How
to
make
more
from
your
online
and
offline
fermentation
data
and
how
to
speed
up
your
bio
process
development
with
statistical
modeling.
I
am
Benjamin
Fürst
from
Clariant
and
today
I
will
show
you
how
to
do
this
in
JMP.
I
will
lead
you
through
a
hands-on
presentation,
how
to
combine
different
modeling
techniques
so
that
in
the
end
you
have
a
combined
profiler
where
you
can
look
at
all
the
responses
online,
offline,
at
one
time
and
see
what
impact
your
process
parameters
have.
I
am
a
biochemical
engineer
by
training.
I
work
for
Clariant
at
the
Group
Biotechnology
and
I'm
group
leader
of
Bioprocess
design.
The
agenda
for
today
will
be
introduction for Clariant and
into
the
process
technology sunl
iquid.
And
then
I
will
dive
into
the
topic
of
my
talk
and
go
to
the
statistical
analysis
of
fermentation
data.
For
the
modeling
offline
responses
I
called it
standard
statistical
modeling.
I
will
use
the
Fit
Model
platform
and
will
especially
focus
here
on
the
extrapolation
control
which
came
in
with
JMP
Pro
16.
Next
level
analysis
will
be
modeling
the
online
data
and
I
will
use
the
same
data
set
and
for
this
I
will
use
the
Functional
Data
platform,
also
a
JMP
Pro
feature.
Let
me
share
some
key
figures
about
Clariant.
Clariant
is
a
global
leader
in
specialty
chemicals.
To
the
right
you
can
see
the
Clariant
business
numbers.
We
have
a
3.9
billion
Swiss franc
sales
in
2020
with
a
15%
EBITDA
margin
and
over
13,000
employees
worldwide
at
85
production
sites.
Clariant
consists
of
three
business
units:
care
chemicals,
catalysis,
and
natural
resources.
Care
chemicals.
They
produce,
for
example,
ingredients
for
shower
gels
and
shampoos,
which
you
use
in
daily
life.
Natural
resources.
They
produce,
for
instance,
bentonites.
40 percent
of
all
vegetable
oils
are
clarified
with
bentonites
from
Clariant.
Catalysis.
Of
course,
these
are
all
kinds
of
catalysts
and
they
also
contain
the
business
line
biofuels
and
derivatives.
This
business
line
sells
sunliquid.
What
is
sunliquid?
Sunliquid
is
a
technology
which
came
from
the
Group
Bio
R&D Center,
where
I
work.
Basically,
it's
a
biotechnological
process
for
producing
bioethanol
from
non-edible
biomass.
How
does
it
work?
Just
to
stress
this
again,
that's
the
second-
generation
bioethanol
production.
So
we
use
non-
edible
biomass
feedstock.
So
basically
agricultural
residues.
This
can
be
wheat,
straw,
bagasse,
corn stover,
municipal
waste
or
forestry
residues.
We
put
that
into
the
sun
liquid
process
and
we
first
turn
them
into
cellulosic
sugars
and
then
to
cellulosic
ethanol.
The
nice
thing
about
this
process
is
that
it
can
be
used
as
a
platform
process
for
not
only
by
bio-ethanol,
but
for
other
sustainable
fuels
or
even
bio-based
chemicals.
The
development
of
this
process
started
in
the
Group
Biotechnology
where
I
work.
You
can
see
if
I'm
not
in
home
office
due to corona,
I'm
working
here
at
that
corner
of
the
R&
D
Center
in
Planegg.
That's
located
just
a
few
kilometers
outside
of
Munich
in
Bavaria
in
Germany.
The
center
was
inaugurated
in
2006
and
we
have
over
100
scientists
and
technicians
working
there.
The
competence
fields
of
Group
Bio
are
biofuels
and
derivatives,
sunliquid,
industrial
enzymes,
and
biobased
chemicals.
Within
the
Biotech
R&D
Center,
we
have
all
the
expertise
to
develop
new
products
and
technologies
under
one
roof,
starting
from
small
tiny
microtiter
plates
over
a shake
flask
to
up
to
100
liter
bioreactors.
Here
in
the
picture,
you
have
a
small
peek
at
our
technical
center
fermentation.
To
the
right
here,
you
can
see
the
sun
liquid
pre-commercial
plant
in Straubing,
which
is
just
one
and
a
half
hours
from
here.
It
was
built
in
2012
and
can
produce
about
1000
tons
of
ethanol
per
year,
and
we
were
able
to
test
the
wide
range
of
different
biomass
feedstocks
there.
Maybe
some
of
you
are
aware
that
Clariant
is
commissioning
a
commercial-
size
bioethanol
plant
in
Podari,
Romania
at
this
time.
