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Copyright © Genomatica 2017 – All Rights Reserved
October 30, 2017
Recent Advances in Fermentation Technology – RAFT™ 12
Jason CraterManager, Scale-up & Technology Transfer
Best practices for successful scale-up of industrial fermentation processes
Jeff LievenseSenior Advisor to the CEO
Topics
2
A brief introduction to Genomatica & 1,4-butanediol
Genomatica’s approach to scaling fermentation
Case study: importance of applying the scale-down approach
Takeaways
3
Begin with the end in mind
Scale-down before scale-up
Leverage models from conception to completion
A brief introduction to Genomaticaand 1,4-butanediol
4
Delivering commercial bio-manufacturing technology
5
We develop microorganisms
and a “how-to”for manufacturing
that use renewablefeedstocks
to make usefulchemicals
that are part ofeveryday products
Making everyday products a better way
First application of Genomatica’s platform technology
6
● Bulk intermediate chemical
● Previously only petro-based feedstocks
● Annual world production ~2 MM tons
● Current market value ~$4 BB
● More than global GDP demand growth
● Industrial solvent, polymers, fibers, polyurethane
Bio-based 1,4-butanediol or BIO-BDO™
● Mater-Bi is a polyester composed of BDO, di-acid, and starch
● Biodegradable, compostable, and now with high renewable content
● Used to make grocery bags, cutlery, food packaging, and more
Novamont licenses the GENO BDOTM process
7
Producing BIO-BDO™ in Italy for Mater-Bi polymer
Genomatica’s approach to scaling industrial fermentation
8
Begin with the end in mind!A commercial-first mindset will reduce development costs, timelines, & mistakes
9
Consider differences between lab and commercial scalesFrom start to finish…
10
Materials
● Industrial grade
● Impurities
● Complex nutrients
● Mixed substrates
● Inducers
● Antibiotics
● Cost
Process
● Control parameters
● KPIs
● Robustness
● Foaming
● Genetic stability
● Sterility
● Waste & recycles
Equipment
● Bioreactor design
● Mode of operation
● Mass transfer
● Heat transfer
● Hydrodynamics
● Heterogeneity
● Hydrostatic pressures
11
Strain
Process
BioreactorTEA
P&IDsGuesses
EmpiricalLiteratureGuesses
EmpiricalLiteratureGuesses
2–10 L
10–2,000 m3
Large-scale model
Scale-down parameters
Scale-down experimentation
OUR
pO2
Temp
pH
Subs
pCO2
Start fermentation scale-up with scale-downAnd start fermentation scale-down by modeling the large-scale design
● Process control parameters
● Acceptable control ranges
● Critical time = time to deviate from acceptable range
● Critical time vs. mixing time○ Assume tmix will be 60-180 sec
○ If tcritical >> tmix then no impact
○ If tcritical ~ tmix then likely important
● Model critical parameters
12
qheatTemp ± 1oC
tcritical >> tmix
qH+pH ± 0.2
tcritical >> tmix
qsSubs ± 10 g/L
tcritical >> tmix
qoO2 ± 5 mM/hr
tcritical << tmix
Calculate critical times to identify key scale parametersSimple comparison of process kinetics to mixing time scales
Substrate: glucose
Example 1:Control target: 50 g/LAcceptable range: 40-60 g/LMax GUR: 12 g/L/hrCritical time = 10 g/L / 12 g/L/hr = 0.83 hr or 3,000 sec
Example 2: Control target: 0 g/LAcceptable range: 0-250 mg/LMax GUR: 12 g/L/hrCritical time = 0.25 g/L / 12 g/L/hr = 0.021 hr or 75 sec
13
qheatTemp ± 1oC
tcritical >> tmix
qH+pH ± 0.2
tcritical >> tmix
qsSubs ± 10 g/L
tcritical >> tmix
qoO2 ± 5 mM/hr
tcritical << tmix
Calculate critical times to identify key scale parametersSimple comparison of process kinetics to mixing time scales
● Macro-scale heterogeneity
● Reactor multi-compartment model
● Organism black box kinetic model
● Solve compartment balances
● Quantify gradient potentials
● Identify critical parameters
● Scale them down in the lab
14
qoqs(qo), qn (qo)
µ(qo), qp(qo), qc(qo), etc.
Model heterogeneity in large-scale fermentorsCompartment model approach works well; must incorporate organism kinetics!
15
● Gas mixing stations
● Pressure-rated reactors
● Heat-traced feed systems
● Dynamic off-gas analysis
● Agitation oscillation
● Gas composition oscillation
● Pulsed substrate feeding
● pH/temp PID controller mods
For process scale-down, you will need to get creativeNon-standard configuration requires new hardware/software and lots of method development
16 Van Dien, Pharkya, Osterhout, 2012, in Engineering Complex Phenotypes in Industrial Strains
Genomics• De novo & variant analysis• Metagenomics• Illumina, PacBio
Transcriptomics• RNAseq• qPCR
Proteomics• Shotgun• iTRAQ (global)• MRM/SRM (targeted)
Fluxomics• Carbon tracing & dynamics• 13C flux analysis
Metabolomics• Quantitative targeted & global• Triple quad LC-MS• Proprietary extraction
● Multi-omics experiments
● Data-driven hypothesis generation
● Data-driven decision making
● Used to diagnose, design, and fix the most intractable:○ Bottlenecks
○ Imbalances
○ Instability
Combine systems biology and scale-downIdeal lab vs. scale-down performance – is there a difference? why? how do we fix it?
