CPAC IFPAC2010 - University of Washingtondepts.washington.edu/cpac/Activities/Meetings/Satellite/... · 2012. 11. 27. · Vegetable Oils (soybean, palm, jatropha etc) ... Note: oils

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CPAC SATELLITE WORKSHOP, March 22-24, 2010 – Rome (Italy) 1 CPAC SATELLITE WORKSHOP, March 22-24, 2010 – Rome (Italy) 2

Outline

!   Introduction

•  PAT in Raw-Materials – i.e., Oils Qualification

•  PAT in Production – i.e., Process Supervision

•  PAT in Final Product – i.e.,Biodiesel QC

!  Conclusions

CPAC SATELLITE WORKSHOP, March 22-24, 2010 – Rome (Italy) 3

PAT Building Blocks


PAT Based Biofuel Production

!   Assessing Raw-Materials' Variability –  Complex raw-materials (RM): fast qualification schemes (QC) –  Opportunity for RMs supervisory systems (QA) –  Formulating mixed lots of RMs for targeted specifications

!   Handling RM Variability During Processing –  Monitoring quality attributes in-process (IPCs) at-line or on-line –  Process supervision (e.g., multivariate trajectories) –  Controlling critical to quality parameters

!   End Product Quality Control –  Biofuels multiparametric QC (e.g., conformity index) –  Blending operations for targeted specifications

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raw-mat process product

investment costs (storage) & profit margins (QC)

a batch process likely to become continuous prone to process spectroscopy monitoring

catalytic reaction phase separation

washing & drying biodiesel

Vegetable Oils (soybean, palm, jatropha etc) ‏

Algae Oils Waste frying Oils

Animal Fats

Biodiesel

How can PAT be important ?


Monitoring with a Lab Instrument

Interfaced with the Process

Process Finger-printing Process Trajectories Process Supervision, Fault Detection End-Point Determination Conformity Index (Multiparametric QC) ‏

Accurate Calibration Models between spectral and analytical data - methyl ester content - residual alcohol content - water ‏ ...

NIR Transflectance Probes (Solvias, AG)‏

ABB MB160 FT-NIR


Outline

!   Introduction




!  Conclusions

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Raw Materials Qualification

Type of Oil PCA of Pure oils

Palm

Rapeseed

Soybean

Iodine number


Raw Materials Monitoring Type of Oil/Blendings PCA of Mixtures of oils

Palm

Rapeseed

Mixtures

Iodine number

Soybean

EN14214 imposes Iodine Number < 120


Raw Materials Monitoring QC of Oil Properties NIR PLS Calibrations

Property Error of the reference method

Error of NIR models

(RMSEP) ‏

Calibration Range

Water content (mg/kg) ‏

50 68 478 – 1776

Iodine Value (g I2/100 g) ‏

3 1.0 60 – 126

Acid Value (mg KOH/g) ‏

0.1 0.41 0.13 – 6.56

Dynamic Viscosity (cP) ‏

2 1.9 5 – 74

Density at 15ºC (kg/m3) ‏

0.5 0.9 875 – 925


Raw Materials Monitoring QC of Oil Blending (%) NIR PLS Calibrations

Property Error of NIR

models (RMSEP in %) ‏

Calibration Range

% Soybean Oil (SO) 2.4 0 -100%

% Rapeseed Oil (RO) 3.2 0 -100%

% Palm Oil (PO) 1.2 0 -100%

Note: oils mixtures were laboratory prepared by weighing and mixing the oils in the desired proportions

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Raw Materials Formulation Oil Mixtures Compositions (% of SO in SO/RO/PO mixtures)

NIR PLS Calibrations

(× = calibration set; ▲ = validation set). CPAC SATELLITE WORKSHOP, March 22-24, 2010 – Rome (Italy) 14

Outline

!   Introduction




!  Conclusions


Following the transesterification reaction

Production Process Monitoring

Raw-‐materials

In process samples

Biodiesel


Production Process Monitoring

Following Biodiesel/Glycerol Separation

Batch Process

NIR Probe in Biodiesel Phase

Reaction End (stirring On)

Phase Separation (stirring Off) ‏

Phase Equilibrium (stirring Off) ‏

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Outline

!   Introduction




!  Conclusions


NIR Fingerprinting- Different Biodiesel Lots

Final Product Quality Control

IV 110 – 120

(opt

imum

)

IV 60 -‐ 6

5

IV 65 -‐ 110

IV 12

0 -‐ 135

Biodiesel before refining

Refined Biodiesel

IV-‐ Iodine Value


Accurate calibrations developed for 15 quality biodiesel quality specs

(EN 14214 25 parameters) ‏

Property EN 14214 limit

Calibration Range

Error of NIR models

(RMSEP) ‏

Error of the reference method

Methyl esters 1.5 0.9 99.3 – 78.4 % 96.5 < ‏ )%(

Monoglycerides (mg/kg)600 654 11738 – 2641 8000 > ‏

Diglycerides (mg/kg) 184 389 7846 – 82 2000 > ‏

Triglycerides (mg/kg) 282 - - 2000 > ‏

Free Glycerol (mg/kg) 56 - - 500 > ‏

Total Glycerol (mg/kg)218 178 4230 – 1371 2500 > ‏

Methanol (mg/kg) 70 67 2864 – 2 2000 > ‏

Linolenic acid methyl ester (C18:3) (%) 1 0.18 8.9 – 0.3 12 > ‏



Property EN 14214 Limit

Calibration Range

Error of NIR models

(RMSEP) ‏

Error of the reference method

Methyl esters composition (%) Not specified Depends on ‏

the ester Depends on

the ester Depends on

the ester

Iodine value (g I2/100 g) 3 0.7 135 – 62 120 > ‏

Density at 15ºC (kg/m3) 0.5 0.9 925 – 875 900 – 850 ‏

Kinematic viscosity at 40ºC (mm2/s) 0.08 0.08 4.91 – 3.67 5.0 – 3.5 ‏

Water content (mg/kg) 50 72 1859 – 218 500 > ‏

Acid value (mg KOH/g) 0.05 0,05 0.68 – 0.09 0.5 > ‏

Oxidative Stability (h)