To
say,
as
a
biochemical
engineer,
seeing
this
plant,
that
is
really,
truly,
impressive thing.
The
size
of
the
buildings
and
the
equipment
there
are,
in
biotechnological
means,
really
tremendous.
And
I
see
this
as
a
real
flagship
for
biomass
conversion
here
in
Europe.
And
to
my
opinion,
that
is
one
of
the
major
biotechnology
projects
we
have
in
Europe
at
this
time
and
it really
makes
me
proud
to
see
that
we
are
turning
our
sun
liquid
technology
from
bench
scale
to
production
scale.
The
plant
transforms
a
quarter
of
a
million
tons
of
wheat
straw
into
50,000
tons
of
bioethanol
per
year.
And
to
give
a
more
handful
number,
that
means
one
huge
bale
of
straw,
about
500
kilos
goes
into
that
plant
per
minute.
The sun
liquid
technology
enables
you
to
produce
bioethanol
in
a
very
integrated
way.
Side
products
like
lignin
are
reused
in
the
CHP
plant
for
energy
supply.`
So
generating
steam
and
power
which
goes
back
to
the
plant,
of
course.
If
you're
curious
to
learn
more
about
that
technology,
please
look
at
the
links
or
feel
free
to
contact
me
or
reach
out
in
the
contacts
which
are
given
on
the
home
page.
Now,
why
I'm
showing
you
all
of
this?
What
do
all
the
stages
have
to
do
with
each
other?
At
all
stages
of
the
development
of
our
biotech
process,
you
have
data
about
the
process.
And
it
doesn't
matter
if
it's
a
micro
titer
plate,
a
one-
liter
bioreactor,
or
a
multi-
cue
production
scale
fermenter,
you
always
have
offline
data
like
titers
you
measure,
and
you
have
online
data
coming
from
sensors,
pressure,
temperatures.
And
you
always
want
to
know:
how
can
you
achieve
the
most
efficient
process?
How
can
you
achieve
the
high
yields?
What
the
process
influences in
process
parameters?
And
what
sensor
do
you
have
to
pay
attention
to
to
get
the
most
of
your
process?
All
these
points
can
be
addressed
with
statistical
modeling
of
your
process,
and
JMP
really
gives
you
the
opportunity
to
make
more
from
your
online
and
offline
fermentation data
and speed
up
your
biotech
process.
Let's
go.
Since
my
talk
is
going
to
evolve
about
data
coming
from
a
fermentation
process,
I
want
to
put
everybody
on
the
same
page
concerning
how
does
a
fermentation
process
look
like.
This is
a
basic
overview
of
the
fermentation
process
with
DSP.
I
will
focus
on
the
main
points
here.
So
you
come
in
with
a
test
tube
that's
a
few
milliliters.
You
propagate
your
organisms
in
a
so-
called
seed
fermenter.
And
then
you
do,
in
the
next
scale,
the
fermentation,
the
main
fermenter
where
the
product
is
going
to
be
produced
and
then
you
go
to
a
downstream.
The
setup
here
really
depends
on
what
the
specs
of
your
product
are.
All
along
the
process,
you
have
different
steps
and
you
have
factors,
which
are
set
for
this
step.
On
the
top
here,
I
show
just
some
examples.
So
seed
inoculum
amount,
how
much
biomass
you
put
in
here,
pH,
temperature
setting
of
the
fermenter,
or
even
other
humid
operations,
and
the
raw
materials
you
put
into
the
process:
which
type,
at
what
concentration.
And
all
of
them
here
are
constant,
they
have
a
fixed
number.
So
I
depicted
them
here
with
dots.
And
of
course,
you
have
responses.
The
responses
are
what
the
outputs
of
your
process.
This
can
be
a
harvest
yield.
So
how
much
product
you
going
to
get
in
your
fermenter
or
how
good
does
your
DSP
perform
in
terms
of
some
specific
response
you
want
to
look
at.
Those
are
constant,
again,
because
you
just
have
a
single
point
and
you
have
online
sensor
data.
For
fermentation,
you
usually
look
at
something
like
a
dissolved
oxygen
called
pO2,
temperature,
pressures.
And
the
important
point
about
that
sensor
data,
it
is
functional.
So
you
have
your
sensor
data
over
duration.
You
have
a
curve
of
data.
And
I
can
really
tell
you
if you're interested
in
knowing,
how
is
everything
connected
there?
How
can
I
make
the
best
of
all
of
the
data
you
have?
And
normally
starts
if
you
want
to
know
from
your fermentation
parameters
what
is
your
response
you're
going
to
have,
your
offline
response
something
like
a
yield
product
character.
