Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit (CDU) are addressed. The main specifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a complicated CDU, an improved wavelet neural network (WNN) is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm (LCA), the superior performance of the proposed approach is verified. Compared with the base operating condition, it is validated that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around (PA2) and third pump-around (PA3).