1 / 36

5th International Symposium on Energy from Biomass and Waste Venice, Italy Nov 17-20 2014

“Use of Near Infrared Spectroscopy for the Rapid Low-Cost Analysis of a Wide Variety of L ignocellulosic 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.

spencer
Download Presentation

5th International Symposium on Energy from Biomass and Waste Venice, Italy Nov 17-20 2014

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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“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

  5. 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

  6. Time for Conventional Analysis Wet Chopped Sample Sample as Collected Dry Sample Air Drying ~ 3+ days Chop sample ~ 10 mins Milling + sieving ~ 1 hour Extractives-free sample Hydrolysis and hydrolysate analysis ~ 3 days Extractives Removal ~ 3 days Completed Lignocellulosic Analysis ~ 10 days !!!! Dry Sample of Appropriate Particle Size “One-day analysis of biomass” www.celignis.com

  7. Interaction of NIR Light with Biomass Specular Reflectance Diffuse Reflectance Absorption Transmittance Refraction Scattering “One-day analysis of biomass” www.celignis.com

  8. 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

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

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

  11. 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

  12. 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 7factors). • 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. 13 Constituents Predicted

  14. Types of Samples Included “One-day analysis of biomass” www.celignis.com

  15. 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

  16. Results for Prediction Set “One-day analysis of biomass” www.celignis.com

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

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

  19. Results for Prediction Set “One-day analysis of biomass” www.celignis.com

  20. Results for Prediction Set “One-day analysis of biomass” www.celignis.com

  21. Feedstock-Specific Models “One-day analysis of biomass” www.celignis.com

  22. 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

  23. Models for Miscanthus Klason Lignin Glucan Xylan “One-day analysis of biomass” www.celignis.com

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

  25. Time for Conventional Analysis Wet Chopped Sample Sample as Collected Dry Sample Air Drying ~ 3+ days Chop sample ~ 10 mins Milling + sieving ~ 1 hour Extractives-free sample Hydrolysis and hydrolysate analysis ~ 3 days Extractives Removal ~ 3 days Completed Lignocellulosic Analysis ~ 10 days !!!! Dry Sample of Appropriate Particle Size “One-day analysis of biomass” www.celignis.com

  26. 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

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

  28. Results for Prediction Set “One-day analysis of biomass” www.celignis.com

  29. 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 hoursof receiving a sample. “One-day analysis of biomass” www.celignis.com

  30. 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. “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. • 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

  33. 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

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

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

More Related