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“Use of Near Infrared
Spectroscopy for One-Day Analysis of a Wide Variety of Lignocellulosic
Feedstocks”
Presentation for EU BC&E 2014
Hamburg, June 25 2014
Dr. Daniel Hayes
dan@celignis.com
www.celignis.com
www.carbolea.ul.ie
“Oil from Carbohydrates”
“One-day analysis of biomass” www.celignis.com
Carbolea Research Group
• Focused on the non-biological methods for obtaining value from biomass.
Biochar and Soils Rapid Biomass Analysis
Heterogeneous Catalysis Pyrolysis + Gasification
Bio-Oil Upgrading Chemical Conversion
“One-day analysis of biomass” www.celignis.com
DIBANET…
Chemical hydrolysis for biofuel and platform chemical production
www.dibanet.org
Important Chemical Properties
Hydrolysis process (e.g. enzymatic hydrolysis). C6 Sugars: Glucose, Galactose, Mannose C5 Sugars: Arabinose, Xylose Lignin content (acid soluble and insoluble) Extractives Ash.
Thermal (e.g. combustion) and thermochemical (e.g. pyrolysis and gasification). Elemental analysis (C, H, N, O, S) Heating value Ash Anions and cations.
“One-day analysis of biomass” www.celignis.com
Time for Conventional Analysis
“One-day analysis of biomass” www.celignis.com
Chop sample ~ 10 mins
Dry SampleSample as Collected
Milling + sieving~ 1 hour
Dry Sample of Appropriate Particle Size
Extractives Removal~ 3 days
Extractives-free sample
0 2 4 6 8 10 12 14 160
50
100
150
200
Hydrolysis and hydrolysate
analysis~ 3 days
Completed Lignocellulosic Analysis
~ 10 days !!!!
Air Drying ~ 3+ days
Wet Chopped Sample
Interaction of NIR Light with Biomass
“One-day analysis of biomass” www.celignis.com
(a) Specular Reflectance(b) Diffuse Reflectance(c) Absorption(d) Transmittance(e) Refraction(f) Scattering
NIR Analysis• FOSS XDS Monochromator.• 400-2500nm (visible and NIR).• Moving sample transport for
inhomogeneous/wet samples.
“One-day analysis of biomass” www.celignis.com
Sample Preparation Process
“One-day analysis of biomass” www.celignis.com
Sample Collected
Wet & Unground
Dry & Unground
Dry & Ground
Scans of One Sample
“One-day analysis of biomass” www.celignis.com
254-WU-A 254-DU-A 254-DG-A 254-DS-A 254-DT-A 254-DF-A
Wavelength (nm)
400 553 708 863 10391238143716361836203522342433
Abso
rbance
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Development of NIR Models (1)
• Target: Predict composition using NIR spectra.• Consider a spectrum as a vector with a dimension equal to
the number of variables (wavelengths).
• xi = (A400 A400.5 A401 …. A2499.5 A2500)• 4200 datapoints• A matrix can be built from the spectra of all samples in the
model
• X = A1,400 A1,400.5 A1,401 …. A1,2499.5 A1,2500
A2,400 A2,400.5 A2,401 …. A2,2499.5 A2,2500
…
An,400 An,400.5 An,401 …. An,2499.5 An,2500
“One-day analysis of biomass” www.celignis.com
Development of NIR Models (2)
• Celignis models are based on Partial Least Squares (PLS1) regression that determines latent variables that consider the variation in X, Y (compositional data) and correlation between X and Y.
• Reduces dimensionality of data (e.g. 4200 variables reduced to 14 factors).
• The loadings for each factor describe its relation to the manifest variables (which ones are important).
• Each sample will have a score for each factor, describing its location on the new coordinate axes of the reduced dimension subspace.
• Models are built on a set of samples (calibration set) and then tested on an independent set of samples (validation set).
“One-day analysis of biomass” www.celignis.com
13 Constituents PredictedLignocellulosic
SugarsLignin and Extractives
Ash
Total Sugars Klason Lignin Total Ash
Glucose Acid Soluble Lignin Acid Insoluble Ash
Xylose Ethanol-Soluble Extractives
Acid Insoluble Residue (KL + AIA)
Mannose
Arabinose
Galactose
Rhamnose
Types of Samples Included
“One-day analysis of biomass” www.celignis.com
Energy Crops Agricultural Residues
Municipal Wastes
Miscanthus Straws Paper/cardboard
Other grasses Animal manures Green wastes
Hardwoods Sugarcane bagasse Black/brown bin waste
Softwoods Forestry residues Composts
Pretreated biomass Mushroom compost
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.
