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Feedstock evaluation and development of rapid analytical methods

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Feedstock Evaluation and
 Development of Rapid
   Analytical Methods



           Daniel Hayes
     DIBANET Networking Se...

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WP2

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    Task 2.1 – Appropriate Feedstock
    Selection and Analysis
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Feedstock evaluation and development of rapid analytical methods

  1. 1. Feedstock Evaluation and Development of Rapid Analytical Methods Daniel Hayes DIBANET Networking Session Thessalonki 31/10/12
  2. 2. WP2  Task 2.1 – Appropriate Feedstock Selection and Analysis − Identify all possible feedstocks for biorefining in Europe and Latin America. − Select those most suitable for the DIBANET process. − Analyse these feedstocks with wet-chemical methods for their lignocellulosic constituents. − UL is responsible for the evaluation of European feedstocks, CTC for sugarcane residues, UNICAMP for other LA biomass.
  3. 3. Task 2.1 Europe (Ireland) Latin America (Brazil) Miscanthus Sugarcane bagasse Pretreated Miscanthus Sugarcane trash Cereal straws Coffee husks Waste paper Banana residues Compost Coconut residues Short rotation coppices Acai residues Animal manures Rice husks Reed canary grass Sawdust Green wastes Soya peel Bamboo Grass
  4. 4. Feedstock Chemical Evaluation  Important chemical characteristics:  C6 Sugars: Glucose, Galactose, Mannose  C5 Sugars: Arabinose, Xylose  Lignin content (acid soluble and insoluble)  Extractives  Ash.  Elemental analysis.
  5. 5. Task 2.1 Data Obtained (Wet Chemistry) Partner Feedstock Lignin Extractives Ash Sugars Elemental UL Miscanthus 221 257 243 211 61 UL Pret. Misc. 47 47 47 47 0 UL Straws 33 44 46 26 15 UL Papers 14 19 13 14 12 UL Others 41 53 46 24 25 CTC Bagasse 80 68 73 60 0 CTC Trash 37 37 37 37 0 UNIC. Coffee husks 42 102 102 42 0 UNIC. Banana 81 104 104 10 0 UNIC. Coconut 30 30 30 30 0 UNIC. Other 7 7 7 7 0 TOTALS UL 356 420 395 322 113 CTC 96 105 110 32 0 UNIC. 160 243 243 89 0 TOTAL 612 768 748 443 113
  6. 6. Initial Evaluation (UL) [1]  DIBANET focuses on Miscanthus and waste samples as target feedstocks.  Generally waste cardboard/paper samples have the highest cellulose/carb contents and so offer the potential for high LvA yields, this however implies also that the residue for subsequent gasification will be low.  Animal Slurries: There is a significant variation between the composition of animal slurries (pig, cattle) but these can offer reasonably attractive carbohydrate contents (up to 50% of dry mass on an extractives free basis).  But moisture content is extremely high, hence these feedstocks could probably only act as “fillers” in a feedstock mix, but there would still be transport problems.
  7. 7. Initial Evaluation (UL) [2]  Spent Mushroom Compost (SMC) is a significant waste resource in Ireland – secondary data suggest it has potential for biorefining purposes but our data suggest its carbohydrate content is too low (max 20%, compared to the pre-compost mix of ~50% carbohydrate).  Forestry residues (wood and leaves) are suitable for utilisation in the DIBANET process but this will require a significant investment in the infrastructure required for their collection and transport.  Sawmill residues are also of value for biorefining but there are other current end-uses for these resources.
  8. 8. Initial Evaluation (UL) [3]  Municipal wastes are predominately composed of waste papers, food waste, garden waste, and waste woods  Waste food does not contain sufficient lignocellulosic sugars to warrant its utilisation in the DIBANET process.  Garden/green waste contains various types of materials such as grasses, leaves, twigs, and branches.  Only the more woody materials have lignocellulosic compositions suitable for the DIBANET process.  Garden/green waste taken as a composite (i.e. a sample from a compost pile) does not have a favourable proportion of wood to foliage. Hence, the sourcing of green waste for biorefineries needs to be specifically tailored to high- carbohydrate materials. This may necessitate for separate collection schemes or for processes to sorting the woody material from the total green-waste resource.
