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Rapid Analysis Methods for Oils and

Biofuels Using IR/NIR Spectroscopy

Ching-Hui Tseng, Nan Wang

Eurofins QTA, Inc.

July 2013

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Biofuels

Fuel produced from renewable resources, especially plant biomass,

vegetable oils, and treated municipal and industrial wastes.

Biodiesel is made by processing vegetable oils and other fats. It

can be used either in pure form or as an additive to petroleum-based

diesel fuel.

Bioethanol is produced by fermenting the sugars in biomass

materials such as corn and agricultural residues etc. It is used in

internal-combustion engines either in pure form or more often as a

gasoline additive.

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Biodiesel Reaction (Trans-esterification)

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Test

Test

Test

Optimization

Segregation

Consistency

Price

Efficiency

Consistency

Quality

Glycerine

ROHOilGrease

Biodiesel

Catalyst

Biodiesel Production & Testing

Test

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Tests for Biodiesel Production

To produce quality biodiesel:

The in-coming fat/oil needs to be tested to see if pretreatment is needed

and the pretreatment outcome is satisfactory.

The trans-esterification reaction can be monitored by testing the in-

process samples.

The finished biodiesel need to meet ASTM D6751 (US) or EN14214

(Europe) standards to be used as biodiesel or blended with diesel fuel.

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Biodiesel Traditional Testing Methods --Specification for Biodiesel (B100) ASTM D6751-11a

Property ASTM Method Limits Units

Methanol EN 14110 0.2 max %mass

Water by KF D6304 0.05 %mass

Kinematic Viscosity, 40C D445 1.9 6.0 mm2/sec

Sulfur, S15 Grade D5453 15 max ppm

Cloud Point D2500 Report C

Acid Number D664 0.5 max Mg KOH/g

Free Glycerin D6584 0.020 max %mass

Total Glycerin D6584 0.240 max %mass

Oxidation Stability EN15751 3 min hours

Cetane D613 47 min

Cold Soak Filtration D7501 360 max seconds

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Biodiesel Traditional Testing Methods-Specification for Biodiesel (B100) EN14214

Property EN Method Limits Units

Methanol, EN 14110 0.2 max %mass

Water by KF EN ISO 12937 500 max mg/kg

Viscosity, 40C EN ISO 3104, ISO 3105 3.5 5.0 mm2/sec

Sulfur EN ISO 20846, EN ISO 20884 10 max mg/kg

Pour Point ISO 3016 Report C

Acid Value EN14104 0.5 max mg KOH/g

Free Glycerin EN14105, EN14106 0.020 max %mass

Total Glycerin EN14105 0.25 max %mass

Oxidation Stability, 110C EN15751/EN14112 6 min hours

Cetane EN ISO 5165 51 min

Ester Content, EN 14103 96.5 min %mass

Cold Filter Plugging Point EN 116 C

Iodine Value EN 14111 120 max g iodine/100g

Density, 15C EN ISO 3675, EN ISO 12185 860 - 900 kg/m3

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Traditional Analysis Methods of Biodiesel Production

In-coming oil:

Free fatty acids: gas chromatography (GC)

Moisture: Karl Fischer

In-Process:

Mono-, di- & tri- glycerides, total & free glycerin: GC method

Finished B100 biodiesel:

Total & free glycerin, methanol: GC method

Acid number, cloud point, moisture, oxidation stability, CFPP, density,

ester content, iodine value etc.: different equipment for each test

By-product: Glycerin

Ash: oven method

Glycerin, methanol: GC

Moisture: Karl Fischer

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Traditional Analysis Method Summary

In summary, the traditional methods

Need a laboratory with all the required instruments

Require chemists with different technical skills

Expensive and resource intensive

Time consuming

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A Desired Technology

One instrument can analyze all the materials, including feedstock,

in-process samples, finished biodiesel and by-product

Easy to operate so no specialized personnel is required

Quick and green (no chemical reagents or waste disposal)

Analyzed results are reliable

Instrument and methods are easy to maintain

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Infrared (IR) Spectroscopy

It has been a very powerful analytical tool for

sample identification and component analysis

since the 1960s.

