1. “Use of Near Infrared Spectroscopy for the Rapid Low-Cost Analysis of a Wide Variety of Lignocellulosic Feedstocks” 5th International Symposium on Energy from Biomass and Waste Venice, Italy Nov 17-20 2014 Dr. Daniel Hayes dan@celignis.com www.celignis.com

2. www.carbolea.ul.ie www.carbolea.ul.ie “Oil from Carbohydrates” “One-day analysis of biomass” www.celignis.com

3. 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

4. DIBANET… Chemical hydrolysis for biofuel and platform chemical production www.dibanet.org

6. 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

7. 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 Hydrolysis and hydrolysate analysis ~ 3 days Completed Lignocellulosic Analysis ~ 10 days !!!! Air Drying ~ 3+ days Wet Chopped Sample

8. 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

9. 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

10. Sample Preparation Process “One-day analysis of biomass” www.celignis.com Sample Collected Wet & Unground Dry & Unground Dry & Ground

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

12. 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). x i = (A 400 A 400.5 A 401 …. A 2499.5 A 2500 ) 4200 datapoints A matrix can be built from the spectra of all samples in the model X = A 1,400 A 1,400.5 A 1,401 …. A 1,2499.5 A 1,2500 A 2,400 A 2,400.5 A 2,401 …. A 2,2499.5 A 2,2500 … A n,400 A n,400.5 A n,401 …. A n,2499.5 A n,2500 “One-day analysis of biomass” www.celignis.com

13. 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 7 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

14. 13 Constituents Predicted Lignocellulosic Sugars Lignin and Extractives Ash Total SugarsKlason LigninTotal Ash GlucoseAcid Soluble LigninAcid Insoluble Ash XyloseEthanol-Soluble Extractives Acid Insoluble Residue (KL + AIA) Mannose Arabinose Galactose Rhamnose

15. Types of Samples Included “One-day analysis of biomass” www.celignis.com Energy CropsAgricultural Residues Municipal Wastes MiscanthusStrawsPaper/cardboard Other grassesAnimal manuresGreen wastes HardwoodsSugarcane bagasseBlack/brown bin waste SoftwoodsForestry residuesComposts Pretreated biomassMushroom compost

16. Important Regression Statistics R 2 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

17. Results for Prediction Set “One-day analysis of biomass” www.celignis.com GlucanXylanKlason Lignin Min: 3.770.590.83 Max: 84.8227.5972.21 R2:R2: 0.9720.9780.972 RMSEP: 2.011.141.83 RER: 36.6523.0031.34

18. Regression Plot – Total Sugars “One-day analysis of biomass” www.celignis.com

19. Regression Plot – Klason Lignin “One-day analysis of biomass” www.celignis.com

20. Results for Prediction Set “One-day analysis of biomass” www.celignis.com MannoseArabinoseGalactoseRhamnose Min: 0.000.040.050.02 Max: 14.046.214.951.56 R2:R2: 0.9560.9030.7830.861 RMSEP: 0.610.350.380.10 RER: 23.1212.238.6014.53

21. Results for Prediction Set “One-day analysis of biomass” www.celignis.com Acid Soluble Lignin ExtractivesAshAcid Insoluble Residue Min: 0.530.000.170.12 Max: 7.7433.2459.3672.64 R2:R2: 0.8990.8820.9140.969 RMSEP: 0.341.732.481.98 RER: 14.8918.8015.3231.86

22. Feedstock-Specific Models “One-day analysis of biomass” www.celignis.com FeedstockStatus Miscanthus (Wet & Dry)Paper Published Peat (Wet & Dry)Paper Submitted Paper/CardboardConference Paper StrawDecember Sugarcane Bagasse (Wet & Dry)January Pre-treated BiomassFebruary CompostsMarch WoodApril

23. 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

24. Models for Miscanthus “One-day analysis of biomass” www.celignis.com DSWUDSWUDSWU Cross Validation 0.966 0.955 0.9570.8610.9570.917 RMSECV 0.9141.082 0.4260.7760.5780.806 RER (CV) 22.9119.35 27.9715.3719.9714.32 Independent Validation 0.9680.9310.9480.9290.9750.958 RMSEP 0.8621.2660.4570.5320.4810.598 RER 23.8116.2020.0517.0518.4915.75 GlucanXylan Klason Lignin

25. Models for Miscanthus “One-day analysis of biomass” www.celignis.com

26. 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 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

27. 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

28. Waste Papers/Cardboards “One-day analysis of biomass” www.celignis.com

29. Results for Prediction Set “One-day analysis of biomass” www.celignis.com GlucanXylanMannoseKlason Lignin Ash Min: 20.743.460.090.050.25 Max: 84.3716.4912.1626.5052.31 R2:R2: 0.9750.9760.8730.9370.915 RMSEP: 2.9380.5890.9871.9824.872 RER: 20.63919.8239.32815.7769.789

30. Celignis Analytical Launched July 2014. 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

31. 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

33. Future Plans Further improve models with more samples. Develop a local calibration algorithm do develop unique models for each sample to be predicted (only select relevant samples for calibration set). Develop models for thermochemical properties (C/H/N/S, heating value, volatile matter, fixed carbon etc.) using existing sample database (1,700 samples) and new samples. 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

34. 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). Enterprise Ireland. Limerick Local Enterprise Office. Assistance provided by colleagues at University of Limerick and Carbolea. “One-day analysis of biomass” www.celignis.com

35. Website: www.celignis.comwww.celignis.com “One-day analysis of biomass” www.celignis.com

36. Thank You! www.celignis.com dan@celignis.com (353) 89 455 5582