pat – the impact for process systems engineering · what is process systems engineering? process...

Julian Morris1, Zeng-ping Chen2, David Lovett2 & David Littlejohn3

Centre for Process Analytics and Control Technologies1Newcastle University, 2Perceptive Engineering, 3Strathclyde University

PAT – the Impact for Process Systems Engineering

Overview of Presentation

What is Process Systems Engineering?

Where are we now, where are we going and how important is variability?

Look at some novel methodologies for building fit-for-purpose calibration and statistical monitoring models

Highlight some process analytical technologies through work aiming to improve understanding of monitoring and controlling batch crystallisation processes using advanced Chemometrics

Show how some new closed loop process PAT control ideas are coming to fruition through innovative process systems engineering - Closing the Analytical Control Loop

Closure

The EU provides 32% of the worlds chemicals manufacturing through some 25,000 enterprises of which 98% are SMEs which account for 45% of the sectors

‘added value’, and 46% of all employees are in SMEs

What is Process Systems Engineering?

Process Systems Engineering (PSE) is a well established scientific area, concerned with the development of methods and computer-based tools for an integrated approach to all aspects of process modelling, simulation, design, operation, control, optimisation and management of complexity in uncertain systems across multiple time and scale lengths.

These tools enable Process Systems Engineers to systematically develop products and processes across a wide range of systems involving chemical, biological and physical changes from molecular and genetic phenomena to manufacturing processes and related business processes.

Where are we Now and Where are we Going

in Process Analytics and Control Technologies?

Impurities and Polymorphism (1)

Impurities and Polymorphism (2)

Impurities effect nucleation and growth processes, and hence canstabilise meta-stable polymorphic forms.

As product purity improves during process chemistry work-up, the “stable” polymorphic form can change !

e.g. RITONOVIR aids drug which changed from anhydrous to hydratecrystal after launch:

with lower solubility and hence bio-availability.

product was withdrawn for a year and reformulated.

new FDA approval needed – mega cost implication !

Where Are We Going?From thisFrom this

Reactor

TurbidityEnablirT, pH PC

Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe

…. to this

Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe Reactor


Manhole

FTIR spectrometer

Turbidityprobe

FTIR probe

N2 purgingtube

pH probe

…. to this…. to this…

Pharma Process Control

Pharmaceutical companies typically operate at around 2 to 2½ Sigmacompared with 6 Sigma in world class manufacturing

Variability (or PAT) by Edwards Deming

Cease reliance on mass inspection to achieve quality.

Eliminate the need for mass inspection by building quality into the product in the first place.

Dr W. Edwards Deming (Circa 1980s)

“Learning is not compulsory, ……. Neither is survival”

Variability and Coping with Different Product Recipes,

Different Processing Units, Different Production Sites,

Different Spectroscopic Probes & Probe Locations,

……, etc

Variability with Different Product Recipes, Processing Units, Production Sites, Spectroscopic Probe Locations, …

The elimination of differences between different modelling or calibration applications is a major issue in flexible process manufacturing

Different spectroscopic probes in different vessels or on different sites

Different reactors (unit operations), multiple recipes, etc

Normally this would require the construction of separate models for each individual situation.

A simple but partial solution is the subtraction of local or global mean levels

A better solution is to develop a multi-group (sub-space) model or calibration such that the interest can then focus on within process (or product) variability.

Martin, E.B. and A.J. Morris, (2003), “Monitoring Performance in Flexible Process Manufacturing”, Proceedings of IFAC ADCHEM’2003, Hong KongLane S., et al, “Performance Monitoring of a Multi-Product Semi-Batch Process." Journal of Process Control, 2001, 11(1), 1-11.

Case Study: Between and Within Group VariationMulti product manufacturing with Unilever (Lever Fabergé).

Approximately 50 different products and five main production units, complicated by multiple recipes.

Monitoring the process using standard MSPC would mean the building and maintenance of approximately 250 statistical monitoring models.

Too complicated for ‘quality’ and operational personnel.

High model-maintenance costs

Composition of the Multi-Recipe Data SetsTo demonstrate the application of multiple group PLS the production of two recipes are considered. Each process mixer is also considered as a separate ‘Recipe’ and thus the process model contains four distinct groups.

Note that Recipe 2 has fewer raw materials and fewer process variables than Recipe 1.

Recipe Group Mixer Number ofBatches

RawMaterials

QualityVariables

1 1 ( ) 1 19 23 1

2 (+) 2 21 23 1

2 3 (o) 1 20 17 1

4 (x) 2 29 17 1

ProcessVariables

120

120

90

90

Impact of Different Recipes

Principal Component 1

-10 -5 0 105 15

5

0

-5

10

-15-10

Prin

cipa

l Com

pone

nt 2

This model contains both within group and between group variation.