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Principle of Infrared (IR) Spectroscopy

h

Low Energy High Energy

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Infrared Vibrational Energy States

Fundamental

1st Overtone

2nd Overtone

3rd Overtone

Harmonic Oscillation

Inharmonic Oscillation

Vib

rational energ

y levels

The mid-infrared (IR) covers

mostly fundamental vibrations

while the near infrared (NIR)

covers the overtone and

combination bands

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What is IR and NIR ?

Infrared (IR) spectroscopy : fundamental absorption of

molecular functional groups, 25-2.5 m (400-4000 cm-1)

Near infrared (NIR) spectroscopy : overtone or combination

absorption of molecular functional groups, 2.5-0.7 m (4000-

14,000 cm-1)

IRNIR

Ab

so

rba

nc

e

Wavenumber cm-1

Oleic Acid

NIR

MIR

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Electromagnetic Spectrum: Infrard is between visible and microwave regions

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MIR/NIR comparison

MIR NIR

Absorption coefficient large (higher sensitive) small (lower sensitive)

Pathlength micrometers mm to tens of cm

Sample container Salt, ATR quartz, glass

Spectrumcomplicated but unique

(can be interpreted)simple but overlapped

(hard to interpret)

Sample Gas, liquid, paste, solid (small

amount, homogeneous, e.g.

biodiesel production samples )

Liquid, paste, solid (large

amount, inhomogeneous, e.g.

bioethanol production samples)

Analyzed materialGood for organic

Possible for inorganic

Good for organic

Poor for inorganic

Available fiber optics 3 m (max.) 100 m (max.)

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How does IR/NIR work for

the qualitative analysis?

Qualitative analysis - confirmation of incoming raw

materials, identification of unknown samples

Method - spectral matching, PCA, ANN etc.

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How does IR/NIR work for the quantitative analysis?

Quantitative analysis - determination of contents of chemical composition or chemical properties.

Chemometric modeling is required: MLR, PLS, ANN

Creating Calibrations

Analyzing Samples1. Samples and data 2. Collect Spectrum 3. Build , Optimize

& Test Model

Report

Sample #081897-049

Component A 81.55%

Component B 5.38%

Component C 13.06%

1. Measure Unknown 2. Access Model3. Predict Concentrations

Component A B C

Units % % %

spectrum1 71.30 7.03 21.67

spectrum2 79.30 3.06 17.64

spectrum3 78.40 8.34 13.26

spectrum4 84.03 4.32 11.65

spectrum11 85.02 1.34 13.64

spectrum12 78.34 3.85 17.81

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Challenges of using IR/NIR

Need to find optimal instrument and sampling device for the application

Need Chemometrics and spectroscopic experts to build and maintain

methods

Need lots of representative samples and good quality primary data to

build the models

Need to have the ability to determine and resolve any issues while

using IR/NIR methods.

Needs to be user-friendly; ideally can be operated by the plant operator

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Example Solution of Overcoming These Challenges

Internet-Enabled IR/NIR

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Why Internet?

Monitor instrument performance remotely

Solve problems remotely

Store and distribute data automatically

Use central models for the prediction

Develop or update models remotely

Expand applications without limit

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Why Central Models?

All the application models are located at the central server

Simple analyzer with a smart brain

No instrument-specific models- one model for the same

application in different instrument

No model transfer or adjustment

Consistent prediction instrument variance compensated

Plug-and-play

No technology limit PCR, PLS, ANN etc.

Model update simultaneous for all users

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Demo of the Internet-Enabled Biodiesel Analyzer (www.QTA.com)

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Works behind the Biodiesel Analyzer

24/7 helpdesk service with experts monitoring and maintaining

performance

Thousands spectra of real world samples with reliable primary data

included in each calibration

Wide range of feedstocks, including soy (virgin, crude and

degummed), canola, rapeseed, poultry, tallow, choice white grease,

yellow grease, waste vegetable oil, sunflower oil, castor oil, corn,

palm, jatropha and blends thereof

Participation in ASTM PTP (Proficiency Testing Programs)

Methods underwent full round robin utilizing AOAC and ASTM

methodologies

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QTA Participation in ASTM PTP

Total Glycerine%

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0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Aug-07 Nov-07 Apr-08 Nov-08 Apr-09 Aug-09

Robust Mean QTA (%)

