Hydrocarbon Processing - July 2021 - 59

Bonus Report The Digital Plant
M. BROOKS, AspenTech, Alamo, California
If you want AI to overcome limitations,
give it more scalability
Artificial intelligence (AI) has caused a significant buzz not
only within the hydrocarbon processing industry but also in
everyday activities. AI seems to be everywhere. It is in autonomous
vehicles, it can recognize images of people and cats vs.
dogs, and bank and credit card companies use it for fraud detection,
among others. The application of AI is seemingly endless.
Since 2015, AI has become more pervasive and obvious as
Alexa, Siri and Google Assistant help us when asked. Even AIpowered
washing machines can cut laundry time, and product
and services companies use AI to tell us what books we might
like to read, what music we can enjoy and what movie will fit
the bill for Friday night.
The essence of AI is about learning from data and using it
to determine the probability of future outcomes based on the
patterns learned from the past. This emulates a human characteristic.
If you watch a specific area, situation or process long
enough, you may develop insights. When similar things occur
again, you could guess with some accuracy what is going
to happen next. Other people have read the books you have
and based on recognizing similar patterns, Amazon suggests
you will enjoy another title just as much as others who read the
same books. It sounds simple enough, but is it?
The Challenge with AI. Standard AI can recognize patterns
but cannot reason why things occur the way they do without
substantial help from humans. We often hear that AI sees correlations,
which may be simplistic and just happen without
being linked. AI cannot determine why it happens-i.e., the
causation. For example, data from literature1
better decisions. The overarching goal of AI is to uncover new
and better business processes that promise significant, positive
business outcomes across key areas, such as manufacturing
operations profitability, safety, environmental impact and
corporate social responsibility.
To be effective, AI cannot be just a technology thrown
at problems. We can refer to the adage " people, process and
technology " to gain solace. We believe that the importance of
incorporating domain knowledge is seriously overlooked by
startup companies and service providers who are trying to enter
the space. Industrial AI is the realization that scalability of
any solution is the most important factor.
The dimensions of industrial AI. Industrial AI incorporates
several important dimensions. These include the following:
* Many people must be able to use AI. To be pervasive,
it cannot just belong to data scientists. Plant personnel
must be able to build and deploy AI solutions based
on what they know now. This is very similar to the
iPhone, which contains deep technology, but the user
does not need to know it since an elegant screen and
a finger as a pointer complete the task. Consequently,
with industrial AI, the competence level of workers
is elevated and gravitates to making judgements on
potential outcomes rather than doing the continuous
and repeated heavy-lifting of deep analytics.
* Industrial AI solutions fit current work processes well
without huge departures.
showed an interesting
correlation: the more lemons imported to the U.S.
from Mexico, the fewer U.S. highway fatalities occurred. Correlation
does not necessarily mean causation-no one would
seriously believe there is cause-and-effect in the referenced example;
however, the problem is that AI cannot tell the difference.
It turns out that humans are better at judgment and have
the ability-unlike AI-to assess whether the observed correlations
make sense. Consequently, despite such challenges for
AI technology within industrial manufacturing, there appears
to be great potential and opportunities if AI applications are
tempered appropriately to overcome the limitations.
AI can create more value than is currently extracted from
industrial data, especially since most data is used for viewing
trend charts for scarce insight development. Intelligent data
gathering and grouping with AI-driven analytics can greatly
assist in helping people make more accurate judgments and
* Judgement from workers' domain knowledge is imparted
as the industrial AI solution building process begins.
This is where domain knowledge from workers provides
guardrails around an AI solution to ensure it will
realize true causation rather than simple correlation.
Embedding is simple and easy so that users can insert
what they know (e.g., when an event occurs, this sensor
shows a condition and then " this " will happen). Those
are the clues that form the guiderails that influence the
solution direction and the best outcomes.
* With industrial AI, the step-by-step work process
is built into the application building to make it
simple and easy (e.g., booking a plane ticket or
ordering a product on a website).
* As much judgment as possible must be built into the
application to assist the user even further, adding even
more domain knowledge. Industrial AI does not use
Hydrocarbon Processing | JULY 2021 59
Hydrocarbon Processing - July 2021

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