Subtle process events cannot be detected; the greater the number of distinct groups in the data set, the greater the impact of between group variation.

Agitator

Location 1Location 2Location 3

Location 4

Spectroscopic Probe Location PCA Plot of Measured Spectra

Impact of Spectroscopic Probe Locationin a Reaction Vessel

PC1P

C2

Location 3

PC

2

Location 2

Location 4

Location 1

Multi-group PLS – Pooled Covariance Model

-6 -4 -2 0 2 4 6 8

6

4

2

0

-2

-4

-6

Latent Variable 1

Late

nt V

aria

ble

2The pooled variance-covariance matrix is constructed as a weighted sum of the individual elements of the individual variance-covariance matrices.

Case Study – Dosing Valve Problem

The monitoring chart shows the scores jumping outside the actionand warning limits as the process malfunction impacts.

-6 -4 -2 0 2 4

-15

-10

-5

0

5

Prin

cipa

l Com

pone

nt 2

Principal Component 1

Fault Diagnostics

Variables (62 - 65) were identified as being related to raw material addition.A faulty dosing-control valve was located as being to source of the problem.

Variables0 20 40 60 80

-2

0

2

4

6

8

Con

tribu

tion

Variability – Modelling and Calibration ChallengesProcess Issues:

Equipment characteristics; site-to-site process differences, etc

Fluctuations in both control and external process variables

Cell improvement; cell line changes; media changes

Small data sets are an issue but can be enhanced through Bootstrap Aggregation

Analytical Issues:

Separating absorbance from multiplicative light scattering effects caused by the variations in optical path length

Inter probe variability: impact of component variance on PLS calibration – can probe differences be accomodated or eliminated?

Can calibration models be made generic for different production unit operations / production lines?

Variability - Coping with Minimal Experimental DataThe need for fit-for-purpose statistical models can be limited due to intrinsic data sparsity resulting from the small number of objects available, e.g. experiments, batches, runs, etc, compared with the large number of process variables wavelengths measured.

This has been successfully addressed in PLS calibrations and in building statistical monitoring and predictive models through the addition of Gaussian noise to the original data and combining multiple models using Bootstrap Aggregated Regression.

This allows the development of robust calibration or process models and has been shown to lead to a decrease in prediction errors as a consequence of the increase in the data density.

Martin, E.B. and A.J. Morris, “Enhanced bio-manufacturing through advanced multivariate statistical technologies”, Journal of Biotechnology 99, 2002, 223-235Conlin, A. et al, “Data augmentation: an alternative approach to the analysis of spectroscopic data”, Chemometrics and Intelligent Laboratory Systems 44,1998,161-173Zhang, J. et al, “Estimation of impurity and fouling in batch polymerisation reactors through the application of neural networks”, Computers and Chemical Engineering 23, 1999, 30, 301-314

Advanced Chemometric Methods

FTIR

Supersa-turation

Size

GrowthkineticsUSS

BatchBatchProcessprocess

monitoringmonitoring& control& control

Processconditions

Heat transferLDA/PIV Reaction

CalorimetryMixing &scale-up

CFD

Shape Video microscopy

MSZWUVvis

Nucleationkinetics

Reactantrheology

Polymor-phic form XRD

FTIR

Supersa-turation

Size

GrowthkineticsUSS

BatchBatchProcessprocess

monitoringmonitoring& control& control

Processconditions

Heat transferHeat transferLDA/PIVLDA/PIV Reaction

CalorimetryMixing &scale-upMixing &scale-up

CFDCFD

Shape Video microscopyVideo microscopy

MSZWUVvis

Nucleationkinetics

Reactantrheology

Polymor-phic form XRD

Chemicals Behaving Badly (CBBII): Integrated In-Process Analytics and Closed Loop Control

CBB#2 Project: collaboration with

Leeds, Heriot-Watt and Newcastle University

PartnersAEA Technology

AstraZenecaBede Scientific Instruments

BNFLClairet Scientific

DTIEPSRC

GlaxoSmithKlineHEL

Malvern InstrumentsPfizer

Syngenta

Smoothed Principal Component Analysis (SPCA)Enhancing signal-to-noise ratio

iii k rQQIXrX )( TT ×+×= λ+×= ],,[],,[ 2121 cc rrrrrrF ΛΛ nsF FxFxFxx +==

mi ,,2,1 Λ=

SPCA

2θ16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.5 19.01200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450a

