Okay, let me start with a quick
introduction about Invasystems.
The company was founded in 2016
at Houston and Texas. That's
where our headquarters is. And
then today we have two
development centers in India one
in one in Pune, and the second
one is at Hyderabad . The
company's focus can be divided
into three areas. The first one
is production solutions, under
which we are focusing on
industrial IoT, enterprise
mobility, as well as employee
engagement. The second area is
digital and cloud services along
with supporting OSI PI OSI
supports PI System. We also
provide services associated with
Microsoft Edge food as well as
SharePoint and the third area is
enterprise applications.
One point I would like to stress
at this juncture
all the industries which are
made And over here, chemicals,
oil and gas power as well as
manufacturing. We do have
subject matter experts in each
and every area so that we can
have effective communication
with our customer, understand
the requirements and provide a
right solution. Diving into each
of those areas little bit
detail. If you look at the
second bucket, the data
analytics and BI. There are two
products mentioned in that
particular case. The first one
is product DNA, which we'll be
talking a little bit more in
today's presentation. Prop DNA
is our Advanced Data Analytics
application. And the second one
is diagnose diagnostic rule
engine. When you're dealing with
predictive maintenance, there
are many scenarios where you
come across where you have to
set up diagnostic rules that
apply that particular
application goes hand in hand
with prompt DNA. Let me share a
couple of points about our
partnership with Orion. It
started About march of 2018.
Initially, we started working
with the company for providing
support for their PI system that
includes global support for
managing their AF tags as well
as the health check. Like many
other companies, Orion has also
put together a variety of
application where they have
successfully amalgamated data
from different sources, like
SAP, then proprietary
engineering applications. Today,
we are providing support for
such applications as well.
Towards the end of last year,
when Orion decided to start
their journey on predictive
maintenance, we did a
presentation on our application
process DNA. And then we have
been selected for the pilot
using that particular product.
And that's what yen's will be
presenting today. A very quick
introduction about probe DNA.
It's an Advanced Data Analytics
application and it has a
connector to PI System It means
you can seamlessly fetch data
into PI System. The application
allows you to have two data
sets, one for training that is
model building. And the second
one is validation. How we have
we have been using this
application. Hence, we'll cover
that in great depth in the
presentation along with the
statistical information, like
mean, or standard deviation. The
application also gives you
information about correlation
index. Where do you need that?
Our Advanced Data Analytics is
based on neural network. What it
means is, there is a concept of
dependent variable and
independent variable. This
particular correlation index
guides you in selecting the
right variables. Nonetheless,
the biggest role subject matter
play in our application is the
selection of those variables.
Last but not the least, when you
get data from PI System You
don't want your analytics to
learn each and everything what
you're getting. The application
allows you to select a specific
range, and then build your
model. Here are a couple of
screen captures from the
application for model training,
as well as model validation. How
this application was used for
predictive maintenance at Orion.
I would like to hand over
microphone to yen's now, he's
going to present today one case
study. And he's also going to
present tomorrow at the panel
session, the second case study.
Yes.
Thank you and good afternoon,
everybody. My name is Ian
Smith's I'm working for Orion
engineered carbons, which is one
of the world's leading companies
producing carbon black. I'm
working as a reliable
reliability director for Orion.
And you may ask yourself, what
the heck is carbon black and
what it's used for and then One
of the products you know which
is currently used everywhere,
but nobody knows about that. So
if you look around in this room,
everything was black is colored
because of carbon black. It
includes for example cases for
laptops or any plastic tires,
ceilings on any products made
are made of made of plastic,
also including toners and
printing in ink coatings and all
black products. We are running
our business with more than 1400
employees in 14 plans worldwide.
And getting back to OSI pi. We
are using OSI pi since more you
know just one just one year. We
are running OSI pi with 45,000
tags actually with a central
Asset Framework server for all
of our plans globally. So, this
is a globally standardized
system for us, which includes
process data quality data and
also your PII data such as s AP
for example. The partnering with
inverse systems regarding
predictive maintenance started
for us with reliability
initiative Orion started last
year, mainly to reduce downtimes
on our production units, mainly
focusing on better planning to
have more reliability and pre
planning phase to do our
maintenance. This initiative
included two parts which are
conditioning condition
monitoring, as well as
predictive maintenance. You In
condition monitoring we we are
trying to implement more sensors
which are relevant for for
maintenance as well as getting
status information about the
condition of our of our
equipment, which we would like
to see also in easy accessible
dashboards. The predict
predictive maintenance part of
this initiative is was mainly
based on the effort to to
predict damages based on the
reliability sensors we installed
but also relying on the process
sensors we already have in our
plant for production purposes,
for example. And one of the main
approaches was also to include
all that of course into into OSI
pi The for for implementing this
initiative we started a
reliability pilot project in one
of our biggest plans which is
located in Germany. And we
improve the OSI pi in dashboards
we already had and implemented
more detailed equipment
dashboards to see more details
for example on vibration
analyzes of of running
equipment. But all of our
displays we prepared in that
pilot plan are always based on a
global Asset Framework standard
for all of our plans globally in
the predictive maintenance pilot
together with inverse systems.
