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

About the Session

Carlos Ruiz, Product Manager for Energy Management Visual MESA at KBC (A Yokogawa Company), is a highly accomplished chemical engineer with decades of experience spanning academia, research, and industrial applications. With a Ph.D. in Chemical Engineering and a rich background in advanced control, simulation, and optimization, he has led numerous international projects focused on real-time energy and process optimization across the chemical and petrochemical industries. As a professor and former director of the Chemical Engineering Applied Research Group in Argentina, Carlos has combined deep theoretical knowledge with hands-on expertise. He is all set to share his views on Real-Time, Model-Based Digital Twin solutions for energy and emissions management, highlighting how these technologies can enable continuous optimization, reduce energy consumption, and support decarbonization strategies within industrial operations.

He will share his views on how process plants must evolve to integrate both traditional and renewable energy vectors into their systems—optimizing production, distribution, and usage to reduce costs and greenhouse gas emissions. The key lies in having real-time data on all energy options, which can fluctuate based on power prices, weather, and renewable availability. In large-scale oil and gas operations, where energy can account for up to 50% of operating expenses, even a 10% reduction in energy use can significantly boost margins. In a renewable-dominated landscape, managing energy variability requires storage strategies and real-time decision-making on when to store, use, or produce energy internally. Carlos emphasizes that such complex, fast-paced decisions demand a shift in traditional mindsets and can only be effectively managed through real-time, model-based digital twin solutions—enabling comprehensive optimization that far exceeds conventional methods.

Key Topics

01 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.
02 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.
03 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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