analysing biomass feedstock s biofuels international expo and conference porto, sep 23 rd 2015...

Analysing Biomass Feedstoc

ks

Biofuels International Expo and Conference

Porto, Sep 23rd 2015

Laurence Corbett

[email protected]

www.celignis.com

mailto:[email protected]

http://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.

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

Second Generation Biofuels….


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.


Cellulose

A glucan polysaccharide.

Most abundant biogenic polymer with an annual global production of 100 x 109 tonnes.


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.


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.


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%.


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.


Analysis of Biomass


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.


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

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).


Interaction of NIR Light with Biomass


(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.


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.


Sample Preparation Process


Sample Collected

Wet & Unground

Dry & Unground

Dry & Ground

Scans of One Sample


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


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).


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


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.


Results for Prediction Set


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


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


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

(%)



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



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


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.


Models for Miscanthus


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


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


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

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…


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.


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.


Website: www.celignis.com



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


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!

www.celignis.com

[email protected]

M: (353) 89 455 5582T: (353) 61 518 440


mailto:[email protected]

analysing biomass feedstock s biofuels international expo and conference porto, sep 23 rd 2015...

Documents

day analysis of biomass

acid hydrolysis

wrong feedstock

right feedstock

enzymatic hydrolysis

feedstock types

different biota

different enzymes