Dr. Daniel Hayes, University of Limerick, Ireland, Dibanet Networking event, 31 October 2013, CERTH, Thessaloniki, Greece. Further info and videos: http://www.dibanet.org/networking_day_greece.php

1. Feedstock Evaluation and
Development of Rapid
Analytical Methods
Daniel Hayes
DIBANET Networking Session
Thessalonki
31/10/12

2. WP2

Task 2.1 – Appropriate Feedstock
Selection and Analysis
−
Identify all possible feedstocks for biorefining
in Europe and Latin America.
−
Select those most suitable for the DIBANET
process.
−
Analyse these feedstocks with wet-chemical
methods for their lignocellulosic constituents.
−
UL is responsible for the evaluation of
European feedstocks, CTC for sugarcane
residues, UNICAMP for other LA biomass.

3. Task 2.1
Europe (Ireland) Latin America (Brazil)
Miscanthus Sugarcane bagasse
Pretreated Miscanthus Sugarcane trash
Cereal straws Coffee husks
Waste paper Banana residues
Compost Coconut residues
Short rotation coppices Acai residues
Animal manures Rice husks
Reed canary grass Sawdust
Green wastes Soya peel
Bamboo
Grass

4. Feedstock Chemical
Evaluation

Important chemical characteristics:

C6 Sugars: Glucose, Galactose, Mannose

C5 Sugars: Arabinose, Xylose

Lignin content (acid soluble and insoluble)

Extractives

Ash.

Elemental analysis.

5. Task 2.1 Data Obtained (Wet Chemistry)
Partner Feedstock Lignin Extractives Ash Sugars Elemental
UL Miscanthus 221 257 243 211 61
UL Pret. Misc. 47 47 47 47 0
UL Straws 33 44 46 26 15
UL Papers 14 19 13 14 12
UL Others 41 53 46 24 25
CTC Bagasse 80 68 73 60 0
CTC Trash 37 37 37 37 0
UNIC. Coffee husks 42 102 102 42 0
UNIC. Banana 81 104 104 10 0
UNIC. Coconut 30 30 30 30 0
UNIC. Other 7 7 7 7 0
TOTALS
UL 356 420 395 322 113
CTC 96 105 110 32 0
UNIC. 160 243 243 89 0
TOTAL 612 768 748 443 113

6. Initial Evaluation (UL) [1]

DIBANET focuses on Miscanthus and waste samples as
target feedstocks.

Generally waste cardboard/paper samples have the highest
cellulose/carb contents and so offer the potential for high
LvA yields, this however implies also that the residue for
subsequent gasification will be low.

Animal Slurries: There is a significant variation between the
composition of animal slurries (pig, cattle) but these can
offer reasonably attractive carbohydrate contents (up to
50% of dry mass on an extractives free basis).

But moisture content is extremely high, hence these
feedstocks could probably only act as “fillers” in a feedstock
mix, but there would still be transport problems.

7. Initial Evaluation (UL) [2]

Spent Mushroom Compost (SMC) is a significant waste
resource in Ireland – secondary data suggest it has
potential for biorefining purposes but our data suggest its
carbohydrate content is too low (max 20%, compared to
the pre-compost mix of ~50% carbohydrate).

Forestry residues (wood and leaves) are suitable for
utilisation in the DIBANET process but this will require a
significant investment in the infrastructure required for
their collection and transport.

Sawmill residues are also of value for biorefining but there
are other current end-uses for these resources.

8. Initial Evaluation (UL) [3]

Municipal wastes are predominately composed of waste
papers, food waste, garden waste, and waste woods

Waste food does not contain sufficient lignocellulosic sugars
to warrant its utilisation in the DIBANET process.

Garden/green waste contains various types of materials
such as grasses, leaves, twigs, and branches.

Only the more woody materials have lignocellulosic
compositions suitable for the DIBANET process.

Garden/green waste taken as a composite (i.e. a sample
from a compost pile) does not have a favourable proportion
of wood to foliage. Hence, the sourcing of green waste for
biorefineries needs to be specifically tailored to high-
carbohydrate materials. This may necessitate for separate
collection schemes or for processes to sorting the woody
material from the total green-waste resource.

