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Gasoline octane number prediction method based on production data

A gasoline octane number and prediction method technology, applied in prediction, data processing applications, instruments, etc., can solve problems such as high-dimensional nonlinear data modeling, and achieve the effect of improving operating efficiency and improving crude oil utilization

Pending Publication Date: 2021-03-26
SHANGHAI MARITIME UNIVERSITY
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  • Description
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Problems solved by technology

[0005] The object of the present invention is to provide a gasoline octane number prediction method based on production data to solve the problem of high-dimensional nonlinear data modeling

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  • Gasoline octane number prediction method based on production data
  • Gasoline octane number prediction method based on production data
  • Gasoline octane number prediction method based on production data

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Embodiment Construction

[0028] The method for predicting gasoline octane number based on production data proposed by the present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples. Advantages and features of the present invention will be apparent from the following description and claims. It should be noted that the drawings are all in a very simplified form and use imprecise ratios, which are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention.

[0029] The core idea of ​​the present invention is that the gasoline octane number prediction method based on production data provided by the present invention solves the problem of modeling high-dimensional nonlinear data that is difficult to solve by traditional methods, and the Boruta algorithm can screen out the most directly affecting octane number. Production variables. Finally, the feature subsets and raw material data of p...

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Abstract

The invention provides a gasoline octane number prediction method based on production data. Data of a gasoline desulfurization device is collected in advance, and the data is preprocessed. And according to the preprocessed production data feature set, feature selection is carried out by using a Boruta algorithm, shadow features are randomly rearranged according to a proportion P when the shadow features are created, optimal sub-features are obtained through multiple times of cyclic marking, and finally, the gasoline octane number is predicted by using an XGboost model. According to the gasoline octane number prediction method based on the production data, a machine learning algorithm model is applied to the field of chemical production, features are extracted through an improved Boruta algorithm, all important features are extracted while the operation efficiency is improved, the independence of the features is reserved, and the gasoline octane number is accurately predicted through anXGboost model on the basis of feature extraction.

Description

technical field [0001] The invention relates to the chemical technology field of gasoline production, in particular to a gasoline octane number prediction method based on production data. Background technique [0002] With the increasing demand for gasoline in my country, there are higher requirements for gasoline quality, and more than 70% of my country's crude oil comes from abroad, and most of them are sulfur-containing and high-sulfur crude oil from the Middle East. Heavy oil in crude oil usually accounts for 40-60%, and this part of heavy oil (which also has a high content of impurities represented by sulfur) is difficult to be directly utilized. In order to effectively utilize heavy oil resources, my country has vigorously developed heavy oil lightening technology centered on catalytic cracking to convert heavy oil into gasoline, diesel and low-carbon olefins. More than 70% of gasoline is produced by catalytic cracking, so the finished gasoline More than 95% of the sul...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/04
CPCG06Q10/04G06F18/214
Inventor 李真宋安军刘慧李中耀
Owner SHANGHAI MARITIME UNIVERSITY
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