We started in December last
year, and we focused on
prediction models for for our
large scale heat exchanges.
Which we which we have and run
in all of our plans globally, as
well as with rotary dryers,
which are also part of our
standard process globally. The
the part within our systems
included workshops on getting
more insight about our process
workshops for better
understanding about these
processes, as well as preparing
the first models which have been
rolled out in in May this year.
I would like to go deeply into
one of these models we we
created. And one of the examples
is the modeling for a large
scale heat exchanger. So these
heat exchangers are 2.5 meters
in diameter and between 13 and
20 meters high. running at
maximum temperature of 900
degrees centigrade. These heat
exchangers are a very important
part of our process equipment.
And for that, I would like to
explain a little bit more how
Carbon Black is produced let you
get an understanding what we are
doing and why we are trying to
predict a certain process
process parameter. So, imagine
at home you have a candle it's
burning, and usually it's just a
flame there should be no carbon
black produced. But if you if
you take a cold spoon and hold
that into the flame, then you
would get something black on
that spoon and that's carbon
black. It's it's pure carbon.
And well this is all about
producing carbon black, so it's
rather easy. But we do that in a
A large scale reactor so it's
it's a tube about two meter two
meters in diameter and up to 50
meters long. So that's, that's
the reactor down here we have a
small candle burning in here
which is provided by oil and
natural gas. So the natural gas
is the candle and the oil gets
sprayed into that burning flame.
So we have a temperature about
1200 degrees centigrade in that
in that reactor. And now here's
the place where the spoon comes
into play. So, our spoon our
cold surface is a water spray
which is sprayed into into the
burning flame. And this is the
zone where the carbon black is
produced. So we get carbon black
carbon particles flowing in a
you know we call that a tail gas
of So, that's a flow with carbon
monoxide, carbon dioxide and
other gases. So, the reactor
produces carbon black, the
carbon black gets transported
together with the tail gas into
the sorry, into the heat
exchanger
you into the heat exchanger
which cools down the carbon
black and tail gas flow and in a
counter flow heats up the air
which is needed to run the
reactor.
These heat exchanges
sometimes have failure which is
is a leakage in the tubes. So,
because they are running at that
high temperature is quite
stressful for for these
equipment. So, we we may have
may have a leakage in the tubes
here
And the leakage in the tubes May
May. So, by the leakage in the
tubes the carbon black may come
into the airflow. So, that's
because of the pressure
differences between Taylor gas
flow and airflow and in this
case the carbon black gets into
contact with oxygen and will
start burning. So, we get more
heat in used in the in the app
or heater in the heat exchanger.
So, this will change the
temperature the temperature
level of of the air exceeding
this temperature leaving leaving
the heat exchanger. So this is
one one damage which we would
like to know in advance because
for obvious reasons, we would
like to be prepared for a case
like this. And to determine this
leakage, we are using a KPI
which we call the leakage index.
And the leakage index is just
the ratio between the
temperature differences between
the airflow and the hot tail gas
flow here. So that's for us a
major indicator about the
condition of this heat exchanger
heat exchanger.
So this is how
heat exchanger looks like when
the tubes are leaking. So you
see the red glowing part of that
of that heat exchanger. And if
you see something like that,
it's nearly too late. So we want
to see that in advance. So our
leakage index gets calculated in
OSI pi, we can see that we have
all the temperatures that are
sensors in that we are
calculating the leakage index.
But because the leakage index is
differing between different
products we run on our reactors.
So we are frequently changing
the products we are running on
these reactor lines. And
different products may have
different leakage indexes
because of different temperature
conditions in the heat
exchanger. We can't just set a
fixed limit fixed alarm limit,
which would indicate that that
we get a leakage at the at the
heat exchanger. So this was one
reason to see if we could use
advanced analytics with with
property and a from inverse
systems to determine which level
of leakage index boots help us
to indicate that we have leakage
occurring here. So, if you think
back what Ashok explained about
independent and dependent
parameters which are used for
the advanced analyst lytic lytic
software, what we do is or what
we did for the for the model is
we have our leakage index which
is calculated by the
temperatures and we have several
parameters which influence the
temperature level at the heat
exchanger. So, this is the the
oil flows or flow of oil which
we put into the reactor the more
oil we put in there so, in
general the the higher the heat
is in the heat exchanger, we
have the natural gas flow which
will also affect temperatures as
well as the airflow rate going
into the reactor for providing
air For combustion, as well as
the air compressed air pressure,
the tail gas pressure which
leaves the heat exchanger, and
the water flow, which cools down
the reaction. So these are our
independent parameters all
influencing the dependent
paramita, which is the leakage
index. And based on the
historical data, proc TNA will
learn the dependencies between
dependent and independent
paramita. So, I'm running proc
DNA with our historical data for
the dependent and independent
variables. We see and we have a
look at the left diagram here.
