EVALUATING FACTORS THAT INFLUENCE THE FEEDSTOCK QUALITY OF COMMINUTED FOREST RESIDUES

Joel A BissonMaster’s CandidateForestry

AdvisorHan-Sup Han, PhD.ProfessorDepartment of Forestry & Wildland Resources

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AcknowledgementsI would like to thank my committee, Dr. David Greene, Dr. Jeffrey Kane and my

advisor Dr. Han-Sup Han.

I would also like to acknowledge Mike Alcorn with Green Diamond Resource Company, Steve and Jake Morris of Jake Morris Logging, Larry Cummings of

Peterson Pacific Corporation, Jim Dooley of Forest Concepts, and the whole team up at Schatz Energy Research Center.

I’d like to tip my hat to George Pease, Maurine Nicholson, Dr. Susan Marshall, Dr. Allison O’Dowd, and all my lab mates.

Most of all… I would like to thank my family for providing loving support and encouragement.

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Outline• Background

• Project Goal

• Research questions

• Literature review

• Research objective and

hypothesis

• Methods

• Results and discussion

• Conclusion

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Background

Forest residues are:• Heterogeneous in size• Low bulk density• Contaminated

Burned to:• prepare sites for regeneration• reduce potential fire hazard

Utilizing residues can be challenging due to high cost to recover and low market value.

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Machine Productivity Cost Ave. Distance1

(BDT/PMH) ($/BDT) (miles)Loader in Unit 46.57 6.43 -Dump Truck 26.16 6.08 1.0Loader 38.04 6.10 -Grinder 38.04 11.78 -AWD Chip Van 26.47 6.38 4.3Highway Chip Van 11.92 7.53 26.0Total Stump to Truck 30.39 1.0Total Transportation 13.91 30.3Total 38.04 44.30 31.3

Operational costs to remove forest residues

1 Round-trip distance. Bisson et al. 2015

1/3 of the cost is in transportation

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A potential solution

Biochar

Densification

Torrefaction

In-woods biomass conversion technologies

• Decrease transportation and handling costs

• Increase product value

• Improve feedstock properties for energy production

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Biomass Conversion Technology

Current desired feedstock specifications

Product Particle size(mm)

Moisture Content (% wet basis)

Ash content(%)

Gasification Biochar < 102 < 25 < 20

Torrefaction Torrefied chips 13 - 38 < 30 no limit

Densification Briquettes < 51 4 - 15 no limit

Gasification Electricity < 38 10 - 30 < 15

New challenges

(Schatz Energy Research Center, 2016) 7

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Typical comminution operation

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Particle size distribution of grindings

Zamora-Cristales et al. 2015

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> 3 3-2 2-1 1 - .5 < .50.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0 Mixed conifer slash (2-mon.)

A-3-4-4 A-2-3-3 N-3-4-4

Size distribution (in.)

Perc

ent o

f tot

al sa

mpl

e w

eigh

t (%

)

Han et al. 2015

Particle size distribution of grindings

Project Goal• Develop methods to improve feedstock quality generated

from forest residues

• Provide recommendations for feedstock procurement managers

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• Could we facilitate the use of a chipper instead of a grinder by sorting out stem wood?

• Would processing stems affect feedstock quality?

• How would air-drying the stems over a period of time affect feedstock quality?

Research Questions

Processed stems

Slash pile

Sawlogs

Image credit: Kizha and Han (in preparation)

Literature Review

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Factors that affect feedstock quality:• Comminution• Particle size distribution• Moisture content• Bulk density• Ash content

Findings:• Majority of the research is on machine productivity• Laboratory setting• Field-based studies on feedstock quality are limited.• Only one study has been done on the production of quality

feedstock from forest residues using local species.

Wood chips

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To evaluate the effect sorting, processing and aging had onparticle size distribution, moisture content, bulk density and ash content of chips generated from forest residues.

Research Objective

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Hypotheses

1. Sorting stems will facilitate comminution with a chipper leading to greater control of the particle size distribution.

2. Moisture content, particle size, bulk density, and ash content of chips generated from processed stems would be significantly different from unprocessed stems.

3. Moisture content, particle size, bulk density, and ash content of chips generated from aged stems would be significantly different from fresh stems.