And
maybe
you
want
to
know
from
a
fermentation
parameter
how
does
a
typical
sensor
curve
look
like
so
that
you
know
how
to
go
with
your
process
in
a
good
way.
And
last
but
not
least,
wouldn't
it
be
great,
here
for
the
last
point,
knowing
at
one
time
point,
online
sensor
has
a
critical
impact
on
your
harvest yield?
Using
standard
software
you
will
really
not
get
far.
You're
probably
going
to
lose
your
focus
looking
at
all
the
points
and
JMP
can
really
help
you
in
getting
those
things
done.
In
the
following
I
want
to
show
you,
with
a
live
demonstration
then,
how
you
can
do
all
those
things
in
JMP.
There
are
several
videos
in
the
community
really
how
to
use
the
Fit Model
platform
or
the
FTE
platform,
but
I'd
like
to
combine
these
platforms
to
put
all
the
results
together.
So
standard statistical
modeling
with
a
Fit
Model
platform
and
with
a
focus
on
the
extrapolation
control.
I
want
to
use
the
fermentation
parameters
and
model
the
harvest
yield.
That
is
where
we're
going
to
go
for
the
first
point.
Normally
the
goal
of
doing
this
is,
you
want
to
know
what
process
parameter
give
a
high
harvest yield.
You
want
to
find
an
optimum
in
the
design
space.
Is
that
optimum
stable?
And
what
parameters
have
interactions?
And
what
parameters
are
sensitive?
And
the
way
you
do
this,
as
I
already
told
you,
you're
going
to
use
the
Fit
Model
platform.
One
important
point
here,
I
call
it
here
detailed
evaluation
by
and
with
subject
matter
experts.
I
believe
that
the
real
speed
for
process
development
comes
from
a
mutual
understanding
among
the
experts
you
have.
It
doesn't
matter
if
it's
a
fermentation
engineer,
data
analyst,
or
a
manager.
Every
expert
must
understand
a
little
bit
of
the
language
of
the
other
experts.
So
it's
really
crucial
that
the
subject
matters
share
a
common
language.
And
I
believe
using
JMP
enables
you
to
speak
that
common
language
using
the
profiles
JMP
provides
and
the
reports
JMP has.
Get
back
to
the
topic.
This
was
called
a real-
life
experiment,
and
the
data
was
coming
from
a
planned
experiment
and
it
was
not
intended
for
the
usage
of
statistical
modeling.
That
means
the
design
space
was
not
optimal.
I
put
a
plot
of
three
parameters
here.
As you
can
see,
of
course,
there
was
some
kind
of
structured
approach,
but
not
in
all
dimensions,
not
in
all
parameters.
You
can
see
on
this
axis,
we
don't
have
so
many
points.
So
one
of
the
challenges
here
was
the
limited
design
space
in
here,
especially
the
extrapolation
control.
JMP
Pro
comes
in
very
handy.
Let's get
into
JMP.
I'm going to load up JMP now.
So
that
is
my
data
table
and
of
course
already
scripted
the
report.
This
is
the
profile
for
that
model I chose.
A
t
this
point,
I
will
not
go
into
the
details
of
the
modeling.
I
assume
you
already
looked
at
your
plots.
You've
done,
you
chosen
your
models,
the
parameters
for
your
model
correctly.
So
how
can
you
use
this
for
your
development?
I
think
that's
pretty
obvious.
You
have
here
the
harvest
yield,
which
you're
interested
for
your
fermenter,
and dependency
of
all
of
the
parameters
which
I
found
significant
for
this
model.
And
one
way
you
can
use
this,
I
want
to
show
you
a
very
nice
thing
you
can
see
with
this
model.
If
you
look
at
the
Seed Inoculum,
that
is
basically
the
amount
of
biomass
you
put
in
the
process.
If
you
have
a
low
biomass
you
put
in
the
seed,
it
means
you're
not
going
to
have
so
much
biomass
during
your
whole
process.
And
you
will
see
that
this
parameter alone
doesn't
have
a
huge
impact
on
the
harvest
yield.
But
now
if
you
see
that
moving,
if
you
move
that
around,
there
is
a
strong
interaction
with
component
A
in
the
main fermenter.
So
with
less
biomass
in
here
you
have,
the
more
component
A
in
the
main
fermenter
you
give
in,
the
more
products
you
will
generate.
You
can
basically
say
that's
a
limitation
on
component
A
that
makes
totally
sense.
Now
the
interesting
thing
is
the
interaction.
If
you
going
to
put
more
biomass
into
the
process,
that
turns
around.
