“Use of Near Infrared Spectroscopy for One-Day Analysis of a Wide Variety of Lignocellulosic...

“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

[email protected]

www.celignis.com

mailto:[email protected]

http://www.celignis.com/

www.carbolea.ul.ie

“Oil from Carbohydrates”

“One-day analysis of biomass” www.celignis.com

http://www.carbolea.ul.ie/

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


DIBANET…

Chemical hydrolysis for biofuel and platform chemical production

www.dibanet.org

http://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.


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

Interaction of NIR Light with Biomass


(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.


Sample Preparation Process


Sample Collected

Wet & Unground

Dry & Unground

Dry & Ground

Scans of One Sample


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


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).


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


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.


Results for Prediction Set


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


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


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

(%)



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



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


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.


Models for Miscanthus


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


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


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


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.


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…


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.


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.


Thank You!

www.celignis.com

[email protected]

(353) 89 455 5582

http://www.celignis.com/

mailto:[email protected]

“Use of Near Infrared Spectroscopy for One-Day Analysis of a Wide Variety of Lignocellulosic...

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