Industrial Application of Surrogate Models to Optimize Crude Oil Distillation Units

Abstract

This work presents a new approach to optimize existing crude oil distillation systems. The main features of this approach are its ability to provide key insights of the main factors affecting product yields and energy consumption; and the consideration of system limitations, such as crude oil changes, column flooding, heat transfer bottlenecks and product quality specifications. The new approach showcases sophisticated and systematic modelling and optimization methodologies, namely artificial neural networks and simulated annealing. The approach has been applied successfully in a debottlenecking study for yield optimization of a medium-scale Spanish refinery. The implementation of the optimization results was carried out successfully in the refinery; initial projections confirm predicted benefits of $7.2 million per year.