Hydrocarbon Processing - January 2022 - 26

Sustainability
that the data lake is a less-than-ideal solution. For significantly
better results, businesses should consider a hybrid approach that
pairs a purpose-designed operations data infrastructure-securely
in a single-tenant cloud-with a cloud-based software as
a service (SaaS) solution, so that data is accessible and shareable.
Another common pitfall to avoid is that many companies
reach for Big Data applications before they have secured a strong
data foundation. Digital twins, machine learning (ML) and artificial
intelligence (AI) all reside in a layer of advanced analytics
that can generate significant value but typically produce poor
results if a robust analytical framework is not already in place.
Once a green fuel company has installed an operations data
infrastructure, they should implement descriptive, diagnostic
and simple predictive analytics. From there, operators can begin
using prescriptive and adaptive analytics that incorporate ML
and AI. Once green fuel companies reach this level of advanced
analytics, it is important to funnel those results directly back
into operations to enable even more optimization.
The following is an example of how that happens. A furnace is
used for heating oil during the refining process. Operators work
to optimize heat output, while minimizing carbon emissions.
They start by modeling the furnace and applying lower level
analytics to examine the correlations between pressure, feed and
fuel composition, temperature, excess air, and oxygen content
from analyzers. Once operators have a foundational understanding
of the process, more advanced technology can be added that
uses laser-based sensors to increase the accuracy of the oxygen
and carbon monoxide content in the excess air. Finally, AI pulls
all those factors together to calculate the changes necessary to
further improve processes. That information is then sent back
into the system so that operators can adjust in near real time.
These same principles can be applied to projected vs. actual
results (i.e., projected GHGs vs. actual GHGs).
The key to implementation. Those who deal in operational
technology (OT), which is designed to run refineries safely
and optimally, and IT, which focuses more on streamlining
back-office operations, often clash when it comes to digital investments.
The IT department-which is expected to be the
steward of technology across the company-typically applies
a traditional IT mentality to operations, but IT departments
rarely appreciate the nuances of operations data and systems.
A typical IT-style solution to data management might see all
operations data dumped into a data lake. Operators will then
apply AI to this reservoir of unstructured, decontextualized
data. This is, in other words, a combination of two mistakes
discussed earlier. Not only does this practice produce very little
in the way of valuable insights, but it can also result in several
lost opportunities.
Companies that excel at digital transformations have redefined
this OT-IT relationship by installing a Chief Digital Officer
or Chief Transformation Officer, with a deep operational
background. By overseeing both OT and IT, a Chief Digital
Officer has a unique vantage from which to normalize operations
across both departments. Such an officer is also well-positioned
to drive sustainable business change by leveraging new
digital technologies.
No digital transformation initiative should be an all-or-nothNEW
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InstruCalc
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ing commitment. Until a company learns what works, the best
advice is to start small, simple and strategic. The key is to start.
Companies should begin with a relatively bounded operational
area-one that is a likely candidate for success. For example,
companies might begin by increasing the runtime of
heat exchangers before fouling, or they might compare several
installations of a given asset type in search of outliers. Starting
small helps green fuel companies apply their digital strategies
appropriately and demonstrate business value clearly. To prove
the success of the business case, companies will need to identify
key performance indicators by which to measure and document
conditions before and after. In a sense, this is using data to
show the value of that data.
To remain competitive over the long term, green fuel producers
must continually strive for operational excellence; they
must have a business model that is viable even without subsidies;
and they must embrace modern carbon accounting. A digital-enabled
business transformation, underpinned by an operations
data infrastructure, is the key to achieving these goals.
CRAIG HARCLERODE is a Global Oil and Gas and HPI Industry
Principal at OSIsoft, where he consults with companies on how to
add value to their organizations utilizing a strategic OT real-time
integration, applications and analytics infrastructure. He has been
focusing on digital-enabled business transformation, addressing
the dimensions of people/culture and process reengineering
leveraging OSIsoft tools in the areas of the Industrial Internet
of Things (IIoT)/edge, PI AF digital twins, layers of analytics, and big data to
deliver transformative business value. Mr. Harclerode's 40-yr career has spanned
engineering, operations and automation in supervisory, executive management and
consultative roles at Amoco Oil, Honeywell IAC and Aspen Tech.
26 JANUARY 2022 | HydrocarbonProcessing.com
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Hydrocarbon Processing - January 2022

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