americas - pharmaceutical manufacturing...commonalities in the chemcial and pharma/biotech...

Author (internally with department name)Page 1; 2002-07-10

Americas

BTS - Vision: Customer Success through technical solutionsYour partner for projects in the chemical and pharmaceutical industries.

BTS Tefen ConsortiumRyan/PTGPage 2; 09/15/04 Americas

Technology Transfer from the Chemical Sectorto the Pharmaceutical/Biotech Sector

Peter J. Ryan

Thomas Daszkowski


Bayer Group: Products

Poly-carbonate

Poly-urethanes

Coatings and Colorants

Fibers, Additivesand Rubber

IndustrialChemicals

Diagnostics

Agriculture

ConsumerCare

Pharma-ceuticals


Bayer Group: Figures

Bayer – Facts and Figures 2003:124,600 employees world-wide

250 companies, 10,000 products

Sales: EUR 28.6 billion

EBITDA: EUR 3.53 billion

Net income: (EUR 1.36 billion)

R&D expenditures: EUR 2.4 billion

Capital expenditures: EUR 1.74 billion

Bayer – Objectives: Leader in research and technology

Continuous growth of expertise in the manufacture of high-qualityand environmentally compatible products


Board of ManagementBoard of Management

Corporate CenterCorporate CenterBusiness

Units

CropScienceCropScience

PolymersPolymers

LanxessLanxess

HealthCareHealthCareService Units

Industry ServicesIndustry Services

Business ServicesBusiness Services

Technology ServicesTechnology Services

Bayer AG

Services offeredon the external market

Services offeredon the external market

Employees worldwide(*) 2.400

Customer segments• HealthCare• Crop Science• Polymers• Chemical

(*) Status: 2003


Bayer Technology Services: International Locations/OfficesRegional OfficeNorth AmericaBaytown, Texas260 employees(*)

RegionalOffice AsiaShanghai, China60 employees(*)

RegionalOfficeBeneluxAntwerp90 employees(*)

HeadquartersBTS EuropeLeverkusen1,700 employees(*)

HeadquartersLocations

(*) Status: 2003

Regional OfficeCentral AmericaMexico City240 employees(*)

Regional OfficeSouth AmericaSao Paulo/Brazil40 employees(*)


Bayer Technology Services: Our Portfolio

Initiate, implement and support technological innovations over the long term.From product and process development through the planning andconstruction of plants to the automation and optimisation of processes.

Design andmanageinvestments

Servicesthroughout theplant life cycle

Optimizefacilities andproducts

Developproducts and

processes


Content of today‘s presentation:

• General Commonalties in the Process and Pharmaceutical/ Biotech Industries

⇒ Example 1: Computer Simulation in the Process and Pharma/Biotech Industries

⇒ Example 2: Total Performance Optimization in the Process and Pharma/Biotech Industries

• Forum for discussion.


Commonalities in the Chemcial and Pharma/Biotech Industries

Reaction Kinetics

Reactors

Crystallization, Distillation

Simulation

Mass and Energy Balances

Process Control

Monitoring and Optimization

Cell Metabolism

Fermenters

Chromatography, UF/DF

Simulation/PK-Sim

in vivo Conc.-Time Profiles

Process Control

Monitoring and Optimization

Process

Methods and Tools


Disactivation

0.0000

2.0000

4.0000

6.0000

8.0000

10.0000

12.0000

14.0000

16.0000

18.0000

20.0000

0.0000 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000

time (h)

Gew

% D

PC

Standard (vers. 32, DPC)

0-60&181-420 std. ver., 61-180 min ohne O2 (vers. 43a, DPC)

0-120 ohne O2, 121-420 min std. vers.(vers. 45, DPC)

vers. 32 DPC (expermn'tl)

vers. 43a DPC (expermn'tl)


Chemical Engineering: Reaction Kinetics

Chemical Engineering Reaction Kinetics mathematically represents chemicalprocesses using parameter estimation algorithms to fit model generated curvesthrough experimental results..


