November 2021

Columns

Digital: Digital twin for refinery production optimization

Digitalization is fundamental to Repsol’s strategy for the future.

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 first principles model with a proprietary data management platform’sc historian and dashboards. 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 digital twin captures the full history and the future of the asset.

Instead of being built on an ad-hoc basis to answer a particular question, the digital twin is automated. Its regular model runs are built-in to business workflows. As a centralized, single version of the truth, the digital twin is used by everyone. Outputs are delivered directly to the business and enable strong corporate governance. Conversely, the simulator is typically owned and used only by isolated departments or groups.

Implementation of the solution required the team to integrate the data from the historian into the model and identify missing data. To identify systematic issues, the team conducted testing based on the historical data and evaluations of failed simulation runs. That enabled the development of trapping error mechanisms to run every week and allowed the team to complete integration of the LP model and development of an LP update tool.

The proprietary visualization toold provides dashboards that can incorporate results from the process simulation software, alongside other data. The displays were developed to allow users to best follow the desired workflow. The digital twin generates a great deal of information that various stakeholders could use in many ways.

By using customized displays, Repsol could ensure adherence to a consistent procedure (FIG. 1). Users can generate alerts to relevant deviations or data quality. Expert users can perform deeper analysis through direct interaction with the process simulation software.

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.

Takeaway

Aside from the experience of subject matter experts, most decision-making activities related to improving plant profitability (e.g., scheduling, planning, real-time operations and retrofitting) rely on a process model. Changing from traditional simulation to a digital twin solution ensures the best decision-making over time.

The digital twin can accelerate the identification and resolution of unit issues and improve productivity. The centralized solution provides information to all stakeholders throughout the organization, with no need for advanced knowledge of the simulation model. The digital twin provides a unified template from which all teams and business units can discuss issues such as model updates and data quality. It constitutes a single source of the truth that drives the alignment of decisions and actions across the value chain. HP

FIG. 2. The centralized solution provides information to all stakeholders throughout the organization.

 NOTES

a Yokogawa’s “Y-NOW 2020: DX Solutions for Tomorrow” event
b KBC’s Petro-SIM
c OSIsoft’s PI System
d OSIsoft’s PI Vision

The Author

Related Articles

From the Archive

Comments

Comments

{{ error }}
{{ comment.comment.Name }} • {{ comment.timeAgo }}
{{ comment.comment.Text }}