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Atmospheric and vacuum product property prediction method and system, equipment and storage medium

A prediction method, atmospheric and decompression technology, applied in neural learning methods, design optimization/simulation, biological neural network models, etc., can solve problems such as poor model generalization ability, single basic data, and insufficient data feature learning, etc. The effect of fast computation, improved generalization performance and robustness

Pending Publication Date: 2022-02-08
北京中智软创信息技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] 1. The traditional process simulation method has great disadvantages in calculation speed, convergence and product special property prediction, and the direct use of traditional process simulation for production operation guidance has great limitations;
[0008] 2. Data-driven prediction models are almost always based on field data or simulated data. The basic data for modeling is too simple to deal with changes in raw material properties, product plans, and equipment performance in the petrochemical process. condition
[0009] 3. Most of the data-driven prediction models currently reported use a mathematical model to model the data. This method is often not comprehensive enough to learn data features, resulting in poor generalization ability of the model

Method used

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  • Atmospheric and vacuum product property prediction method and system, equipment and storage medium
  • Atmospheric and vacuum product property prediction method and system, equipment and storage medium
  • Atmospheric and vacuum product property prediction method and system, equipment and storage medium

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Embodiment 1

[0049] Such as figure 1 As shown, a method for predicting properties of atmospheric and vacuum products provided in this embodiment, specifically, includes the following steps:

[0050] 1) Based on the pre-obtained sample data set, train the pre-built machine learning models, and fuse the trained machine learning models to obtain the final atmospheric tower fusion model;

[0051] 2) Using the final atmospheric tower fusion model to predict the properties of the atmospheric and vacuum products, and obtain the prediction results of the properties of the atmospheric and vacuum products.

[0052]Preferably, in the above-mentioned step 1), the method for obtaining the final atmospheric column fusion model comprises the following steps:

[0053] 1.1) Data set acquisition: Collect on-site production data and process simulation data for different typical working conditions, and aggregate them to form a sample data set.

[0054] 1.2) Machine learning model selection: select several m...

Embodiment 2

[0077] This embodiment provides a method for machine learning atmospheric tower prediction model based on model fusion, including:

[0078] 1) Sample data set generation, according to the actual process flow of a refinery, establish process simulation process models of different typical working conditions, and use these typical working conditions as the fulcrum, apply process simulation technology to generate 1000 sets of different Process simulation data, and then aggregate the process simulation data and 1000 sets of data actually produced on site to form a sample data set.

[0079] 2) Data preprocessing, the obtained sample data set is randomly divided into two parts of training set and test set according to 4:1, and the input and output variables of each machine learning model are determined, wherein:

[0080] Input variables: naphtha flow at the top of the tower, flow in the middle of Chang No. 1, heat load in the middle of Chang No. 1, flow in the middle of No. 2, heat l...

Embodiment 3

[0094] Embodiment 1 above provides a method for predicting properties of atmospheric and vacuum products. Correspondingly, this embodiment provides a system for predicting properties of atmospheric and vacuum products. The recognition system provided in this embodiment can implement a method for predicting properties of atmospheric and vacuum products in Embodiment 1, and the recognition system can be realized by software, hardware or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute corresponding steps in the methods of Embodiment 1. Since the identification system of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple. For relevant parts, please refer to the part of the description of Embodiment 1. The embodiment of the system of this embodiment is only schematic .

[0095] Such as image 3 As shown, a...

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Abstract

The invention relates to an atmospheric and vacuum product property prediction method and system, equipment and a storage medium, and the method comprises the steps: training each pre-constructed machine learning model based on a pre-obtained sample data set, and fusing each trained machine learning model to obtain a final atmospheric tower fusion model; and predicting the properties of the atmospheric and vacuum product by adopting the final atmospheric tower fusion model to obtain a property prediction result of the atmospheric and vacuum product. According to the method and system, the machine learning model is modeled by adopting the process simulation data and the field data, and the model has a self-learning capability and can cope with complex working conditions such as crude oil property change and product scheme change. The twin model is trained by using the fused machine learning model, so that the model can fully learn data features, and the generalization performance and robustness of the model are improved, thereby ensuring that the model has adaptability to multiple working conditions and also has calculation stability and convergence. The method and system can be widely applied to the field of product detection.

Description

technical field [0001] The invention relates to a method, system, equipment and storage medium for predicting properties of atmospheric and vacuum products based on model fusion, and belongs to the field of product detection. Background technique [0002] The atmospheric and vacuum distillation unit is the crude oil processing equipment in the oil refining industry and the first process of oil refining. As the "leading" device of petroleum refining, its operation level plays a vital role in the operation performance of the secondary processing device and the optimization of the whole plant. In order to improve the operation level of atmospheric and vacuum devices, RTO (Real Time Optimization, real-time optimization) technology has gradually been applied in industry. As one of the key technologies, the property prediction of atmospheric and vacuum sideline products plays a key role in the application level of RTO technology. [0003] The atmospheric and vacuum distillation ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/08G06N20/00
CPCG06F30/27G06N20/00G06N3/08
Inventor 王海向云刚
Owner 北京中智软创信息技术有限公司