Multi-scale Optimization for Advanced Energy Processes

Advanced energy systems demand powerful and systematic optimization strategies for analysis, high performance design and efficient operation. Such processes are modeled through a heterogeneous collection of device-scale and process scale models, which contain distributed and lumped parameter models of varying complexity. This work addresses the integration and optimization of advanced energy models through multiscale optimization strategies. In particular, we consider the optimal design of advanced energy processes by merging device-scale (e.g., CFD) models with flowsheet simulation models through sophisticated model reduction strategies. Recent developments in surrogate-based optimization have led to a general decomposition framework with multiple scales and convergence guarantees to the overall multi-scale optimum. Here, we sketch two trust region-based algorithms, one requiring gradients from the detailed model and one that is derivative-free; both demonstrate multi-scale optimization of advanced energy processes. Motivated by an advanced Integrated Gasification Combined Cycle (IGCC) process, we present two case studies that include PSA models for carbon capture and CFD models for gasification and combustion.