17
Case study: importance of applying the scale-down approach
Novamont’s plant goes live in Q3 2016
● 30,000 tons per year
● 100 million euros
● Reliable process
● High-quality product
● 56% lower greenhouse gas emissions
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0%20%40%60%80%
100%120%140%
0 10 20 30 40
Tite
r (%
Tar
get)
Fermentation Time (hrs)
Titer
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Rate
(% T
arge
t)
Fermentation Time (hrs)
RateLab performance
Plant performance
Plant target
Plant vs. lab
● Lower TRY
● Higher byproducts
● Higher cell mass
● Excess respiration
Commercial fermentation KPI targets achievedDespite performance gap from lab-scale data
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Yiel
d (%
Tar
get)
Fermentation Time (hrs)
Yield
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Cell
Mas
s (%
Lab
)
Fermentation Time (hrs)
Cell Mass
● Contamination?
● Cell bank issue?
● Process control deviation?
● Seed culture compromised?
● Fermentation media?
● Fermentation water?
● Fermentation feedstock?
● Large-scale conditions?
20
Why is plant performance deviating from the lab? Many potential sources
21
Samples plated, sequenced
Validated at Geno and plant
Redundant probes, offline samples
Why is plant performance deviating from the lab? Eliminate the obvious
● Contamination?
● Cell bank issue?
● Process control deviation?
● Seed culture compromised?
● Fermentation media?
● Fermentation water?
● Fermentation feedstock?
● Large-scale conditions?
● Contamination
● Cell bank issue
● Process control deviation
● Seed culture compromised
● Fermentation media
● Fermentation water
● Fermentation feedstock
● Large-scale conditions?
Geno lab control
Plant lab AI
Plant performance
After-Inoculation (AI)1L of extracted culture
Run in lab reactor using plant materials
Performance aligns with Geno lab dataPlant seed, media, and dextrose performance verified
0%
20%
40%
60%
80%
100%
120%
140%
0 10 20 30 40
Tite
r (%
Tar
get)
Fermentation Time (hrs)
Titer
22
Why is plant performance deviating from the lab? Use satellite lab fermentations for more rigorous troubleshooting
● Contamination
● Cell bank issue
● Process control deviation
● Seed culture compromised
● Fermentation media
● Fermentation water
● Fermentation feedstock
● Large-scale conditions?
23
Why is plant performance deviating from the lab?
● Existing fermentors
● Vessel geometry
● Agitation system○ Impeller types
○ Locations
○ Power input
● Operating range limits○ Agitation rate
○ Aeration rate
24
qoqs(qo), qn (qo)
µ(qo), qp(qo), qc(qo), etc.
Novamont fermentor model and scale-down protocol Scale-down modeling and experimentation conducted well ahead of plant start-up
Top/middle zones● Low kLa
● Lower pressure
● O2 depletion
Bottom zone● High kLa
● Higher pressure
● Higher O2
0%
20%
40%
60%
80%
100%
0 10 20 30 40
DO
2(%
air
sat
)
Fermentation Time (hrs)
Dissolved O2
0
20
40
60
80
100
0 10 20 30 40
vOU
R(m
ol/m
3 /hr
)
Fermentation Time (hrs)
O2 Uptake Rate
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Bottom
Middle
Top
Novamont fermentor model and scale-down protocol Scale-down modeling and experimentation conducted well ahead of plant start-up
0.0
0.1
0.2
0.3
0.4
0.5
0 10 20 30 40
pCO
2(b
ar,a
bs)
Fermentation Time (hrs)
Partial Pressure CO2
0.00
0.05
0.10
0.15
0.20
0.25
0 10 20 30 40
Gas
yCO
2(m
ol/m
ol)
Fermentation Time (hrs)
CO2 Accumulation
26
Bottom
Middle
Top
Top/middle zones● Lower pressure
● CO2 accumulation
Bottom zone● Higher pressure
● Lower CO2
Novamont fermentor model and scale-down protocol Scale-down modeling and experimentation conducted well ahead of plant start-up
Agitation oscillation algorithmkLa heterogeneity
Gas mixing O2/CO2 enrichmentGas phase heterogeneity
Developing a scale-down protocol to simulate the large scale
27
Novamont fermentor model and scale-down protocol Scale-down modeling and experimentation conducted well ahead of plant start-up
28
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Yiel
d (%
Tar
get)
Fermentation Time (hrs)
Yield
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Tite
r (%
Tar
get)
Fermentation Time (hrs)
Titer
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Rate
(% T
arge
t)
Fermentation Time (hrs)
Rate
0%20%40%60%80%
100%120%140%
0 10 20 30 40
Cell
Mas
s (%
Lab
)
Fermentation Time (hrs)
Cell Mass
Lab performance
Plant performance
Plant target
Scale-down protocol
Plant vs. scale-down
● Similar KPI deviations
● Same byproduct shifts
● Increased cell mass and respiration
Scale-down model closely predicted scale-up performanceOrganism response under scale-down protocol identical to large-scale conditions
29
Strategy Cost Lead Time Complexity Issues/Risks
Install new impellers withbetter power distribution Med Med Med Loss of production during
down time/installation
Modify blades on bottom radial impeller Low Low-Med Low-Med Modifications don’t work,
replacement blades needed
Remove blades from bottom radial impeller Low Low Low Insufficient gas dispersion,
broth out of spec
Turn agitator off and operate as bubble column Low None Low Target OUR not achieved, poor
mixing, broth out of spec
Reduce OTR gradients by distributing power more evenly along vertical axis
Improvement options identified before plant start-upReactor engineering vs. strain engineering
30
Takeaways
Begin with the end in mind
Scale-down before scale-up
Leverage models from conception to completion