Processed stems

Limbs and chunks

Unprocessed stems

Sawlogs

MethodsSorting forest residues during a timber harvest, June, 2014

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Material generated from sorting and processing residues

PC PH UC

UH

PC = processed coniferPH = processed hardwood

UC = unprocessed conifer

UH = unprocessed hardwood

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Characterizing the different material types

Material type Bark cover (%)

Average volume (m3/piece)

Processed conifer PC 68 0.14

Processed hardwood PH 71 0.15

Unprocessed conifer UC 92 0.08

Unprocessed hardwood UH 95 0.07

• 24% reduction in bark cover as a result of processing

• Processed material was greater in volume

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Material type Bark cover (%)

Average volume (cubic feet/piece)

Processed conifer PC 68 5.1

Unprocessed conifer UC 92 2.9

Processed hardwood PH 71 5.3

Unprocessed hardwood UH 95 2.5

• 24% reduction in bark cover as a result of processing

• Processed material was greater in volume

Sawlog Processed stem Unprocessed stem

• Merchantable sawlog tree

Unprocessed stemProcessed stem

• Non-merchantable tree

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Research design

Statistical Analysis

Processed Hardwood

Processed Conifer

Chipper

Unprocessed Hardwood

Unprocessed Conifer

Units 1, 2, 3

Collect samples

Grinder

ChipperMicro-chipper

Slash

Units 1, 2, 3

August, 2014

June, 2015

Lab Analysis

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Chipping 2- and 12-month sorted stemsMorbark 76 cm disc chipper• 875 hp• 3-knives• 3-pockets• Set to cut 19 mm

chips

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Grinding 2-month slashPeterson Pacific 5710C drum grinder• 1035 hp• 11 knife bits

(center)• 7 hammer bits

(outer edge)• 2– 76 mm and 2-

100 mm screens

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Micro-chipping 12-month sorted stems

Peterson Pacific 4300B micro-chipper • 760 hp• 12-knife• 12-pocket• Set to cut 6

mm chips

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• Particle size distribution• Moisture content• Bulk density• Ash content

Laboratory analysis

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Material type Machine*

Age (months)

Average moisture content

Geometric mean particle

length

Average bulk density

Average ash content

(%) (mm) (kg/m3) (%)

PCC 2 26 17 228 0.27C 12 18 12 203 0.26M 12 18 6 236 0.25

PHC 2 29 15 322 1.03C 12 21 17 252 0.69M 12 23 5 300 0.88

UCC 2 27 18 239 0.64C 12 22 15 217 0.43M 12 20 4 227 0.35

UHC 2 27 20 310 1.07C 12 19 15 252 0.99M 12 20 7 293 1.18

Slash G 2 19 48 138 1.50

Results and Discussion

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* C = Chipper, M = Micro-chipper, G = Grinder

Material type Age

Average moisture content

SD n(%)

PC2 26 5 43

12 18 4 1812-micro 18

PH2 29 4 24

12 21 6 1812-micro 23

UC2 27 5 44

12 22 6 1812-micro 20

UH2 27 3 30

12 19 4 1812-micro 20

Slash 2-grinder 19

Moisture content

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Air-dying stems for an additional 10 months resulted in 7.25% reduction in moisture content across all material types.

There was no significant difference in moisture content between processed and unprocessed material.

2-month 12-month micro-chip12-month

Conifer Hardwood

Material type Age

Geometric mean particle

length

SD n(mm)

PC2 17.23 19.40 43

12 11.46 2.24 1812-micro 5.77

PH2 15.41 2.09 24

12 16.95 2.14 1812-micro 5.41

UC2 18.10 20.11 44

12 14.75 2.51 1812-micro 4.28

UH2 20.41 2.05 30

12 15.15 2.53 1812-micro 6.45

Slash 2-grinder 47.49

Particle size

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Significant difference in GMPS for PC, UC, and UH due to aging.

Processing significantly influenced GMPS for the aged conifer.

Fine fractions increased 10 and 7% for PC and UH, respectively.