51 L

AB

S

48 L

AB

S

47 L

AB

S

65 L

AB

S

63 L

AB

S

63 L

AB

S

Blue bar = range of accepted reference lab results for 47 65 labs

X = Mean of QTA results

= Robust Mean of reference lab

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Example Calibration Curves on Server

QTA Model Statistics

Range, % R2, % QTA Std. Error, %

0.0 0.64 93.5 0.03

QTA Model Statistics

Range, % R2, % QTA Std. Error, %

0.1 2.8 94 0.14

Total glycerin of B100 samplesMonoglycerides of in-process samples

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EQTA IR Models of Biodiesel Process

In-Process Reference Method Range Standard Error

Monoglyceride, % ASTM D6584 0.1 3.0 0.14

Diglyceride, % ASTM D6584 0.1 5.5 0.11

Tri-glyceride, % ASTM D6584 0.1 9.9 0.18

Incoming Oil Reference Method Range Standard Error

FFA, % AOCS Ca 5a-40 0.0 44.5 0.3

Moisture, % KF Moisture 0.0 1.0 0.05

Recovered Methanol Reference Method Range Standard Error

Moisture, % KF Moisture 0.0 20.0 0.5

Crude Glycerin Reference Method Range Standard Error

MeOH, % GC/FID 0.0 25.0 0.32

Glycerin, % AOCS Ea 6-94 80.0 99.9 0.9

Ash, % AOCS Ea 2-38 0.0 15.0 0.4

Moisture, % AOCS Ea 8-58 0.3 25.0 0.77

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EQTA IR Models of B100 Biodiesel Product

for ASTM D6751 and EN14214 Specifications

QTA algorithms Reference Method Range Standard Error

Free Glycerin#*, % ASTM D6584 0.00 0.03 0.004

Total Glycerin#*, % ASTM D6584 0.00 0.50 0.03

Total Acid Number#, mg KOH/g ASTM D664 0.0 1.0 0.09

Cloud Point#*, C ASTM D2500 -6.0 12.0 1.5

Moisture#, % ASTM D6304 0.0 0.1 0.09

Methanol#*, % EN14110 0.0 3.0 0.06

Oxidative Stability#, Hrs EN14112 0.5 12.0 1.5

Sulfur#, ppm ASTM D6453 0.3 16.2 1.9

Ester, % EN14103 83 99.9 0.9

Iodine Value AOCS Cd 1b-87 46 152 1.2

Density, kg/m3 ASTM D4052/D1217 873 891 1

Kinematic Viscosity, mm2/s ASTM D445 3.8 5.2 0.1

CFPP, C ASTM D6371 -14 12 1

Monoglyceride#*, % ASTM D6584 0.1 0.9 0.09

Diglyceride, % ASTM D6584 0.1 0.6 0.06

Tri-glyceride, % ASTM D6584 0.1 0.6 0.08

# AOCS Ck 2-09 standard procedure* Approved alternative methods included in the ASTM D6751

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Ethanol Production (Example: Corn Ethanol)

Corn receiving and storage

Cleaning

milling/grinding

Mixing: H2O/enzyme

--corn mash

Cooking to reduce bacterial

more enzyme --simpler sugar Fermentation

Distillation column

Molecular Sieve

Ethanol

Centrifuge

Thin stillage

Evaporator

Syrup

Solids

Rotary dryer

DDGS

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Traditional Analysis for Bioethanol Production

To ensure the quality of the bioethanol produced, quality tests are

conducted for incoming corn, processed corn in the slurry tank,

fermentation tank, DDGS (Dried Distillers Grains with Solubles)

produced.

Time consuming analytical methods have to be used to analyze

moisture, oil, protein and starch of the incoming corn.

HPLC method is commonly used to analyze carbohydrates (glucose,

maltose, maltotrios, dextrin etc.), acids (potential inhibitors), ethanol,

and other alcohols for samples from the slurry tank and the

fermentation tank.

