analysing biomass feedstock s biofuels international expo and conference porto, sep 23 rd 2015...
TRANSCRIPT
Analysing Biomass Feedstoc
ks
Biofuels International Expo and Conference
Porto, Sep 23rd 2015
Laurence Corbett
www.celignis.com
Feedstock is Important!
Right technology but wrong feedstock…..heavy losses.
Right feedstock, wrong price.Balance composition, price, and conversion efficiency.
Ethanol: corn, wheat, cane, beet.Biodiesel: oil palm, soybean, rapeseed.
Few constituents to determine.
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Second Generation Biofuels….
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Important Chemical Properties
Hydrolysis process (e.g. enzymatic hydrolysis). Cellulose content (structural glucose). Hemicellulose content (and the constituent
sugars). Lignin content (acid soluble and insoluble) Extractives Ash.
Thermal (e.g. combustion) and thermochemical (e.g. pyrolysis and gasification). Elemental analysis (C, H, N, O, S) Heating value Ash Anions and cations.
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Cellulose
A glucan polysaccharide.
Most abundant biogenic polymer with an annual global production of 100 x 109 tonnes.
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Hemicelluloses Polysaccharides that are mostly not extractable in hot water but,
unlike cellulose, are extractable in aqueous alkali. Have several sugars and tend to be branched. The major sugars are
the pentoses xylose and arabinose and the hexoses mannose, glucose, and galactose; with smaller amounts of rhamnose, in addition to uronic acids.
Unlike cellulose, they are not arranged in a highly ordered state and also have a much lower molecular weight. This means they are comparatively easy to hydrolyse with acid.
They will require different enzymes than cellulose for enzymatic hydrolysis. Also, the sugars may require different biota to those used for glucose fermentation.
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Galactoglucomannan – the principal hemicellulose in softwoods
Lignin A supporting agent in cell structure, also
assists in resistance against microbes. A complex three-dimensional polymer of
phenylpropane units. The ether linkages are very resistant to cleavage, which explains the low lignin degradation rates by most biota.
Can inhibit enzymes, hence pre-treatments that disrupt the lignocellulose macrostructure are used before hydrolysis.
Is a solid residue of most hydrolysis technologies, often used as a fuel for process heat and energy. Lignin does usually contribute to the direct biofuel output of most thermochemical processes.
A small fraction is acid-soluble with the amount varying between lignin and feedstock types. It can interfere with enzymatic and acid hydrolysis.
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Extractives Extraneous components that may be separated from the
insoluble cell wall material by their solubility in water or neutral organic solvents, with solvents of different polarities required to remove different types of extractives.
There are a large number of extractives, some feedstock-specific. Many have roles in the metabolic processes of a plant.
Can cause complications in hydrolysis technologies and should preferentially be removed in pre-treatment. Certain extractives (e.g. fats and waxes) may add to the heating value of biomass, a benefit for some thermochemical processes.
In analysis, not removing extractives before determining lignin can lead to significant overestimations. E.g. we found Klason lignin to be 19.5% higher in a non-extracted versus a fully-extracted bark sample with acid-soluble lignin 2.87% vs. 0.95%.
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Ash The residue remaining after biomass has been
incinerated. Content can vary greatly between plant species and will
depend on stage of growth and location. With wastes (municipal solid wastes in particular), ashes
are often more abundant and more diverse. Where acid hydrolysis is used, ash may necessitate a
higher consumption of acid due to the alkaline nature of some ash.
Enzymes may also be sensitive to ash components, such as silica.
For thermochemical processes ash is also detrimental since it results in a decrease in heating value.
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Analysis of Biomass
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Thermochemical vs. Hydrolysis
Analytical techniques for properties most relevant to thermochemical processing are well established, due to their use in the fossil fuel industry, and relatively rapid and low cost. Moisture and ash contents ovens and furnaces. Heating values oxygen bomb calorimeter. C, H, N, S elemental analyser. Volatile matter/fixed carbon VM furnace.
However, it takes significantly longer to determine the properties relevant to hydrolysis processes and these methods are much more reliant on careful work by the analyst.
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Time for Conventional Analysis
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Chop sample ~ 10 mins
Dry SampleSample as Collected
Milling + sieving~ 1 hour
Dry Sample of Appropriate Particle Size
Extractives Removal~ 3 days
Extractives-free sample
0 2 4 6 8 10 12 14 160
50
100
150
200
Hydrolysis and hydrolysate
analysis~ 3 days
Completed Lignocellulosic Analysis
~ 10 days !!!!
Air Drying ~ 3+ days
Wet Chopped Sample
Chemical Analysis Methods
Advantages: Established for decades. Most accurate method. Accurate for all sample types.
Disadvantages: Destructive. Needs careful sample preparation. Array of equipment required. Need highly-trained analysts. Slow (requires ~2 weeks). Costly. Hence, number of samples that can be
analysed is limited (time/cost).
