v3 case studies

7/30/2019 V3 Case Studies

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Bioprocess SimulationCase Studies

E. Heinzle

Biochemical Engineering Department

Saarland University


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Training Case: CellulaseProduction

Cellulose is most common organic compound on earth


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Process Flow Diagram (Cellulase)


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Model parameter Value Source

Bioreaction

Initial cellulose concentration (g/L) 45 [17, 20]

Yield (g cellulase/g cellulose) 0.33 [17]

Productivity (g cellulase/L h) 0.125 [17]

Utilization cellulose (%) 90 own estimate

Initial CSL concentration (g/L) 7.5 [15, 16]

Nutrients/trace elements (g/L) (sum) 4.1 [15, 16]

Utilization CSL + nutrients (%) 75 own estimate

Ammonia added (g/L) 1.0 own estimate

CO2 formation (g/L fermenter volume) 18 [20]

Final cellulase concentration (g/L) 13.4 calculated

Fermentation time (h) 107 calculated

Final biomass concentration (g dcw/L) 15 [20]

Bioreaction conditions

Inoculum volume (% of working volume) 5.0 [15, 16]

Working volume vessel (%) 80 [15, 16]

Aeration rate (vvm) 0.58 [17]

Specific agitator power (W/m3

) 500 [17]Fermentation temperature (C) 28 [20]


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Material Balance of Cellulase Production

Component Input (kg/kg P) Output (kg/kg P)

Ammonia 0.08 -

Biomass - 1.17

Carbon dioxide - 1.48

Cellulase

in final product

product loss

-

1.0

0.04

Cellulose 3.62 0.35

Corn steep liquor 0.61 0.15

Nutrients 0.33 0.08

Water 77.4 77.8Sum 432 432


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Cellulose Production Scenarios

Scenario

Annual

production

(metric tons)

Capital

investment

($ million)

Unit production

cost ($/kg

cellulase)

Base case 456 20.6 15.4

10% inoculum 475 23.4 16.4

Additional

chromatography385 22.1 20.4


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Sensitivity Analysis Yield Cellulase

0 10 20 30 40 50 600

10

20

30

40

50

60

Unitproductio

n

cost($/kg)

Yield (%)


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Data Variation for MCS Cellulase

ParameterValue base

modelProbabilitydistribution

Variation

Yield (g/g) 0.33 normal V = 20%; range: 0.22 - 0.44

Productivity (g/L h) 0.125 normal V = 20%

Aeration rate (vvm) 0.58 even 0.3 to 0.8

Specific power(kW/m3)

0.5 triangular0.4 to 1.2, 0.5 as the most

likely


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Probability Distribution of UnitProduction Cost (UPC)

10 15 20 25 30

0

50

100

150

200

250

300

350

400

Frequency

Unit production cost ($/kg P)

Aeration rate

Productivity

Yield

-75 -50 -25 0 25 50 75

Contribution to variance (%)


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Total Capital Investment CellulaseCost item Multiplier Base Cost ($ thds.)

Delivered purchased equipment cost (PC) 3,290

Installation variable PC 1,060

Process piping 0.35 1,150

Instrumentation/control 0.4 1,320

Insulation 0.03 100

Electrical systems 0.10 330

Buildings 0.45 1,480

Yard improvement 0.15 490

Auxiliary facilities 0.4 1,320

Total plant direct cost (TPDC) 10,550

Engineering 0.25 TPDC 2,640

Construction 0.35 3,690

Total plant indirect cost (TPIC) 6,330

Total plant cost (TPC) = TPDC + TPIC 16,880

Contractors fee 0.05 TPC 840

Contingency 0.1 1,690

Direct fixed capital cost (DFC) 19,410

Land 0.015 DFC 290

Start up & validation 0.05 970

Working capital 30 days - 270

Total capital investment (TCI) 20,650


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Annual Total Production Cost CellulaseCost item Multiplier Cost ($ thds./year)

Variable costs

Raw materials 256

Consumables 112

Labor

Basic labor cost (BLC)