Biotech Engineering: Cell Metabolism:

Processcontrol

FluxComputation

G lu

H e xo s e

T rica rb oxy lic Acid C yc le

100

78

G lu tam in e

E le ctronTrans port

Chain

17

B io m as s A c C o A

B io m a s s♦-K G

CO 2

C O2

O 2

R

L a cta te

G ly colys is P P P

B io m as sG A P

R N AD N A

A la

R

G 6P

G A P

RP YR

R

C O 2

O AAR

C O 2

♦-K G

A cCo A

C O2 R

100

1.6

781.1

2222

133

191

18 9

4

36

26

88

36

96

62

58

-2C O2R

496

43 8

24 9

20 6

PP P

Real-TimeComputationof Metabolic

Fluxes

Processcontrol

FluxComputation

G lu

H e xo s e


100

78

G lu tam in e


Chain

17


B io m a s s♦-K G

CO 2

C O2

O 2

R

L a cta te

G ly colys is P P P

B io m as sG A P

R N AD N A

A la

R

G 6P

G A P

RP YR

R

C O 2

O AAR

C O 2

♦-K G

A cCo A

C O2 R

100

1.6

781.1

2222

133

191

18 9

4

36

26

88

36

96

62

58

-2C O2R

496

43 8

24 9

20 6

PP P

Real-TimeComputationof Metabolic

Fluxes

• Metabolic Flux Model of a CHO Cell:⇒ Build up cell model to represent a fermenter.⇒ Develop open-loop control of fermentation system.⇒ Extend to closed loop control.


Chemical Engineering: Reactors

Maleic Anhydride Unit, Baytown, TX


Biotech Engineering: Fermentation

0,97

0,215

• Data Mining

• CFD - Simulation

• Operational Concepts

pH

Conc.

Shear Stress

Improvement byIndividualized

Process Strategy

Tangible Results through AdvancedData Mining Technology

Processcontrol

FluxComputation

G lu

H e xo s e


100

78

G lu tam in e


C hain

17


B io m a s s♦-K G

C O 2

C O2

O 2

R

L a cta te

G ly colys is P P P

B io m as sG A P

R N AD N A

A la

R

G 6P

G A P

RP YR

R

C O 2

O AAR

C O 2

♦-K G

A cCoA

C O 2 R

100

1.6

781.1

2222

133

191

18 9

4

3 6

26

88

36

96

62

58

-2C O2R

4 96

43 8

24 9

20 6

PP P

Real-TimeComputationof M etabolic

Fluxes

Processcontrol

FluxComputation

G lu

H e xo s e


100

78

G lu tam in e


C hain

17


B io m a s s♦-K G

C O 2

C O2

O 2

R

L a cta te

G ly colys is P P P

B io m as sG A P

R N AD N A

A la

R

G 6P

G A P

RP YR

R

C O 2

O AAR

C O 2

♦-K G

A cCoA

C O 2 R

100

1.6

781.1

2222

133

191

18 9

4

3 6

26

88

36

96

62

58

-2C O2R

4 96

43 8

24 9

20 6

PP P

Real-TimeComputationof M etabolic

Fluxes


Chemical Engineering: Distillation

Distillation, Leverkusen, Germany

Reboiler Steam Flow-Rate0 2000 4000 6000 8000 10000 12000 14000 16000

Tray

Product 1756 2083 2328

R = 4,6

Case A Case B Case C

Side-draw

Condenser Duty ACondenser Duty BNo Condneser


Pharma Engineering: Chromatography

0

0.2

0.4

0.6

0.8

1

3 lotsElution Rate = n l/min

Nor

mal

ized

Col

umn

Rec

over

y

3 lotsElution Rate = n* l/min


Example 1: Process Simulation

Chemical Process:

Simulation of a

Polymer Process

Biotech Process:

Simulation of the

Human Body


Example 1: Polymer Process Simulation

300 Co

Werkstoff 2.4605

El.

RKW

KW KW

MCB

MCB MCB

auf +-0m

auf +-0mauf +-0m

o20 C

20 Co

o

diskontinuierlich

El.