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Interaction of NIR Light with Biomass
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(a) Specular Reflectance(b) Diffuse Reflectance(c) Absorption(d) Transmittance(e) Refraction(f) Scattering
NIR Analysis• FOSS XDS Monochromator.• 400-2500nm (visible and NIR).• Moving sample transport for
heterogeneous/wet samples.
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History of NIR Analysis• In development since 70’s for
the analysis of forage crops and grains.
• Now the primary method of analysis for these sectors.
• To date application of NIR for lignocellulose analysis limited to research papers.
• Celignis is the only company to offer NIR analysis as a commercial service for the lignocellulosic constituents of a wide variety of biomass samples.
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Sample Preparation Process
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Sample Collected
Wet & Unground
Dry & Unground
Dry & Ground
Scans of One Sample
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254-WU-A 254-DU-A 254-DG-A 254-DS-A 254-DT-A 254-DF-A
Wavelength (nm)
400 553 708 863 10391238143716361836203522342433
Abso
rbance
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Development of NIR Models (1)
• Target: Predict composition using NIR spectra.• Consider a spectrum as a vector with a dimension equal to
the number of variables (wavelengths).
• xi = (A400 A400.5 A401 …. A2499.5 A2500)• 4200 datapoints• A matrix can be built from the spectra of all samples in the
model (~1,200 samples currently).
• X = A1,400 A1,400.5 A1,401 …. A1,2499.5 A1,2500
A2,400 A2,400.5 A2,401 …. A2,2499.5 A2,2500
…
An,400 An,400.5 An,401 …. An,2499.5 An,2500
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Development of NIR Models (2)
• Celignis models are based on Partial Least Squares regression that reduce the dimensionality of data (e.g. 4200 variables reduced to 7 factors).
• Models are built on a set of samples (calibration set) and then tested on an independent set of samples (validation set).
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13 Constituents PredictedLignocellulosic
SugarsLignin and Extractives
Ash
Total Sugars Klason Lignin Total Ash
Glucose Acid Soluble Lignin Acid Insoluble Ash
Xylose Ethanol-Soluble Extractives
Acid Insoluble Residue (KL + AIA)
Mannose
Arabinose
Galactose
Rhamnose
Types of Samples Included
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Energy Crops Agricultural Residues
Municipal Wastes
Miscanthus Straws Paper/cardboard
Other grasses Animal manures Green wastes
Hardwoods Sugarcane bagasse Black/brown bin waste
Softwoods Forestry residues Composts
Pretreated biomass Mushroom compost
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.
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Results for Prediction Set
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Glucan Xylan Klason Lignin
Min (%): 3.77 0.59 0.83Max (%): 98.58 27.59 72.21
R2: 0.972 0.978 0.972RMSEP (%): 2.01 1.14 1.83
RER: 36.65 23.68 31.34
Regression Plot – Total Sugars
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0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Reference (%)
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Reference (%)
Regression Plot – Klason Lignin
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0 10 20 30 40 50 600
10
20
30
40
50
60
Reference (%)
NIR
- Pr
edic
ted
(%)
0 10 20 30 40 50 600
10
20
30
40
50
60
Reference (%)
NIR
- Pr
edic
ted
(%)
Results for Prediction Set
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Mannose Arabinose Galactose RhamnoseMin (%): 0.00 0.04 0.05 0.02Max (%): 14.04 6.21 4.95 1.56
R2: 0.956 0.903 0.783 0.861RMSEP (%): 0.61 0.35 0.38 0.10
RER: 23.12 12.23 8.60 14.53
Results for Prediction Set
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Acid Soluble Lignin
Extractives Ash Acid Insoluble Residue
Min (%): 0.53 0.00 0.17 0.12Max (%): 7.74 33.24 59.36 72.64
R2: 0.899 0.882 0.914 0.969RMSEP (%): 0.34 1.73 2.48 1.98
RER: 14.89 18.80 15.32 31.86
Feedstock-Specific Models
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Feedstock StatusMiscanthus (Wet & Dry) Paper PublishedPeat (Wet & Dry) Paper PublishedPig Manure Paper PublishedPaper/Cardboard In PreparationStraw 2016Sugarcane Bagasse (Wet & Dry) 2016Pre-treated Biomass 2016Composts 2016Wood 2016
Miscanthus Models• Approx. 115 Miscanthus plants sampled. • These plants were separated according to the fractions, resulting
in a total of around 700 samples.• “I” = Internodes• “N” = Nodes (each plant also sampled by the metre).• “K” = Live blades (>60% green by visual inspection)• “M” = Live Sheaths• “F” = Dead blades (<60% green by visual inspection)• “H” = Dead sheaths• “FL” = Flowers• “WP” = Whole plant (sometimes separate metre sections are
collected)• All samples analysed via NIRS, selected samples processed to
DS state and analysed via wet-chemical methods.