2θ2θ16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.5 19.01200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450a b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 17.5 18.0 18.5 19.0In

tens

ity1250

1300

1350

1400

1450b

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 18.0 18.5 19.01250

1300

1350

1400

1450

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

2θ

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 17.5 18.0 18.5 19.0In

tens

ity1250

1300

1350

1400

1450b

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 18.0 18.5 19.01250

1300

1350

1400

1450

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

2θ2θ

Raw (a) and Processed (b) XRD profiles (by SPCA) for 6 XRD data sets of mannitol-methanol suspensions with the contents of mannitol equalling to 0.0%, 0.178%, 0.389%, 0.533%, 0.8% and 1.0% g/ml, respectively

SPCA in Morphology Monitoring (β - Form)

Concentration (%)0 2 4 6 8 10

Pea

k he

ight

-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R = 0.9951

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R = 0.9951

2θ= 29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R = 0.9951

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R = 0.9951

2θ= 29.8272

The raw and pre-processed XRD profiles of the β-form with concentration varying from 0.02% to 8.00%; relationships between concentrations and peak heights at peaks B3 of the raw and processed spectra.

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000β - form

B3

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000β - form

B3

B2B1

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000β - form

B3

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000β - form

B3

B2B1

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000

6000

B1B2

B3

? - form

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000

6000B1

B2

B3

β - form

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000

6000

B1B2

B3

? - form

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000

6000B1

B2

B3

β - form

Loading Space Standardisation (LSS)Correcting non-linear shift and broadening in spectral bands caused by temperature fluctuations

)()()(1

ii

K

kikkiii ttct esx += ∑

=,

)()(

)()(

m

11

tt

tt

PCAm

PCA

'

'

TPX

TPX

→

→

ΜΜΜ LSStt ii ⇒~)(P )()( refLSS

test tt xx →

LSS

Wavelength (nm)850 900 950 1000 1050

Abs

orba

nce

(AU

)

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050

Wavelength (nm)850 900 950 1000 1050

Abs

orba

nce

(AU

)

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Abs

orba

nce

(AU

)

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Abs

orba

nce

(AU

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050

Abs

orba

nce

(AU

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Abs

orba

nce

(AU

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050

Wavelength (nm)850 900 950 1000 1050

Optical Path-Length Estimation and Correction (OPLEC)Separating absorbance from multiplicative light scattering effects caused by the variations in optical path length

], , ,[] , , ,[),( 2121 mjjjjmji cccOPLECb ΛΛ === cxxxXcX ,,

i

J

jjjiii cb εsx += ∑

=1, multiplicative parameter-ib

OPLEC

OPLEC has recently been applied for Raman Scattering applications incomplex crystallisation processes.

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Abs.

(AU

)

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2.0

2.2

2.4

2.6

2.8

3.0

3.2

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Abs.

(AU

)

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2.0

2.2

2.4

2.6

2.8

3.0

3.2

Abs.

(AU

)

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2.0

2.2

2.4

2.6

2.8

3.0

3.2

Abs.

(AU

)1.85

1.9

1.95

2.0

2.05

2.1

2.2

2.15

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Abs.

(AU

)1.85

1.9

1.95

2.0

2.05

2.1

2.2

2.15

Abs.

(AU

)1.85

1.9

1.95

2.0

2.05

2.1

2.2

2.15

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Wavelength (nm)850 900 950 1000 1050850 900 950 1000

Bede MONITORTM In-process XRDCrystal Polymorph Monitoring & Control Using SPCA

Typically circa 1 wt % detectable via in-process XRD, much lower with advanced chemometric analysis (Smoothed PCA)

Temperaturereaders

Temperaturemonitors

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 302Theta [degree]

Inte

nsity

t = 200 s

t = 600 s

t = 1000 s

t = 1400 s

t = 1800 s

t = 2200 s

t = 2600 s

t = 3000 s

t = 3400 s

t = 3800 s

t = 4200 s

t = 4400 s

t = 4600 s

αpeaks

βpeaks

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 302Theta [degree]

Inte

nsity

t = 200 s

t = 600 s

t = 1000 s

t = 1400 s

t = 1800 s

t = 2200 s

t = 2600 s

t = 3000 s

t = 3400 s

t = 3800 s

t = 4200 s

t = 4400 s

t = 4600 s

αpeaksαpeaks

βpeaksβpeaks

System provides capability to monitor polymorphic form “in-process”unaffected by product separation prior to analysis.