  9. 9. Conclusion (UL)  Focus on Miscanthus as a DIBANET feedstock.  Also investigate straws and waste papers.
  10. 10. Initial Evaluation (UNICAMP)  A total of 10 different feedstocks were investigated and analysed (wet-chemical methods): − Soy peel, bamboo, banana residues, rice husks, sawdust, acai seeds, elephant grass, coconut residues, coffee residues.
  11. 11. CHEMICAL COMPOSITION OF ALL BIOMASSES ANALYSED BY UNICAMP (%) TOTAL TOTAL TOTAL BIOMASS ARABINOSE GALACTOSE RHAMNO GLUCOSE XYLOSE MANOSE SUGARS LIGNIN EXTRACTIVE ASH COMP. SOY BEAN 4.64 3.13 0.92 35.05 9.85 4.31 57.90 7.58 6.81 4.14 77.00 RICE husks 1.70 0.83 0.13 36.17 16.65 0.49 55.98 23.90 2.32 12.5 95 SADWUST 0.26 1.23 0.25 38.79 9.72 0.35 50.60 32.87 8.12 0.63 92 BAMBOO 0.81 0.32 0.06 44.65 14.78 0.07 61.57 17.64 12.62 2.81 95 GRASS 3.56 1.22 0.10 27.52 16.12 0.24 48.84 15.61 11.54 12.66 89 COCONUT 1.79 0.71 0.30 32.41 14.37 0.35 49.94 35.87 1.41 2.63 90 AÇAÍ seed 0.69 1.43 0.17 8.66 3.18 53.59 67.71 17.26 9.5 0.46 95 BANANA Stalk 2.89 1.18 0.27 26.83 6.94 1.46 39.56 10.68 22.85 10.33 84 BANANA Stem 2.37 0.72 0.16 36.32 5.36 0.61 45.53 8.38 25.15 10.30 90 COFFEE husks 1.62 1.54 0.51 35.33 21.89 1.68 62.55 24.46 4.21 4.00 95 11
  12. 12. Conclusion (UNICAMP)  Three feedstocks were selected for more detailed analysis and evaluation (considering levels of supply, environmental factors, price, and composition) − Banana residues. − Coffee Residues. − Coconut Residues.
  13. 13. Conclusion (Early Evaluation)  There are a number of viable feedstocks for utilisation in DIBANET.  However, secondary analytical data relevant to our needs are severely lacking.  There is a clear need for primary wet-chemical analysis in DIBANET.  This will also be of value to others in the biorefining industry.
  14. 14. Task 2.1 – Detailed Evaluation  Focused on obtaining primary analytical data in the laboratories of UL, CTC, UNICAMP.  Analytical methodologies (written at UL) were kept consistent for all partners.  Strict quality thresholds placed upon results.
  15. 15. Example: DIBANET WP2 Method
  16. 16. Example: DIBANET WP2 Datasheet
  17. 17. D2.2 - Database  A database containing compositional data of samples analysed by DIBANET partners can be downloaded from the DIABNET website.  It contains the results from the wet- chemical and/or spectroscopic analysis of 1281 samples from Europe and Latin America.
  18. 18. D2.2 – Report: Miscanthus (1)  Miscanthus is a productive energy crop that does not require significant time or expense for maintenance after plantation. It can be productive for up to 20 years without the need for replantation.  The lignocellulosic composition of the crop once at full production is highly attractive for use in biorefining technologies.  The stem fractions of the plant, when processed in biorefining technologies, will provide higher chemical/biofuel yields than the leaf fractions. This is due to their higher total sugars contents and increased heating values. The lower acid soluble lignin, protein, and extractives contents in the stem sections are also likely to present fewer complications in many conversion processes (e.g. acid and enzymatic hydrolysis).
