In this lesson we'll be doing some data exploration and engineering using asset
analytics. We need to take a look beyond the sensor data we've obtained from the refinery
to allow us to gain a better understanding of how the drying process is performing.
So we have two primary objectives: first we need to define and label dryer operating
states so based on the patterns of the dryer outlet temperatures we'll need to configure
a rule in asset analytics that allows us to identify the operating state of
each dryer at any point in time. Secondly, we'll be adding two descriptive operating
features and these have to do with the life of the drying bed. You remember the age of
the molecular sieve is really identified by how many barrels of feed it processes.
We'll add to descriptive operating features. The first, drying cycle barrels will be the
total volume of hydrocarbon fed during each drying cycle. The second, lifetime barrels
will be the total volume of hydrocarbon feed processors during all drying cycles.
So the next step is to define and label the dryer operating states and we're going to
take an iterative approach to this. You can see in the trend there are three trends and
the yellow traces are the outlet temperature of one of the dryers and if you recall what we'd
like to do is identify the regenerating state as the time that that temperature rises. The
green trace shows the results of our analytic that's going to basically identify the cycles
for each dryer. So we're going to be creating an analytic and we're going to improve it throughout
this lesson. We're going to start with the good state rule, which will give us a few problems
we'll have a little bit of sort of static or false starts in our evaluation which isn't
really going to work well for us. So we're going to try again and we're going to make a second
rule or actually we're going to revise the first one to create the better state rule and you can
see that we've got a much more definitive start and end to the regenerating cycle.
However, we'll recognize that there are actually other places in there where we
we actually have a third state and once we identify that we're going to call that the
standby state and then finally we're going to create an analytic that covers all possibilities,
does it very clearly, we're going to call that the best state we're going to use that
one for the rest of the lessons in the course. And finally we're going to be
using several different evaluation features of asset analytics so you could get a feel
for what's available for you to test out your analytics before you put them into production.
Once we have the dryer states defined we can calculate the other two descriptive operating
features. The first one will be the drying cycle barrels and you can see a trend of
that value in the PI Vision display at the top. It's the light blue trace you can see it's kind
of a sawtooth trend where we are accumulating the barrels or totaling the barrels in between
regeneration cycles. So it only adds to the total when the dryer is in drying mode and
then it resets to zero. At the bottom you can see the math where we are basically adding up
the barrels in between the regenerating cycles and then zeroing it out. The other descriptive feature
that we'll calculate are the lifetime barrels, so this one is going to be an ever-increasing
accumulation or totalization of the barrels that went through the dryer for its entire lifetime.
So this will be accumulating over several years and will always have an indication of
the total barrels at any given point in time that went through the dryer. Well now we're
ready to get started, in the next lesson we'll begin building our asset analytics.