Energy optimization in industrial plants: AI and digital twin to reduce carbon footprint

Energy consumption in industrial facilities remains one of the main levers for reducing carbon emissions and improving operational efficiency. This session explores how artificial intelligence and digital twin technologies can be applied to optimize energy-intensive systems in real production environments. By combining physics-based simulation with machine learning models, it is possible to predict system behavior, dynamically adjust operating parameters and identify optimal setpoints under real constraints. Through a practical use case, the session will show how these approaches enable continuous optimization, significant energy savings and measurable reductions in carbon footprint, while maintaining process performance and reliability.