So this lesson we're going to be covering some domain knowledge about the process
we'll be working with in this course and really this is kind of essential so that
as you perform the hands-on exercises you have a better feel for exactly what you're doing.
We'll be working with some data that was obtained from an oil refinery about their molecular sieve
drying process, you can see the dryers depicted there this is just an example of what they look
like there are two large vessels and they're filled with molecular sieve which looks like
the picture shown in the middle. Molecular sieve can be used for various purposes but in this case
we'll be removing trace amounts of moisture from a liquid hydrocarbon feed that will be eventually
mixed with some acid further on downstream and we want to make sure that all the moisture is
taken out of that material because moisture and acid are bad combination for metallurgy a lot
of corrosion can occur. So it's very important that we move all the moisture from this stream.
Below you see a microscopic view of how things work the molecular sieve has very tiny pores
and the water molecules are adsorbed into the molecular sieve and the hydrocarbon molecules
aren't so that's how the water is removed. So the way it works is the flow this liquid hydrocarbon
stream with the trace amount of moisture in it goes through one of the dryers from the top
to the bottom. When it's at 80 to 100 degrees the water is absorbed by the molecular sieve,
eventually the molecular sieve becomes saturated with water and needs to be regenerated and
at that point in time the liquid feed is switched over to the second dryer and the
first dryer goes into a regenerating cycle. The regenerating cycle is achieved by raising the
temperature of the bed to over 220 degrees and that basically reverses the process,
the adsorption process and the water molecules leave the molecular sieve so
that it's ready to be put back into service once the second dryer becomes saturated.
One other important factor is the age of the molecular sieve drying bed and the age of the
sieve is not really determined by time, it's determined by the volume of feed it processes
over time. So this is an oil refinery and things are measured in barrels and so we'll
be looking at the total number of barrels that each of these dryers see and we'll use that as
a way of measuring their age. These beds will be in service for three or four years when the
refinery does its next annual turnaround or maintenance period. They shut the process down
and they may choose to replace a molecular sieve and what we like is at that point in
time to have both dryers be at the exact same age or as close as possible so measuring the
total volume of feed that goes through each of these is an important metric. Keeping
in mind that the feed rate changes so this process can have a variability in the amount
of barrels that go through it and so we'll have to accommodate for that too in our calculation.
So let's take a look at the drying process in PI Vision. If you open the display drying process
before in your VM you'll get to this display and it shows the two feed dryers dryer A and
dryer B. Dryer A is in drying mode because you can see the cool moist feed is going in the top
and coming out the bottom dried. Dryer B has a hot vapor stream signified by the red line
going into the top so it's being regenerated. In between the two dryers you can see a trend
that shows the two outlet temperatures. One for dryer a and one for dryer B and
we've color-coded them. So dryer A's outlet temperature is orange and dryer B's outlet
temperature is yellow. So in the trend in the middle you can certainly see that the
one dryer's outlet temperature goes up while the other stays down and vice versa and that
just shows how the two dryers are operated over time, this is a seven-day period and
you can see that one's in regeneration when the other's in drying mode and back and forth.
If you'd like to see what the other configuration looks like just go into the trend and scoot it
along one way or the other until you see the opposite temperature peak. So in this case now
you can see by the orange trace that it's at high temperature which means dryer a is
being regenerated and dryer B is in drying mode. So feel free to play around with the
graphic to get a better feel for how the process works. So switch the PI Vision display to be the
display that's titled drying process after and you should see this one here. Now you'll
notice it looks just like the first one except we've added some things to it some symbols,
so we have some multi-state values that show the state of each dryer,
we have some additional values and then we also have a trend titled total barrels dried.
Now when we talked about the age of the molecular sieve drying beds we mentioned that that's really
measured by their total lifetime barrels. In other words a continuous aggregation of
the number of barrels of feed that actually goes through each dryer and that's what's shown here.
So let's take a closer look. So you can see that we have one trace for each dryer and the
trace increases when one increases the other doesn't and that's because only one dryer is
in a drying state at a time. So when the dryers in a drying state the barrels get accumulated
and then they stop when it's in a regenerating state and when one's in a regenerating state
the other's in a drying state so they alternately increase which is what you'd expect. Let's take a
look at how these dryers have been treated throughout the year 2019. So I'm going to
change the start time to January 1st 2019 and now we have a nice view of how things have gone
for the last year and you can see that we've done a pretty good job, there is a period of
time throughout April and May when they deviated quite a bit which isn't really what we're after.
Remember that these dryers will be in service for a number of years so we still got some
time to go here and we really want to make sure that after three or four years we can
maintain this nice consistency, actually make sure it's consistent the whole way through. So
we have an idea for improvement and that idea is going to be to be able to predict how long
it will take for the next dryer regeneration cycle and if we can make that prediction what
we can do is develop a notification rule that will send a note to the operator reminding them
when it's time to switch dryers and hopefully by making that reminder we can make the regeneration
schedule more consistent and help maintain a good curve here and equal treatment of these beds.
So that's going to be our objective for the class. So let's talk about some of the other
values we have here. First you see the multi stating, so under dryer A and dryer B you now
see you there's a state that shows that dryer A is in the drying mode and that dryer B is
actually going through some regeneration. So before we didn't have any of that because there
was no digital state tag that we could rely on to tell us that. So we're going to have to calculate
that based on the temperature profiles you see in the outlet temperature trend. So that'll be
the first thing and that will define the state of the dryer at any point in time. Next what
we're going to do is we're going to calculate the drying cycle's barrels. So what that means
is we want to know how many barrels are processed through a dryer during any given drying cycle and
then we're going to reset it so it's going to have a different shape than the ever-increasing
curve that we see with the total barrels dried calculation. So let's take a look at that briefly.
So I'm going to go ahead and select a couple of these variables so I'll hold the CTRL key
down to select two things and I'll go up here to the ad hoc trend and take a look. So you
can see that trace in blue so it's a sawtooth wave so it increases and then once the dryer
goes into regeneration it gets reset to zero and then it goes up again for the next drying
cycle and then it gets reset to zero when we go into regeneration and so forth throughout the
year and so we'll know how many barrels actually went through the dryer during its drying cycle.
The last thing we're going to do is based on those barrels we're going to predict
how long we anticipate it will take for the next regeneration cycle. See the idea here
is to assume and our hypothesis is that if we process more barrels of feed it's
probably going to take longer to regenerate the bed, so if we can manage and monitor how
much barrel how many barrels actually go through a particular dryer during the time
its drying we can basically estimate how long it's going to take for the next regeneration
cycle and then send a notification when that duration reaches a certain limit. Remember,
the feed rates to these units varies quite a bit, let's take a look.
You can see it kind of goes up and down it's not a nice steady flow so it's not correct to assume
that the amount of barrels is always going to be the same for a given time because the feed
rate changes so much, so we're going to use the power of PI to basically calculate this
total based on this unevenly spaced data and the fact that it moves up and down.
So this wraps up this step, the next lesson we'll take a look at
the underlying AF asset framework model that we're using to support our entire course.