9. Conclusion (UL)

Focus on Miscanthus as a DIBANET
feedstock.

Also investigate straws and waste
papers.

10. Initial Evaluation
(UNICAMP)

A total of 10 different feedstocks
were investigated and analysed
(wet-chemical methods):
−
Soy peel, bamboo, banana residues, rice
husks, sawdust, acai seeds, elephant
grass, coconut residues, coffee residues.

11. CHEMICAL COMPOSITION OF ALL
BIOMASSES ANALYSED BY UNICAMP
(%)
TOTAL TOTAL TOTAL
BIOMASS ARABINOSE GALACTOSE RHAMNO GLUCOSE XYLOSE MANOSE SUGARS LIGNIN EXTRACTIVE ASH COMP.
SOY BEAN 4.64 3.13 0.92 35.05 9.85 4.31 57.90 7.58 6.81 4.14 77.00
RICE husks 1.70 0.83 0.13 36.17 16.65 0.49 55.98 23.90 2.32 12.5 95
SADWUST 0.26 1.23 0.25 38.79 9.72 0.35 50.60 32.87 8.12 0.63 92
BAMBOO 0.81 0.32 0.06 44.65 14.78 0.07 61.57 17.64 12.62 2.81 95
GRASS 3.56 1.22 0.10 27.52 16.12 0.24 48.84 15.61 11.54 12.66 89
COCONUT 1.79 0.71 0.30 32.41 14.37 0.35 49.94 35.87 1.41 2.63 90
AÇAÍ seed 0.69 1.43 0.17 8.66 3.18 53.59 67.71 17.26 9.5 0.46 95
BANANA Stalk 2.89 1.18 0.27 26.83 6.94 1.46 39.56 10.68 22.85 10.33 84
BANANA Stem 2.37 0.72 0.16 36.32 5.36 0.61 45.53 8.38 25.15 10.30 90
COFFEE husks 1.62 1.54 0.51 35.33 21.89 1.68 62.55 24.46 4.21 4.00 95
11

12. Conclusion (UNICAMP)

Three feedstocks were selected for
more detailed analysis and
evaluation (considering levels of
supply, environmental factors, price,
and composition)
−
Banana residues.
−
Coffee Residues.
−
Coconut Residues.

13. Conclusion
(Early Evaluation)

There are a number of viable
feedstocks for utilisation in DIBANET.

However, secondary analytical data
relevant to our needs are severely
lacking.

There is a clear need for primary
wet-chemical analysis in DIBANET.

This will also be of value to others in
the biorefining industry.

14. Task 2.1 – Detailed
Evaluation

Focused on obtaining primary
analytical data in the laboratories of
UL, CTC, UNICAMP.

Analytical methodologies (written at
UL) were kept consistent for all
partners.

Strict quality thresholds placed upon
results.

15. Example:
DIBANET WP2 Method

16. Example:
DIBANET WP2 Datasheet

17. D2.2 - Database

A database containing compositional data
of samples analysed by DIBANET partners
can be downloaded from the DIABNET
website.

It contains the results from the wet-
chemical and/or spectroscopic analysis of
1281 samples from Europe and Latin
America.

20. D2.2 – Report: Miscanthus (1)

Miscanthus is a productive energy crop that does not
require significant time or expense for maintenance after
plantation. It can be productive for up to 20 years without
the need for replantation.

The lignocellulosic composition of the crop once at full
production is highly attractive for use in biorefining
technologies.

The stem fractions of the plant, when processed in
biorefining technologies, will provide higher
chemical/biofuel yields than the leaf fractions. This is due
to their higher total sugars contents and increased heating
values. The lower acid soluble lignin, protein, and
extractives contents in the stem sections are also likely to
present fewer complications in many conversion processes
(e.g. acid and enzymatic hydrolysis).