Fringe benefits

Supervision

Administration

Total labor cost (TLC)

25,840 h/year

$26/h

0.4 BLC

0.2 BLC

0.5 BLC

-

672

269

134

336

1,410

Operating supply 0.1 BLC 67

Laboratory/QC/QA 0.15 TLC 222

Utilities 1,161

Waste treatment & disposal 64

Royalties -

Fixed costs

Depreciation period 9.5 years

Depreciation 0.095 DFC 1,840

Insurance 0.01 DFC 194Local tax 0.02 DFC 388

Maintenance & repair equipment specific 1,280

Plant overhead cost 0.05 DFC 970

General expenses

Distribution & marketing - -Research & development - -

Total product cost 6,990


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Case Studies


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Citric acid 5Aspargillus

Stoichiometric modelFilamentous fungus

Pyruvic acid 6

Escherichia coli Detailed stoichiometric model, liquid-

liquid extraction versus electrodialysis,scenario analysisBacterium

L-Lysine 7Corynebacterium glutamicum Dynamic bioreaction model coupled to

process model, sensitivity analysisBacterium

Riboflavin 8Eremothecium ashbyii

Batch productionFilamentous fungus

a-Cyclodextrin 9Cyclodextrin glycosyl transferase

Enzymatic conversion, scenario analysisEnzyme

Penicillin V 10Penicillium chrysogenum Detailed process model, uncertainty

analysis using Monte-Carlo simulationFilamentous fungus

Recombinant HumanSerum Albumin

11Pichia pastoris

New process, recombinant therapeuticprotein from yeast, comparison ofadsorption processes, scenario analysisYeast

Recombinant HumanInsulin

12Escherichia coli

Therapeutic protein from E. coli, proteinprocessing and refolding, detailed model

of complex process, schedulingBacterium

Monoclonal Antibody 13Chinese hamster ovary cells Animal cell culture, uncertainty analysis

using scenarios, sensitivity analysis andMonte-Carlo simulationMammalian cell

-1-Antitrypsin fromTransgenic Plant Cell

Suspension Culture

14Transgenic rice cells

Plant cell culture, feasibility studyPlant cell

Plasmid DNA 15 Therapeutic DNADNA for gene therapy and genevaccination


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Case Studies

Pyruvic acid (National Research Center Jlich, IBT I + II)

Fermentation (E. coli) Optimization microorganism

Optimization process

-Cyclodextrin (TU Harburg, Technische Mikrobiologie)

Existing enzymatic process

Optimization enzyme (CGTase)

OH

O

O


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-Cyclodextrin: Starting PointExisting enzymatic process

Optimization enzyme(CGTase, evolution of extremophilic enzyme)

1. Modeling and assessment of the existing process

(based on literature, patents and expert knowledge)2. Modeling and assessment of potential process improvements


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Reaction Scheme

Cyclodextrin production

Starch Dextrin

MaltoseGlucose

-Cyclodextrin

-Cyclodextrin

-Cyclodextrin

Complexing agent

-Cyclodextrin-Complex

Biwer et al., Appl.Microbiol.Biotech. 2002: 59, 609-617


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Process Schemes

Cyclodextrin Production

Enz. Convers. + Complex

Separation Complex

Washing Complex

Steam Distillation

Decolorization

Crystallization

Vacuum Filtration

Drying

Solvent-Process

Enzymatic Conversion

Adsorption Column

Decolorization

Crystallization

Vacuum Filtration

Drying

Non-Solvent-Process


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0,00

0,02

0,04

0,06

0,08

0,10

Solvent Process Non-Solvent Process

EI[IndexPoin

ts/kgP]

Decanol

other C-Compounds

Cyclodextrins

Glucose + Maltose

Starch + Dextrin

Output

Comparison:Solvent Process Non-Solvent Process

Carbohydratesin waste

Product loss

Decanol lessimportant

Energy DemandSolvent-Process:

18 MJ/kg P

Energy DemandNon-Solvent-Process:

