Online multi-object crude oil blending optimization method

Abstract

The invention discloses an online multi-object crude oil blending optimization method. The method comprises the steps of: firstly, performing blending task parameter initialization; secondly, settingan optimization period and a target function, and setting the upper limit and the lower limit of each attribute index of blended crude oil, the oil reserve of each component, the maximum flow of eachrefining line component coil and the unit quality cost of each component oil; then, according to the predetermined optimization period, obtaining each blending component and tank bottom oil attributedata, and updating a tank bottom oil gauge, the component oil reserve and the blending remaining time of the batch; and finally, solving the optimal formula of each blending component in the current optimization period, and sending to a blending control system to execute.

Description

technical field [0001] The invention relates to the field of crude oil processing in oil refining enterprises, in particular to an optimization method for crude oil blending. Background technique [0002] In order to maximize the comprehensive benefits, it is often necessary to weigh various optimization objectives in the process of crude oil blending. However, in the actual industry of crude oil blending, due to the limitation of the computing power of traditional algorithms, most refineries can only perform single-objective optimization while ignoring other objectives, so that they cannot achieve sufficient trade-offs. For example, when the blending cost is required to be the lowest, properties such as density cannot be controlled to a certain target value, and only within a certain range, which sacrifices the controllability of the production process and limits the further improvement of the production level and product quality. However, when making requirements on attri...

AI Technical Summary

Problems solved by technology

However, in the actual industry of crude oil blending, due to the limitation of the computing power of traditional algorithms, most refineries can only perform single-objective optimization while ignoring other objectives, so that they cannot achieve sufficient trade-offs

For example, when the blending cost is required to be the lowest, it is impossible to control the properties such as density to a certain target value, only within a certain range, sacrificing the controllability of the production process, and limiting the further improvement of the production level and product quality

However, when making requirements on attributes such as density, due to the lack of the goal of minimum cost, the comparison of the schemes is lost, resulting in unnecessary waste

Images