21. D2.2 – Report: Miscanthus (2)

The total sugars content of the crop in its first year of
production is significantly less than in subsequent years.
The total biomass yield of the crop is also less in this first
year.

Given this situation, the commercial harvest of the first
year growth of Miscanthus and the subsequent transport
of this crop to the biorefinery is not economical and is not
advised.

Instead, the crop should be cropped after the first year of
growth with the biomass either left on the land for soil
conditioning or used for other local uses.

22. D2.2 – Report: Miscanthus (3)

The harvest window for Miscanthus is between
October and April.

Between October and early December a relatively
small amount of standing biomass is lost as leaf
fall. This period is termed the “Early Harvest”.

Between mid-December and the end of February
there is a rapid loss of leaves from the plant.

By March the only remaining leaf materials tend
to be the sheaths. These are lost from the plant
at a much slower rate than the leaf blades.
Hence, the loss of standing biomass is much less
after March. This period is termed the “Late
Harvest”.

23. D2.2 – Report: Miscanthus (4)

The best time for harvesting Miscanthus will be
dependent on how the crop will be processed.

The dry biomass yield associated with an “Early”
Harvest can be approximately 30% more than
that associated with a “Late” Harvest.

If the maximal biomass yield is the primary
desire the crop should be harvested in the “Early”
period. The crop will have a significant amount of
moisture (approximately 50% on a wet basis) at
this time.

An “Early” harvest will not provide a feedstock
suitable for most thermochemical biorefining
technologies (e.g. pyrolysis, gasification) since
these will require lower moisture contents.

24. D2.2 – Report: Miscanthus (5)

An “Early” harvest is feasible for most hydrolysis
biorefining technologies, e.g. DIBANET, providing
they do not use pretreatment method that
require dry feedstock (e.g. ionic liquids).

An “Early” harvest will remove leaves that would,
in a “Late” harvest, fall to the field. The amount
of carbon and nitrogen provided to the soil would
therefore be reduced.

The removal of leaf material from the land can be
addressed with increased fertiliser input.

25. D2.2 – Report: Miscanthus (6)

There are significant changes in lignocellulosic
composition of the standing plant over the
harvest window. The most important of these are
an increase in the glucan and Klason lignin
content.

On a dry mass per-tonne basis the biomass
collected during the “Late” harvest period is of
more value for biorefining processes (hydrolysis
and thermochemical) than the biomass collected
in the “Early” harvested period.

If a feedstock payment scheme at a biorefinery,
using the hydrolysis platform, is based on total
sugars content then the Late harvest crop would
be worth approximately 10% more per tonne
than the Early harvest crop.

26. Loss of Leaves over
Harvest Window

28. Levulinic Acid Yield per Ha

31. Database

32. Sugarcane Residues

33. Production in Brazil

34. Sugarcane in Brazil

35. D2.2:
Sugarcane
Residues
(Bagasse)

36. D2.2:
Sugarcane
Residues
(Bagasse)

37. D2.2: Sugarcane Residues

Both sugarcane bagasse and sugarcane trash have
sufficient amounts of lignocellulosic sugars to justify their
processing in hydrolysis biorefining technologies.

The compositions of the bagasse samples that were
analysed tended to be more varied than those of the trash
samples analysed.

Ash, in particular, can vary significantly in bagasse
samples. There also seems to be a tendency for the ash
contents of bagasse to be higher in some mills.

Hence, it is recommended that careful determinations and
observations, over a period of time, of the ash contents,
associated with the harvesting/milling process of any mill
that is being considered for a biorefining scheme, be
carried out.

38. D2.2: Sugarcane Residues (2)

Using the average lignocellulosic compositions of sugarcane
bagasse and sugarcane trash determined in DIBANET
analyses along with the estimated arisings (165m dry
tonnes of bagasse and 128m dry tonnes of straw), the total
potential yields possible from processing these feedstocks
in representative biorefining technologies were calculated.