  19. 19. D2.2 – Report: Miscanthus (2)  The total sugars content of the crop in its first year of production is significantly less than in subsequent years. The total biomass yield of the crop is also less in this first year.  Given this situation, the commercial harvest of the first year growth of Miscanthus and the subsequent transport of this crop to the biorefinery is not economical and is not advised.  Instead, the crop should be cropped after the first year of growth with the biomass either left on the land for soil conditioning or used for other local uses.
  20. 20. D2.2 – Report: Miscanthus (3)  The harvest window for Miscanthus is between October and April.  Between October and early December a relatively small amount of standing biomass is lost as leaf fall. This period is termed the “Early Harvest”.  Between mid-December and the end of February there is a rapid loss of leaves from the plant.  By March the only remaining leaf materials tend to be the sheaths. These are lost from the plant at a much slower rate than the leaf blades. Hence, the loss of standing biomass is much less after March. This period is termed the “Late Harvest”.
  21. 21. D2.2 – Report: Miscanthus (4)  The best time for harvesting Miscanthus will be dependent on how the crop will be processed.  The dry biomass yield associated with an “Early” Harvest can be approximately 30% more than that associated with a “Late” Harvest.  If the maximal biomass yield is the primary desire the crop should be harvested in the “Early” period. The crop will have a significant amount of moisture (approximately 50% on a wet basis) at this time.  An “Early” harvest will not provide a feedstock suitable for most thermochemical biorefining technologies (e.g. pyrolysis, gasification) since these will require lower moisture contents.
  22. 22. D2.2 – Report: Miscanthus (5)  An “Early” harvest is feasible for most hydrolysis biorefining technologies, e.g. DIBANET, providing they do not use pretreatment method that require dry feedstock (e.g. ionic liquids).  An “Early” harvest will remove leaves that would, in a “Late” harvest, fall to the field. The amount of carbon and nitrogen provided to the soil would therefore be reduced.  The removal of leaf material from the land can be addressed with increased fertiliser input.
  23. 23. D2.2 – Report: Miscanthus (6)  There are significant changes in lignocellulosic composition of the standing plant over the harvest window. The most important of these are an increase in the glucan and Klason lignin content.  On a dry mass per-tonne basis the biomass collected during the “Late” harvest period is of more value for biorefining processes (hydrolysis and thermochemical) than the biomass collected in the “Early” harvested period.  If a feedstock payment scheme at a biorefinery, using the hydrolysis platform, is based on total sugars content then the Late harvest crop would be worth approximately 10% more per tonne than the Early harvest crop.
  24. 24. Loss of Leaves over Harvest Window
  25. 25. Levulinic Acid Yield per Ha
  26. 26. Database
  27. 27. Sugarcane Residues
  28. 28. Production in Brazil
  29. 29. Sugarcane in Brazil
  30. 30. D2.2: Sugarcane Residues (Bagasse)
  31. 31. D2.2: Sugarcane Residues (Bagasse)
  32. 32. D2.2: Sugarcane Residues  Both sugarcane bagasse and sugarcane trash have sufficient amounts of lignocellulosic sugars to justify their processing in hydrolysis biorefining technologies.  The compositions of the bagasse samples that were analysed tended to be more varied than those of the trash samples analysed.  Ash, in particular, can vary significantly in bagasse samples. There also seems to be a tendency for the ash contents of bagasse to be higher in some mills.  Hence, it is recommended that careful determinations and observations, over a period of time, of the ash contents, associated with the harvesting/milling process of any mill that is being considered for a biorefining scheme, be carried out.
  33. 33. D2.2: Sugarcane Residues (2)  Using the average lignocellulosic compositions of sugarcane bagasse and sugarcane trash determined in DIBANET analyses along with the estimated arisings (165m dry tonnes of bagasse and 128m dry tonnes of straw), the total potential yields possible from processing these feedstocks in representative biorefining technologies were calculated.
