Real-time, Model Based Digital Twin for Energy and Emissions Management

Carlos Ruiz, Product Manager of Energy Management Visual MESA at KBC (Yokogawa Company). With extensive experience spanning over two decades, Carlos is a leader in energy management. In this blog, we’ll delve into his strategies for Real-time, Model-Based Digital Twins for Energy and Emissions Management, as shared at the O & G Digital Twin Conference 2022.

Carlos Ruiz shared a fascinating perspective on managing energy in big factories. He likened it to a factory that wants to be smart about using energy, like electricity or fuel, to save money and protect the environment by reducing harmful emissions. However, choosing the right energy source can be tricky because it depends on factors like energy costs, weather conditions, and the availability of renewable energy sources like wind and solar power. Making this decision quickly is a challenge too complex for human hands alone. That’s where the concept of a Digital Twin comes into play. It’s like a super-smart computer that examines all the options and instantly picks the best one. It can even handle energy storage and production.

Process plants need to consider how the available energy vectors, either traditional or renewables sources, can be produced, distributed, mixed and used by integrating them within or reformulating existing energy systems. The objective is to reduce both costs, and GHGs emissions. Selecting which energy source should be used at any given time depends on having data related to all the possible options. Data values could fluctuate in time, depending on power price, weather conditions, renewable sources availability, etc.
For large-scale O&G plants, energy normally accounts for 50% of operating expenses. Consequently, reducing energy use by 10% can often improve margins by 5%. As companies seek to maintain profits and reduce emissions, energy optimization is one of the first places to look.
In a renewable dominated scenario, due to the variability of the climate factors impacting the power generation, energy storage mechanisms should be available to capture the surplus and be used as a backup when renewable generation is expected to decline. A decision is to be taken, in real-time, about when to fill or deplete the storage and when to activate internal power production. A great challenge for the person or group in charge of optimally managing the energy system is the change in traditional mind set and optimization objectives. This kind of analysis cannot be done manually to the extent and with the necessary speed for a large and complex facility, especially when renewables are involved. Using a real-time, model based, Digital Twin approach, can consider both the sources and uses of energy, making it possible to optimize selections well beyond conventional energy and emissions reduction efforts.

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