“One-day analysis of biomass” www.celignis.com
Results for Prediction Set
“One-day analysis of biomass” www.celignis.com
Glucan Xylan Klason Lignin
Min: 3.77 0.59 0.83Max: 84.82 27.59 72.21
R2: 0.972 0.978 0.972RMSEP: 2.01 1.14 1.83
RER: 36.65 23.00 31.34
Regression Plot – Total Sugars
“One-day analysis of biomass” www.celignis.com
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Reference (%)
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Reference (%)
Regression Plot – Klason Lignin
“One-day analysis of biomass” www.celignis.com
0 10 20 30 40 50 600
10
20
30
40
50
60
Reference (%)
NIR
- Pr
edic
ted
(%)
0 10 20 30 40 50 600
10
20
30
40
50
60
Reference (%)
NIR
- Pr
edic
ted
(%)
Results for Prediction Set
“One-day analysis of biomass” www.celignis.com
Mannose Arabinose Galactose RhamnoseMin: 0.00 0.04 0.05 0.02Max: 14.04 6.21 4.95 1.56
R2: 0.956 0.903 0.783 0.861RMSEP: 0.61 0.35 0.38 0.10
RER: 23.12 12.23 8.60 14.53
Results for Prediction Set
“One-day analysis of biomass” www.celignis.com
Acid Soluble Lignin
Extractives Ash Acid Insoluble Residue
Min: 0.53 0.00 0.17 0.12Max: 7.74 33.24 59.36 72.64
R2: 0.899 0.882 0.914 0.969RMSEP: 0.34 1.73 2.48 1.98
RER: 14.89 18.80 15.32 31.86
Feedstock-Specific Models
“One-day analysis of biomass” www.celignis.com
Feedstock StatusMiscanthus (Wet & Dry) Paper PublishedPeat (Wet & Dry) Paper SubmittedPaper/Cardboard July 2014Straw AugustSugarcane Bagasse (Wet & Dry) SeptemberPre-treated Biomass OctoberComposts NovemberWood December
Miscanthus Models• Approx. 115 Miscanthus plants sampled. • These plants were separated according to the fractions, resulting
in a total of around 700 samples.• “I” = Internodes• “N” = Nodes (each plant also sampled by the metre).• “K” = Live leaves (>60% green by visual inspection)• “M” = Live Sheaths• “F” = Dead leaves (<60% green by visual inspection)• “H” = Dead sheaths• “FL” = Flowers• “WP” = Whole plant (sometimes separate metre sections are
collected)• All samples analysed via NIRS, selected samples via processed
to DS/DF state and analysed via wet-chemical methods.
“One-day analysis of biomass” www.celignis.com
Models for Miscanthus
“One-day analysis of biomass” www.celignis.com
DS WU DS WU DS WUCross Validation
CV - 0.966 0.955 0.957 0.861 0.957 0.917RMSECV 0.914 1.082 0.426 0.776 0.578 0.806RER (CV) 22.91 19.35 27.97 15.37 19.97 14.32
Independent Validation0.968 0.931 0.948 0.929 0.975 0.958
RMSEP 0.862 1.266 0.457 0.532 0.481 0.598RER 23.81 16.20 20.05 17.05 18.49 15.75
Glucan Xylan Klason Lignin
Models for Miscanthus
“One-day analysis of biomass” www.celignis.com
25 35 4525
30
35
40
45
50CalibrationLinear (Calibration)ValidationLinear (Validation)
Reference Glucose
Pred
icte
d (D
S M
odel
) (%
)
25 30 35 40 45 5025
30
35
40
45
50CalibrationLinear (Calibration)ValidationLinear (Validation)
Reference Glucose
Pred
icte
d (W
U M
odel
) (%
)
Time for Conventional Analysis
Chop sample ~ 10 mins
Dry SampleSample as Collected
Milling + sieving~ 1 hour
Dry Sample of Appropriate Particle Size
Extractives Removal~ 3 days
Extractives-free sample
0 2 4 6 8 10 12 14 160
50
100
150
200
Hydrolysis and hydrolysate
analysis~ 3 days
Completed Lignocellulosic Analysis
~ 10 days !!!!
Air Drying ~ 3+ days
Wet Chopped Sample
“One-day analysis of biomass” www.celignis.com
Discriminant Analysis
• Plant fraction: Stem section vs. leaf section.• Plant fraction (detailed): internode; node;
live leaf blade; dead leaf blade; dead leaf sheath.
• Harvest period: Early (Oct-Dec) vs. Late (Mar-Apr).
• Stand age: 1 year vs. over one year.• Variety: Miscanthus x giganteus vs. other
varieties
“One-day analysis of biomass” www.celignis.com
Launch of Celignis!
• June 23rd at EU BC&E.• Based on personal experience 10 yrs.• Work on NIR models ~ 20 person-years.• Provision of characterisation services for
biomass (lignocellulosic and thermal properties).
• NIR data provided within 24 hours of receiving a sample.
“One-day analysis of biomass” www.celignis.com
Remove Risk from NIR Analysis…
• NIR analysis carried out without payment.• Figures for Deviation in Prediction for the
Total Sugars and KL contents provided for free.
• Can then decide whether to pay for NIR data, wet-chemical analysis, or nothing!
• All operations carried out online with interactive database…
“One-day analysis of biomass” www.celignis.com
“One-day analysis of biomass” www.celignis.com
Future Plans
• Further improve models with more samples.
• Expand range of analytes predicted using NIR (e.g. thermochemical properties).
• Open to collaboration in future Horizon 2020/JTI research projects for models for new feedstocks or analytes.
“One-day analysis of biomass” www.celignis.com
Acknowledgements
• This work was part funded by:• DIBANET project, funded by the European
Community’s Seventh Framework Programme (FP7/2007–2013), grant agreement #227248-2.
• Irish Department of Agriculture Fisheries and Food• Irish EPA.• Irish Research Council for Science Engineering and
Technology (IRCSET).
• Assistance provided by colleagues at University of Limerick and Carbolea.
“One-day analysis of biomass” www.celignis.com
Thank You!
www.celignis.com
dan@celignis.com
(353) 89 455 5582
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