

Poplar

8.3

1.4

5.1

10.8

Chestnut

9.7

1.2

7.4

12.2

Robinia

9.5

2.9

5.8

16.6

Speci

fic consumption dm

3

t

− 1

Poplar

1.8

1.3

1.4

4.4

Chestnut

3.3

1.2

2.0

5.4

Robinia

3.4

1.5

1.8

8.9

Speci

fic consumption dm

3

odt

− 1

Poplar

3.4

1.3

2.6

8.1

Chestnut

4.2

1.2

2.6

7.0

Robinia

4.3

1.5

2.2

11.3

Speci

fic power consumption kWh odt

− 1

Poplar

6.8

0.7

5.2

8.6

Chestnut

9.6

1.9

6.0

14.4

Robinia

9.3

2.5

5.3

16.6

Table 6
ANOVA table for productivity and fuel consumption.

Effect

DF

SS

MS

F-value P-value

Power

Productivity t h

− 1

Material

2

49.49 24.74 24.00

b0.0001 1.00

Residual 102 105.15

1.03

Productivity odt h

− 1

Material

2

0.89

0.45

0.88

0.4172 0.19

Residual 102

51.69

0.51

Avg. consumption

dm

3

h

− 1

Material

2

13.93

6.96

3.49

0.0342 0.64

Residual 102 203.58

2.00

Peak consumption

dm

3

h

− 1

Material

2

41.25 20.62

5.29

0.0065 0.84

Residual 102 397.91

3.90

Speci

fic consumpt.

dm

3

t

− 1

Material

2

1.58

0.79 45.83

b0.0001 1.00

Residual 102

1.75

0.02

Speci

fic consumpt.

dm

3

odt

− 1

Material

2

0.22

0.11

6.45

0.0023 0.91

Residual 102

1.75

0.02

Speci

fic consumpt.

kWh odt

− 1

Material

2 168.05 84.03 24.56

b0.0001 1.00

Residual 102 348.92

3.42

Table 7
Data for particle size distribution, in percent by weight.

Species

Mean

SD

Min

Max

45

–16 mm

Poplar

1.0

0.5

0.0

8.7

Chestnut

2.6

1.0

0.0

23.2

Robinia

4.3

1.0

0.0

31.6

16

–8 mm

Poplar

87.8

0.3

78.9

92.9

Chestnut

85.4

0.6

70.9

93.2

Robinia

80.1

0.4

55.3

90.9

8

–3 mm

Poplar

9.0

0.3

4.8

16.8

Chestnut

9.3

0.5

2.3

22.1

Robinia

12.3

0.1

7.8

19.8

b3 mm

Poplar

1.7

0.0

1.0

4.3

Chestnut

1.7

0.2

0.1

4.9

Robinia

2.5

0.1

1.2

5.4

Note: Mean and standard deviation

figures are obtained from the back-transformation

of the means and standard deviations of the arcsine-transformed data. Totals may not
add exactly to 100% due to the rounding of

figures.

Table 8
ANOVA table for particle size distribution.

Effect

DF

SS

MS

F-value

P-value

Power

45

–16 mm

Material

2

0.21

0.11

13.17

b0.0001

1.00

Residual

102

0.81

0.01

16

–8 mm

Material

2

0.20

0.10

23.39

b0.0001

1.00

Residual

102

0.45

0.00

8

–3 mm

Material

2

0.06

0.03

10.88

b0.0001

1.00

Residual

102

0.30

0.00

b3 mm

Material

2

0.02

0.01

8.47

0.0004

0.97

Residual

102

0.10

0.00

Note: the values are calculated after arcsine transformation.

73

R. Spinelli et al. / Fuel Processing Technology 99 (2012) 69

–74

References

[1] R. Chum, R. Overend, Biomass and renewable fuels fuel processing technology 71

(2001) 187

–195.

[2] M. De Wit, A. Faaij, European biomass resource potential and costs, Biomass and

Bioenergy 34 (2010) 188

–202.

[3] Aebiom, Annual Statistical Report on the Contribution of Biomass to the Energy

Systems in the EU 27, Aebiom, Bruxelles

— Belgium, 2011 107 pp.

[4] A. Strelher, Technologies of wood combustion, Ecological Engineering 16 (2000)

25

–40.

[5] B. Schneider, M. Kaltschmitt, Heat supply from woody biomass

— an economic

analysis, Ecological Engineering 16 (2000) 123

–135.

[6] J. Moran, E. Granada, J.L. Miguez, J. Porteiro, Use of grey relational analysis to

assess and optimize small biomass boilers, Fuel Processing Technology 87
(2006) 123

–127.

[7] L. Gustavsson, A. Karlsson, Heating detached houses in urban areas, Energy 28

(2003) 851

–875.