35 Co

AbluftanlageAbluftanlage Abluftanlage Abluftanlage

/2

n.VD70PA21/22

30mbar

KW

Diphyl

Diphyl

VD71BA44Auskoch-LMG

300 C

MCB MCB

CB-Destillation

GOS

GOS

Diphyl, Dampf

Diphyl-V, flüssig

Diphyl-R, flüssig

Diphyl-Kondensat

/59

524MCB

von VD70PA21/22

PC-Lösung (70%ig)

503

55

58 59

5859 60

60

60

60

52

Diphyl

Diphyl

-400-

Stand: 00-06-26 Cn

UE307779

LISA

TIA+

LIA-

FIA-

TITI

LICSA

FICSA

TISA+

FSA+

TIA+

TI

PIR

anlage

Abluft-

TICA+

UE307779

n.WA70 WA31Kondensator

1. Stufe

Voreindampfung

PISA+

UE307779

n. VD70PA21/22

UE307779

v. VD41PA44 ( Spülkreislauf )

PC-Lösung ( Spülstrom ) v.VD70FA13/14

Frisch CB 25 bar

( 16 bar / 25 bar)

LMG-Spülung

TISA+

LISA

UE307783

n. GR70HA72

UE307783

n. GR70HA71

UE307780

v. DTPA80

PIRSA+

LICRSA+

PIRSA+

TIRCA

UE307779

LIRCSA

30mbar

PIRCA

2x-900

PIRA

PICA+

UE307779

n. VD71BA44 (Spülkreislauf)

PIRSA+

TI

1mbar

TITI

TIA+

UE307781

UE307781

UE307781

UE307781

UE307779

( 15% ig) UE307779

n.VD71PA43 UE307779

VA71FA55/ VA71FA56/

VA71VA56/VA71VA55/ VA71FA57/

VA71VA57/

VA71BA58 VA71BA54

02.03.99 Lentzen

VA71BA51

VD71RM50

VD71AX10

Eindampfung/Granulierung Verfahrensfließbild

Bearbeiter:incae ue307782 .DGN

UE307782-0

VD71BA521

VD71BA52

VD71WV52

/2

58 59 60 60

VA71PA57/

VA71WA57/

/2

55 605258 59 60

VD71WV51

VA71PA51/ VA71PA54/

/59

VA71PA58

VD71BA51

UE307779

Str. 7 ZT ENGKU

UE307781

Diphyl-R, flüssig

UE307781

Diphyl-V, flüssig

VD71PA52

VD71WA51 VD71WA52

VD71PA51

VA71FA51

VA71VA51

VA71FA53VA71VA53

VA71PA53

VA71WA53

Benennung

Technische Daten

zul. Betriebs}berdruck

Werkstoff

Bemerkung

bar

Co

Zeichnung Nr

zul. Betriebstemperatur

Technische Einrichtung

23-M1-V1-VD71/VA71

Makrolon N175UER

VD71/VA71 V1 M1 23

Vorlage

6,3 m3 d=1800x3200

-1 / 6

200

1.4571

VA71BA51

61491

ue233164-0

Vorlage

1,6 m3 1200x1800

-1 / 6

120

1.4571

VA71BA54 Vorlage

1,6 m3 1200x1800

-1 / 6

200

1.4571

VA71BA58 Abscheider

0,52 m3 800x1200

B=-1/3HS=-1/18

200/300

1.4571

VA71FA51 Abscheider

0,5 m3 600x1400

-1/3

200

1.4571

VA71FA53 Abscheider

1 m3 1000x1500

-1/3

200

1.4571

VA71FA55/ Abscheider

0,52 m3 800x1200

-1/3

200

1.4571

VA71FA56/ Abscheider

0,3 m3 600x1150

-1/3

200

HII

UE241801

VA71FA57/ Kreiselpumpe

6 m3/h 2900 upm

51mFLS, 6,6KW

1.4408

magnetgek.

VA71PA51/ Kreiselpumpe

11 m3/h 2900 upm

12mFLS, 2,5KW

1.4408

magnetgek.

VA71PA53 VA71PA54/

magnetgek.

1.4408

51mFLS, 6,6KW 2900 upm 6 m3/h

Kreiselpumpe

VA71PA57/

magnetgek.

