Hi.
I'm Samvith Rao.
In this video series, we will look
at fault detection and diagnosis in process and chemical plants.
Specifically, I will talk about multivariate analysis
techniques like PCA, walk through a MATLAB demo
on fault detection and diagnosis.
And also, we will look at multiple industry success
stories in this application.
Before we delve into this topic, it helps to take a step back.
Traditionally, chemical engineers
have relied on process simulations
and first principles modeling to understand the processes.
Of late, the advent of large amounts of process data
has given rise to techniques in data-driven modeling.
These help obtain actionable insights
from process data and aid in simulations and engineering
studies, such as debottlenecking,
increasing water quality, and increasing production.
Today, we will touch on a part of data analytics technologies.
All right.
So what does advanced data analytics exactly
mean in this industry?
This two-by-two matrix helps summarize my view
of data analytics in this case.
Analysis on the first column, the first two convention
analysis tools, things that the industry has
been doing for quite some time.
Even if they don't put it into practice,
they are certainly aware of it to a greater extent
than machine learning analytics here, just as all
the current ways right now.
Big data, the first two data that
satisfies the three Vs criteria of volume, velocity,
and variety.
This could be process data, x logs, email data, and so on.
Data in this industry refers to conventional neatly tagged
[INAUDIBLE] data.
This industry generates the huge stream of data.
But it is mostly used for making reports
other than forward-looking analysis, something
that I was guilty of in my previous job as well.
Each of the squares indicates MathWorks' value proposition
in this case.
You can use MATLAB for automating your data analytics,
performing multivariate analysis on big data,
predictive maintenance by using machine learning,
and fault detection and image processing using
machine learning and big data.
In this presentation today, we will only
be looking at multivariate analysis.
Process data are often characterized
by the presence of large numbers of variables
from different sources, something that is generally
much more difficult to handle than just
large numbers of observations.
How do I determine, what are the principal variables
I need to be looking at?
What is their effect on the process?
And how can I control these variables
so I can consistently achieve a high-quality
and keep costs low?
Latent variable methods like principal component analysis
and projection through latent structures
are uniquely suited for the analysis and interpretation
of process data because these are
methods that exploit the correlations amongst variables
in the data sets.
Because these methods tell you exactly what variables
that we choose, they can-- you can take very specific action
such as avoid unplanned downtime,
improve plant performance through cycle time reduction,
reduce waste, the root cause investigation
of defective batches, and controlling product quality.
There have been several success stories using these techniques
in industry.
Here, I have highlighted a few of them.
The first story talks about a batch process
for drying and agrochemical materials
at FMC Corp., a fertilizer company.
They used custom MATLAB toolboxes, and analyzed
the process, and isolated the process variables that
were responsible for the product quality differences.
The second paper is from Pfizer.
And the objective was to determine
the materials available in the inventory that
should be combined and the ratios of those that
should be blended in order to obtain the best
next lot of finished product.
In this project at Frito-Lay, an online vibrational sensor
was used to develop a soft sensor for real-time prediction
of product texture in chips.
An accelerometer is used to record
the vibrations generated by chips
falling onto a metal surface.
The blister level and brittleness of the chips
are predicted by analyzing various sensor data together
using PLS.
Another example is from GSK.
They spoke at MATLAB Expo 2019.
One of the products is Sensodyne,
which is the toothpaste.
Toothpaste is almost regulated like medicine
in some countries.
So there is a lot of regulation and quality check, of course.
Change and experimentation is quite difficult in the process.
You can see some of the challenges they faced here.
Batch possess quality depends on the experience of the operator.
Databases are silos.
You need different logons, et cetera.
In this presentation, they spoke about how
they used their process data to improve the performance
of future batches.
If you remember the two-by-two framework from the early slide,
you can see it as well replicated here.
They use univariate statistics, multivariate analysis,
and machine learning to get knowledgeable insights
from their data.
And finally, they also spoke about why they chose MATLAB.
You can see multiple different reasons mentioned here,
linking different files, rapid prototyping, consulting support
on projects, and app building.