So
what
happens
here?
I
have
to
say
you
cannot
see
that
here
what
happens
there
because
you
have
a
different
limit.
Now
it
gets
to the point
where
it's
interesting.
You
have
to
put
more
data
into
your
model,
for
instance,
online
data.
You're going to
see
that
in
a
different
sensor.
That
is
something
I
will
show
you
then
if
we
go
into
the
online
data
analysis.
You
can
exactly
see
this
behavior
there.
And
just
seeing
this
was
quite
obvious
for
me;
an
explanation
of
that
was
clear.
But
just
the
other
day
I
talked
to
a monitoring and
fermentation
engineer
and
he
just
said,
"Having
that
behavior
for
that
other
parameter
and
temperature,
that's
totally
clear
and
we
have
to
look
at
that."
That's
something
that
I
talked
about,
that
common
language
that
I
will
be
able,
as
data
analyst,
to
understand
some
things
but
not
to
the
same
extent
as
the
fermentation
engineer
does.
So
it's
very
crucial
that
everybody
has
access
to
this
kind
of
looking
at
that.
I
wanted
to
talk
about
Extrapolation
Control.
Extrapolation
Control
comes
with
JMP
Pro
16
and
it
can
be
found
here.
There
are
different
criterion
you
can
set.
I
set
it
here
to
the
first
one
that
basically
is
JMP
to
stay
within
the
context
of
the
data
you
have.
If
you
want
to
learn
more
about
that,
there's a
talk
about
control
extrapolation
by
Laura
Lancaster
and
Jeremy
Ash.
The
videos
in
the
community.
They
talk
a
lot
about
how
you
can
use
that
and
how
are
the
statistical
details
behind
that.
As
you
already
probably
noticed,
moving
around
the
parameters,
those
traces
here
move
that
wasn't
there
before
and
that
really
gives
you
an
advantage
in
using
the
model
because
you
will
not
go
outside
the
model,
outside
of
your
design
space.
You're
not
going
to
make
a
wrong
conclusion
from
your
data.
And
you
can
see
even
JMP
splits
data
here
if
it thinks
that
is
not
sufficient
data
there
to
do
some
extracting
data
from
that.
So
for
limited
design
space,
Extrapolation
Control
is
very
neat
way
to
go.
We're
going
to
use
that
later.
So
we're
going
to
need
to
save
our
prediction
formula.
Going to save
it
here,
save
columns,
hit
the
prediction
formula,
and
then
it's
going
to
turn
up
in
your
data
table
down
here
because
we're
going
to
need
that.
Going
to
get
back
to
my
presentation.
So
just
to
make
a
short
summary.
The
models
shown
could
show
expected
behavior
and
most
important
unexpected
behavior
and
use
that
for
next
runs.
The
extrapolation
control
especially
on
limited
design
space
comes
in
very
handy,
and
we
were
able
to
find
optimized
parameters
of
the
model
in
the
design
space.
So
we
identified
potential
parameters
for
a
higher
yield
for
the
next
runs.
Just
a
nice
side
fact.
How
to
interpret
stability.
I
normally
prefer
to
use
the
simulator
here.
You
can
just
add
that
to
the
profiler
and
also
I
like
to
use
the
contour
plot
especially
when
you
have
multiple
responses.
So
not
only
the
titer
but
imagine
you
have
some
DSP
performance
you
want
to
put
into
your
model
as
well
and
then
you
put
them
all
in
a
contour
plot
and
limit
the
areas
where
you
want
to
have
the
process
and
then
you
have
the
target
area
where
you
need
to
run
your
process
to
be
within
all
the
specs.
Especially
here
because
of
this
limited
design
space,
you
have
to
emphasize,
you
have
to
verify
the
results
with
an
additional
experimental
runs
because
you
were
on
the
limit.
The
optima
we
chose
with
the
profiler
are
on
the
limit
of
the
model.
So
you
have
to
be
sure
that
those
are
correct.
But
here
in
this
case,
we
just
use
them
to
guide
us
in
the
correct
directions
for
parameter
setting.
At
this
point,
we
have
the
profiler
with
the
response
of
the
harvest
yield
over
the
fermentation
parameters.
Now
I
already
hinted
it.
Wouldn't it
be
great
to
have
now
all
your
online
sensors
on
there
too
so
you
can
see
other
effects
as
well?
Let's
go
there.
So
the
next
step
is
online
data
analysis
with
a
functional
data
platform.
And
there
you
are
able
to
use
the
same
set
of
fermentation
parameters
and
model
the
functional
data,
the
online
response
data
and
combine
them
with
your
model.
We
just
use
that.