  34. 34. 102 SAMPLES OF COFFEE - INCLUDED: Leaves Different kinds: Husks - etiopia - árabica -robusta - mundo novo Grain Ground coffee 39
  35. 35. SOME HISTOGRAMS FOR COFFEE SAMPLES 40
  36. 36. 102 SAMPLES OF BANANA - INCLUDED: Leaves Steam Rhizome Stalk Husks 41
  37. 37. SOME HISTOGRAMS FOR BANANA SAMPLES 42
  38. 38. 30 SAMPLES OF COCONUT - INCLUDED: Husks Fibers 43
  39. 39. SOME HISTOGRAMS FOR COCONUT SAMPLES 44
  40. 40. SOME HISTOGRAMS FOR COCONUT SAMPLES 45
  41. 41. Task 2.2  “Development of Lab-Based NIR Calibration Equations”  “Equations will be targeted for at least 5 feedstocks, each, from Europe and Latin America.” Europe Latin America UL CTC UNICAMP 1. Miscanthus 1. Bagasse 2. Pret. Misc. 2. Trash 3. Straws 3. Coffee rds. 4. Papers. 4. Banana rds. 5. Global 5. Coconut rds.
  42. 42. Time for Conventional Analysis Sample as Collected Wet Chopped Sample Dry Sample Chop sample Air Drying ~ 10 mins ~ 3+ days Extractives-free Milling + sample sieving 15 ~ 1 hour 10 5 Hydrolysis and Extractives 0 0 2 4 6 8 10 12 14 16 hydrolysate Removal Completed analysis ~ 3 days Lignocellulosic ~ 3 days Dry 47 Sample of Analysis Appropriate Particle
  43. 43. Sample Preparation
  44. 44. Scans of One Sample
  45. 45. WU Spectra
  46. 46. 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.
  47. 47. UL Misc. Glucose Models
  48. 48. Glucose DS and WU Models 50 50 Predicted GLU_SRS (WU Model) (%) Predicted GLU_SRS (DT Model) (%) 45 45 40 40 Linear () Linear () 35 35 Linear () Linear () 30 30 25 25 25 30 35 40 45 50 25 30 35 40 45 50 Reference GLU_SRS Reference GLU_SRS (DS Samples of All Varieties) (%) (DS Samples of All Varieties) (%)
  49. 49. UL Misc. Xylose Models
  50. 50. UL KL Models (Gig)
  51. 51. Miscanthus Models - Summary DS DU WU RMSEPWU RERWU Glucose A B A 1.26% 16.20 Xylose A A A 0.53% 17.05 Rhamnose B B C 0.06% 8.38 Mannose C C C 0.07% 5.52 Arabinose B B B 0.27% 11.04 Galactose C C C 0.12% 7.97 Total Sugars A B A 1.21% 18.59 KL A A B 0.93% 10.86 ASL A B B 0.42% 10.62 Ash A B B 0.93% 10.66 EXTR_PD B B C 1.38% 7.83 Nitrogen A C C 0.28% 6.84 Moisture - - A 2.52% 23.80
  52. 52. Pretreated Miscanthus  Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
  53. 53. UNICAMP Lignin Models-DS Sample Pre- Matrix R2  RMSE  Y Range LV Outlier RE Set1 Treatment2 Cal      Val SEC     SEP B+C 2d+OSC NIR 89x4200 2 3  0.96 0.95 1.193 1.278 6.90 TL   B 2d+OSC NIR 64x2800 3 - 0.96 0.93 0.883 1.142 7.80 42X2800   C 1d(25) NIR 6 4 0.88 0.85 1.62 1.80 7.00 30X2800   COC. 2d(1) NIR 4 2 0.91 0.94 1.03 0.92 4.00 134X2800   B+C+COC. 2d(25) NIR 7 3 0.94 0.91 1.55 1.96 10.00 89x4200 B+C 2d+OSC Full 3 - 0.93 0.92 1.379 1.489 10.80 KL 64x2800 B 2d+OSC NIR 3 1 0.96 0.92 0.779 1.186 11.20 42X2800   C 1d(25) NIR 5 5 0.90 0.91 1.28 1.27 6.00 30X2800   COC. 2d(1) NIR 4 2 0.90 0.91 1.00 0.96 4.00 134X2800   B+C+COC 2d(25) NIR 5 5 0.94 0.90 1.531 1.988 12.50 B+C: pooled datset (banana a and coffee); B: banana alone;C: coffee; COC: coconut; B+C+Coc.: banana+coffee+coconut. 3: The asterisks indicates statistically significant difference (* p < 0.05) between RMSEP and RMSEC.
