1. “Use of Near Infrared Spectroscopy for the Rapid Low-Cost Analysis of a Wide Variety of Lignocellulosic Feedstocks” International Fuel Ethanol Workshop & Expo Minneapolis, MN, USA June 3 rd 2015 Daniel Hayes, PhD dan@celignis.com www.celignis.com

2. Feedstock is Important!  Right technology but wrong feedstock…..heavy losses.  Right feedstock, wrong price.  Balance composition, price, and conversion efficiency.  Ethanol: corn, wheat, cane, beet.  Biodiesel: oil palm, soybean, rapeseed.  Few constituents to determine. “One-day analysis of biomass” www.celignis.com

3. Second Generation Biofuels…. “One-day analysis of biomass” www.celignis.com

4. 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

5. 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

6. Chemical Analysis Methods  Advantages:  Established for decades.  Most accurate method.  Accurate for all sample types.  Disadvantages:  Destructive.  Needs careful sample preparation.  Array of equipment required.  Need highly-trained analysts.  Slow (requires ~2 weeks).  Costly.  Hence, number of samples that can be analysed is limited (time/cost). “One-day analysis of biomass” www.celignis.com

7. 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

8. NIR Analysis FOSS XDS Monochromator. 400-2500nm (visible and NIR). Moving sample transport for heterogeneous/wet samples. “One-day analysis of biomass” www.celignis.com

9. History of NIR Analysis In development since 70’s for the analysis of forage crops and grains. Now the primary method of analysis for these sectors. To date application of NIR for lignocellulose analysis limited to research papers. Celignis is the only company to offer NIR analysis as a commercial service for the lignocellulosic constituents of a wide variety of biomass 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 (~1,200 samples currently). 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 regression that reduce the dimensionality of data (e.g. 4200 variables reduced to 7 factors). 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 (%): 98.5827.5972.21 R2:R2: 0.9720.9780.972 RMSEP (%): 2.011.141.83 RER: 36.6523.6831.34

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 Published Pig ManurePaper Published Paper/CardboardIn Preparation StrawJune Sugarcane Bagasse (Wet & Dry)July Pre-treated BiomassAugust CompostsSeptember WoodOctober

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 processed to DS 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 DryWetDryWetDryWet 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. Celignis Analytical Launched August 2014  personal experience (10 yrs), ~ 25 person-years for NIR models. Laboratory analysis of biomass (lignocellulosic and thermal). Cellulosic analysis by chemical and NIR. Current NIR models require dry, ground biomass samples and we provide data within 24 hours of receiving a sample. www.celignis.com

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

29. 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

31. Celignis NIR Method Advantages: Results provided in one day (versus ~2 weeks). Significantly lower price than chemical analysis. Allows for a large number of samples to be screened for their suitability in a cost-effective manner. Proven on ~1200 biomass samples covering a wide variety of feedstock types. Disadvantages: Less accurate than chemical analysis - however models provide an estimate of the deviation (error) in prediction and this may be low enough for many clients. “One-day analysis of biomass” www.celignis.com

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

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

34. Publications Hayes, D.J.M., Hayes, M. H. B., Leahy, J. J. (2015), Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions, Fuel 150: 261-268. Wnetrzak, R., Hayes, D. J. M., Jensen, L. S., Leahy, J. J., Kwapinski, W. (2015), Determination of the Higher Heating Value of Pig Manure, Waste and Biomass Valorization, doi: 10.1007/s12649-015-9350-y Hayes, D. J.M., Hayes, M. H. B., Leahy, J. J. (2014), Rapid analysis, using near-infrared spectroscopy, of lignocellulosic components of waste papers and cardboards, 5 th International Symposium on Energy from Biomass and Waste Hayes, D.J.M. (2013) Biomass composition and its relevance to biorefining, The Role of Catalysis for the Sustainable Production of Biofuels and Bio-chemicals, K. Triantafyllidis, A. Lappas, M. Stoker, Elsevier B. V. 27-65 Hayes, D. J. M. (2012) Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet Miscanthus samples, Bioresource Technology 119:393-405 “One-day analysis of biomass” www.celignis.com

35. The Number One Question….Price! Number of Samples in an Order 1-45-910-1920-4950+ Near Infrared Analysis 1501251007560 Chemical Analysis 450350300 Thermal Analysis 145120 Preparation (if unground) 25 Chemical/NIR analysis = total sugars, glucan, xylan, mannan, arabinan, galactan, rhamnan, Klason lignin, acid soluble lignin, ash, ethanol extractives. Thermal Analysis: moisture, ash, volatile matter, fixed carbon, heating value, C, H, N, S. Competitor price $900 per sample (x 20) = $18,000 Celignis price (20 samples NIR) = $1,500, save $16,500 (92%). $18,000 would get analysis of 300 samples by NIR method! We will undertake chemical analysis for NIR price if the biomass type is currently under-represented in our NIR models!!

36. Thank You! www.celignis.com dan@celignis.com M: (353) 89 455 5582 T: (353) 61 518 440