To
put
that
a
little
bit
in
graphics,
w
e
have
the
fermentation
parameters,
we
have
a
whole
bunch
of
sensor
data.
I
depicted
all
the
patches
here.
And
from
that
data,
we
want
to
go
to
a
profiler
where
we
have
the
lower
line
is
the
model
I
just
showed
you
and
you
also
have
other
responses
there.
We
can
see
what
is
the
typical
response
of
that
set
of
parameters
you've
chosen.
I
drafted
a
short
workflow
here
of what
we're
going
to
do.
I'm
going
to
show
you
that
in
JMP,
of
course.
And
we
are
basically
going
to
use
that
we
did
already,
the
fit
model
platform,
save
the
prediction
formula.
And
now
we're
going
to
use
the
FDE
platform, f
it splines,
do
the
functional
DOE
and
save
that
again,
and
then
put
everything
together
in
one
place.
Let's
get
back
to
JMP.
Here's
a
set
of
data.
I
normalized
all
the
data
from
zero
to
one.
I
would
want
to
show
you
an
example
of
the
pO2
,
that
is
dissolved
oxygen.
This
is
an
important
parameter
in
fermentation
because
microorganism
in
this
case,
needs
oxygen.
And
so
having
sufficient
oxygen
all
the
time
is
important.
The
graph
builder,
essentially,
if
you
want
to
get
people
to
use
JMP,
I
always
like
to
stress
out
using
the
graph
builder,
because
that's
just
the
easiest
way
to
put
data
together
and
make
nice
graphs.
So
using
other
software
probably
would
have
taken
me
to
do
this,
not
30
seconds.
And
this
is
a
very
nice
way
to
have
all
the
overviews.
And
where
do
you
want
to
go?
We
want
to
have
another
parameter
here,
the
pO2
.
Also,
all
the
information
we
have
in
all
those
batches
we
want
to
collect
them
here
and
see
what
is
the
typical
curve
for
this
set
of
fermentation
parameter
we
chose.
The
functional
data
platform
is
here
under
specialized
modeling.
You
need
your
outputs,
which
are
functional.
I'm
just
going to
take
two
here,
pO2
and
CO2.
On
the
X,
you're
going
to
put
the
duration,
and
you
have
to
tell
JMP
how
to
discriminate
between
the
matches.
So
you're
going
to
put
your
batch
ID
here.
Now,
it's
really
important
to
supplement
everything
you
want
to
need
about
so the
fermentation
parameters,
or
even
other
things
like
the
harvest
yield.
You
want
to
know,
you
want
to
do
analysis
on,
you
have
to
put
supplementary
here
at
this
point.
If
you
forget
something
here,
you
have
to
redo
a
lot
of
stuff
again.
And
then
you
have
the
first
report
on the Functional
Data
Explorer.
JMP
gives
you
here
that's
everything
together.
One
graph
that's
not so nice,
but
here
you
can
see
all
the
data
by
Batch
ID.
Just
want
to
point
out,
there
are
nice
clean-
up
tools
here.
Have
a
look
at
them,
use
them.
You
don't
have
to
clean
up
your
data
before,
even
you
can
fill
your
data
here
very
easily.
Now
we
have
the
data
here,
and
now
we
have
to
build
a
functional
model
of
the
data.
And
JMP
does
that
with
doing
splines.
There
are
different
kinds
of
splines
you
can
use.
I'm
first
going
to
go
to
the
easiest
one,
B-S
pline.
That is pretty
fast,
and
you
can
see
what
it
does
there.
First,
you're
going
to
see
here
in
red,
the splines
,
which
were
done
over
the
other
user
data.
And
the
second,
it
does
a
functional
PCA
down
here.
So
PCA
basically
is
about
reducing
the
dimensionality.
And
JMP
produces
a
set
of
Eigen
functions
and
a
set
of
FPCs,
functional
principle
components.
And
if
you
multiply
each
corresponding
FPC
with
the
Eigen
function,
you
will
end
up
with
a
functional
model.
So
you
can
use
that
to
individually
model
each
batch
of
fermenter
you
have.
So
the
FPCs
are
the
individual
characteristic
of
a
batch
and
the
Eigen
functions
are
valid
for
all
of
them.
In
multiplying
them,
you
get
each
individual
curve.
That's
important
to
understand
that
those
FPCs
are
the
characteristic
for
the
functional
data
you
have.
Then
you
have
to
design
now.
Now,
looking
into
this.
Basically
you
see
that
spline
doesn't
get
the
data
very
well.
I
know
that
up
and
downs
here,
those
are
important
and
that
doesn't
suit
me
very
well.