13 MJ/kg P0 2 4 6 8Reactor

Centrifugation

Steam Distillation

Adsorption

Crystallization

Vacuum Filtration

Dryer

Specific Energy Demand [MJ/kg P]

Solvent Process

Non-Solvent Process


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Sensitivity Analysis:

Yield Enzymatic Conversion

Environmental Indexhighly sensitive for yield

Specific energy demandhardly sensitive for yield

0

1

2

3

4

5

6

20 30 40 50 60 70 80 90Yield Enzymatic Conversion [%]

EI[IndexPo

ints/kgP]

Non-Solvent Process

Solvent Process

0

5

10

15

20

25

20 30 40 50 60 70 80 90Yield enzymatic conversion [%]

Sp.

EnergyDeman

d[MJ/kgP]

Solvent Process

Non-Solvent Process


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Sensitivity Analysis:

Volume Eluate Adsorption

0

10

20

30

40

50

0 0,5 1 1,5 2 2,5 3

Volume Eluat [bv]

specificEnergyDemand[MJ

/kgP]

Non-Solvent ProcessSolvent Process


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Conclusion Cyclodextrin

Standard process already shows manyadvantages of biotechnological processes

Increase of yield = highest potential for

improvement Purification including adsorption yields no

significant improvements


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Monoclonal Antibodies (Mab) Scientific:

adaptive proteins

In vitrouse (antigen identification, antigen purification)

In vivouse (therapeutic applications, diagnostic tools)

Commercial: Growing market: 2,400 kg in 2006 (Chovav et al., 2003)

New MAb entering the market; in the biopharmaceutical

development pipeline Need for new production facilities and optimization of

existing plants

Chovav et al.: The state of biomanufacturing; UBS's Q-series: London, 2003.


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Process Steps MAb ProductionRaw material + inoculum

preparetion

Hybridoma fermentation

Biomass removal

Chromatographic steps (2-4)

Viral inactivation

Formulation of final product

Typical steps of proteinproduction

Mammalian cell lines

Exact process flow diagramdepending on MAb


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Basic Assumptions

2 x 15 m3 fermenter

1 g/L Mab

5% of batches fail

326 operating days

14 days fermentation time + 1.5 days lack

time

90% yield in each downstream step


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InoculumPreparation

Seed train:

Starting from T-flasks

Six steps

Volume factor: 7.5 Duration: 24 days

Inoculum Prep

P-1 / TFR-101

T-Flask (225 mL)

P-2 / RBR-101

Roller Bottle (2.2 L)

P-5 / SBR1

First Seed Bioreactor (1000 L)

P-6 / V-102

Media Prep

P-7 / DE-101

Sterile Filtration

P-11 / V-104

Media Prep

P-12 / DE-102

Sterile Filtration

P-10 / SBR2

Second Seed Bioreactor (5000 L)

S-101

S-102

S-103

S-111

S-112 S-113

S-115

S-114

S-119

S-130

S-121

S-122S-123

S-125

S-124

S-129

S-120

S-

P-3 / BBS-101

Bag Bioreactor (20 L)

S-104

S-105

S-107

P-4 / BBS-102

Bag Bioreactor (100 L)

S-106

S-108 S-110

S-109

P-9 / AF-101

Air Filtration

P-8 / G-101

Gas Compression

S-116

S-117

S-118

P-13 / G-102

Gas Compression

P-14 / AF-102

Air Filtration

S-126

S-127

S-128


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Bioreaction

2 x 15 m3 working volume in staggered mode 1 g/l MAb after 14 days fed-batch

25 g media/Liter (equal $5/L fermenter volume)

P-20 / PBR1

Production Bioreactor (20000 L)

P-21 / V-106

Media Prep

P-22 / DE-103

Sterile Filtration

-

S-149

S-140

S-141 S-142

S-144

S-143

Bioreaction

P-24 / AF-103

Air Filtration

P-23 / G-103

Gas Compression

S-145

S-146

S-147


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Primary Recovery + Protein A

