(132b) Using Digital Utilities Twin Technology on a Refinery to Drive Carbon and Energy Reductions

Digital replicas of operating assets that combine plant data with high-fidelity process models are bringing a new level of decision support to operations of steam, condensate, fuel gas and power systems, otherwise known as ‘utilities’ systems. They provide many benefits to operators, from information on monitoring and ‘soft-sensing’ to advice on optimal set-points, diagnostics, and prognostics such as future maintenance interventions. At the heart of such shadowing systems is an always-current digital simulation model of the assets which updates itself periodically based on real-plant performance.

In the context of utility systems such as steam, condensate, electricity and power, digital utilities twin technology is now being implemented in many process industry applications. The associated benefits include: equipment monitoring to determine the real state of equipment; real-time ‘soft-sensing’ to provide up-to-date performance information which is either difficult or impossible to measure; forecasting to determine future performance such as maintenance intervals based on current equipment state and anticipated operation; operational optimisation to give advice to operators regarding set-points, diagnostics, prognostics and performance benchmarks; and finally ‘what-if’ analysis to anticipate how to operate for future/alternative operating scenarios.

One very specific focus for Utilities Twins is in the area of decarbonisation and energy reduction. The urgency in these areas has recently grown, with global warming and world events combined with the underlying drive by refining companies to find new ways to address their environmental commitments. Such twins have a significant contribution to make in this area.

The Digital Utilities Twins described in this paper use high-fidelity predictive mathematical models of the assets to exploit redundancy between model prediction and plant data and maintain themselves in an always-current state. This means that ‘drift’ in key parameters such as gas turbine efficiency, steam pressure levels, equipment fouling and so on is taken into account in all monitoring, forecasting and optimisation calculations, to provide reliable information for decision support.

This paper describes how Galp has been combining equation-based general-purpose modelling technologies with a next-generation digital application framework to form an environment for easy construction and application of fast, robust online solutions based on high-fidelity utility system models. The paper describes how the digital twin of the utilities system has been used to improve system performance across all areas of utilities in the refinery. The twin is linked to plant data systems, updating itself through machine-learning capabilities, validating actual performance and, where appropriate, identifying departures from normal operation. Beneficial operating changes are highlighted in web-based dashboards which operators can view and act upon. These dashboards have been developed through a collaboration between all stakeholders and they provide a comprehensive summary of utility system performance, all presented on a consistent basis. Visualisation of the twin is seen to be critically important to give operators greater insight and confidence to operate the process safely at the optimum point with a view to reducing green-house gas emissions and reducing energy consumption.

The Utilities Twin has also had a wider beneficial impact. It has been used to improve understanding and knowledge of energy consumption across the refinery within different stakeholders and been a focal point to make improvements in many different aspects of energy consumption. As such, the twin is delivering improvements, keeping the refinery utility system running optimally, and making a significant contribution to the refinery’s decarbonisation and energy reduction goals.