So
I'm
going
to
remove
this
fit
and
I
will
do...
There
are
other
models.
A P-Spline is a penalized
B-S
pline
model
and
that
will
do
the
thing
here.
Just
want
to
point
out
there
are
other
ways
as
well.
The
direct
function
of
PCA
does
it
without
fitting
a
basis
function.
If
you
have
a
huge
data
set
that
works
faster.
For
me,
I
already
know
from
my
data
set
that
the
P-Spline
is
that
what
I'm
going
to
need.
You
can
already
see
here
this
takes
some
time.
So
think
about
maybe
some
data
reduction
that
you
can
get
faster
through
that
process
if
it's
important.
But
be
aware
that
if
you
reduce
your
data,
you
will
be
losing
information
about
your
process
and
maybe
those
will
be
the
important
ones.
So
maybe
just
take
your
time
and
just
wait
for
some
minutes,
grab
a
coffee.
So
you
can
see
here
already
that
those
lines
do
fit
very
good.
So
this
is
something
I
was
concerned
with
to
use.
Of
course,
you
can
look
at
the
diagnostic plots
and
see
if
everything
suits
you
well.
I
will
focus
on
that
where
I
wanted
to
go
originally.
Keep
in
mind,
where
did
you
want
to
go?
Wanted
to
add
a
pO2
curve
here
depending
on
the
fermentation
parameters.
JMP
is
going
to
give
the
option
to
do
a
functional
DOE
analysis.
That
is
exactly
where
we
want
to
go.
Basically
in
the
background,
it
does
a
generalized
progression
with
a
two-
degree
factorial
model.
And
the
estimation
method,
it
usually
depends
on
the
amount
of
parameters
you
have.
It's
either
best
subset
or
I
think
it's
a
forward
selection.
Let
me
make
that
smaller.
Don't need this.
But
we
do
need
this.
That's
where
we
wanted
to
go.
So
exactly
what
we
need.
We
have
the
pO2
,
the
online
response,
dependency
of
our
parameters.
Just
as
a
side
point,
if
you
don't
see
this
modelization
fitting,
you
have
to
do
that
maybe
more
by
using
the
fit
model
platform.
But
if
it's
fine
for
you,
you
can
go
with
that.
Again,
you
have
to
save
your
prediction
formula
as
you
want
to
put
that
together.
Be
aware
that
I'm
now
in
different
data
tables
than
I
used
before.
So
you
can
hit
save
prediction
formula,
click,
and
then
you
're going to
end
up
with
your
prediction
formula
here
in
that
table.
And
my
original
model
was
in
another
data
type.
So
you
have
to
just
combine
them
whatever
you
like,
however
you
like.
I
just
basically
copied
the
formula
and
that
was it.
Then,
let
me
show
you
if
you
want
to
put
them
together.
I
put
them
together
in
my
original
table.
I
have
them
both
here
now:
the
prediction
formula
of
the
harvest
yield
and
the
pO2 of the
prediction
formula.
And
how
to
put
that
together?
Very
easy.
Use
the
profiler,
put
the
function,
and
very
important,
tick
the
expand
immediate
formulas
because
you
have
that
Eigen
function
there,
so
you
need
to
expand
the
immediate
formulas.
Click
okay.
I,
again,
prepared
a
strip
because
the
original
evaluation,
that's
not
so
colorful.
So
I
prefer
coloring
at
some
point.
So
I
just
put
those
two
in
here.
So
this
is
where
we
wanted
to
go.
We
have
our
harvest
here
and
we
have
our
pO2,
and
we
have
all
our
parameters
in
here
for
the
dependencies.
Now
I
promised
you
that
we're going to
see
more.
That is the
seed
inoculum
in
the
component
A
we
looked
before,
if
you
remember,
and
we
saw
that
for
low
biomass
we have
a
positive
impact
of
component
A
in
the
main fermenter.
And
having
more
biomass,
that
behavior
turns
and
now
we
are
able
to
see
what's
happening.
You
see
here
that
is
the
typical
pO2
response
to
that
set
of
parameters.
And
already
going
down
here,
you
can
see
what
happens.
I'm
going
to
go
here
a
little
bit
more
down.
The
pO2
goes
down.
So
in the fermenter,
we
don't
have
a
limit.
Now
of
component
A,
we
have
a
limit
of
oxygen.
That
is
just
the
reason
why
that
is
not
good
for
the
process.
Now
with
modeling
your
online
data
and
your
offline
data,
you
can
go
one
step
deeper
to
understand
what
exactly
causes
all
your
behavior.
That
is
a
really
great
example
how
you
can
approach
the
understanding,
especially
the
speed-
up
of
your
process
development.
