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.
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