Biomass removal: Centrifugation + Filtration

1st Chromatography step: Protein A (affinity) Viral inactivation: Acid treatment

P-30 / V-101

Surge Tank

P-33 / V-103

Storage

P-40 / C-101

Prot-A Chromatography

P-42 / V-108

Pool Tank / Viral Inactivation

PrA-Equil

PrA-Wash

PrA-Eluat

PrA-Reg

S-161

P-41 / DE-105

Polishing FIlter

S-160

S-163

S-162

S-165

P-31 / DS-101

Centrifugation

P-32 / DE-108

Polishing Fitler

S-150

S-152

S-151

S-154

S-153

Primary RecoveryProtein-A

S-155

S-164

S-156


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IXC, HIC + Final Formulation Ion exchange chromatography (gradient elution) Hydrophobic interaction chromatography

Concentration, buffer change and product stabilization

P-50 / C-102

IEX Chromatography

P-52 / V-109

IEX Pool Tank

IEX-Equil

IEX-Wash

IEX-WFI

IEX-Eluat

IEX-Strip

S-170

S-174

P-60 / C-103

HIC Chromatography

P-62 / DE-106

Nanofiltration

P-70 / V-110

HIC Pool Tank P-71 / DF-103

Diafiltration

P-72 / DE-107

Final Polishing Filtration

P-73 / DCS-101

Final Cooling

S-175

HIC-Equil

HIC-Wash

HIC-Eluat

HIC-Reg

S-180

S-185

S-184

S-190

S-191

S-192

S-193

S-195

S-197

S-196

Final Product

IEX-Rinse

IEX Chrom HIC Chrom

Final Filtration

S-194

P-51 / MX-102

Mixing

P-61 / MX-103

Mixing

S-171

S-172

S-173

S-181

S-182

S-183


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Base Case: Inventory Analysis

9.5 kg Mab per batch

x 34 batches per year

= annual production 307 kg

4,600 tons/year raw materials 14,900 kg/kg Mab in final product

Purchased Equipment cost: $9.3 million

Total Capital Investment (TCI): 129 million


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Operating Costs Total Operating Costs: $40 million/year

TOC: 55% bioreaction + inoculum; 45% downstream

Unit Production Costs: $131/g final product

0 5 10 15 20 25

Raw Materials

Labor-Dependent

Facility-Dependent

Laboratory/QC/QA

Consumables

Waste Treatment/Disposal

Utilities

Operating Cost

$ million/year


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Status Mab case

Base case model Based on assumptions and estimates

Best guess for economic key metrics

Existing variability not know

Possible alternatives and variations not

considered


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Areas of Uncertainty

Waste

Consumables

FinalProductUtilities

Labor

RawMaterials

BioreactionDownstreamProcessing

Supply Chain MarketProcess


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Uncertainty Analysis: Elements

EnvironmentalAssessment

Process Model

Uncertainty Analysis

Process Data, Literature,Estimates

EconomicAssessment

Monte CarloSimulations

SensitivityAnalysis

Same PFD

ScenarioAnalysis

Different PFD


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Objective Functions

First Step: Selection of objective functions:

Technical performance: Annual amount of

productEconomic performance: Unit ProductionCost, Return on Investment

Environmental performance: EnvironmentalIndices


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Scenario: # of Fermenter

Parameter 1 x 30 m3 2 x 15 m3

Investment

Annual Amount ofProduct

Unit Production Cost

Lower investment in downstream units overweights higherfermenter cost

Higher labor cost + lower amount of product cause higher UPC

Handling, failure risks


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Scenarios: Chromatography steps

Parameter TCI UPC AnnualAmount

Additional gel filtration + 33%

Additional IXC + 18%

Removal HIC - 16%

Replacement Prot A byIXC

- 9%

Base case: Protein A IXC - HIC


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Sensitivity Analysis

PFD usually not changed

Impact of a single key parameter

Technical, supply chain or market parameters

Identification of the most sensitive parameters

Does not expresses probabilities


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Sensitivity: Mab Concentration I

Curves level off at around 2g/l

Further improvement only through change of PFD

0

50

100

150

200

250

0 0.5 1 1.5 2 2.5 3 3.5

Final Product Concentration (g/l)