Going back
to
my
presentation.
Basically,
that
is
just
what
I
showed
you,
just
with
more
parameters.
I
added
more
online
responses
and
also
some
DSP
responses
in
here.
You can
put
that
at
whatever
extent
you
like.
But
be
aware,
here
is
no
extrapolation
control.
There
are
different
models
behind
each
response.
So
JMP
cannot
put
that
together
into
extrapolation
control.
So
here
you
might
limit
your
factors
over
the
factor
settings
of
the
profiler
that
you
stay
within in
your
design
space.
You
can
use
JMP Standard
to
look
at
the
profiles.
You
just
have
to
do
the
analysis
with
JMP Pro,
the
FDE
analysis.
Okay,
one
more
thing.
Wouldn't
it
be
great
now
to
know
at
which
time
points
an
online
sensor
has
a
critical
influence
on
your
yield?
So
that
you
basically
have
the
sensor
as
an
input
parameter
and
your
yield
on
this
side.
So
you
can
exactly
say,
"Okay,
at
this
point,
it's
very
critical
to
have
my
pO2
up
or
down
to
have
a
good
harvest
yield."
Let's
go.
Next
level.
Now,
I
want
to
use
the
online
data
to
model
the
harvest
yield.
I
wanted
to,
before
we
start,
put
that
graphically
again
and
on
this
side,
we
have
all
the
pO2.
The
graph
I
already showed
you
before.
That's
the
pO2
of
all
the
batches.
And
you
can
see
that
in
the
end
here,
we
have
this
one's
going
up;
the
pO2
sensor
here,
the
pO2
is
very
low;
and
here
it's
somewhere
in
the
lower
region
of
the
sensor.
And
in
each
of
these
cases,
the
harvest
yield
has
different
behavior.
So
somehow
the
individual
curves
at
this
time
point
have
influence
on
the
harvest
yield.
That
is
exactly
what
I
want
to
model
so
you
can
say
which
sensor
profile
really
leads
to
a
good
yield.
This
is
where
I
want
to
go.
So
I
can
have
all
the
responses
here
and
my
harvest
yield
of
course
depicted
and
modelized
over,
this
case,
the
FPCs.
Remember
that
I
told
that
the
FPCs
are
the
individual
characteristic
of
the
response.
That
is
exactly
what
you
do.
So
I'm
going
to
show
that
to
you in
JMP
as
well.
We
can
start
working
through
that
workflow
and
basically,
it's
the
same
but
you
just
use
the
data
in
a
different
way.
You
use
your
online
data,
the
summaries
of
the
FPCs
to
model
the
generalized
regression.
Then
you
put
everything
again
together,
save the
to
prediction
formulas
in
one
data
table,
and
there
you
go.
Okay,
where
did
we
leave?
So
this
is
where
we
stopped
the
last
time.
We
had
our
pO2
over
the
fermentation
parameters.
We
already
have
the model
of
pO2
over
the
FPCs.
Now
what
we
need
is
the
individual
function
summaries.
So
we
need
to
modelize
the
FPCs
we
have
for
that
response
with
the
harvest
yield
because
that
is
what
I
want
to
know.
How
do the
F
PCs
contribute
to
the
harvest
yield?
Now
you
can
set
here
what
you
want
to
export.
Basically,
tick
all of
this.
You
need
to
save
formulas
because
you
want
to
have
the
formula
of
the
pO
2
dependent
of
the
FPC,
and
you
want
to
have
all
the
FPCs.
Now
you
have
to
extract
that
data
because
you
want
to
modelize
that,
and
either
save
data
here
or
if
you
have
modelize
more
than
one
response,
you
can
do
that
up
here
in save
data
or
save
the
summaries.
Then
you
get
everything
neatly
arranged
in
one
data
table.
Click
that
and
then
you
will
end
up
with
a
table
like
this.
So
you
basically
have
all
your
FPCs
in
here
and
the
formulas
you
will
need
here
are
there.
Now
we
have
to
model
the
FPCs
to
get
dependency
to
the
harvest
yield.
You
just
go
to
the
fit
model
platform,
then
you
will
choose
what
you
want
to
model
and
that
is
your...
It's
supplemented.
That
is
why
I
hinted
that
you
really
have
to
be
aware
of
what
you
supplement
in
the
very
first
step
of
the
FTE.
Now
we're
going
to
need
the
harvest
yield
here.
It
has
to
be
supplemented.
And
then,
depending
on
how
you
want
to
approach
it,
I
normally
take
the
FPCs.
I
use
the
response
surface
model.
That
might
be
sufficient
for
you.