250l Pilot Plant Batch Agitated Vessel

Outlet (8mm) port Inlet (12mm) port

α-sizer

Supersaturation Control System Upgrade to PI Capability

INPUTBlock

User DefineModel

User Define “S”set point

HEL PC

Solubility Model

Macro

S(t)

PI ControllerControl Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIR PC Spectra

PLS model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

INPUTBlock

User DefineModel

User Define “S”set point

HEL PC

Solubility Model

MacroMacro

S(t)

IMC Based PI ControllerControl Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIR PC Spectra

PLS model

C(t)

FTIR PC Spectra

Cal. model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

Supersaturation Control of L-Glutamic Acid250 litre Plant Crystalliser

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300Time (min)

Tem

pera

ture

, Con

cent

ratio

n, S

olub

ility

& T

urbi

dity

0.0250.0750.1250.1750.2250.2750.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

Sup

ersa

tura

tion

(S =

C/C

*)

Temperature (°C) Concentration (g/500ml)

Solubility (g/500ml) Turbidity (%)Supersaturation SlimitSlimit

Smax = 1.125S = 1.1

Smin = 1.075Started SupersaturationControl

5% seedsadded

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300Time (min)

Tem

pera

ture

, Con

cent

ratio

n, S

olub

ility

& T

urbi

dity

0.0250.0750.1250.1750.2250.2750.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

Sup

ersa

tura

tion

(S =

C/C

*)





Smax = 1.125S = 1.1

Smin = 1.075Started SupersaturationControl

5% seedsadded

Model Based Closed Loop Process Control

Challenges for Real Time Closed Loop ControlAt least 15 years worth of academic control theory, simulations and occasionally pilot studies have reported on the benefits and challenges of improving operational performance through improved control, more recently in the Pharmaceutical Industry.

Since then the FDA has opened the way to a technology-led quality assurance regime there has been hectic activity directed towards defining an implementation methodology for measurement, modelling and data analysis.

Many of the larger automation companies have focussed on the overall infrastructure to ensure that the integration of data from many sources is recorded securely and a traceable batch record is generated.

Hopefully one of the key outcomes is that the integration of the data will also integrate the users of the data such as the chemists, biologist, engineers, to provide a process process systems engineering approach to design, build, operate, monitor and improve production facilities.

The Impact of Process Dynamics (Auto-correlated Data)

In the presence of auto-correlation, the action and warning limits calculated for a process performance monitoring scheme based on the usual assumptions that the observations are Independent and Identically Distributed (IID), are inappropriate making the monitoring charts unreliable, if not useless.

Developing an MSPC representation for the monitoring of processes, exhibiting time varying characteristics, will result in an increase in the number of false alarms and operational changes not being detected.

So, how do we deal with auto-correlated data?

Model-based Performance Monitoring

Plant

FirstPrinciples Model

orDynamic Empirical

Model

DynamicEmpirical

‘Error’ Model

+-

+-

Multivariate Monitoring

Charts

Inputs OutputsPlant

First Principles Modelor

Dynamic Empirical Model

Dynamic Empirical‘Error-whitening’

Model‘

+-

+-

+-

MultivariateMonitoring Charts

Inputs Outputs

Plant-ModelMismatch

Plant

FirstPrinciples Model

orDynamic Empirical

Model

DynamicEmpirical

‘Error’ Model

+-

+-

+-

Multivariate Monitoring

Charts

Inputs OutputsPlant

First Principles Modelor

Empirical Model

Dynamic Empirical‘Error-whitening’

Model‘

+-

+-

+-

MultivariateMonitoring Charts

Inputs Outputs

Plant-ModelMismatch

At the heart of a model-based control or monitoring system is a DYNAMIC model

Per

cent

age

99

95

80

402010

1

Process Data

60

5

80 130 180

Data

Model ResidualsDynamic Non-linear PLS

-1 0 1

9995

80

402010

60

5

1

Per

cent

age

-0.5 0.0 0.5 1.0

Per

cent

age

Model ResidualsLinear PLS

1

99

95

80

402010

60

5

Data

-1Plant-Model Residuals

Per

cent

age

99

95

80

402010

1

60

5

0 1-1 2-3 -2-4

Impact of Non-linearity – Normal Probability Plots

2 12 22 32 42 52 622 12 22 32 42 52 62

Process Data0.81.0

0.4

0.0

-1.0-0.8

-0.4

2 12 22 32 42 52 62

ResidualsPlant-Model Mismatch0.8

1.0

0.4

0.0

-1.0-0.8

-0.4

2 12 3222 5242 62

Model ResidualsDynamic PLS

0.81.0

0.4

0.0

-1.0-0.8

-0.4

2 12 22 32 42 52 62

Model ResidualsTime Series Model

0.81.0

0.4

0.0

-1.0-0.8

-0.4

Impact of Autocorreltion – Partial Correlation Plots

Incorporating PAT Sensors into Real Time Process Control

and

PAT in Process Systems Engineering

Key Challenges in PAT Pharma-Batch ControlAvailability of Critical to Quality (CtQ) measurements:

Are the CtQ parameters continuously measured?Are the CtQ parameters measured only by lab assay at the end of the batch or a-periodically during the batch?Can the CtQ parameters be inferred from PAT sensors with associated calibration model(s)?

