Adaptive fast prediction method for crude oil properties based on near infrared spectrum

Abstract

The invention discloses an adaptive rapid prediction method for crude oil properties based on near-infrared spectrum. A method for constructing a model for predicting the crude oil properties comprises the following steps of: (1) determining the property data of a crude oil sample; (2) determining a near-infrared spectrum of the crude oil sample; (3) preprocessing the near-infrared spectrum obtained in step (2) to establish an initial training set; (4) using the method of combining principal component analysis (PCA) with the Hotelling T2 statistical to eliminate abnormal sample points from theinitial training set to obtain a final training set; (5) performing the principal component analysis on the sample obtained in step (4), and storing the load vector with the contribution value of thefeature value greater than 95% as P<pca>; (6) selecting a local training set by the similarity index (SI) after the dimensional reduction of the near-infrared spectrum of the sample to be tested by P<pca>; and (7) determining one or more wavenumber segments according to the local training set, and using the partial least squares (PLS) method to establish a local model for the crude oil properties.

Description

technical field [0001] The invention relates to an adaptive near-infrared rapid prediction method of crude oil properties. Background technique [0002] With the rapid development of modern industry, petroleum, as a national material, plays a vital role in the national economy. Crude oil is the most important raw material for refining and chemical enterprises. The demand for crude oil has increased sharply, the import volume has expanded, and the price has remained high and fluctuated frequently. According to the "BP World Energy Statistical Yearbook 2016" report, in 2015, China's net oil imports increased by 770,000 barrels per day, and China once again became the world's largest oil importer. There are many types of imported crude oil, many of which are so-called "Opportunity oil", they either have a large specific gravity, or have a high acid content, or have a lot of impurities. These have brought tremendous pressure to refining and chemical enterprises. Timely access...

AI Technical Summary

Problems solved by technology

For the process industry, the continuity of production often requires the model to be able to track the on-site working conditions in real time; and when the model deviates greatly from the current working conditions and the prediction accuracy cannot meet the needs of online detection, the model can be updated in a timely and effective manner

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