UPC($/g)


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Sensitivity: Mab Concentration II

Annual raw materialcost (media, buffers)and consumables

(resins) increase withincreasing amount ofproduct

Facility dependent and

annual labor almostcost constant

Cause lower UPC

0

5

10

15

20

25

30

0 0,5 1 1,5 2 2,5 3 3,5


($

Million) Raw Materials

Facility

Labor

Consumables

0

25

50

75

100

125

150

0 0,5 1 1,5 2 2,5 3 3,5


($/g)

Raw Materials

Facility

Labor

Consumables


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Sensitivity: Yield

Relatively small impact on UPC

100

110

120

130

140

150

0 10 20 30 40 50

Yie ld Ra tio (g/g)

UPC($/g)


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Figure 3-9

Random Number

Generation

SuperPro Model Crystal Ball

MS Excel

ResultsResultsVariables

Initiatessimulation

VBA Script


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Technical parameters: Duration, yield + final Mab concentration in the

fermenter

Aeration rate fermenter Replacement frequency chromatography resins

Yield chromatography steps

MCS: Selection Input Variables

Market parameter- Selling price Mab

Supply Chain parameters- Price media- Price resins

- Price electricity

Parameters that routinely exhibit uncertainty in model/process:


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Probability Distribution Variables

0,000

0,005

0,0100,015

0,020

0,025

10 12 14 16 18

0,000

0,005

0,010

0,015

0,020

0,63 0,71 0,78 0,85 0,93

0,000

0,005

0,0100,015

0,0200,025

4,1 4,4 4,7 5,0 5,4

Triangular distribution: e.g.

Fermentation time (base casevalue as the most likeliest)

Normal distribution: e.g. HICyield (std-dev. 10%)

Weibull distribution:Electricity price (empiricaldata)

S P b bili Di ib i


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Source Probability Distributions

Experimental data, e.g.:

Fermentation parameters like yield, concentration,duration

Empirical/statistical data, e.g.:

World market price sugar (1996-2002)

Electricity price (U.S. Market 2000-2004)

Supplier information, e.g.: Price, yield, service life resins + membranes

Literature data, e.g.:

Fermentation parameters Raw material prices

Expert opinion and own estimates:

All parameters without directly accessible data


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Random Number

Generation

MCS: Computational Structure

SuperPro Model Crystal Ball

MS Excel

ResultsResultsVariables

Initiatessimulation

VBA Script

P b bilit di t ib ti UPC


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Probability distribution UPC

Mean: $138/g

St-Dev: $34/g Var: 25%

Min: $62/g

Max: $424/g

90%-Perc.:$183/g

80%-Perc.:$163/g

Forecast: Unit Production Cost

0

500

1000

1500

2000

2500

52 112 172 232 292($/g MAb)

F

req

u

en

cy

UPC: Fermentation versus


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UPC: Fermentation versus

Downstream Parameter

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

50 100 150 200 250 300 350

UPC ($/g)

Probability MCS-FP

MCS-DSP

FP: 126 +/- 22 $/g

DSP:144 +/- 24 $/g

UPC: All Groups


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UPC: All Groups

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

50 100 150 200 250 300 350UPC ($/g)

Probability

MCS-AP

MCS-TP

MCS-SCMP

MCS-FP

MCS-DSP

TP: 138 +/- 34 $/g

SCP:131 +/- 2 $/g

Parameter Contribution


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Parameter Contribution-75% -50% -25% 0% 25% 50% 75%

Media Pow der Price

Unit Cost Protein A Resin

Unit Cost IXC Resin

Unit Cost HIC Resin

-75,0% -50,0% -25,0% 0,0% 25,0% 50,0% 75,0%

Final MAb Concentration

Product Yield HIC

Product Y ield IXC

Product Yield Prot A Chr.

