Breve introdução às Tecnologias...

1Aplicações NIR na Indústria Agroalimentar | Seminário CATAA| Castelo Branco| Maio. 28, 2015 | © 4Tune Engineering 2015

Breve introdução às Tecnologias PAT

Prof. José Cardoso MenezesProfessor (IST) e CEO 4Tune Engineering Ltd

better processes, better products ©

tune (verb)

adapt, adjust, conform, fix, integrate, modulate, regulate, tighten, bring into harmony


Professional highlights

• Chemical, Petrochemical, Food & Pharma

Project Management in PAT & QbD (1996 - )

• 200+ papers & conference talks, graduated 12 PhDs & 50+ MScs theses

• Active member of ISPE, APV, PDA, ACS, AICHE, IFPAC

• Founder of The Lisbon Pharmaceutical Engineering MSc Program (2006 - )

• Trained “Portuguese FDA” (INFARMED) PAT Teams (2006, 2009, 2012)

• Built PAT Teams at six major pharma and biopharma companies

CV highlights

• Professor at TU Lisbon (2005 - )

• Invited Professor at Faculty of Pharmacy Lisbon (2009 – )

• Chemical Engineer (1985), Chemical and Nuclear Industry (1985-1986)

• MSc Organometallic Chemistry (1988), PhD in Bioengineering (1996)

• Post-Docs and Sabbatical leaves in Pharma Industry (1999, 2004, 2011)

Dr. Jose C. Menezes, 4Tune Engineering

Founder and Senior Partner of 4Tune Engineering

(2004 – )


raw-mat process

product

When final-specs depend on

RMs and/or processing conditions

easy to monitor by process NIR

high lot-to-lot variability of RMs

end-product multiple specifications

When should a PAT Strategy be Applied ?


Systems EngineeringProcess & Information Flows

________________________________________________

* production-line, process-step, analytical-instrument

SYSTEM*... ...

INPUTS

operating variablesOUTPUTS

quality specs

• Ask “what-if” questions• Be capable of system redesign

• Ability to understand parts• See interconnections


PAT: Process Understanding & Control

more than putting probes on a process !

6


P Felizardo, T Robalo, F Folque

Improving biofuels manufacturing through

NIRS and MIRS quality control.

Biofuels International 2010,9,57-58

PAT in Biodiesel Manufacturing


Process Development & ManufacturingSample, Process-Step, Production Line, Life-Cycle

Food Industry Example


PAT Implementation Scenarios

Marketed “robust” products

PAT used as IPCs to improve QC efficiency

Cost reductions associated with QC

ROI: 100’s k EUR /Year

Marketed products needing improvement

Step-wise PAT implementations to improve QA

Major process efficiency gains

ROI: 1-5 Mio EUR/Year

New products / processes

PAT tools used throughout development,

industrialization & routine manufacturing

Faster time to market and better product QA

ROI: 100’s Mio EUR /Year

A

B

C

without PAT with PAT


Opening a Window onto the Process

PAT instrumentation


PAT Monitoring: from A to Z

ABB, Bruker, Fogale, Kaiser, Parsum, Thermo, Yokogawa, Zeiss-Corona

at-line or on-line 2D-FLUO, Acoustics, Dielectrics, PSD, MIR, MRT, NIR, RAMAN

liquids, suspensions, powders, solids


Multivariate Calibration Methods

Ab

sorb

an

ce

(a

.u.)

Wavenumber (cm-1)

Chemical and

physical

information

PCA

PLS

Pro

pe

rty

X p

red

icte

d

Property X measured

Multivariate fingerprint of

sample properties –

understanding

relationships

Multivariate fingerprint for

classification purposes

Multivariate

calibration (e.g.,

moisture, API content)


Good-Modeling Practices

• Know your Process– Dynamics, process phases and how these are represented in the available data

(sampling probs etc)

– Are process variables (PVs) cross-correlated or not ?

– Which PVs affect the CQA of interest and should threfore be included in the model)

• Know the Data acquired on your Proceess– Structure (clusters), quality, outliers, missing data points, to be able to choose which

samples for model calibration and validation

• Choose Model Strategy: MLR – PLS– model structure defined by the calibration data set; model should be able to model

well trends in Y (.e., main CQA features)

• Calibrate and Validate the Model– model accuracy evaluated with a validation set (data not used before)

– Use several figures of merit: (1) predicted x fitted; (2) residual plots; (3) statistical tests

RMSE (root mean square error)


Batch Trajectories & Batch Variability

industrial

bioreactors


• PCA using batch wise unfolding

• Correlation with relative yield

On-line NIR Fermentation Monitoring


Batch Process


Life-Cycle Analysis

• Product Design / Realizationrisk-assessment tools

• Process Development / Industrializationknowledge, design and operating spaces

• Routine Manufacturingcontrol strategies

• Continuous Improvementcontinued verification and troubleshooting

“Knowledge has temporal spread. Information is not knowledge.”Deming


Process Analytical TechnologyData – Information - Knowledge

DATA INFORMATION

KNOWLEDGE


DIKI* SolutionsIntelligence for your data

Added value increase

Data Inform ation Knowledge Intelligence

A Systems-Engineering Approach

to PAT Implementations

Unit

operationsProcess Products

Overall

system

Monitor Control QualityContinuous

improvement


What we at 4Tune Engineering

learned over 10 years ?