1.4408

12mFLS, 2,5KW 2900 upm

11 m3/h

Kreiselpumpe

VA71PA58

magnetgek.

1.4408

51mFLS, 6,6KW 2900 upm

6 m3/h

Kreiselpumpe

VA71VA51

RO 3500 II

GG20

9,7KW, 1000upm 25 mbar

3000 m3/h

pumpe Wälzkolben-

VA71VA53

SIHI/GLRD

1.4408

75KW, 750upm 70 mbar 1800 m3/h

ringpumpe Flüssigkeits-

VA71VA55/

RO 16000

GG20

20KW, 1000upm 1,3 mbar

10500 m3/h

pumpe Wälzkolben-

VA71VA56/

RO 3500 II

GG20

9,7KW, 1000upm 4 mbar

2700 m3/h

pumpe Wälzkolben-

VA71VA57/

SIHI/GLRD

GG-25

46KW, 1000upm 30 mbar

600 m3/h

ringpumpe Flüssigkeits-

VA71WA53

HII / 1.4571

200/200

M=-1/10,R=-1/6

370x3250 20 m2

Kühler

VA71WA57/

H II / St.35.8

200/200

M=-1/10,R=-1/6

370x1940 15 m2

Kühler

VD71AX10

Schmelzarmatur

VD71BA51

45 Konus

1.0425/2.4605

350/350

M=-1/16,R=-1/8

3200x2690 9,5 m3

Abscheider Rohrverdampfer

GOS

-300-

-700-

-700-

Str.4

zum CB/MC Tank

über WA70FB40

VD71WV52

Innenverteiler

2.4605

350

200

d=1 mm 100 000

Bohrungen:

verdampfer Strang-

VD71WV51

HII/2.4605

400

M=-1/10/R-1/25

1750R 12,5x1,1 1100x2500

172 m3

Verdampfer Rohr-

VD71WA52

1.4571

350/200

M=-1/10/R=-1/8

1200 25x1,65x3500

285 m2

Kondensator

VD71WA51

1.4571

350/200

M=-1/10/R=-1/8

2000 33,7x2x3500

592 m2

Kondensator

VD71RM50

2.4605

350

200

Stat. Mischer

VD71PA52

1.4802/1.4112

350

250 bar

12 m3/h

Zahnradpumpe

VD71PA51

1.4408/1.8550

350

250 bar

12 m3/h

Zahnradpumpe

VD71BA521

1.4571

300/300

M=-1/6 / R=-1/

18l

323,9x540

Abscheider

VD71BA52

1.0425/2.4605

350/350

M=-1/16 / R=-1

3200x6833 46m3

Abscheider Strangverdampf

VD71BA511

1.4571

300/300

M=-1/6 / R=-1/

18l

323,9x540

Abscheider

Technische Einrichtung

zul. Betriebstemperatur

Zeichnung Nr

o C

bar

Bemerkung

Werkstoff

zul. Betriebs}berdruck

Technische Daten

Benennung

L405

L414

L391

L419

L791L413

L944

L412

L971 L943

L402

L403

L973

L407

/2VD71BA511

projekt=PC7 variante=0 fbid=351668

L422

L421

Preheater Simulation ResultsExisting DIN-2 Preheater

4.5ft active tube length, 2275 tubes, 10,000 pph RS-ABS

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Tube Length (ft)

0

50

100

150

200

250

300

Molar Vapor FractionResidence Time (min)Pressure (psig)Polymer Temperature (°C)

Polymer DevolatilizationProcess showing onsetof two-phase flow in thetubes of the first-stage

preheater


Disactivation

0.0000

2.0000

4.0000

6.0000

8.0000

10.0000

12.0000

14.0000

16.0000

18.0000

20.0000

0.0000 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000

time (h)

Gew

% D

PC

Standard (vers. 32, DPC)

0-60&181-420 std. ver., 61-180 min ohne O2 (vers. 43a, DPC)

0-120 ohne O2, 121-420 min std. vers.(vers. 45, DPC)


vers. 43a DPC (expermn'tl)


Chemical Engineering: Reaction Kinetics

Chemical Engineering Reaction Kinetics mathematically represents chemicalprocesses using parameter estimation algorithms to fit model generated curvesthrough experimental results..