[8] W. Heschel, L. Rweyemamu, T. Scheibner, B. Meyer, Abatement of emissions in

small-scale combustors through utilisation of blended pellet fuels, Fuel
Processing Technology 61 (1999) 223

–242.

[9] T. Kai, Y. Uemura, Y. Teraoka, T. Takahashi, Y. Hatate, M., Yoshida, Design and

operation of an air-conditioning system fueled by wood pellets, Renewable
Energy 33 (2008) 720

–725.

[10] A. García-Maraver, V. Popov, M. Zamorano, A review of European standards for

pellet quality, Renewable Energy 36 (2011) 3537

–3540.

[11] M. Zamorano, V. Popov, M.L. Rodríguez, A. García-Maraver, A comparative study

of quality properties of pelletized agricultural and forestry lopping residues,
Renewable Energy 36 (2011) 3133

–3140.

[12] A. Kumar Biswas, W. Yang, Blasiak Steam pretreatment of Salix to upgrade

biomass fuel for wood pellet production, Fuel Processing Technology 92 (2011)
1711

–1717.

[13] B. Hillring, World trade in forest products and wood fuel, Biomass and Bioenergy

30 (2006) 815

–825.

[14] R. Spinelli, B. Hartsough, N. Magagnotti, Testing mobile chippers for chip size

distribution, International Journal of Forest Engineering 16 (2005) 29

–36.

[15] R. Spinelli, L. Ivorra, N. Magagnotti, G. Picchi, Performance of a mobile mechanical

screen to improve the commercial quality of wood chips for energy, Bioresource
Technology 102 (2011) 7366

–7370.

[16] C. Nati, C. Lombardini, Cippatino da scarti di lavorazione: prezzi bassi e meno

cenere in stufa, Terra e Vita 17 (2009) 54

–55.

[17] M. Pottie, D. Guimier, Preparation of forest biomass for optimal conversion, FERIC

Special Report SR-32, Pointe Claire, Canada, 1985 112 pp.

[18] C. Nati, R. Spinelli, P. Fabbri, Wood chips size distribution in relation to blade wear

and screen use, Biomass and Bioenergy 34 (2010) 583

–587.

[19] R. Spinelli, N. Magagnotti, G. Paletto, C. Preti, Determining the impact of some

wood characteristics on the performance of a mobile chipper, Silva Fennica 45
(2011) 85

–95.

[20] SAS Institute Inc., StatView Reference, SAS Publishing, Cary, NC, 1999, pp. 84

–93,

ISBN-1-58025-162-5.

[21] R. Spinelli, E. Cavallo, A. Facello, N. Magagnotti, C. Nati, G. Paletto, Performance and

energy ef

ficiency of alternative comminution principles: chipping vs. grinding. Scan-

dinavian Journal of Forest Research. (in press). doi:

10.1080/02827581.2011.644577

.

[22] M. Bouchard, P. Savoie, High-speed processing of woody stems with a

flail

hammer shredder, ASABE Paper n° 083590. Presented at the ASABE Annual
International Meeting, Providence, Rhode Island June 29

–July 2, 2008, 13 pp.

[23] R. Spinelli, B. Hartsough, A survey of Italian chipping operations, Biomass and

Bioenergy 21 (2001) 433

–444.

[24] R. Björheden, K. Apel, M. Shiba, M. Thompson, IUFRO Forest Work Study Nomen-

clature, Swedish University of Agricultural Science, Dept. of Operational Ef

ficien-

cy, Garpenberg, 1995 16 pp.

[25] R. Spinelli, R. Visser, Analyzing and estimating delays in wood chipping opera-

tions, Biomass and Bioenergy 33 (2009) 429

–433.

[26] P. Harstela, Principle of comparative time studies in mechanized forest work,

Scandinavian Journal of Forest Research 3 (1988) 253

–257.

[27] R. Spinelli, C. Nati, L. Sozzi, N. Magagnotti, G. Picchi, Physical characterization of

commercial woodchips on the Italian energy market, Fuel 90 (2011) 2198

–2202.

[28] S. Paulrud, J.E. Mattsson, C. Nilsson, Particle and handling characteristics of wood

fuel powder: effects of different mills, Fuel Processing Technology 76 (2002)
23

–39.

[29] R. Papworth, J. Erickson, Power requirements for producing wood chips, Forest

Products Journal 16 (1966) 31

–36.

[30] M. Gil, P. Oulego, M. Casal, C. Pevida, J. Pis, F. Rubiera, Mechanical durability and

combustion characteristics of pellets from biomass blends, Bioresource Technolo-
gy 101 (2010) 8859

–8867.

[31] G. Giordano, Tecnologia del legno, Vol. III, UTET, Torino, Italy, 1986, 868 pp. (In

Italian).

74

R. Spinelli et al. / Fuel Processing Technology 99 (2012) 69

–74