Hydrocarbon Processing - November 2021 - 20

Digital
C. A. GARCIA
Repsol, Madrid, Spain
Digital twin for refinery production optimization
Digitalization is fundamental to Repsol's
strategy for the future. To meet
emerging challenges, the company has
developed an ambitious program comprising
of multiple projects. Within the
company's industrial business, the development
of a digital twin for refining processes
leads the digitalization program.
The digital twin maximizes production,
while optimizing energy consumption.
At a virtual industry eventa
, I described
the project in which a digital
twin has improved the accuracy and
scope of the refinery linear programming
(LP) model that makes decisions regarding
crude feedstock purchasing and refinery
unit operations.
Key
objectives. Repsol devised the
project to increase the accuracy and frequency
of updates for their planning
models to improve decision-making. A
cross-functional team-consisting of
personnel from KBC (a Yokogawa Company)
and the author's company-developed
the technology. The initial project
took place at a refinery in northern Spain.
The team deployed a digital twin that
combines a proprietary process simulation
softwareb
historian and dashboards.
first principles model
with a proprietary data management
platform'sc
Key objectives included simplifying
workflow, the planning model and model
evaluation. By automating data collection
and processing, the digital twin
enables more focus on analyzing results
rather than generating data. It also provides
indicators that can be monitored
briefly to check the health of LP vectors
and the simulation model vs. actual process
conditions.
To simplify the planning model, the
digital twin can, if necessary, update the
LP vectors based on the rigorous process
simulation software's model. The digital
twin also provides model assurance
through early detection and notification
of relevant deviations between actual
data and LP vector results.
A single version of the truth was another
key objective. The digital twin provides
access not only to the process and
lab data but also to derived indicators that
can be used throughout the organization.
Implementing the solution. The key
technologies are the proprietary process
simulation software and data management
platform. The process simulation
softwareb
is a digital twin that is based on
a first principles model originally used
for process simulation. Deployed in a
backcasting prediction mode, it provides
the calculation of critical operating parameters
that allow an improved understanding
and monitoring of the process.
It is sensitive to changes in feeds, operating
conditions, catalyst used and fractionation.
It also provides updated calibration
generation for the digital twin.
The process simulation software
generates indicators to monitor input
data quality, reality vs. model results
(LP vector and simulation model) and
health of the tool. If there are deviations,
it also generates new LP vectors based
on monitoring criteria. The model is
automatically run on a regular basis, as
required, typically daily or weekly. Data
transfers between the simulation software
and data management platform are
bi-directional.
The differences between a digital
twin and a traditional simulator are
worthy of review. The digital twin is a
replication of the actual process, and it
allows for improved operation and understanding
of the facility. While a simulator
provides an accurate representation
of a particular operating case, the
digital twin is an accurate representation
of the asset over its full range of operation.
Rather than a snapshot in time, the
FIG. 1. Workflow calls for regularly scheduled execution of the digital twin, with validation
checks on data quality parameters, key performance indicator checks, LP model validation
and, if necessary, LP model updating and recalibration.
20 NOVEMBER 2021 | HydrocarbonProcessing.com
FIG. 2. The centralized solution provides
information to all stakeholders throughout
the organization.

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Hydrocarbon Processing - November 2021

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