Example 1: Biotech Engineering Simulation

Dose strategy forsuccessful therapy ???

Absorption and

distribution of the agent

inside the body ???

Concentrationof the agent

inside organ / tumoras a function

of time???

Segmental fraction doseabsorbed inside cellularspace ???

PK-Sim®Fast and cost-saving drug development ?Therapeutical effect ???


GI-tract

liver

organs

lung

Ve n

ous

b loo

d

Ar t e

ri al b

l ood

intra

cel

lula

r c

ompa

rtmen

t

inte

rstit

ial

com

partm

ent

vasc

ular

com

partm

ent

Simulation ofconcentration profile in

liverlungpancreasplasmaskinbrain.......tumoretc.

t

c

Simulation ofconcentration profile int

Dosing Scheme 1

Dosing Scheme 2

vascular spaceof organ

cellular spaceof organ

t

t

PK-Sim®The Whole-Body ADME Simulation Tool

2 4 6 8

100

50

Succ

ess

r ate

[%]

Days after infection

Control

Dosing Scheme 1

Dosing Scheme 2

Simulation ofpharmaco-dynamics



Goal

Results

ProcedurePhysiology-based modeling was usedto „scale-up“ the known PK oflaboratory animals to humans

Estimate pharmacokinetic (PK)behavior of a new compound inhumans prior to clinical studies

New compound has a favorable PKprofile in humans (e. g. threefold higherCmax compared to compound B)

x 3


Compound BCompound B


GI-tract

liver

organs

lung

Ven

ous

b loo

d

Ar t e

ri al b

l ood

intra

cel

lula

r c

ompa

rtmen

t

inte

rstit

ial

com

partm

ent

vasc

ular

com

partm

ent

PK-Sim®The Whole-Body ADME Simulation ToolFuture Development …

Pharmaco-Dynamic Module

affinity to target


Future Development: Cancer Cell Metabolic Model


Process Simulation: Summary

• Dynamic and steady-state material and energy balances developed in the polymer and chemical sector to support design, construction and optimization of processes.

⇒ First simulation methods developed 30-years ago!⇒ Industrial standards now recognized (Aspen, Hypertech, gProms).

• Dynamic material balance simulation models are available in the pharmaceutical and biotech sectors to support:

⇒ Drug development!⇒ Optimized Dosages (individualized therapies)


Example 2: Total Process Performance Optimization

UltrafiltrationCapturing

Protein A Chrom.VirusInactivation?

PurificationIEX Chrom.

PolishingHIC Chrom.Diafiltratio

nDiafiltratio

nSterile FiltrationFormulation

air

waste airM

V

Fermentation

Based on six-sigma principles and chemical engineering tools: √ Simulation √ Datamining √ Statistics √ Process Control

Typical Batch Biotech Process


Cp Sigma Defects Cost of Quality Class

0.67 +/- 2 5% 25-35%Not Capable Competitive

1 +/- 3 0.13% 20-25% Average1.33 +/- 4 60 ppm 12-18% Healthy1.66 +/- 5 1ppm 4-8% Superior

2 +/- 6 2 ppb 1-3% World Class

Example 2: Total Process Performance Optimization - Motivation

FDA Science Board November 16, 2001 Meeting:D. Dean, F. Bruttin, Price Waterhouse Coopers,

Modified by Ajaz S. Hussain, Ph.D.,Deputy Director, Office of Pharmaceutical

Science, CDER, FDA

Under cGMP when failures/recalls exceed 10% - process no longer “validated”

Pharma

Semicon


Notes:1: * Based on sold-out conditions

2: All results are based on past projects: (two years ago mainly energy and yield savings and quality improvements, currently capacity increase (higher throughput and reliability) OEE Overall Equipment Efficiency

Example 2: Total Process Performance Optimization - Motivation

Project Payout (mts)

Annual Savings (k$)

Project Payout (mts)

Annual Savings (k$)

Energy 9-12 150-350 N/A N/AYield 9-12 up to 450 12-24 >> 1,000Capacity Increase* 8 > 1,000 12-24 >> 1,000

Polychem Biotech


A step-by-step quantitative framework (rooted in six sigma principles) toassess, improve and sustain process efficiencies.

