Oil refining process product prediction method and based on variable weighting deep-learning and system

Abstract

The invention provides an oil refining process product prediction method based on variable weighting deep-learning and a system. The method includes: acquiring process variables in a debutanizer process, and using the process variables as input of a trained deep-learning model to obtain a product quality prediction value on the basis of the deep-learning model, wherein the deep-learning model includes at least three variable weighting autoencoders, and when the deep-learning model is trained, hidden-layer feature data of a variable weighting autoencoder which is in every two adjacent variableweighting autoencoders and is arranged at a front are used as input variables of a variable weighting autoencoder arranged at a rear, and the variable weighting autoencoder arranged at the rear is trained. The multiple weighting autoencoders are utilized to be stacked to form the deep network model, deep output-related features from a low level to a high level can be gradually obtained, features related to a quality index can be enhanced, the accurate prediction value can be provided for product quality, and the method has the advantages of high prediction precision, good generalization and the like.

Description

technical field [0001] The present invention relates to the field of chemical technology, and more specifically, to a method and system for predicting products in an oil refining process based on variable weighted deep learning. Background technique [0002] Oil refining production is a complex process industrial process with multiple raw materials, multiple devices, multiple processes and multiple products. In oil refining production, after crude oil from different oil fields is evenly mixed, it passes through primary processing equipment such as initial distillation tower, atmospheric and vacuum distillation tower, secondary processing equipment such as catalytic cracking, hydrocracking, delayed coking, and catalytic hydrogenation, Fine production of tertiary processing equipment such as catalytic reforming and hydrofining, and finally obtain petroleum products such as gasoline, diesel, aviation kerosene, fuel oil, etc. A variety of intermediate petrochemical products, as...

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Problems solved by technology

[0003] The present invention provides a method and system for predicting oil refining process products based on variable weighted deep learning that overcomes the above problems or at least partially solves the above problems, and solves the problem that the deep learning model in the prior art only focuses on the feature representation of the process data itself and ignores the Feature extraction related to the output quality index, so that the correlation between the extracted features and the quality index cannot be guaranteed

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