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Protein, moisture, oil,

starch

Rejects loads of corn

on the basis of

moisture

Tracks corn suppliers

more closely

More information about

the crop for process

optimization

Using NIR for the analysis on in-coming corn

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Measure ethanol, carbohydrates, acids, and glycerol at any time period during fermentation

Quality control

Trouble shooting

Enzyme evaluations

Supplement evaluations

Using NIR for the Analysis on Corn Mash

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Typical fermenter profile

Increase in ethanol

Decrease in dextrins , maltose, dextrose

Increase in lactic acid

Increase in glycerol

Using NIR for fermenter monitoring

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A Profile of Ethanol Production during Fermentation

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

Hours of Fermentation

% E

tha

no

l

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A Profile of Carbohydrate Reduction during Fermentation

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

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Hours of Fermentation

% C

arb

oh

yd

rate

Dextrose

Maltose

Dextrins

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A Profile of Lactic Acid Production During Fermentation

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

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Hours of Fermentation

% L

ac

tic

Ac

id

Lactic Acid is produced by the bacteria -Lactobacillus sp.

Competes with yeast for dextrose -reduce yield

Lactobacillus comes from improper cleaning of fermenters undercooked corn contaminated yeast

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A Profile of Glycerol Production During Fermentation

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

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Hours of Fermentation

% G

lyc

ero

l

Produced from yeast when under stressIncreased ethanolIncreased lactic acidHigh sugar concentrationLow pHHigh temperatureLack of nutrients (nitrogen)

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Corn Models

QTA Starch Model Statistics

Range, %DB R2, % QTA Std. Error, %DB

61.0 72.4 96.3 0.8

QTA Oil Model Statistics

Range, %DB R2, % QTA Std. Error, %DB

2.6 10.7 96.6 0.4

QTA Protein Model Statistics

Range, %DB R2, % QTA Std. Error, %DB

6.3 15.5 97.6 0.4

QTA Moisture Model Statistics

Range, % R2, % QTA Std. Error, %

7.3 24.8 99.3 0.3

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EQTA NIR Models of Corn and DDGS

Corn DDGS Range R2, % Standard Error

Oil, %DB 8.8 13.8 85.0 0.4

Protein, %DB 25.0 34.4 97.7 0.5

Moisture, % 5.4 16.6 98.3 0.5

Corn Range R2, % Standard Error

Oil, %DB 2.6 10.7 96.6 0.4

Protein, %DB 6.3 15.5 97.6 0.4

Moisture, % 7.3 24.8 99.3 0.3

Starch, % DB 61.0 72.4 96.3 0.8

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EQTA NIR Models for Biethanol Slurry Tank

Corn Mash form

slurry tank

Range R2, % Standard

Error

DP2 (maltose), % 0.18 1.57 78.0 0.18

DP3 (maltotrios), % 0.0 5.2 80.4 0.63

DP4+ (dextrin), % 0.0 24.5 97.2 1.4

Acetic Acid, % 0.002 0.030 96.4 0.004

Glucose, % 0.01 2.53 97.3 0.14

Glycerol, % 0.1 2.2 87.8 0.2

Lactic acid, % 0.01 0.88 90.8 0.07

Dextrose, % 0.08 1.1 93.4 0.09

Total Solids, % 10.0 38.5 98.5 1.1

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EQTA NIR Models for Bioethanol Fermentation Tank

Corn Mash form

fermentation tank

Range R2, % Standard

Error

Brix 10.0 27.9 99.3 0.66

DP2, % 0.18 9.20 87.5 0.95

DP3, % 0.06 2.75 90.8 0.20

DP4+, % 0.2 13.9 97.2 0.74

Ethanol, % 0.54 13.47 99.8 0.23

Glucose, % 0.0 16.1 96.9 0.78

Glycerol, % 0.1 2.0 87.8 0.16

Lactic acid, % 0.01 0.28 95.5 0.02

pH 3.7 5.7 76.9 0.23

Total Sugar, % 1.2 30.9 99.4 0.84

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Demo of the Internet-Enabled NIR Analyzer (www.QTA.com)

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Summary

Productions of biofuels, including biodiesel and bioethanol,

need analytical tests at different stages to ensure the

quality and yield of the products.

Traditional analytical methods are expensive, time-

consuming and need many analytical devices and skills.

Spectroscopic methods, IR/NIR, can be used to perform

rapid analysis for multiple traits but there are some

challenges when using them.

An Internet-enabled IR/NIR system has been developed

and successfully used in production plants worldwide for

the rapid analysis of biodiesel and bioethanol.

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Thank You

Question?

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