PAT MonitoringMeasure whole samples to assess process, not sample parameters

Batch Processing1st sample is not just one more sample

Consecutive Batch StepsEnd-to-End Analysis

PAT SolutionsSupport Quality over the life-cycle, not just process-understanding


Industry Challenges

What drives Food companies to become

“operationally excellent” is …

Global competition

Price pressure from Competitors

Regulatory initiatives for Process Innovation

Customer expectations

Shareholder expectations

Manufacturing costs

Increasing standards and regulatory requirements


Lean-Six Sigma vs PAT & QbDfor already existing processes/products

Six Sigma

• Minimizes process variation

• Stable and predictable process results.

• Improves process understanding

• Identify & control causes of defects

Focus on Accuracy

Lean Manufacturing

• Eliminates Waste

• Standardizes work

• Continuous Flow

• Pull strategy

Focus on Productivity

Lean Six Sigma (LSS)


PAT-Based Manufacturing

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 (MPC, feed-forward)

End-Product Quality Control

– Multiparametric QC (e.g., conformity index)

– Blending operations for targeted specifications (when allowed)


Publically Available

References and Credentials

of 4Tune Engineering Ltd


tune (verb)adapt, adjust, conform, fix, integrate, modulate, regulate, tighten,

bring into harmony


• Felizardo P, Folque F, Machado JE, Menezes JC (2012), Process Analytical Technology: a common

approach across different industries. NIRNEWS 23(10),10-13.

• Alcalà, M., Blanco, M., Menezes, J. C., Felizardo, P. M., Garrido, A., Pérez, D., Zamora, E., Pasquini,

C. and Romañach, R. J. (2012). Near-Infrared Spectroscopy in Laboratory and Process Analysis.

Encyclopedia of Analytical Chemistry, pp. 1-46, John Wiley & Sons, Ltd.

• Menezes, J.C. Correia J.N., The Use of PAT Tools in Biodiesel Production., IFPAC - 21st Int. Process

Analytical Chemistry & Technology Forum, January 27-30, 2008 Baltimore, Maryland (USA)

• Jaumot J, Menezes JC, Tauler R (2006), Quality assessment of the results obtained by multivariate

curve resolution analysis of multiple runs of gasoline blending processes. J. Chemom. 20,54-67 (doi:

10.1002/cem.985).

• Brás LP, Bernardino SA, Lopes JA, Menezes JC (2005), Multiblock PLS as an approach to compare

and combine NIR and MIR spectra in calibrations of soybean flour. Chemom. & Intell. Lab Syst. 75(1),

91-99.

• Felício CC, Brás LP, Lopes JA, Cabrita L, Menezes JC (2005), Comparison of PLS algorithms in

gasoline and gas oil parameter monitoring with MIR and NIR, Chemom. Intell. Lab Syst. 78(1-2),74-

80.

General References


• Canha N, Felizardo P, Menezes JC, Neiva Correia MJ (2012), Multivariate near infrared spectroscopy models for predicting the

oxidative stability of biodiesel: Effect of antioxidants addition. Fuel 97, 352–357.

• Felizardo P, Menezes JC, Neiva-Correia MJ, PAT Use in Biofuels Manufacturing, chpt 11, pp 201-221, in PAT Applied in

Biopharmaceutical Process Development and Manufacturing An Enabling Tool for Quality-by-Design. Eds Cenk Undey,

Duncan Low, Jose C. Menezes, Mel Koch CRC Press (2011).

• Folque F, Lewis I, Menezes JC (2011), Use of RAMAN as a PAT Tool in Biodiesel Analysis., IFPAC – 25th Int. Process

Analytical Technology Forum, January 17-21, 2011, Baltimore, Maryland (USA).

• Baptista P, Felizardo P, Menezes JC, Neiva-Correia MJ (2008), Multivariate near infrared spectroscopy models for predicting

the iodine value, CFPP, kinematic viscosity at 40ºC and density at 15ºC of biodiesel. Talanta 77(1),144-151.

• Baptista P, Felizardo P, Menezes JC, Neiva-Correia MJ (2008) Monitoring the quality of oils for biodiesel production using

multivariate near-infrared spectroscopy models. J. Near Infrared Spectrosc. 16(5), 445–454.

• Baptista P, Felizardo P, Menezes JC, Neiva-Correia MJ (2008), Multivariate near infrared spectroscopy models for predicting

the methyl esters content in biodiesel. Anal. Chim. Acta 607,153–159.

• Menezes JC, Felizardo P, Neiva-Correia MJ (2008), The Use of Process Analytical Technology in Biofuels Production.

Spectroscopy 23(9),30-33.

• Felizardo P, Baptista P, Menezes JC, Neiva-Correia MJ (2007), Multivariate near infrared spectroscopy models for predicting

methanol and water content in biodiesel. Anal. Chim. Acta 595(1-2),107-113.

• Felizardo P, Baptista P, Sousa-Uva M, Menezes JC, Neiva-Correia MJ (2007) Monitoring biodiesel fuel quality by near infrared

spectroscopy, J. Near Infrared Spectrosc. 15(2), 97–105.

Biofuels


THANK YOU, any questions ?

Chief Business Development ManagerJoão E Machado, 4Tune Engineering Ltd

[email protected]

4Tune Engineering LtdAv. Antonio Augusto Aguiar 108, 4th floor1050-019 Lisbon, Portugalwww.4TuneEngineering.com

Tel. (+351) 21 606 2788 Fax (+351) 21 606 2790


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