You see the actual measured and
calculated heat IX, heat Sorry
leakage index, which is
calculated based on the actual
temperatures is the red line
property and a will provide us
with the black line which is the
predicted heat in the leakage
index based on the historical
data on which property and a
uses to learn about calculating
of the leakage index. So, you
see it follows the current and
actual leakage index quite well.
And we see that on the on the
right side, which is the
deviation between actual and
predicted leakage index. So, you
see, the maximum deviation here
is around plus minus 1.4%. So,
that's a rather small deviation
we have here and when you also
look at the form of that w It's
quite narrow. So that means that
the quality of prediction is
rather good. So the more narrow
this distribution is, the better
the prediction is if if we would
find a very broad distribution,
we would see that the dependent
parameters, the independent
parameters are not okay, so you
need to play around with that
bit to see which are the right
independent parameters here.
So, unfortunately,
we were a bit too late with our
prediction models remove the
rolling out of the prediction
models, because before we were
able to run, roll this out, we
had a damage on one of our heat
exchanges and that was the
picture I showed you before. So
in this case, I used the
historical data we had about
that damage to verify our model
with with the real time data
here. So, on the on the upper
diagram, we see the red line is
our actual calculated leakage
index. And at that time, we
there is a leakage starting in
this heat exchanger that was
noticed one night yeah, by
chance by the shift personnel
because there was some light in
the night where that shouldn't
be, you know, light and that's
the point of time where we shut
down this line, because we
noticed there's there's leakage
and and damage of this this heat
exchanger, but when we look at
the deviation between the actual
and the printed addicted heat
exchanger of leakage index, we
see that five days before we
could have noticed that already
because the deviation of between
actual and predictive already
exceeds 10% you remember the
other quality of the deviation
while learning the system is
1.4%.
So, this is
giving us would would have given
us a lead time from additional
five days. Five days might sound
short but the problem is also
that five days before would know
the damage of the heat exchanger
would not be that extensive as
it had been. To further monitor
these these leakage indexes we
are while we are Calculating the
the predicted leakage index with
proc DNA. Also proc DNA
calculates the deviation. And
both of these values are
delivered back into OSI pi. So
we have all the data available.
So property in a learns, creates
that model calculates the the
predicted leakage index, and the
values get back into it was i pi
where we can use them to
calculate and to determine what
I call a prediction status. So,
the prediction status is rather
simple. It says if the deviation
between both is more than 10% we
have an alarm level. If it's
less than we have a warning and
if it's within a certain
bandwidth, it's good. This is
also good calculated in OSI pi
and that means we have a tag
prediction status which says
good warning and alarm level.
So, these calculations go back
into into OSI pi. we visualize
them in PI Vision on we have a
equipment dashboard here for for
the app preheaters for the heat
exchanger, which shows the major
the major temperature and
pressure values which we need to
evaluate our heat exchanger heat
exchanger leakage index, it
shows the leakage index itself
it shows the leakage index
compared to the to the predicted
leakage index. It shows the
deviation and it shows also the
status Here, including the
prediction status. You see that
over here, I just included in
the Asset Framework, the
prediction status, as I said,
alarm warning and good level,
which is here displayed in
green, yellow and red. And we
have the current utilization of
that displayed in gray. That
means our overall limit is 10%.
Beginning with 10% deviation, we
would like to get an alarm here.
And these utilization is just
calculated the as as a 100%.
We're taking the 10% and
currently we have an actual
value of 0.01% deviation. That
means we have a utilization of
this complete warning level from
about 1.8%. So that's indicated
above, down here we have the
same just displayed as a time, a
time range for, for the whole
prediction. So that would allow
us to have equipment dashboards
which are accessible for all the
guys from production to
immediately Look at that, and to
see what happens in regards to
the leakage index on their upper
heaters on the different lines.
