A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm

作     者：Yang Huihua Ma Wei Zhang Xiaofeng Li Hu Tian Songbai 

作者机构：Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Research Institute of Petroleum ProcessingSINOPEC 

出 版 物：《China Petroleum Processing & Petrochemical Technology》 (中国炼油与石油化工（英文版）)

年 卷 期：2014年第16卷第4期

页      面：70-78页

核心收录：

学科分类：081702[工学-化学工艺] 08[工学] 0817[工学-化学工程与技术] 

基　　金：supported by the National Natural Science Foundation of China(No.21365008) the Science Foundation of Guangxi province of China(No.2012GXNSFAA053230) 

主　　题：crude oil similarity crude oil selection blending optimization mixed-integer nonlinear programming CuckooSearch algorithm 

摘      要：Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.