Conifer Hardwood

2-month 12-month micro-chip12-month

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1 10 1000

10

20

30

40

50

60

70

80

90

100PC2

PC12

PH2

PH12

% P

assin

g

1 10 100

UC2

UC12

UH12

UH2

Sieve Size (mm)

0.5 10

PC

UC

PH

UH

Processed material Unprocessed material Micro-chip material

Cumulative distribution graphs

Material type Age

Average bulk density

SD n(kg/m3)

PC2 228.13 12.70 36

12 203.22 12.13 1812-micro 236.26

PH2 322.17 11.82 22

12 251.5 2.41 1812-micro 299.53

UC2 239.09 15.76 37

12 217.12 14.96 1812-micro 226.82

UH2 309.84 14.21 28

12 251.46 41.77 1812-micro 292.85

Slash 2-grinder 137.28

Bulk density

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Species and age significantly influenced bulk density.

No significant difference as a result of processing.

Micro-chips increased bulk density by 13% over larger chip of same material.

Conifer Hardwood

2-month 12-month micro-chip12-month

Material type Age

Average ash content

SD n(%)

PC2 0.27 0.07 31

12 0.26 0.09 3212-micro 0.25 0.09 30

PH2 1.03 0.24 43

12 0.69 0.18 3312-micro 0.88 0.24 30

UC2 0.64 0.68 45

12 0.43 0.16 3312-micro 0.35 0.32 30

UH2 1.07 0.21 39

12 0.99 0.24 3312-micro 1.18 0.18 30

Slash 2-grinder 1.50 0.04 45

Ash content

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Species significantly influenced ash content.

Significant difference in 2-month PC and UC.

Significant difference in 2- and 12- month PH.

Conifer Hardwood

2-month 12-month micro-chip12-month

Further Discussion

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• The results of this work show the complexity in refining a feedstock to a desired specification.

• Managers should decide which feedstock quality is most important and base their management accordingly.

• The results are limited to the species used in this study. • Further research can be done to tease out the

differences in ash content.

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Hypotheses

1. Sorting stems will facilitate comminution with a chipper leading to greater control of the particle size distribution.

Yes2. Moisture content, particle size, bulk density, and ash

content of chip generated from processed stems would be significantly different from unprocessed stems.

Yes, but only in two cases. Therefore, NO.3. Moisture content, particle size, bulk density, and ash

content of chips generated from aged stems would be significantly different from fresh stems.

Yes, for moisture content, particle size, and bulk density

Conclusions• Through sorting and chipping we were able to improve

feedstock quality compared to ground slash. This may justify the additional cost to sort.

• Additional stem processing does not have a big impact on feedstock quality.

• Allowing material to age can have a significant impact on moisture content, particle size, and bulk density

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Thank you very much

Questions?

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This material is based upon work supported by a grant from the U.S. Department of Energy under the Biomass Research and Development Initiative program: Award Number DE-EE0006297.

Appendix slides

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Boxplots

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Quartiles are insensitive to outliers and preserve information about the center and spread. Consequently, they are preferred over the mean and s.d. for population distributions that are asymmetric or irregularly shaped and for samples with extreme outliers.

Geometric mean

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=

Where: xi = diagonal of screen opening of the ith screen x(i-1) = diagonal of screen opeing in the next screen larger than ith screen = geometric mean length = geometric mean length of particles on the ith screen = (xi * xi-1)1/2

Mi = mass on the ith screen

Particle size

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Fine fractions increased 10 and 7% for PC and UH, respectively.

Could be related to moisture content as it can influence friction from compression and shear forces.

Weak positive correlationr = 0.37 0.10 0.15 0.20 0.25 0.30 0.35 0.40

1015

2025

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Moisture content (%)

Geo

met

ric m

ean

leng

th (m

m)

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Statistical analysisRegression analysis

Y = β0 + β1X1 + β2X2 + β3X3 +β4X1X2+ β5X2X3 + β6X1X3 + β7X1X2X3 + εWhere:

Y = geometric mean particle size, moisture content, bulk density, or ash content

X1 = Species (conifer / hardwood)

X2 = Treatment (processed / unprocessed)

X3 = Age (2-month / 12-month)

+ Interactions

Transformations → 2- and 3-way ANOVA → Tukey HSD multiple comparison test and interaction plots were used.Alpha (p < 0.05)

X3

X1

X2