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Intelligent prediction and diagnosis method for salt content of crude oil after removal of electric desalting system of atmospheric and vacuum distillation unit

An atmospheric and decompression device, intelligent prediction technology, applied in the direction of prediction, manufacturing computing system, resources, etc., can solve the problems of long analysis and testing time, low efficiency, impact on personnel level, etc.

Pending Publication Date: 2021-12-31
HEFEI GENERAL MACHINERY RES INST +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In order to overcome the traditional manual sampling analysis method in the prediction of the salt content of crude oil after de-salting, the analysis and testing time is long, the problem is affected by the level of personnel and the efficiency is low, and a random forest-based electric desalting system is provided for crude oil de-salted crude oil Intelligent Prediction and Diagnosis Method of Salt Content

Method used

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  • Intelligent prediction and diagnosis method for salt content of crude oil after removal of electric desalting system of atmospheric and vacuum distillation unit
  • Intelligent prediction and diagnosis method for salt content of crude oil after removal of electric desalting system of atmospheric and vacuum distillation unit
  • Intelligent prediction and diagnosis method for salt content of crude oil after removal of electric desalting system of atmospheric and vacuum distillation unit

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Embodiment 1

[0036] Such as figure 1 As shown, an intelligent prediction and diagnosis method for the salt content of crude oil after desalting by the electric desalination system of an atmospheric and vacuum device comprises the following steps:

[0037] S1. Construct an index system that affects the salt content of crude oil after desalination by the atmospheric and vacuum device's electric desalination system, and obtain the historical and real-time operation data of the atmospheric and vacuum device's electric desalination system as sample data according to the indicators in the system;

[0038]In this embodiment, the data of the electric desalination system is obtained from the LIMS system and the sampling and analysis system of the atmospheric and vacuum device as sample data; Try to select data with a relatively high degree of data integrity as sample data.

[0039] The above index system includes 46 parameters including electric desalting process, operation data and crude oil anal...

Embodiment 2

[0058] Construct an intelligent prediction and diagnosis model for the salt content of crude oil after desalination in the atmospheric and vacuum device electric desalination system based on random forest, the steps are as follows:

[0059] S31. Divide the preprocessed data into a training sample set and a test sample set at a ratio of 0.8:0.2;

[0060] S32. Generate N sample subsets from the training sample set by using a random sampling method that can be replaced. Considering the running time of the model and the accuracy of the model, the number N of decision trees selected in this embodiment is 100. The number of samples in each of the sample subsets is the same as the number of samples in the training sample set.

[0061] S33. When performing regression analysis with the random forest algorithm, select partition features to select parameters according to the minimum mean error (MSE), and then use a recursive method to construct the entire decision tree and random forest....

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Abstract

The invention relates to the technical field of petrochemical devices, in particular to an intelligent prediction and diagnosis method for salt content of crude oil after removal of electric desalting system of atmospheric and vacuum distillation unit. The method comprises the following steps: constructing an index system influencing the salt content of crude oil after the electric desalting system of the atmospheric and vacuum distillation unit, and obtaining sample data according to indexes in the system; preprocessing the sample data; utilizing the preprocessed data to construct an intelligent prediction and diagnosis model of the salt content of crude oil after the electric desalting system of the atmospheric and vacuum distillation unit based on the random forest; performing real-time prediction and diagnosis on the electro-desalting system through the constructed intelligent prediction and diagnosis model; and comparing the prediction and diagnosis result with a true value, and judging and optimizing the intelligent prediction and diagnosis model for the salt content of the crude oil after the electric desalting system is demineralized. The prediction model is constructed by adopting the random forest algorithm to perform intelligent early warning on the condition that the salt content of the removed crude oil exceeds the standard, so that the cost of electro-desalting sampling analysis is effectively reduced, and the efficiency of detecting the salt content of the removed crude oil of the electro-desalting system is improved.

Description

technical field [0001] The invention relates to the technical field of petrochemical device detection, in particular to an intelligent prediction and diagnosis method for the salt content of crude oil after being desalted by an electric desalination system of an atmospheric and vacuum device. Background technique [0002] Atmospheric and vacuum units are the first process in oil refineries, providing qualified and high-quality raw materials for many downstream secondary processing units. The electric desalination system is the "leader" of the atmospheric and vacuum unit, and is an essential crude oil pretreatment process to provide high-quality raw materials for subsequent units. After electric desalination, the salt content in crude oil increases, which is likely to cause scaling, blockage and corrosion of equipment and pipelines. The heavy metals contained in it may also easily cause catalyst poisoning in the subsequent processing process, and may affect the product qualit...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06K9/62
CPCG06Q10/04G06Q10/06393G06Q10/067G06Q50/04G06F18/214G06F18/24323Y02P90/30
Inventor 朱建新袁文彬乔松吕宝林亢海洲方向荣庄力健
Owner HEFEI GENERAL MACHINERY RES INST
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