  54. 54. UNICAMP Lignin Models-DU Sample Pre- Matrix R2  RMSE  Y Range LV Outlier RE Set1 Treatment2 Cal      Val SEC     SEP 55X2800 B SNV+DT(2) NIR 9 3 0.88 0.86 1.513 1.872 14.00 TL   60x2800 C SNV+1d(1) NIR 7 5 0.97 0.90 0.700* 1.831* 8.00 30x2800   COC. 2d(1) NIR 5 4 0.97 0.97 0.55 0.56 2.00 55X2800 B SNV+DT(2) NIR 9 4 0.86 0.84 1.480 1.811 16.00 KL 60x2800 C SNV+1d(1) NIR 7 5 0.97 0.90 0.714 1.693 9.00 30x2800   COC. 2d(1) NIR 5 4 0.98 0.96 0.67 0.47 3.00 55X2800 ASL B SNV+1d NIR 8 5 0.97 0.96 0.170 0.181 6.00 60x2800   C SNV+1d(1) NIR 4 5 0.80 0.73 0.085 0.105 2.00 30x2800   COC. 2d(1) NIR 3 2 0.89 0.85 0.118 0.121 8.00 52x2800 AIR B SNV+1d(1) NIR 9 2 0.95 0.94 1.039 1.227 10.00 60x2800   C SNV+1d(1) NIR 7 5 0.97 0.90 0.743* 1.835* 9.00 30x2800   COC. 2d(1) NIR 5 4 0.96 0.91 0.65 0.91 4.00
  55. 55. UNICAMP Lignin Models-WU Sample Pre- Matrix R2  RMSE  Y Range LV Outlier RE Set1 Treatment2 Cal      Val SEC     SEP B 1d(7) NIR 62x4200 8 5 0.89 0.74 1.452 1.916 15.00 TL 64x2800 C 2d(1) NIR 6 5 0.95 0.80 0.903* 2.450* 11.00 30X2800   COC. 2d(25) NIR 5 2 0.96 0.84 0.858* 1.939* 8.00 62x2800 KL B 1d(7) NIR 7 4 0.85 0.76 1.590 1.772 17.00 42X2800   C 2d(1) NIR 6 5 0.96 0.90 0.790 1.779 9.00 30X2800   COC. 2d(25) NIR 4 2 0.81 0.80 1.842 1.973 9.00 62x2800 ASL B 1d(7)  NIR 7 3 0.82 0.81 0.362 0.424 16.00 42X2800   C SNV+1d NIR 5 5 0.80 0.76 0.440 0.591 16.00 30X2800   COC. 2d(25) NIR 6 3 0.92 0.84 0.082* 0.136* 9.00 62x2800 AIR B 1d(7) NIR 8 5 0.89 0.75 1.470 1.788 16.00 42X2800   C 2d(1) NIR 7 5 0.98 0.89 0.587* 1.914* 10.00 30X2800   COC. 2d(25) NIR 7 3 0.93 0.92 1.13 1.30 5.50
  56. 56. UNICAMP Ash and Extractives Models-DS Sample Pre- Matrix R2  RMSE  Y Range LV Outlier RE Set1 Treatment2 Cal      Val SEC     SEP 205x2800 B+C 2d+SNV NIR 7 2  0.83 0.80 0.526 0.556 22.00 ASH 103x4200 B 2d Full 7 2  0.76 0.70 0.559 0.711 18.00 102X2800   C 1d(25)  NIR 7 5 0.73 0.60 0.22* 0.39* 20.00 30X2800   COC. 2d(1) NIR 4 1 0.86 0.86 0.34 0.40 20.00 233X2800   B+C+COC. 2d(25)  NIR 6 5 0.80 0.75 0.587 0.619 22.00 205x2800 B+C 2d+SNV NIR 7 1  0.81 0.79 0.947 1.063 11.00 Extr. 103x2800 B 2d NIR 6 - 0.86 0.86 0.794 0.985 12.00 102X2800   C 1d(25) NIR 6 5 0.80 0.75 1.08 1.22 12.00 30X2800   COC. 2d(1) NIR 2 3 0.84 0.84 0.88 0.84 8.00 233X2800   B+C+COC. 2d(25) NIR 7 4 0.82 0.81 1.04 1.25 13.00
