Hydrocarbon Processing - April 2021 - 36

Clean Fuels

FIG. 9. A smart renewable diesel facility configuration from digital twin asset building blocks.

dressed to leverage a proven, powerful
and configurable operational data infrastructurea used by many HPI companies
worldwide. The key is the ability to enable SMEs to configure smart asset digital twins that can be combined to create
an operational digital twin of the renewable diesel facility, including associated
utility and infrastructure systems.
The operational data infrastructure
forms the foundation for real-time operational intelligence and proactive, exception-based decision support. It also
enables the use of modern digital technologies, such as analytics, financial base
modeling and optimization, and safe and
secure ecosystem digital integration.
The result is the ability to increase
safety and to mitigate processing challenges in the PTU and RDU, as well as to
increase effective capacity by 2%-4% by
boosting asset reliability, reduce O&M
costs by 3%-5%, and increase EBITDA
performance by 3%-5%.
NOTES
Refers to OSIsoft's PI System
b
Refers to PI System's Asset Framework (AF)
a

REFERENCES
Chan, E., " Converting a Petroleum Diesel Refinery
for Renewable Diesel, " Burns & McDonnell,
December 2020, online: https://www.burnsmcd.
com/insightsnews/tech/converting-petroleumrefinery-for-renewable-diesel
2
Komróczki, T., " Supporting Strategic Initiatives at
MOL with the PI System Infrastructure, " OSIsoft PI
World 2015, April 2015.
3
Haragovics, M., M. Bubálik, and A. Frouillac,
" Digital Bridge (PI Cloud Connect) Between Axens
and MOL to Maximize Profit from Conversion
Catalysts, " OSIsoft PI World 2018, April 2018.
4
Tate, S. and J. Rose, " Best Practices in the Integration
of Modeling Software with the PI System, " OSIsoft
PI World 2019, April 2019.
5
Mandani, M. and A. Brodskiy, " Overview of the PI
System Enabled Integrated Refinery Information
System (IRIS) at YASREF, " OSIsoft PI World 2015,
April 2015.
1

FIG. 10. Using incremental cost/value evolution through layers of analytics and hybrid data lakes.

value as they move to more complex analytical methods. The costs include not
only the technology, but also the costs
associated with lost time to value, scalability, configuration, sustainment and
risk of attainment.
The foundation of this " layers of analytics " approach (FIG. 10) relies on the
use of an operational data infrastructurea
to enable SMEs, not IT, to configure realtime descriptive, diagnostic and simple
predictive analytics by using formulas,
empirical correlations and rule-based
expressions. These lower-level analytics
form the foundation for more advanced
predictive, prescriptive and adaptive
analytics that use machine learning and
other methods, and require collaborative
support from data science teams.
These foundational analytical layers
generally provide over 80% of the value
for about 20% of the cost vs. more advanced analytical layers that only use
36

APRIL 2021 | HydrocarbonProcessing.com

technologies such as machine learning.
Once higher layers of analytics are
utilized, it is imperative to feed back the
results of these advanced layers to the
lower-level layers as forecasts or targets,
where appropriate, to operationalize the
advanced analytical output. This is key to
the development of the smart renewable
diesel, as results from the integration with
process simulation optimization models
and financial data for real-time gas plant
financial optimization are fed back to the
operational data infrastructure.a
Takeaway. The growth of renewable
diesel, by either building new facilities or
modifying existing petroleum refineries,
is global, and opportunities are expanding rapidly to address social, regulatory
and financial needs and opportunities.
Renewable diesel PTUs and RDUs present both operating challenges and opportunities that can successfully be ad-

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.

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