Oil dry point prediction calculation method and device, computer equipment and storage medium

A calculation method and computer program technology, applied in the field of oil refining, can solve problems such as large gaps, large differences in the properties of isomers, and inaccurate calculations

Pending Publication Date: 2022-04-08
石化盈科信息技术有限责任公司
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0003] At present, the commercialized modeling software in the field of continuous reformer online optimization (RTO) generally has the limitation of inaccurate calculation in the calculation of "dry point". The main reason is that the properties of isomers are very different, especially It is because of the process simulation software that after complex fractionation calculations, many physical property matching results are not ideal. Taking the simulation of the reformer as an example, the dry point of the reformed oil generally has a large gap between the calculated value and the measured value of the real value. , the degree of error has lost the meaning of reference and prediction
However, in the process of online optimization, a relatively accurate dry point value is required, so it is necessary to improve the defects of the current process simulation software

Method used

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  • Oil dry point prediction calculation method and device, computer equipment and storage medium
  • Oil dry point prediction calculation method and device, computer equipment and storage medium
  • Oil dry point prediction calculation method and device, computer equipment and storage medium

Examples

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Effect test

Embodiment 1

[0055] In this example, if figure 1 As shown, a method for predicting and calculating oil dry point is provided, which includes:

[0056] Step 110, acquire sample data.

[0057] In this embodiment, the amount of sample data is LIMS data, which is used as a sample of the neural network model. In this embodiment, the LIMS data is firstly screened to select appropriate data, and then the screened data is sorted to obtain sample data.

[0058] Step 120, input the sample data into a preset neural network algorithm to generate a neural network model with at least one hidden layer.

[0059] In this step, a neural network algorithm is applied according to the samples to generate a neural network model. In this embodiment, the sample data is the composition of oil and gas, the input of the neural network model is the composition, and the output is the dry point temperature. The specific calculation process of the neural network model is to input the measured values ​​of all the cor...

Embodiment 2

[0081] It should be understood that online optimization is the most advanced optimization technology developed to the present stage of the whole process optimization control technology. It applies optimization technology to process control, and seeks to achieve the objective function to achieve the optimum value under the condition of meeting various production technical indicators. An optimal set of operating parameters is obtained, and this set of parameters is used for the actual control of the device.

[0082] Online optimization technology can make the production process in the best running state by only adjusting the operating parameters without modifying the process flow or adding production equipment. Online optimization technology can set the maximum benefit, high value-added yield, etc. as the objective function, without modifying the process flow, without increasing or reducing production equipment, only by adjusting parameters such as pressure, temperature, load, et...

Embodiment 3

[0110] In this example, if figure 2 As shown, an oil dry point prediction calculation device is provided, including:

[0111] A sample data acquisition unit, configured to acquire sample data;

[0112]A neural network model generation unit, configured to input the sample data into a preset neural network algorithm to generate a neural network model with at least one hidden layer;

[0113] A proxy model generating unit, configured to transform each hidden layer of the neural network model using a preset matrix to generate a proxy model;

[0114] A prediction module obtaining unit, configured to input the calculation results of the proxy model into a preset modeling program for encapsulation, and obtain a prediction module for predicting dry points;

[0115] The dry point predicted value obtaining unit is configured to input the preset component values ​​in the preset modeling program into the prediction module for calculation to obtain the dry point predicted value.

[0116...

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Abstract

The invention provides an oil dry point prediction calculation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring sample data; inputting the sample data into a preset neural network algorithm to generate a neural network model with at least one hidden layer; transforming each hidden layer of the neural network model by using a preset matrix to generate an agent model; the prediction module is used for inputting a calculation result of the agent model into a preset modeling program for packaging to obtain a prediction dry point; and inputting a preset component value in the preset modeling program into a prediction module for calculation to obtain a dry point prediction value. After a neural network model is generated by combining a large amount of sample data with an algorithm of a neural network, a corresponding agent model is developed based on parameters generated by the neural network model, and a corresponding online optimization software module is developed based on the agent model, so that accurate calculation and prediction of a dry point value are realized.

Description

technical field [0001] The invention relates to the technical field of oil refining, in particular to an oil dry point prediction calculation method, device, computer equipment and storage medium. Background technique [0002] The "dry point" is when the oil is distilled to the highest vapor phase temperature, which is called the final boiling point or dry point. The "dry point" properties of plant logistics products are usually obtained through on-site testing, while the online optimization system is obtained through physical property calculations within the software. [0003] At present, the commercialized modeling software in the field of continuous reformer online optimization (RTO) generally has the limitation of inaccurate calculation in the calculation of "dry point". The main reason is that the properties of isomers are very different, especially It is because of the process simulation software that after complex fractionation calculations, many physical property ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/70G16C20/30G06N3/04G06N3/08
Inventor 朱宏韬郑文刚周建华田健辉吴奕琛赵毅
Owner 石化盈科信息技术有限责任公司
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