We propose an optimization-based framework for process synthesis under variability in two frequencies. Low-frequency variability is represented through scenarios and high-frequency variability is modeled using modes. The proposed framework allows for the selection of different process configurations during different modes, a feature necessary to model systems under wide high frequency variability (e.g., solar-based technologies). The optimization problem is formulated as a two-stage stochastic programming model with mode subproblems nested inside each scenario. The proposed framework is applied to the design of concentrating solar power plants with thermochemical energy storage, leading to the formulation of a computationally efficient model, as well as the identification of a superior design.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Chemical Engineering(all)
- mixed-integer nonlinear programming
- stochastic programming
- superstructure-based process synthesis