Composite Optimization Algorithm for Consistent Solutions of Different Plant Model Abstractions by Solving Linear Subsets of Equations

This work focuses on finding consistent solutions to plant models that have different levels of model abstractions (different levels of accuracy) such as planning, scheduling, or operating condition optimization models. All models are viewed as subsets of one rigorous model, and all are based on the same process network representation. The modeling basis is changed from moles and fractions to mass component flows and local thermophysical properties to increase accuracy at higher abstraction levels, thereby enabling the development of hybrid plant models that have accuracy on par with rigorous models. Increasingly more accurate, mutually consistent solutions are computed by iterative solutions of linear subsets of model equations (partitions corresponding to abstraction levels) instead of solving the entire nonlinear model simultaneously. Linear equation parameters are computed (updated) at the solutions of the linear subsets from more accurate models. Such partitioning enables optimization based on linear subsets of a plant model, providing a basis for the development of high-accuracy linear multiperiod planning LP models or MILP models for superstructure optimization or discrete-time scheduling instead of the MINLP approach to such problems. A hydrogen plant model demonstrates that a highly accurate hybrid model with accuracy appropriate for real-time optimization can also be used for multiperiod planning.