  57. 57. UNICAMP Sugar Models-DS Sample Pre- Matrix Outli R2 RMSE Y Range LV RE Set1 Treatment2 er Cal Val SEC SEP GLUC. B C 2d(25) NIR 41X2800 7 2 0.78 0.70 1.99 2.22 14.00 COC. SNV+1d(1) NIR 30X2800 4 2 0.92 0.82 1.13 1.25 5.00 XYL. B C EMSC NIR 41X2800 7 5 0.95 0.80 0.723 0.780 16.00 COC. SNV+1d(1) NIR 30X2800 5 2 0.88 0.82 0.94 1.44 11.00 GALA. B C 2d(25) NIR 41X2800 5 5 0.87 0.83 0.443 0.476 15.00 COC. 2d(1) NIR 30X2800 5 2 0.91 0.85 0.06* 0.14* 10.00 RHAM B C SNV+1d(1) NIR 41X2800 7 2 0.94 0.86 0.053 0.055 8.00 COC. SNV+1d(1) NIR 30X2800 7 3 0.79 0.72 0.01 0.04 12.00 ARAB. B 41X2800 C 2d(25) NIR 5 5 0.87 0.86 0.479 0.582 12.00 30X2800 COC. 2d(1) NIR 3 3 0.90 0.83 0.15 0.17 7.00 MAN. B 41X2800 C SNV+1d(1) NIR 7 4 0.89 0.75 1.574 1.676 22.00 30X2800 COC. SNV+1d(1) NIR 6 3 0.52 0.53 0.16 0.10 16.00 TS B C EMSC NIR 41X2800 7 5 0.75 0.72 4.40 4.06 13.00 30X2800 COC. SNV+1d(1) NIR 7 2 0.95 0.94 1.41 2.05 5.50
  58. 58. UNICAMP Sugar Models-WU Sample Pre- Matrix Outli R2 RMSE Y Range LV RE Set1 Treatment2 er Cal Val SEC SEP GLUC. B C SNV+DT NIR 41X2800 9 3 0.81 0.77 1.399 1.547 10.00 30X2800 COC. 1d(1) NIR 5 3 0.92 0.88 1.030 1.768 7.00 XYL. B C EMSC NIR 41X2800 COC. SNV+1d(1) NIR 30X2800 5 3 0.90 0.85 0.80* 1.34* 12.00 GALA. B C 2d(25) NIR 41X2800 7 3 0.91 0.88 0.192 0.294 9.00 COC. 2d(25) NIR 30X2800 6 4 0.95 0.80 0.06* 0.12* 11.00 RHAM B C 2d(25) NIR 41X2800 6 2 0.77 0.70 0.054 0.106 6.50 COC. 2d(1) NIR 30X2800 1 1 0.91 0.60 0.014 0.015 5.00 ARAB B C 2d(25) NIR 41X2800 4 4 0.73 0.70 0.589 0.971 20.00 COC. 2d(1) NIR 30X2800 1 1 0.92 0.71 0.12* 0.31* 11.50 MAN. B C 2d(25) NIR 41X2800 4 4 0.72 0.71 0.370 0.476 25.00 COC. 2d(1) NIR 30X2800 3 4 0.98 0.77 0.031* 0.103* 19.00 TS B C 2d(25) NIR 41X2800 6 4 0.82 0.76 1.975* 3.085* 9.00 COC. SNV+dt(2) NIR 30X2800 4 2 0.96 0.90 1.40 2.52 5.00
  59. 59. Banana Residues
  60. 60. Coffee Residues
  61. 61. Coconut Residues
  62. 62. Banana+ Coffee + Coconut (pooled set)
  63. 63. Sugarcane Trash (KL) DS (ALL) WU (ALL) R2 = 0.90 R2 = 0.87 RMSECV = 0.30% RMSECV = 0.35%
  64. 64. Sugarcane Trash (Extractives) DS (ALL) WU (ALL) R2 = 0.87 R2 = 0.85 RMSECV = 0.63% RMSECV = 0.69%
  65. 65. Sugarcane Trash (Ash) DS (ALL) WU (ALL) R2 = 0.84 R2 = 0.88 RMSECV = 0.50% RMSECV = 0.44%