39. 102 SAMPLES OF COFFEE - INCLUDED:
Leaves
Different kinds:
Husks
- etiopia
- árabica
-robusta
- mundo novo
Grain
Ground coffee
39

40. SOME HISTOGRAMS FOR COFFEE SAMPLES
40

41. 102 SAMPLES OF BANANA - INCLUDED:
Leaves
Steam
Rhizome
Stalk
Husks 41

42. SOME HISTOGRAMS FOR BANANA SAMPLES
42

43. 30 SAMPLES OF COCONUT - INCLUDED:
Husks
Fibers
43

44. SOME HISTOGRAMS FOR COCONUT SAMPLES
44

45. SOME HISTOGRAMS FOR COCONUT SAMPLES
45

46. Task 2.2

“Development of Lab-Based NIR Calibration
Equations”

“Equations will be targeted for at least 5
feedstocks, each, from Europe and Latin America.”
Europe Latin America
UL CTC UNICAMP
1. Miscanthus 1. Bagasse
2. Pret. Misc. 2. Trash
3. Straws 3. Coffee rds.
4. Papers. 4. Banana rds.
5. Global 5. Coconut rds.

47. Time for Conventional
Analysis
Sample as Collected Wet Chopped Sample Dry
Sample
Chop sample Air Drying
~ 10 mins ~ 3+
days
Extractives-free Milling +
sample sieving
15
~ 1 hour
10
5
Hydrolysis and Extractives
0
0 2 4 6 8 10 12 14 16 hydrolysate Removal
Completed analysis ~ 3 days
Lignocellulosic ~ 3 days Dry
47 Sample of
Analysis Appropriate Particle

48. Sample Preparation

49. Scans of One Sample

50. WU Spectra

51. Important Regression
Statistics

R2 for the validation set.

RMSEP.

RER (range error ratio) =
Range/SEP.

RER > 15 model is good for
quantification.

RER 10-15, screening control.

RER 5-10, rough sample screening.

52. UL Misc. Glucose Models

53. Glucose DS and WU Models
50 50
Predicted GLU_SRS (WU Model) (%)
Predicted GLU_SRS (DT Model) (%)
45 45
40 40
Linear () Linear ()
35 35
Linear () Linear ()
30 30
25 25
25 30 35 40 45 50 25 30 35 40 45 50
Reference GLU_SRS Reference GLU_SRS
(DS Samples of All Varieties) (%) (DS Samples of All Varieties) (%)

54. UL Misc. Xylose Models

55. UL KL Models (Gig)

56. Miscanthus Models - Summary
DS DU WU RMSEPWU RERWU
Glucose A B A 1.26% 16.20
Xylose A A A 0.53% 17.05
Rhamnose B B C 0.06% 8.38
Mannose C C C 0.07% 5.52
Arabinose B B B 0.27% 11.04
Galactose C C C 0.12% 7.97
Total Sugars A B A 1.21% 18.59
KL A A B 0.93% 10.86
ASL A B B 0.42% 10.62
Ash A B B 0.93% 10.66
EXTR_PD B B C 1.38% 7.83
Nitrogen A C C 0.28% 6.84
Moisture - - A 2.52% 23.80

58. Pretreated Miscanthus
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Click to edit Master
text styles
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Second level
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Third level
•
Fourth level
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Fifth level

59. UNICAMP Lignin Models-DS
Sample Pre- Matrix R2 RMSE
Y Range LV Outlier RE
Set1 Treatment2 Cal Val SEC SEP
B+C 2d+OSC NIR 89x4200 2 3 0.96 0.95 1.193 1.278 6.90
TL
B 2d+OSC NIR 64x2800 3 - 0.96 0.93 0.883 1.142 7.80
42X2800
C 1d(25) NIR 6 4 0.88 0.85 1.62 1.80 7.00
30X2800
COC. 2d(1) NIR 4 2 0.91 0.94 1.03 0.92 4.00
134X2800
B+C+COC. 2d(25) NIR 7 3 0.94 0.91 1.55 1.96 10.00
89x4200
B+C 2d+OSC Full 3 - 0.93 0.92 1.379 1.489 10.80
KL
64x2800
B 2d+OSC NIR 3 1 0.96 0.92 0.779 1.186 11.20
42X2800
C 1d(25) NIR 5 5 0.90 0.91 1.28 1.27 6.00
30X2800
COC. 2d(1) NIR 4 2 0.90 0.91 1.00 0.96 4.00
134X2800
B+C+COC 2d(25) NIR 5 5 0.94 0.90 1.531 1.988 12.50
B+C: pooled datset (banana a and coffee); B: banana alone;C: coffee; COC: coconut; B+C+Coc.: banana+coffee+coconut.
3: The asterisks indicates statistically significant difference (* p < 0.05) between RMSEP and RMSEC.

