Optimization Model of Crude Oil Distillation Units for Optimal Crude Oil Blending and Operating Conditions

This work presents the mathematical formulation of a nonlinear programming (NLP) model which optimizes simultaneously crude oil blending and operating conditions for a system of several crude oil distillation units (CDUs) at a Colombian refinery. The CDU system consists of three industrial units processing a blending of five extra-heavy crude oils and producing two commercial fuels, Jet-1A and Diesel. The NLP model involves typical restrictions (e.g., flow rate according to capacity of pumps, distillation columns, etc.) and the heat integration of streams from atmospheric distillation towers (ADTs) and vacuum distillation towers (VDTs) with the heat exchanger networks for crude oil preheating. A metamodeling approach is used so as to represent the ADTs. Preheating networks are modeled with mass, energy balances, and design equations of each heat exchanger. The NLP model has been implemented in GAMS using CONOPT as solver. Different cases are solved by the NLP model such that the optimal case with less profit increment had an economical benefit of 13% with respect to its case without optimization. In each optimal case the extra-heavy crude oils in the feed blending of each CDU required more severe operating conditions such as higher temperature of the crude oil at the entrance to the towers, greater flow rate of stripping steam at the bottom, and minor pressure of the tower tops