  66. 66. Sugarcane Bagasse (KL) WU (ALL) WU (Low Ash)  Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level R2 = 0.72 R2 = 0.80 RMSECV = 0.50% RMSECV = 0.46%
  67. 67. Ash Variability According to Mill
  68. 68. Task 2.2: Spectra Collected Partner DS DG DU WU TOTAL UL Miscanthus 562 492 759 1,884 3,697 Pret. Misc. 94 - - - 94 Straw 78 78 117 - 273 Papers 60 60 90 - 210 Global (excl. above) 310 - - - 310 CTC Bagasse 404 404 606 606 2,020 Bagasse (online system) - - 249 249 498 Trash 74 74 111 111 370 UNICAMP Banana Residues 206 216 186 186 794 Coffee Residues 204 243 198 198 843 Coconut Residues 60 90 90 90 330 TOTAL 2,052 1,657 2,406 3,324 9,439
  69. 69. D2.3 - BACI  (1) Wet Chemical Analysis:  Sugars  Lignin (Klason and acid soluble)  Ash  Extractives (ethanol and water)  Elemental  More planned in mid-term. − Accuracy of DIBANET methods has been identified as superior to other competing companies and the literature. − Company will offer guarantees on precision of analysis.
  70. 70. D2.3 - BACI  (2) NIR Analysis.
  71. 71. D2.3 – Markets for Company 1. Biorefining Companies − Biorefining database has been prepared. − BACI has identified 115 companies that are developing technologies and facilities for the commercial production of second-generation biofuels from lignocellulosic biomass. − Also numerous other companies that invest in biorefining projects, and examples include BIOeCON, BP, Cargill, Chevron, Honda, Khosla Ventures, Mitsubishi, Petrobras, Shell, Total SA, Toyota, and UOP.
  72. 72. D2.3 Markets for Company
  73. 73. D2.3: Markets for BACI 2. Farmers and consumers of energy crops (particularly Miscanthus) 3. Energy crop breeders. 4. Waste service providers and waste producers. 5. Scientific researchers.
  74. 74. D2.3: Business Plan Objectives  Secure the Irish market for the analysis of lignocellulosic materials in the short-term.  To obtain the necessary licenses to allow for the importation of biomass samples from outside the country.  To utilise the equipment and laboratory space of UL in the short to mid-term.  Continue to improve NIR models.  Continue and strengthen EU/LA ties: Develop a working relationship with DIBANET partners CTC and UNICAMP allowing for their NIR models to be used, under license, at BACI.  CTC may also form a spin-out company that could offer DIBANET analytical services.