60. UNICAMP Lignin Models-DU
Sample Pre- Matrix R2 RMSE
Y Range LV Outlier RE
Set1 Treatment2 Cal Val SEC SEP
55X2800
B SNV+DT(2) NIR 9 3 0.88 0.86 1.513 1.872 14.00
TL
60x2800
C SNV+1d(1) NIR 7 5 0.97 0.90 0.700* 1.831* 8.00
30x2800
COC. 2d(1) NIR 5 4 0.97 0.97 0.55 0.56 2.00
55X2800
B SNV+DT(2) NIR 9 4 0.86 0.84 1.480 1.811 16.00
KL
60x2800
C SNV+1d(1) NIR 7 5 0.97 0.90 0.714 1.693 9.00
30x2800
COC. 2d(1) NIR 5 4 0.98 0.96 0.67 0.47 3.00
55X2800
ASL B SNV+1d NIR 8 5 0.97 0.96 0.170 0.181 6.00
60x2800
C SNV+1d(1) NIR 4 5 0.80 0.73 0.085 0.105 2.00
30x2800
COC. 2d(1) NIR 3 2 0.89 0.85 0.118 0.121 8.00
52x2800
AIR B SNV+1d(1) NIR 9 2 0.95 0.94 1.039 1.227 10.00
60x2800
C SNV+1d(1) NIR 7 5 0.97 0.90 0.743* 1.835* 9.00
30x2800
COC. 2d(1) NIR 5 4 0.96 0.91 0.65 0.91 4.00

61. UNICAMP Lignin Models-WU
Sample Pre- Matrix R2 RMSE
Y Range LV Outlier RE
Set1 Treatment2 Cal Val SEC SEP
B 1d(7) NIR 62x4200 8 5 0.89 0.74 1.452 1.916 15.00
TL
64x2800
C 2d(1) NIR 6 5 0.95 0.80 0.903* 2.450* 11.00
30X2800
COC. 2d(25) NIR 5 2 0.96 0.84 0.858* 1.939* 8.00
62x2800
KL B 1d(7) NIR 7 4 0.85 0.76 1.590 1.772 17.00
42X2800
C 2d(1) NIR 6 5 0.96 0.90 0.790 1.779 9.00
30X2800
COC. 2d(25) NIR 4 2 0.81 0.80 1.842 1.973 9.00
62x2800
ASL B 1d(7) NIR 7 3 0.82 0.81 0.362 0.424 16.00
42X2800
C SNV+1d NIR 5 5 0.80 0.76 0.440 0.591 16.00
30X2800
COC. 2d(25) NIR 6 3 0.92 0.84 0.082* 0.136* 9.00
62x2800
AIR B 1d(7) NIR 8 5 0.89 0.75 1.470 1.788 16.00
42X2800
C 2d(1) NIR 7 5 0.98 0.89 0.587* 1.914* 10.00
30X2800
COC. 2d(25) NIR 7 3 0.93 0.92 1.13 1.30 5.50

