Hydrocarbon Processing - April 2022 - 73

Digital Technology
S. REYNOLDS, AVEVA, Chicago, Illinois
High risk, high stakes: Why digitalization
is critical for hydrocarbon processing
Hydrocarbon processing, by nature, is a high-risk operation.
Safety is paramount, and even minor deviations in asset
performance can lead to explosions, leaks, fires and environmental
contamination. Highly specialized, large and expensive
pieces of equipment are often single points of failure. In addition,
distributed workforces have further compounded previous
operational visibility hurdles. Now, teams must advance digital
transformation efforts to enable real-time monitoring, improve
collaboration among teams and deploy predictive tools in hydrocarbon
processing plants.
Data accessibility and analysis tools are essential to an organization's
ability to stay competitive, meet current challenges
and optimize production outcomes. First, hydrocarbon processing
plants must move beyond old, manual processes and
centralize operations data into a management platform. While
a data management platform collects and stores sensor-based,
time-series data, these platforms are far more capable than traditional
historians. Users can contextualize data, add metadata
and build hierarchies to create standardized data structures and
subsequent key performance indicators (KPIs). Contextualization
also enables hydrocarbon processors to capture knowledge
within a centralized location, ensuring that no information losses
occur in the event of a staffing change.
Contextualization not only standardizes tag naming conventions,
asset hierarchies and KPIs across the plant and even multiple
locations, it lays the foundation for advanced tools, such
as artificial intelligence- (AI-) based guidance, digital twins and
visualization. Centralizing and standardizing data management
eliminates manual processes and calculations, enabling teams to
layer in advanced and predictive analytics for operations, maintenance
and more. Once plant users have immediate access to
real-time contextualized insights, they can use that information
to focus on optimizing operations and production volume, and
minimizing unplanned downtime.
By centralizing and contextualizing data management efforts,
users from across the organization-and not just operations
teams-can leverage operations data in a relevant and helpful
way for their needs. For example, the process engineering team
can connect operations data to a process simulation tool and
run scenarios using real-time data. This enables engineers to
identify equipment inefficiencies and provide direct guidance
about which actions operations and maintenance teams must
take to bring efficiency back into the desired range.
When hydrocarbon processing plants use the centralized
platform in conjunction with mobile tools, team members can
access operational data and analytics from remote locations. Previously,
if an operations manager received a late-night call about
an anomaly, he or she would have to drive to the plant, evaluate
the situation and make a recommendation. With remote access
to contextualized insights, that manager can perform an analysis
from home and make recommendations over the phone. Similarly,
a field worker can use a mobile app to perform root cause
analysis without setting foot into a control room.
Real-time visibility and predictive analytics alert teams of
current and potential asset failure and abnormal process behavior,
allowing them to get the right people onsite to perform
maintenance on the right parts, ensuring the asset is down for the
least amount of time possible. Not only does this increase safety
measures by preventing catastrophic failures, but teams can also
increase uptime to ensure contractual obligations are met.
Real-world benefits. SCG Chemicals, one of Asia's largest integrated
petrochemical companies, needed to increase operational
visibility to boost efficiencies and minimize unplanned downtime.
The company built a single platform to centralize data collection,
analysis, visualization and maintenance. The platform
integrates online and offline equipment data to visualize plant
performance, enhance workforce efficiency and apply AI for
predictive maintenance. The hybrid cloud approach provided
teams real-time visibility into operations and KPIs to coordinate
asset performance management and use smart analytics and the
cloud to streamline operations and provide decision support.
The combination of real-time data in the digital reliability
platform (DRP) and AI-driven analytics tools allows users to
predict equipment health, monitor performance and make better
decisions about anomalies and repairs. Users have access to
actionable information in 10 sec or less from one single source of
operational truth. Teams use digital twins and virtual plant operations
to view equipment alarms, health status and efficiently
respond to emergency conditions.
The DRP not only achieved a 9X return on investment on
the project in only 6 mos, but insights from the platform enabled
teams to reduce overall maintenance costs. In addition,
SCG increased plant reliability from 98% to 100%.
STEPHEN REYNOLDS is the Chemicals Industry Principal at
AVEVA. He has 20 yr of experience in chemical engineering,
specializing in polyethylene, polypropylene, methacrylate and
phenol processes. Mr. Reynolds has worked in unit engineering,
operations excellence facilitation, operations management
and continuous improvement management. Mr. Reynolds
holds a BS in chemical engineering from Texas A&M.
Hydrocarbon Processing | APRIL 2022 73
Hydrocarbon Processing - April 2022

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