  75. 75. BACI Prices Constituent(s) Price for 1 Price per Extra Bulk, Microbac ($/sample) Sample (incl. Sample Round (€/ ref) sample) Moisture Content This cost is included in the price for the relevant methods in 80 BACI Sample Preparation 70 70 70 150 Wet-Chemical Analysis Ethanol-Extractives 220 150 160 150 Water-Extractives 220 150 160 150 Water+Ethanol Extraction 330 200 220 200 Ash 50 50 50 80 Lignocellulosic sugars 515 300 340 550 KL, ASL, AIR, AIA 350 200 230 275 (no AIR, AIA) Sugars, KL, ASL, AIR, AIA 575 350 395 825 (no AIR,AIA) NIR Analysis Wet unchopped (WU Model) 250 - 150 N/A Wet unchopped (DU Model) 250 - 175 N/A Wet unchopped (DS Model) 300 - 200 N/A Wet chopped (WU Model) 200 - 125 N/A Wet chopped (DU Model) 250 - 150 N/A Wet chopped (DS Model) 300 - 175 N/A Dry chopped (DU Model) 200 - 125 N/A Dry chopped (DS Model) 250 - 175 N/A Dry Sieved (DS Model) 200 - 125 N/A
  76. 76. Task 2.2: Other Outputs (1)  “Discrimination between samples” − Have developed NIR models to differentiate Miscanthus samples on the basis of: − Early/Late Harvest. − Variety (giganteus or other). − Plant fraction (e.g. stems, internodes, leaves). − Stand age (1st year of growth or later).
  77. 77. Task 2.2: Other Outputs (2)  “Where available, NIR tools will also analyse process outputs of WP3”. − Chemometric models have been developed for: − Acid hydrolysis residues. − Pretreated samples (Miscanthus, bagasse). − Analytical hydrolysates (WP2) (UV). − Hydrolysates from WP3 reactor (UV).
  78. 78. Task 2.2: Reactor Yields N = 188 R2 (CV) = 0.962
  79. 79. Task 2.2: Hydrolysate Analysis N = 201 R2 (CV) = 0.955
  80. 80. Task 2.3 Transfer of Methods to an Online NIR Facility Bagasse NIR Analysis Cane NIR Analysis
  81. 81. Task 2.3  “Transfer of Methods to an Online NIR Facility” − Objectives: − (1) Install online NIR equipment at a Brazilian sugar-mill. − Status: An older NIR model has been operational at a sugar mill in Quata, Sao Paulo. Its performance was monitored Nov-Dec 2011.
  82. 82.  Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
  83. 83. T2.3  (1) Install online NIR equipment at a Brazilian sugar-mill. • Status (Cont’d): Following failure of the Quata system it was decided that an improved sample presentation method and a more modern device would be needed. The ProFOSS system: − Diode array detection (no moving parts). − Analysis over 1100-1650nm. − Capable of operating in industrial environments. − Was installed in Aug 2012.
  84. 84.  Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
  85. 85. LAB ONLINE
  86. 86. D2.4  “Report on Potential Markets for The FOSS Systems Following DIBANET Development – This report will examine under which conditions the online device (BAS or a variant of it) will be attractive for biomass analysis and will examine the market for this in the EU and LA.”  Status: Ongoing. Planned for Month 38, expected completion Month 42 (due to delays in setting up and validating the online NIR system).  On advice of FOSS this report will also include market evaluations for lab-based NIR systems.  E.g. an NIR/DIBANET-model package that would allow biorefinery operators to characterise biomass in an at-line (rather than online) basis.
  87. 87. Conclusions 1. A large number of samples have been analysed to high accuracy via wet-chemical methods. Data available on DIBANET analytical database. 2. The state of the art in NIR analysis of lignocellulosic feedstocks has been advanced by allowing accurate predictions for wet heterogeneous biomass. 3. A spin-out company offering biomass analytical services is being developed. 4. The use of NIRS as an online analysis tool for biorefineries is being demonstrated. 5. Strong EU-LA links have been developed and will be maintained.
  88. 88. Thank You!!! daniel.hayes@ul.ie www.carbolea.ul.ie

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