62. UNICAMP Ash and Extractives
Models-DS
Sample Pre- Matrix R2 RMSE
Y Range LV Outlier RE
Set1 Treatment2 Cal Val SEC SEP
205x2800
B+C 2d+SNV NIR 7 2 0.83 0.80 0.526 0.556 22.00
ASH
103x4200
B 2d Full 7 2 0.76 0.70 0.559 0.711 18.00
102X2800
C 1d(25) NIR 7 5 0.73 0.60 0.22* 0.39* 20.00
30X2800
COC. 2d(1) NIR 4 1 0.86 0.86 0.34 0.40 20.00
233X2800
B+C+COC. 2d(25) NIR 6 5 0.80 0.75 0.587 0.619 22.00
205x2800
B+C 2d+SNV NIR 7 1 0.81 0.79 0.947 1.063 11.00
Extr.
103x2800
B 2d NIR 6 - 0.86 0.86 0.794 0.985 12.00
102X2800
C 1d(25) NIR 6 5 0.80 0.75 1.08 1.22 12.00
30X2800
COC. 2d(1) NIR 2 3 0.84 0.84 0.88 0.84 8.00
233X2800
B+C+COC. 2d(25) NIR 7 4 0.82 0.81 1.04 1.25 13.00

63. UNICAMP Sugar Models-DS
Sample Pre- Matrix Outli R2 RMSE
Y Range LV RE
Set1 Treatment2 er Cal Val SEC SEP
GLUC. B
C 2d(25) NIR 41X2800 7 2 0.78 0.70 1.99 2.22 14.00
COC. SNV+1d(1) NIR 30X2800 4 2 0.92 0.82 1.13 1.25 5.00
XYL. B
C EMSC NIR 41X2800 7 5 0.95 0.80 0.723 0.780 16.00
COC. SNV+1d(1) NIR 30X2800 5 2 0.88 0.82 0.94 1.44 11.00
GALA. B
C 2d(25) NIR 41X2800 5 5 0.87 0.83 0.443 0.476 15.00
COC. 2d(1) NIR 30X2800 5 2 0.91 0.85 0.06* 0.14* 10.00
RHAM B
C SNV+1d(1) NIR 41X2800 7 2 0.94 0.86 0.053 0.055 8.00
COC. SNV+1d(1) NIR 30X2800 7 3 0.79 0.72 0.01 0.04 12.00
ARAB. B
41X2800
C 2d(25) NIR 5 5 0.87 0.86 0.479 0.582 12.00
30X2800
COC. 2d(1) NIR 3 3 0.90 0.83 0.15 0.17 7.00
MAN. B
41X2800
C SNV+1d(1) NIR 7 4 0.89 0.75 1.574 1.676 22.00
30X2800
COC. SNV+1d(1) NIR 6 3 0.52 0.53 0.16 0.10 16.00
TS B
C EMSC NIR 41X2800 7 5 0.75 0.72 4.40 4.06 13.00
30X2800
COC. SNV+1d(1) NIR 7 2 0.95 0.94 1.41 2.05 5.50

64. UNICAMP Sugar Models-WU
Sample Pre- Matrix Outli R2 RMSE
Y Range LV RE
Set1 Treatment2 er Cal Val SEC SEP
GLUC. B
C SNV+DT NIR 41X2800 9 3 0.81 0.77 1.399 1.547 10.00
30X2800
COC. 1d(1) NIR 5 3 0.92 0.88 1.030 1.768 7.00
XYL. B
C EMSC NIR 41X2800
COC. SNV+1d(1) NIR 30X2800 5 3 0.90 0.85 0.80* 1.34* 12.00
GALA. B
C 2d(25) NIR 41X2800 7 3 0.91 0.88 0.192 0.294 9.00
COC. 2d(25) NIR 30X2800 6 4 0.95 0.80 0.06* 0.12* 11.00
RHAM B
C 2d(25) NIR 41X2800 6 2 0.77 0.70 0.054 0.106 6.50
COC. 2d(1) NIR 30X2800 1 1 0.91 0.60 0.014 0.015 5.00
ARAB B
C 2d(25) NIR 41X2800 4 4 0.73 0.70 0.589 0.971 20.00
COC. 2d(1) NIR 30X2800 1 1 0.92 0.71 0.12* 0.31* 11.50
MAN. B
C 2d(25) NIR 41X2800 4 4 0.72 0.71 0.370 0.476 25.00
COC. 2d(1) NIR 30X2800 3 4 0.98 0.77 0.031* 0.103* 19.00
TS B
C 2d(25) NIR 41X2800 6 4 0.82 0.76 1.975* 3.085* 9.00
COC. SNV+dt(2) NIR 30X2800 4 2 0.96 0.90 1.40 2.52 5.00

