A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation

Abstract

Renewable-dominated power grids will require industry to run their processes in accordance with the availability of energy. At the same time, digitalization introduces new possibilities to leverage the untapped optimization potential to provide this flexibility. Mathematical optimization methods such as mixed-integer linear programming (MILP) are widely used to predict optimal operation plans for industrial systems. MILP models are difficult to adapt, but the viability of the predicted plans relies on accurate underlying models of the actual behavior. New automation paradigms, such as the digital twin (DT), can overcome these current drawbacks. In this work, we present the implementation and experimental evaluation of several micro-services on a standardized five-dimensional DT platform that automate MILP model adaption and operation optimization. These micro-services guarantee that, (1) deviations between the physical entity and its virtual entity models are detected, (2) the models are adapted accordingly, (3) subsequently linearized to suit the MILP approach and (4) used for live operational optimization. These novel services and DT workflows that orchestrate them were experimentally tested with a packed bed thermal energy storage (PBTES) test rig that acts as a physical entity. A waste heat recovery use case in steel production is used as the evaluation scenario. While the model error of a static simulation model would increase to 60% over 7 days of operation, the model error remains well below 25% as a result of successful model adaption. The prediction error of the optimization model remains in a typical magnitude of 10 to 20% during the evaluation period, despite the degradation of the PBTES power.