Capacity Yield Energy Environment Quality Reliability

D e f i n e Define goals and targets

M e a s u r e Measure goals and process, validate measurements

A n a l y z e Analyze variability, historical process system data & control system performance data

I m p r o v e Perform DOE’s, Lab Experiments to discover variable relationships, establish “best” control limits C o n t r o l Implement SPC Implement process control, implement monitoring to sustain performance

Voice of the Customer

Economic and Quality Goals for process

Example 2: Total Process Performance Optimization


Total Process Performance Optimization:Define and Quantify Performance

EXAMPLE: Specific Energy Consumption Lbs SteamLbs Product

Spec

ific

Ener

gy C

onsu

mpt

ion

Best Achieved(best achieved values)

Production Rate

Target based (averaged values)7%

15%


2n

4n

6n

8n

10n

12n

0 n*1000 n*2000 n*3000 n*4000 n*5000 n*6000 n*7000

Production Rate (#/h of PC based on BPA feed)

Spec

ific

Mix

ed S

olve

nt C

onsu

mpt

ion

(#m

s/#p

c)

If the unit can operate consistently at the best observed performancepoints, it can increase capacity and improve

quality!

30%

Total Process Performance Optimization:Define and Quantify Performance

Production Rate


0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

7/24/1998 0:00 2/9/1999 0:00 8/28/1999 0:00 3/15/2000 0:00 10/1/2000 0:00 4/19/2001 0:00 11/5/2001 0:00 5/24/2002 0:00

Date

Dur

atio

n (D

ays)

Filter Suspend/Harvest Duration

100%

Total Process Performance Optimization:Define and Quantify Performance, Biotech Process


0

5

10

15

20

25

30

0.00-0.30 0.30-0.35 0.35-0.40 0.40-0.45 0.45-0.50 0.50-0.55 0.55-0.60 0.60-0.65 0.65-0.99

Duration (days)

%

Filter Suspend/Harvest Duration

Best ObservedPerformance

Cpk = 0.003

Total Process Performance Optimization:Define and Quantify Performance, Biotech Process


-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

Varia

ble

1

Varia

ble

2

Varia

ble

3

Varia

ble

4

Varia

ble

5

Varia

ble

6

Varia

ble

7

Varia

ble

8

Varia

ble

9

Varia

ble

10

Varia

ble

11

Varia

ble

12

Varia

ble

13

Varia

ble

14

Varia

ble

15

Varia

ble

16

Varia

ble

17

Varia

ble

18

variables with little to no effect on KPI

variables with a large positive effect on KPIvariables with a large negative effect on KPI

Fingerprints and Impact Charts

-4-3-2-

1012

- 2- 101234

Varia

ble

1Va

riabl

e 2

Varia

ble

3Va

riabl

e 4

Varia

ble

5

Varia

ble

20Va

riabl

e 21

Varia

ble

22Va

riabl

e 23

Varia

ble

24Va

riabl

e 25

Varia

ble

26Va

riabl

e 27

Varia

ble

6Va

riabl

e 7

Varia

ble

8Va

riabl

e 9

Varia

ble

10Va

riabl

e 11

Varia

ble

12Va

riabl

e 13

Varia

ble

14Va

riabl

e 15

Varia

ble

16Va

riabl

e 17

Varia

ble

18Va

riabl

e 19

Good Lot

Bad Lot

Total Process Performance Optimization:Data Analysis and Datamining


•Daily Summary •TDI 2 •06/02/2001•General Information

•Number of Controllers: •20•Unit PI: •0.75•Unit Service factor: •88.00

•Controllers •PI •Service •Factor

•Saturation •(%)

•Oscillation •Index

•Period •(min)