The way we did that, together
with inverse systems is of
course a bit more than what I
just explained. So what's
required for preparing these
prediction models is at first
you need to have the data. So if
you have all the pie, you
hopefully have all this data
available. You need to have
these data available also in in
a time range from about at least
half a year maybe more to get
enough historical data to train
your system. And as well as to
test the data. You need to
cleanse that data because you
should not include any failure
data because you don't want to
train your model with data which
already indicates a failure. So
that that would mislead the the
model. So, we we implemented the
proc DNA application together
with inverse systems. We
deployed prediction models for
heat exchangers, as well as for
our rotary dryers. We will speak
about that tomorrow during the
panel session and the rotary
dryer For the rotary dials, we
are predicting the bearing
temperatures of the rollers
where the rotary dryer sits on.
So that's a completely different
application, but which proves
the practicability of inverse
systems proc DNA, because in my
eyes the main opportunity we
have and the main advantage is
that we don't need a physical
model for the prediction. So for
property and a It doesn't matter
if if you're including the moon
phases or whatever, as long as
it has influence on your on your
parameter, which you want to
control. So we deployed all
these models, they are linked
into AWS hi pi, they bring the
data the results into OSI pi,
and they provide us some extra
lead time to be prepared about
the damages. So It doesn't help
of course to to prevent these
damages because especially
leakage index will indicate the
leakage once it happens. So it
doesn't stop the heat exchanger
from from leaking. But for us,
the main important part is that
we get some more time to be
prepared. And that's, that's our
main outcome of this of this
project. And as well, the main
focus for us was also to bring
all that into AWS i pi to have
one system to to look at and to
provide to our to our shift
personnel to look at the air
preheaters to to be prepared,
what happens here. Yeah, so,
this is for us a major
improvement and we are
continuing working with inverse
systems on Also with other
equipment types. And yeah, we
are hoping that we still will
have some more advantages from
that system. Thank you very much
for your time. Thank you.
And if there are some questions,
the mic is over here.
So yeah.
Cultural show you Okay, can you
just say a little bit more about
how you actually write it, but
how you do the real time
prediction calculations. So you
are you doing the calculation
and AF right into Pio, you're
doing in some third party
system, right to get via AF
Okay, the predictions are done
using problem DNA because you
are collecting data in PI
System. Once you build your
Advanced Data Analytics model
that is that the system is
deployed in the real time
environment the problem DNA. As
soon as the data hits OSI pi,
then we basically get the data
into proc DNA perform all the
calculations. Now you have one
number first is the predicted
number. Those results are sent
back to PI System for file
system of record. Then we
compute the percentage deviation
and then generate the alerts as
defined in PI System. Today, we
are doing the predictions after
every five minutes, but
obviously that frequency is
configurable. Okay.
Okay. Any other questions?
was another
Chuck could could you hand over
the mic, please?
Cyrano de from Twitter, if you
have to update your model,
meaning your prediction model
who is doing that your company?
Oh,
well, it's a, it's may happen
that that the implemented
prediction model is not up to
date anymore because the process
conditions change overall, for
example. So if we would exchange
one of our heat exchanges
completely and completely remove
and building a new one, you
would need to retrain the models
because the conditions are
completely different. In this
case, I would download the
historical data about in this
case, we don't have any so we
need to wait for some time to
have new new historical data. So
I would I would download that I
would feed that into proc DNA.
And we are even able to provide
these models into into the
property in a server and also do
the connection in towards our PI
on our own. We usually don't do
that because I don't want to do
all that work. So we have some
support from from inverter
systems here. But we would be
able to do that on our own. So
the training and the testing of
the models is done by us. So we
do that
is another question on here. My
question.
Okay, so fortunately, and
unfortunately, you had the
problem with a heat exchanger,
but it did help you to have
ranges for the alarms if you
hadn't had this problem. In
which other way will proc DNA or
the PI stem help you with this
kind of notifications?
Well, if if you don't don't know
how how the damage looks like
and what it causes in regards of
the data you have, well, you
don't have any approach to do
that, you could try to find
something which where you think
it might indicate a damage, but
you should know at least have
some idea about how the damage
affects your sense of beta what
will happen with the data
because protein a has no
physical model about these these
equipment. So I think you have
no other choice other to know
what the damage means to your
equipment and to your sense of
data.
What I would like to add to what
Ian said is that that is where
the physics comes into picture.
So for example, if you have a
following problem in your heat
exchanger, you can start looking
at thermal resistivity as one of
your variables, thermal
resistivity will give you an
indication about how your heat
exchanger is performing, then
that becomes your dependent
variable. Then you figure out
all your independent variables
depending upon your process.
That's exactly what we did in
case of Orion as well which we
are not presenting this time.
But for fouling, we use thermal
resistivity for tubing leaks, we
use leakage index.
Thank you. Okay.
Okay. If there are any other
questions so far, then I would
like to thank you for paying
attention.
That was the last presentation
before today. Thank you very
much, and have a nice evening.
Thanks.