65. Banana Residues

66. Coffee Residues

67. Coconut Residues

68. Banana+ Coffee + Coconut
(pooled set)

69. Sugarcane Trash (KL)
DS (ALL) WU (ALL)
R2 = 0.90 R2 = 0.87
RMSECV = 0.30% RMSECV = 0.35%

70. Sugarcane Trash (Extractives)
DS (ALL) WU (ALL)
R2 = 0.87 R2 = 0.85
RMSECV = 0.63% RMSECV = 0.69%

71. Sugarcane Trash (Ash)
DS (ALL) WU (ALL)
R2 = 0.84 R2 = 0.88
RMSECV = 0.50% RMSECV = 0.44%

72. Sugarcane Bagasse (KL)
WU (ALL) WU (Low Ash)

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Fifth level
R2 = 0.72 R2 = 0.80
RMSECV = 0.50% RMSECV = 0.46%

73. Ash Variability According to Mill

74. Task 2.2: Spectra Collected
Partner DS DG DU WU TOTAL
UL
Miscanthus 562 492 759 1,884 3,697
Pret. Misc. 94 - - - 94
Straw 78 78 117 - 273
Papers 60 60 90 - 210
Global (excl. above) 310 - - - 310
CTC
Bagasse 404 404 606 606 2,020
Bagasse (online system) - - 249 249 498
Trash 74 74 111 111 370
UNICAMP
Banana Residues 206 216 186 186 794
Coffee Residues 204 243 198 198 843
Coconut Residues 60 90 90 90 330
TOTAL 2,052 1,657 2,406 3,324 9,439

75. D2.3 - BACI
 (1) Wet Chemical Analysis:
 Sugars
 Lignin (Klason and acid soluble)
 Ash
 Extractives (ethanol and water)
 Elemental
 More planned in mid-term.
− Accuracy of DIBANET methods
has been identified as superior
to other competing companies
and the literature.
− Company will offer guarantees
on precision of analysis.

76. D2.3 - BACI

(2) NIR Analysis.

77. D2.3 – Markets for
Company
1. Biorefining Companies
−
Biorefining database has been prepared.
−
BACI has identified 115 companies that are
developing technologies and facilities for the
commercial production of second-generation
biofuels from lignocellulosic biomass.
−
Also numerous other companies that invest in
biorefining projects, and examples include
BIOeCON, BP, Cargill, Chevron, Honda,
Khosla Ventures, Mitsubishi, Petrobras, Shell,
Total SA, Toyota, and UOP.

78. D2.3 Markets for
Company

79. D2.3: Markets for BACI
2. Farmers and consumers of energy
crops (particularly Miscanthus)
3. Energy crop breeders.
4. Waste service providers and waste
producers.
5. Scientific researchers.

80. D2.3: Business Plan
Objectives

Secure the Irish market for the analysis of lignocellulosic
materials in the short-term.

To obtain the necessary licenses to allow for the
importation of biomass samples from outside the country.

To utilise the equipment and laboratory space of UL in the
short to mid-term.

Continue to improve NIR models.

Continue and strengthen EU/LA ties: Develop a
working relationship with DIBANET partners CTC and
UNICAMP allowing for their NIR models to be used, under
license, at BACI.

CTC may also form a spin-out company that could offer
DIBANET analytical services.

