Data-Driven Mathematical Modeling and Global Optimization Framework for Entire Petrochemical Planning Operations

In this work we develop a novel modeling and global optimization-based planning formulation, which predicts product
yields and properties for all of the production units within a highly integrated refinery-petrochemical complex. Distillation
is modeled using swing-cut theory, while data-based nonlinear models are developed for other processing units.
The parameters of the postulated models are globally optimized based on a large data set of daily production. Property
indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables
are introduced to denote unit and operation modes selection. The planning model is a large-scale non-convex mixed
integer nonlinear optimization model, which is solved to e-global optimality. Computational results for multiple case
studies indicate that we achieve a significant profit increase (37–65%) using the proposed data-driven global optimization
framework. Finally, a user-friendly interface is presented which enables automated updating of demand, specification,
and cost parameters.