 6 4.32 – 16.8 0.28 0.12

CFPP (ºC) Varies -13 to 5 1.0 1 ‏

CP (ºC) Not specified -3 to 9 0.4 1 ‏


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EN 14214 vs ASTM

Critical Specification Quality Parameters According to BQ 9000


Property EN 14214 limit

ASTM 6751 limit

NIR Calibration

Methanol Content √ 0.2 > 0.2 > ‏ )%(

Water & Sediment (% Vol.) - 0.05 > - ‏

Water Content √ - 0.05 > ‏ )%(

Sulfur (mg/kg) or 500 X 15 10 > ‏

Free Glycerol X 0.02 > 0.02 > ‏ )%(

Total Glycerol √ 0.24 > 0.25 > ‏)%(

Acid number (mg KOH/g) ‏

< 0.5 < 0.5 √

Oxidation Stability (h) ‏

> 6 > 3 √ Cloud Point

(º C)‏ Contract Contract √ CPAC SATELLITE WORKSHOP, March 22-24, 2010 – Rome (Italy) 22

Biodiesel Quality Control

Biodiesel analyses are very expensive and time consuming

It takes + 2 days to perform by the reference methods the analyses of the 15 quality parameters calibrated with NIRS

To perform the simultaneous analyses of the calibrated properties by NIRS, it is only necessary to acquire the NIR spectrum of the sample, a procedure that takes less than 2 minutes

The prediction errors of NIRS models are comparable to the reference methods errors


Good Chemometric Practices Robust long-term accurate calibrations established using industrial and lab-scale samples

Methanol

(x calibration set; ▲ validation set)

Example: methanol and FAME in biodiesel

FAME


Mixtures

of oil,

biodiesel

and

fossil diesel Diesel

Biodiesel

Oil

End-Product QA: Blending & Counterfeit

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Outline

!   Introduction




!  Conclusions


!   Assessing Raw-Materials' Variability –  Qualification (QC) of Complex Raw-Mat‏ –  Opportunity for Raw-Mat Supervisory Systems –  Formulating Oil-Mixes for targeted specifications

!   Handling RM Variability During Processing –  Monitoring IPCs at-line or on-line –  Process supervision (e.g., mapping process trajectories)‏ –  Controlling critical to quality parameters

!   End Product Quality Control –  Biodiesel multiparametric QC (e.g., conformity index)‏ –  Blending operations for targeted specifications

Conclusions


Conclusions

NIR Spectroscopy is a very powerful analytical technique in biodiesel production specially if used as

process analytical technology

fingerprinting, multiparametric and whole-process-based


Conclusions

NIR Spectroscopy is a very powerful analytical technique in biodiesel production specially if used as

process analytical technology

fingerprinting, multiparametric and whole-process-based

NIRS is accepted as analytical technique for parametric release of

food products (that we eat – when healthy) pharmaceuticals (that we take – when ill) why not for Biodiesel ? (oh no, not in my car !)

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Breewing & Dairy Biomanufacturing

Pharma Petrochemical


4TUNE Engineering Ltd better processes, better products


Offices in Lisbon, Clients Everywhere a charming history-rich European capital


Biotech

Chemical (CPI)

Pharma (API)

Petrochemical

Biofuels

Food

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Thank you !

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Recent Papers by Us

P. Felizardo, P. Baptista, J.C. Menezes and M.J. Neiva Correia, Predicting methanol and water content in biodiesel by near infrared spectroscopy, NIR news 19(3), 7–9, 2008.

Baptista P., Felizardo P., Menezes J.C., Correia M. J. N., Multivariate Near Infrared Spectroscopy Models for Predicting the Methyl Esters Content in Biodiesel, Analytica Chimica Acta 607, 153-159, 2008.

Felizardo P., Baptista P., Menezes J.C., Correia M. J. N., Multivariate Near Infrared Spectroscopy Models for Predicting Water and Methanol Content in Biodiesel, Analytica Chimica Acta V. 595, Issues 1-2, 107-113, 2007.

Baptista P., Felizardo P., Menezes J.C., Correia M. J. Neiva, Monitoring the Quality of Oils for Biodiesel Production Using Multivariate NIR Spectroscopy Models, J. of Near Infrared Spectroscopy, 16, 445-454, 2008

Baptista P., Felizardo P., Menezes J.C., Correia M. J. Neiva, Multivariate Near Infrared Spectroscopy Models for Predicting the iodine value, CFPP, Kinematic Viscosity at 40ºC and Density at 15ºC of Biodiesel, Talanta, 77, 144-151, 2008

J.C. Menezes, P. Felizardo, M.J. Neiva Correia, The Use of Process Analytical Technology (PAT) Tolls in Biofuels Production, Spectroscopy News, Sept. , 2008



Documents

CPAC IFPAC2010 - University of Washingtondepts.washington.edu/cpac/Activities/Meetings/Satellite/... · 2012. 11. 27. · Vegetable Oils (soybean, palm, jatropha etc) ... Note: oils