If
you
think
you
need
another
model,
you're
free
to
use
whatever
modelization
you
seem
necessary
to.
In
this
case,
I've
put
them
individually.
Sometimes,
even
maybe
just
modeling
the
mean
would
be
enough.
The
mean
here,
maybe
just
modeling
the
mean
is
enough.
I
know
that
in
my
case
it
isn't.
By
the
way,
at
this
point,
I would like to
say
thank
you
to
Imanuel
Julio, JMP
engineer,
who
helped
me
out
in
going
through
that
procedure
as
well.
Thanks,
Imanuel,
for
giving
me
a
heads
up
for
FTE.
We
need
a
generalized
regression
here.
Hit
run.
And
then,
that
dialog
opens
where
you
can
choose
different
estimation
methods.
Basically,
you
could just
try
them.
Then,
the
model
is
going
to
be
done.
The nice
thing
is
that
up
here
you're
going
to
have
a
model
comparison.
So
if
you
put
in
more
than
one,
you
will
directly
see
the
comparison
of
them.
So
how
they
behave
and
which
has
the
best
information
criterion.
Then,
you
have
to
choose
whatever
you
want
to
go
with.
Be
aware
that
something
like
best
subset
can
take
some
computational
time.
So
maybe
go
to
the
see
how
far
that
should
go.
You
chose,
then,
your
model.
You
do,
then,
save
the
prediction
formula
of
that
estimation
method
you
chose.
You
hit
that
and
then
JMP
will
put
that
here.
Then
you
have
everything
together
that
you
need,
this
time
in
one
place.
You're
going
to
take
the
profiler,
put
in
your
prediction
formula
for
the
harvest
yield,
and
of
course,
of your
online
responses
as
well,
add
them
here
depending
on
how
many
you
have
modeled.
Expand
immediate formulas, as well,
and
click
okay.
I,
again,
scripted
that,
having
it
a
little
bit
nicer.
And
then
this
comes
up.
You
have
your
prediction
formulas
for
pO2,
your
online
responses
and
your
harvest
yield;
dependency
of
the
FPCs
down
here.
I will
just
show
you
a
short...
If
you
turn
on
the
FPC
1,
you
can
see
what
happens.
So
if
you
have
a
lower
harvest
yield,
then
it shows
you
that
here,
you
are
basically
on
a
rather
low
level.
We
already
learned
that
low
levels
of
oxygen
are
not
so
good
for
fermentation
process.
So
that
can
be
seen
here
as
well.
And
if
you
have
high yield
you
have no
more
harvest,
more
tighter,
more
product
than
your
fermenter,
you're
going
to
see
basically,
this
goes
up.
That's
good.
Same
behavior
here.
And
you
can
already
see
here
that
here
goes
up.
So
it's
positive
for
your
process
that
the
pO2
goes
up
in
the
end.
Now
this
is
the
point
where,
at
least,
I
have
to
go
to
the
subject
matter
expert
and
to
give
him
that
data
because
he
is
the
one
who
understands
that
this
level
of
analysis
may
be
not
good
for
a
manager.
But
here
you
can
really
go
into
detail
with
all
the
subject
matter
expert
on
the, let's say,
fermentation
level.
And
having
this
option,
this
really
speeds
up
your
process,
understanding
very
much.
And
you
can
see
the
impact
with
the
plots
JMP
gives
you
very
easily.
And
sharing
that
profiler
with
a
process
engineer
gives
you
a
real
head
start
in
what
is
important
during
the
course
of
the
process,
and gives
you
really
the
opportunity to
save
a
lot
of
time
in
finding
that
out.
And
if
you
do
that
by
chance,
find
it
out
by
chance,
that' s
going
to
take
so
much
longer,
so
don't
do
that.
I'm
heading
towards
the
end
of
my
talk.
And
I
want
to
say
that
the
statistical
analysis
of
fermentation
data,
JMP
really
gives
you
the
power
to
explore
and
visualize
those
complex
process
very
easy.
You
can
deepen
your
process
understanding
and
which
process
parameters
are
important,
which
interact
and
which
do
I
have
to
look
at
at
which
time
points
of
the
process.
And
with
the
profilers
and
the
different
setups,
JMP
really
gives
you
the
possibility
to
speak
that
one
mutual
language
to
all
levels
from
technician
to
manager
so
that
really
everybody
can
make
more
from
the
online
and
offline
fermentation
data
and
really
speed
up
your
biotech
process
development.
Thanks
for
listening.
I'm
Benjamin
Fürst
from
Clariant
and
feel
free
to
comment
and
ask
questions
over
the
b
eta channels.