81. BACI Prices
Constituent(s) Price for 1 Price per Extra Bulk, Microbac ($/sample)
Sample (incl. Sample Round (€/
ref) sample)
Moisture Content This cost is included in the price for the relevant methods in 80
BACI
Sample Preparation 70 70 70 150
Wet-Chemical Analysis
Ethanol-Extractives 220 150 160 150
Water-Extractives 220 150 160 150
Water+Ethanol Extraction 330 200 220 200
Ash 50 50 50 80
Lignocellulosic sugars 515 300 340 550
KL, ASL, AIR, AIA 350 200 230 275 (no AIR, AIA)
Sugars, KL, ASL, AIR, AIA 575 350 395 825 (no AIR,AIA)
NIR Analysis
Wet unchopped (WU Model) 250 - 150 N/A
Wet unchopped (DU Model) 250 - 175 N/A
Wet unchopped (DS Model) 300 - 200 N/A
Wet chopped (WU Model)
200 - 125 N/A
Wet chopped (DU Model) 250 - 150 N/A
Wet chopped (DS Model) 300 - 175 N/A
Dry chopped (DU Model) 200 - 125 N/A
Dry chopped (DS Model) 250 - 175 N/A
Dry Sieved (DS Model) 200 - 125 N/A

82. Task 2.2: Other Outputs (1)

“Discrimination between samples”
−
Have developed NIR models to
differentiate Miscanthus samples on the
basis of:
−
Early/Late Harvest.
−
Variety (giganteus or other).
−
Plant fraction (e.g. stems, internodes, leaves).
−
Stand age (1st year of growth or later).

83. Task 2.2: Other Outputs (2)

“Where available, NIR tools will also
analyse process outputs of WP3”.
−
Chemometric models have been
developed for:
−
Acid hydrolysis residues.
−
Pretreated samples (Miscanthus, bagasse).
−
Analytical hydrolysates (WP2) (UV).
−
Hydrolysates from WP3 reactor (UV).

84. Task 2.2: Reactor Yields
N = 188
R2 (CV) = 0.962

85. Task 2.2: Hydrolysate
Analysis
N = 201
R2 (CV) = 0.955

86. Task 2.3 Transfer of Methods to an Online
NIR Facility
Bagasse
NIR
Analysis
Cane
NIR
Analysis

87. Task 2.3

“Transfer of Methods to an Online
NIR Facility”
−
Objectives:
−
(1) Install online NIR equipment at a Brazilian
sugar-mill.
−
Status: An older NIR model has been
operational at a sugar mill in Quata, Sao Paulo.
Its performance was monitored Nov-Dec 2011.

88. 
Click to edit Master text styles
−
Second level
−
Third level
•
Fourth level
−
Fifth level

94. T2.3

(1) Install online NIR equipment at a Brazilian
sugar-mill.
• Status (Cont’d): Following failure of the Quata system it
was decided that an improved sample presentation method
and a more modern device would be needed. The ProFOSS
system:
−
Diode array detection (no moving parts).
−
Analysis over 1100-1650nm.
−
Capable of operating in industrial environments.
−
Was installed in Aug 2012.

96. 
Click to edit Master text styles
−
Second level
−
Third level
•
Fourth level
−
Fifth level

97. LAB
ONLINE

98. D2.4

“Report on Potential Markets for The FOSS
Systems Following DIBANET Development –
This report will examine under which conditions
the online device (BAS or a variant of it) will be
attractive for biomass analysis and will examine
the market for this in the EU and LA.”

Status: Ongoing. Planned for Month 38, expected
completion Month 42 (due to delays in setting up
and validating the online NIR system).

On advice of FOSS this report will also include
market evaluations for lab-based NIR systems.

E.g. an NIR/DIBANET-model package that would
allow biorefinery operators to characterise
biomass in an at-line (rather than online) basis.

99. Conclusions
1. A large number of samples have been analysed
to high accuracy via wet-chemical methods.
Data available on DIBANET analytical database.
2. The state of the art in NIR analysis of
lignocellulosic feedstocks has been advanced by
allowing accurate predictions for wet
heterogeneous biomass.
3. A spin-out company offering biomass analytical
services is being developed.
4. The use of NIRS as an online analysis tool for
biorefineries is being demonstrated.
5. Strong EU-LA links have been developed and will
be maintained.

100. Thank You!!!
daniel.hayes@ul.ie
www.carbolea.ul.ie