IT in Manufacturing


Digital twin for refinery production

June 2021 IT in Manufacturing

Digitalisation is fundamental to Repsol’s strategy for the future. To meet emerging challenges, the company has developed an ambitious program comprising a multitude of projects.

Cristina Aguilar Garcia, Optimization and Simulation advisor at Repsol, 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 improve the accuracy and frequency of updates for its planning models in order to improve decision-making. A cross-functional team consisting of personnel from Repsol and KBC (a Yokogawa company) developed the technology. The initial project took place at a refinery in northern Spain.

The team deployed a digital twin that combines KBC’s Petro-SIM first principles model with the OSIsoft PI System historian and dashboards. Key objectives included simplifications in terms of workflow, the planning model and model evaluation. By automating data collection and processing, the digital twin enables more focus on analysing results rather than generating data. It also provides indicators that can be monitored at a glance to check the health of LP vectors and the simulation model compared to actual process conditions.

To simplify the planning model, the digital twin can, if necessary, update the LP vectors based on the rigorous Petro-SIM 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 laboratory data, but also to derived indicators that can be used throughout the organisation.

Implementing the solution

The key technologies are Petro-SIM and PI Vision. Petro-SIM is a digital twin that is based on a first principles model originally used for process simulation. Deployed in a back-casting 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.

Petro-SIM 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 Petro-SIM and the PI System 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. As a centralised, single version of the truth, the twin is used by everyone. Outputs are delivered directly to the business and enable strong corporate governance. The simulator, on the other hand, is typically owned and used only by isolated departments or groups.

PI Vision provides dashboards that can incorporate results from Petro-SIM alongside other PI 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.

Conclusion

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

The digital twin can accelerate the identification and resolution of unit issues and improve productivity. The centralised solution provides information to all stakeholders throughout the organisation 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.

For more information contact Yokogawa South Africa, +27 11 831 6300, eugene.podde@ao.yokogawa.com, www.yokogawa.com/za


Credit(s)



Share this article:
Share via emailShare via LinkedInPrint this page

Further reading:

Mitigate industrial network vulnerabilities
July 2021, RJ Connect , IT in Manufacturing
It must not be forgotten that ignoring common system vulnerabilities in today’s world could put your entire network at risk.

Read more...
Digital transformation in mining
July 2021 , IT in Manufacturing
Rapid advances in technology have disrupted industries and businesses around the world, which are now being forced to accept this new reality rather quickly because the new technologies have tremendous potential to deliver value for those who adopt them.

Read more...
Digital twins require accurate and reliable data to be effective
July 2021 , IT in Manufacturing
At the 25th ARC Industry Forum, hosted virtually, many industry experts gave their perspectives on accelerating digital transformation in the post-Covid world.

Read more...
Frequency analysis without programming requirements
July 2021, Beckhoff Automation , IT in Manufacturing
Beckhoff expands TwinCAT Analytics with easy-to-configure condition monitoring functions.

Read more...
Siemens adds AI to Simcenter
July 2021, Siemens Digital Industries , IT in Manufacturing
Siemens Digital Industries Software has announced the latest release of Simcenter Studio software, a web application dedicated to discovering better system architectures, faster. Simcenter Studio offers ...

Read more...
Water resource sustainability management
July 2021, Yokogawa South Africa , System Integration & Control Systems Design
One way Yokogawa is successfully pursuing its sustainability goals is through its service to the global water industry.

Read more...
ABB technology can help make SA steel industry competitive
June 2021, ABB South Africa , IT in Manufacturing
South Africa’s steel industry needs to invest in technology like automation and data analytics if it is to improve its productivity to the point where it is globally competitive.

Read more...
Siemens expands CFD simulations
June 2021, Siemens Digital Industries , IT in Manufacturing
Siemens’ Simcenter portfolio expands capabilities for frontloading computational fluid dynamics (CFD) simulation and increased productivity.

Read more...
Building secure networks
June 2021, RJ Connect , IT in Manufacturing
This article explores how to build resilient industrial networks and deploy cybersecurity defences in order to sustain continuous industrial operations.

Read more...
Improving the state of OT cybersecurity by sharing experiences
June 2021 , IT in Manufacturing
It is essential that we view improvements in cybersecurity as requiring improvements in multiple areas, including people and skills development, governance and process development and improved technology.

Read more...