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Feedstock Evaluation and
 Development of Rapid
   Analytical Methods



           Daniel Hayes
     DIBANET Networking Session
            Thessalonki
             31/10/12
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.
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
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.
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
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.
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.
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.
Conclusion (UL)



    Focus on Miscanthus as a DIBANET
    feedstock.

    Also investigate straws and waste
    papers.
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.
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
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.
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.
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.
Example:
DIBANET WP2 Method
Example:
DIBANET WP2 Datasheet
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.
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).
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.
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”.
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.
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.
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.
Loss of Leaves over
 Harvest Window
Levulinic Acid Yield per Ha
Database
Sugarcane Residues
Production in Brazil
Sugarcane in Brazil
D2.2:
Sugarcane
 Residues
(Bagasse)
D2.2:
Sugarcane
 Residues
(Bagasse)
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.
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.
102 SAMPLES OF COFFEE - INCLUDED:



Leaves




                   Different kinds:
Husks
                        - etiopia
                       - árabica
                       -robusta
                    - mundo novo
Grain




Ground coffee

                                            39
SOME HISTOGRAMS FOR COFFEE SAMPLES




                                     40
102 SAMPLES OF BANANA - INCLUDED:


Leaves




Steam




Rhizome




 Stalk




Husks                                 41
SOME HISTOGRAMS FOR BANANA SAMPLES




                                     42
30 SAMPLES OF COCONUT - INCLUDED:




 Husks




  Fibers




                                    43
SOME HISTOGRAMS FOR COCONUT SAMPLES




                                      44
SOME HISTOGRAMS FOR COCONUT SAMPLES




                                      45
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.
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
Sample Preparation
Scans of One Sample
WU Spectra
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.
UL Misc. Glucose Models
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) (%)
UL Misc. Xylose Models
UL KL Models (Gig)
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
Pretreated Miscanthus

         
             Click to edit Master
             text styles
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                 Second level
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                 Third level
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                      Fourth level
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                          Fifth level
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.
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
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
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
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
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
Banana Residues
Coffee Residues
Coconut Residues
Banana+ Coffee + Coconut
      (pooled set)
Sugarcane Trash (KL)
 DS (ALL)            WU (ALL)




    R2 = 0.90           R2 = 0.87
    RMSECV = 0.30%      RMSECV = 0.35%
Sugarcane Trash (Extractives)
   DS (ALL)            WU (ALL)




      R2 = 0.87            R2 = 0.85
      RMSECV = 0.63%       RMSECV = 0.69%
Sugarcane Trash (Ash)
DS (ALL)            WU (ALL)




   R2 = 0.84            R2 = 0.88
   RMSECV = 0.50%       RMSECV = 0.44%
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%
Ash Variability According to Mill
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
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.
D2.3 - BACI


    (2) NIR Analysis.
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.
D2.3 Markets for
   Company
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.
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.
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
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).
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).
Task 2.2: Reactor Yields

       N = 188
       R2 (CV) = 0.962
Task 2.2: Hydrolysate
       Analysis
     N = 201
     R2 (CV) = 0.955
Task 2.3 Transfer of Methods to an Online
               NIR Facility




                              Bagasse
                                NIR
                              Analysis




      Cane
       NIR
     Analysis
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.

    Click to edit Master text styles
    −
        Second level
    −
        Third level
         •
             Fourth level
               −
                 Fifth level
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.

    Click to edit Master text styles
    −
        Second level
    −
        Third level
         •
             Fourth level
               −
                 Fifth level
LAB



      ONLINE
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.
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.
Thank You!!!
 daniel.hayes@ul.ie
 www.carbolea.ul.ie

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Feedstock evaluation and development of rapid analytical methods

  • 1. Feedstock Evaluation and Development of Rapid Analytical Methods Daniel Hayes DIBANET Networking Session Thessalonki 31/10/12
  • 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. 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. 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. 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. 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. 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. 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. Conclusion (UL)  Focus on Miscanthus as a DIBANET feedstock.  Also investigate straws and waste papers.
  • 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. 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. 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. 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. 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.
  • 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.
  • 19.
  • 20. 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).
  • 21. 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.
  • 22. 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”.
  • 23. 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.
  • 24. 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.
  • 25. 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.
  • 26. Loss of Leaves over Harvest Window
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  • 37. 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.
  • 38. 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.
  • 39. 102 SAMPLES OF COFFEE - INCLUDED: Leaves Different kinds: Husks - etiopia - árabica -robusta - mundo novo Grain Ground coffee 39
  • 40. SOME HISTOGRAMS FOR COFFEE SAMPLES 40
  • 41. 102 SAMPLES OF BANANA - INCLUDED: Leaves Steam Rhizome Stalk Husks 41
  • 42. SOME HISTOGRAMS FOR BANANA SAMPLES 42
  • 43. 30 SAMPLES OF COCONUT - INCLUDED: Husks Fibers 43
  • 44. SOME HISTOGRAMS FOR COCONUT SAMPLES 44
  • 45. SOME HISTOGRAMS FOR COCONUT SAMPLES 45
  • 46. 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.
  • 47. 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
  • 49. Scans of One Sample
  • 51. 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.
  • 53. 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) (%)
  • 54. UL Misc. Xylose Models
  • 55. UL KL Models (Gig)
  • 56. 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
  • 57.
  • 58. Pretreated Miscanthus  Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
  • 59. 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.
  • 60. 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
  • 61. 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
  • 62. 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
  • 63. 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
  • 64. 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
  • 68. Banana+ Coffee + Coconut (pooled set)
  • 69. Sugarcane Trash (KL) DS (ALL) WU (ALL) R2 = 0.90 R2 = 0.87 RMSECV = 0.30% RMSECV = 0.35%
  • 70. Sugarcane Trash (Extractives) DS (ALL) WU (ALL) R2 = 0.87 R2 = 0.85 RMSECV = 0.63% RMSECV = 0.69%
  • 71. Sugarcane Trash (Ash) DS (ALL) WU (ALL) R2 = 0.84 R2 = 0.88 RMSECV = 0.50% RMSECV = 0.44%
  • 72. 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%
  • 74. 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
  • 75. 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.
  • 76. D2.3 - BACI  (2) NIR Analysis.
  • 77. 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.
  • 78. D2.3 Markets for Company
  • 79. 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.
  • 80. 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.
  • 81. 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
  • 82. 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).
  • 83. 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).
  • 84. Task 2.2: Reactor Yields N = 188 R2 (CV) = 0.962
  • 85. Task 2.2: Hydrolysate Analysis N = 201 R2 (CV) = 0.955
  • 86. Task 2.3 Transfer of Methods to an Online NIR Facility Bagasse NIR Analysis Cane NIR Analysis
  • 87. 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.
  • 88. Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
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  • 94. 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.
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  • 96. Click to edit Master text styles − Second level − Third level • Fourth level − Fifth level
  • 97. LAB ONLINE
  • 98. 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.
  • 99. 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.
  • 100. Thank You!!! daniel.hayes@ul.ie www.carbolea.ul.ie