•Weight

•10TC207 •0.65 •100.00 •8.61 •0.12 •0.00 •1.00•10PC104 •0.11 •10.00 •90.00 •0.88 •6.00 •1.00•10PC105 •0.16 •20.00 •85.00 •0.82 •6.00 •1.00•10FC107 •0.15 •70.00 •65.00 •0.85 •6.00 •1.00•10FC109 •0.90 •100.00 •21.53 •0.20 •6.00 •1.00•10PC110 •0.95 •100.00 •11.88 •0.30 •6.00 •1.00•10TC113 •0.70 •85.00 •17.71 •0.78 •6.00 •1.00•10AC115 •0.80 •65.00 •21.04 •0.86 •6.00 •1.00•10PC116 •1.00 •100.00 •10.76 •0.60 •6.00 •1.00•10PC106 •0.08 •100.00 •80.00 •0.91 •8.00 •1.00•10FC108 •0.79 •100.00 •14.31 •0.83 •8.00 •1.00•10FC111 •1.00 •100.00 •15.14 •0.82 •8.00 •1.00•10PC112 •0.80 •100.00 •12.36 •0.83 •8.00 •1.00•10PC114 •1.00 •100.00 •8.40 •0.86 •8.00 •1.00•10TC204 •0.87 •100.00 •8.89 •0.80 •12.00 •1.00•10LC206 •0.95 •100.00 •5.90 •0.63 •12.00 •1.00•10TC201 •0.90 •100.00 •0.00 •0.92 •14.00 •1.00•10AC203 •0.75 •100.00 •2.15 •0.61 •16.00 •1.00•10FC202 •0.45 •100.00 •0.00 •0.58 •22.00 •1.00•10TC205 •0.90 •100.00 •0.00 •0.50 •24.00 •1.00

Daily Summary

Total Process Performance Optimization:Sustain Reactor Controller Performance

LowerSpecLimit

UpperSpecLimit

GOOD: High CapabilityHigh Capability

LowerSpecLimit

UpperSpecLimit

BAD: Low CapabilityLow Capability

LowerSpecLimit

UpperSpecLimit

Process is capable

LowerSpecLimit

UpperSpecLimit

Process is not capableFDA Science Board November 16, 2001 Meeting:

Price Waterhouse Coopers Presentation,


Performance

Process & Equipment

Control

Total Process Performance Optimization:Sustainability, Monitoring and SPC


Total Process Performance Optimization:Sustain Batch Operations

Golden Batch Profile


Total Process Performance Optimization - Results

• Baytown and New Martinsville set monthly production records, againThey’ve done it again; Baytown and New Martinsville have once again set newmonthly MDI production records. The facilities first set monthly production recordsfor MDI in March with a combined output of 27,238 metric tons. In June, they brokethe record again, increasing production by an additional 1,367 metric tons… Thisgood news is no surprise to Kirk Bourgeois, Head Production and Technology –MDI USA, who has helped lead six sigma-based optimization teams charged withimproving and sustaining process efficiencies... BMS Website News, September,2004

• Bayer BP … has bounced back thanks to a successful process transformation projectBayer BP’s Operational Excellence initiative helped increase by 35% the plant’soverall output of Kogenate FS in 2003 and should help to double 2000’s productionlevels by 2006… So far, the process changes brought about by the OperationalExcellence project have not been accompanied by big Bayer BP financialexpenditures in new technology… Managing Automation, July, 2004, pages 43-46.


Total Process Performance Optimization: Summary

• Powerful Tools Developed in the Chemical Process Sector√ Methodologies√ Mathematical Tools√ Monitoring Infrastructure

In Polymer and Chemical Industries In Pharma and Biotech Industries √ To Increase Capacity √ To Increase Capacity √ To Improve Quality √ To Improve Quality √ To Reduce Energy Costs √ To Improve Release Times √ To Increase Productivity √ To Reduce DER’s

• And … process optimization leads to a better understanding of the unit operations within the process; a well implemented optimization project can inherently be used as a diagnostic platform to recover from disturbances … both in the process and pharma/biotech sectors!

Bayer Group: ProductsBayer Group: FiguresBayer Technology Services: International Locations/Offices

americas - pharmaceutical manufacturing...commonalities in the chemcial and pharma/biotech...

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