Rplc. Frequency Protein A

Fermentation Time

Allparameters

Supply chainparameters

ROI: Technical versus Supply


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ROI: Technical versus Supply

Chain/Market Parameters

0

0,01

0,02

0,03

0,04

0,05

0,06

0 50 100 150 200 250

ROI (%)

P

robability

MCS-TP

MCS-SCMP

TP: 110 +/- 33%

SCMP:110 +/- 25%


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ROI: Parameter Contribution

-75% -50% -25% 0% 25% 50% 75%


MAb Selling Price

Fermentation Time

Product Yield HIC

Product Yield IXC


Contribution all parameters

Environmental Indices


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Environmental Indices

0

100

200

300

20 36 52 68 84

EI Input (IP/kg P)

Frequency

Parametercontribution

similar to UPC

EI Input

-75% -50% -25% 0% 25% 50% 75%


Product Yield HIC

Product Yield IXC


-75% -50% -25% 0% 25% 50% 75%

Product Y ield HIC

Product Y ield IXC

Fermentation Yield


Input Output

Results Mab


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Results Mab

Better understanding of key parameters and processalternatives

Most contributing parameters largely the same foreconomic and environmental variability

Mab concentration and chromatography yields contributemost to the uncertainty

Potential improvements:

Increase MAb concentration to around 2 g/L Reduce to only two chromatography steps (media

composition, strain requirements, by-products)

Conclusion


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Conclusion

Uncertainty addressed by a combination of scenario +sensitivity analysis + Monte Carlo simulation

Representative process model and a thoroughevaluation of base case are crucial

The connection of a process simulator with MonteCarlo software enables a time-efficient quantification

Monte Carlo simulations provide a most probablevalue and an expected range of values

Allows to prioritize the parameters that impacteconomic and environmental performance

R&D effort can be directed to the most important risksand the most promising opportunities


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Acknowledgement

Intelligen Inc. (N.J.): Demetri Petrides, JohnCalandranis, Evdokia Achilleos

Charles Cooney and Group at MIT


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Process Diagram

Final

Product

RawMaterials

Utilities

Waste

Consumables

Labor

BioreactionDownstream

Processing

Consumables

Waste

Facility-Dependent Costs: MAb


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Facility Dependent Costs: MAb

Direct Fixed Capital (DFC) $ 165.7 Million

Insurance 0.015 = 2.5

Local Taxes 0.025 = 4.1DFC

Contractors Fee 0.055 = 9.1

Annual Facility-Dependent Cost $ 42.3 Million

Depreciation period 10 years

Depreciation 0.10 = 16.6TCI

Maintenance 0.06 = 9.9

Operating Cost MAb


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Operating Cost MAb

Laboratory/QC/QA 0.6 = 2.3TLC

Operating Cost $ 60.3 Million

Raw Materials 3.5

Consumables 8.2

Total Labor Cost (TLC) 3.9

Utilities 0.024

Waste Treatment/Disposal 0.006

Facility-Dependent Costs 42.3


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Figure 3-5

P-1 / V-101

Seed Fermentation

P-12 / G-104

Gas Compression

P-18 / MX-101

Mixing

S-101

S-102

S-103

S-106

S-107

S-108

S-109

S-110

P-2 / V-102

Seed Fermentation

P-3 / G-101Gas Compression

P-5 / MX-102

Mixing

S-111

S-112

S-113

S-116

S-118

S-119

S-120

S-117

P-4 / ST-101

Heat Sterilization

S-114

S-115

P-6 / V-103

Seed Fermentation

P-7 / G-102

Gas Compression

P-8 / MX-103

Mixing

S-122

S-123

S-124

S-125

S-126

S-127

S-128

S-121

P-9 / ST-103

Heat Sterilization

S-129

S-130

P-11 / G-103

Gas Compression

P-13 / MX-104

Mixing

S-132

S-133

S-134

S-136

P-10 / ST-104

Heat Sterilization

P-15 / V-104

Fermentation

S-131

S-135

S-137 S-138

S-139

S-140

P-16 / RVF-101

Rotary Vacuum Filtration

S-141

S-143S-144

P-17 / UF-101

Ultrafiltration

S-142

S-145

S-146

P-14 / ST-102

Heat Sterilization

S-104

S-105

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