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Models for Miscanthus
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Dry Wet Dry Wet Dry WetCross Validation
0.966 0.955 0.957 0.861 0.957 0.917RMSECV (%) 0.914 1.082 0.426 0.776 0.578 0.806
RER (CV) 22.91 19.35 27.97 15.37 19.97 14.32Independent Validation
0.968 0.931 0.948 0.929 0.975 0.958RMSEP (%) 0.862 1.266 0.457 0.532 0.481 0.598
RER 23.81 16.20 20.05 17.05 18.49 15.75
Glucan Xylan Klason Lignin
Models for Miscanthus
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25 35 4525
30
35
40
45
50CalibrationLinear (Calibration)ValidationLinear (Validation)
Reference Glucose
Pred
icte
d (D
S M
odel
) (%
)
25 30 35 40 45 5025
30
35
40
45
50CalibrationLinear (Calibration)ValidationLinear (Validation)
Reference Glucose
Pred
icte
d (W
U M
odel
) (%
)
Time for Conventional Analysis
Chop sample ~ 10 mins
Dry SampleSample as Collected
Milling + sieving~ 1 hour
Dry Sample of Appropriate Particle Size
Extractives Removal~ 3 days
Extractives-free sample
0 2 4 6 8 10 12 14 160
50
100
150
200
Hydrolysis and hydrolysate
analysis~ 3 days
Completed Lignocellulosic Analysis
~ 10 days !!!!
Air Drying ~ 3+ days
Wet Chopped Sample
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Celignis Analytical• Launched August 2014 CEO experience (10 yrs),
~ 25 person-years for NIR models.
• Laboratory analysis of biomass (lignocellulosic and thermal). Cellulosic analysis by chemical and NIR.
• Current NIR models require dry, ground biomass samples and we provide data within 24 hours of receiving a sample.
www.celignis.com
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Remove Risk from NIR Analysis…
• NIR analysis carried out without payment.• Figures for Deviation in Prediction for the
Total Sugars and KL contents provided for free.
• Can then decide whether to pay for NIR data, wet-chemical analysis, or nothing!
• All operations carried out online with interactive database…
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“One-day analysis of biomass” www.celignis.com
Celignis NIR Method
• Advantages:• Results provided in one day (versus ~2 weeks).• Significantly lower price than chemical analysis.• Allows for a large number of samples to be screened
for their suitability in a cost-effective manner.• Proven on ~1500 biomass samples covering a wide
variety of feedstock types.
• Disadvantages:• Less accurate than chemical analysis - however
models provide an estimate of the deviation (error) in prediction and this may be low enough for many clients.
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Future Plans
• Further improve models with more samples.• Develop a local calibration algorithm do
develop unique models for each sample to be predicted (only select relevant samples for calibration set).
• Develop models for thermochemical properties (C/H/N/S, heating value, volatile matter, fixed carbon etc.) using existing sample database (1,700 samples) and new samples.
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Publications• Hayes, D.J.M., Hayes, M. H. B., Leahy, J. J. (2015), Analysis of the
lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions, Fuel 150: 261-268.
• Wnetrzak, R., Hayes, D. J. M., Jensen, L. S., Leahy, J. J., Kwapinski, W. (2015), Determination of the Higher Heating Value of Pig Manure, Waste and Biomass Valorization, doi: 10.1007/s12649-015-9350-y
• Hayes, D. J .M., Hayes, M. H. B., Leahy, J. J. (2014), Rapid analysis, using near-infrared spectroscopy, of lignocellulosic components of waste papers and cardboards, 5th International Symposium on Energy from Biomass and Waste
• Hayes, D.J.M. (2013) Biomass composition and its relevance to biorefining, The Role of Catalysis for the Sustainable Production of Biofuels and Bio-chemicals, K. Triantafyllidis, A. Lappas, M. Stoker, Elsevier B. V. 27-65
• Hayes, D. J. M. (2012) Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet Miscanthus samples, Bioresource Technology 119:393-405
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The Number One Question….Price!Number of Samples
in an Order 1-4 5-9 10-19 20-49 50+
Near Infrared Analysis 150 125 100 75 60
Chemical Analysis 450 350 300
Thermal Analysis 145 120
Preparation (if unground) 25
• Chemical/NIR analysis = total sugars, glucan, xylan, mannan, arabinan, galactan, rhamnan, Klason lignin, acid soluble lignin, ash, ethanol extractives.
• Thermal Analysis: moisture, ash, volatile matter, fixed carbon, heating value, C, H, N, S.
• Competitor price €900 per sample (x 20) = €18,000
• Celignis price (20 samples NIR) = €1,500, save €16,500 (92%).
• €18,000 would get analysis of 300 samples by NIR method!
• We will undertake chemical analysis for NIR price if the biomass type is currently under-represented in our NIR models!!
Thank You!
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M: (353) 89 455 5582T: (353) 61 518 440