Process non-linearity:As a function of process operating point (e.g. Arrhenius reaction rate, etc)Time varying dynamics as batch evolves - varying time constants and process gains

• Changes in sign of process gain can be especially challenging

Trajectory tracking:Many control systems are excellent at regulating to a fixed set-point and rejecting disturbances BUT not so good at tracking a time varying set-point profiles.

Real Time Quality Control (Batch Control)

Pre-ProcessingSequence Batch PLS

Spectral Data

Imported from Model Development File

Dat

a Q

ualit

y M

onito

r

Process Data

Discrete Data

Critical to Quality Parameters

21 CFR part 11 Records

Batch Controller

Pre-ProcessingSequence Batch PLS

Spectral Data

Imported from Model Development File

Dat

a Q

ualit

y M

onito

r

Process Data

Discrete Data

Critical to Quality Parameters

21 CFR part 11 Records

Batch Controller

Courtesy Perceptive Engineering

Batch Model Predictive TrackingCtQ parameters controlled indirectly by virtue of controlling a process variable.

At the heart of the controller is a dynamic process model that captures dynamic, multivariable process interactions.

The model predicts the trajectory of controlled variable(s) over a future horizon (typically much shorter than the batch duration).

The controller computes a trajectory of MV moves over this future horizon to minimise the current and future SP error.

Only the first move is applied; at the next control interval the whole process is repeated - this is a moving window approach


Process Systems Engineering – The Future

A Process Systems Engineering, holistic, approach should help integrate all aspects - sensor, data, chemometrics, and the process and control system in a modular way that provides a readily validatable system.

Clean Data Set

Spectral Data

Data Quality MonitorProcess Data

Discrete Data

Real TimePre-

ProcessingSequence

Imported fromModel Development File

Spectral Data

Clean Data Set

Spectral Data

Data Quality MonitorProcess Data

Discrete Data

Real TimePre-

ProcessingSequence

Imported fromModel Development File

Spectral Data


• Continuously measured OR• A-periodically measured OR• Real time value Inferred from calibration model OR• End-point value inferred from calibration model OR• Scores of calibration model are CTQ parameters

CtQ Parameters

Process Systems Engineering – The Future

Design Space

PLS/PCA Calibration

Model

DynamicPCA

Controller

Spectral Data

Process Data

Discrete Data

Real time pre-processed data

21 CFR part 11 Compliant Records

NormalOperating

Space


PAT Sensors in Closed Loop Process Control– Some Challenges

Real-Time Management of Process Data - maintaining a traceable record of Data Quality.

Real time pre-processing – needs to be consistent with and traceable to the unique calibration model.

No control system is going to control a spectrum of several hundred simultaneous values; so what is important?

Is there is a fit-for-purpose calibration model to infer specific product properties?

Are there particular features / segments of the spectrum of interest?

Should the scores of the PCA/PLS calibration model be controlleddirectly using Latent Variable controllers?

Round upBe realistic about what can be achieved at each development stage.

Use “generic” nature of development process to your advantage.

Identify which unit operations are the source of greatest product quality variation, eg upstream or downstream processing.

Start early and use data derived from existing development strategies.

Data infrastructure is critical for acquisition and collation of large data sets such as NIR spectra.

Data Robustness is critical to operate in a validated environment.

Essential to work with equipment vendors on system specification (both analytical and control systems providers).

Ease-of-Use and highly visual software is critical

On-line Spectral methods coupled with closed loop process control will be critical and useful tools for process transfer and comparability.

So - Where will IFPAC be in 2020?

Courtesy Prof Al Sacco, Chemical Engineer, Space Scientist

Closure and Thanks

In this talk, we have tried to…

Overview some novel methodologies for building robust calibration and statistical monitoring models using Data Augmentation and Bootstrap Aggregated Regression.

Highlight some process analytical technologies through work aiming to improve understanding of batch crystallisation processes and its scale-up, and the provision of enhanced process understanding through advanced chemometrics.

And show how some new closed loop process PAT control ideas are coming to fruition through innovative process systems engineering.

I will be happy to attempt to answer your questions

Many thanks to the IFPAC Organisers for their kind invitation

and of course you for your kind attention

pat – the impact for process systems engineering · what is process systems engineering? process...

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