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

Reifneries often need to ifnd similar crude oil to replace the scarce crude oil for stabilizing the feedstock prop-erty. We introduced the method for calculation of crude blended properties ifrstly, 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 trans-forms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We ap-plied 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 efifciency. The blending solution can provide a reference for reifneries to ifnd the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.