A detection method for multi-category properties of oil based on pattern recognition and spectral mapping

Abstract

The present invention is a method for detecting multi-type properties of oil based on pattern recognition and spectrogram mapping, including: step S1: collecting the near-infrared spectrum of an unknown oil sample, and mapping and projecting the near-infrared spectrum to the training on the feature plane determined by the sample set; step S2: according to the area position of the unknown oil sample falling on the feature plane of the training set sample, classify the unknown oil sample point into a certain type of sample of the training set sample through the naive Bayesian classifier Middle; Step S3: In the sample points of the type of unknown oil sample point, select and within the search radius, search and find the adjacent points of the unknown oil sample point; Step S4: Calculate the relationship between the adjacent points of the unknown oil sample point and The Mahalanobis distance of the unknown oil sample point, and the Mahalanobis distance is normalized as the weight, and the oil properties of the unknown oil sample point are calculated by using the weighted sum of the properties of the adjacent points of the unknown oil sample point, so as to obtain Multiclass properties of unknown oil sample points.

Description

technical field [0001] The invention belongs to the field of petroleum and petrochemicals, and relates to a method for detecting multi-category properties of oil products based on pattern recognition and spectrogram mapping. Background technique [0002] The near-infrared spectra of gasoline, diesel oil, and crude oil include a wealth of information on the composition of oil products, and the properties of oil products such as octane number, vapor pressure, cetane number, freezing point, cold filter point, flash point, distillation range, etc. It is the result of the comprehensive action of its family composition, and the spectrum corresponding to each oil product is uniquely determined. Therefore, the traditional method generally characterizes the near-infrared spectrum and directly correlates it with various properties of the oil, using multiple linear regression (MLR), stepwise regression (SMR), principal component analysis (PCA), principal component regression (PCR), par...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the existing technology, the single model of each property of the oil product has a narrow scope of application, large errors, and a large workload for establishing and maintaining the model. It is difficult to meet the long-term requirements of the industrial site and cannot meet the requirements of stable operation of many advanced petroleum and petrochemical companies. The bottleneck problem of control implementation, for this reason the purpose of the present invention is to provide a kind of detection method of oil product property accurate, fast detection, based on pattern recognition and spectrogram mapping oil product multi-category property

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