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Oil refining process pattern recognition and optimization method based on big data

An oil refining process and pattern recognition technology, applied in character and pattern recognition, design optimization/simulation, special data processing applications, etc., can solve the problem of real-time monitoring of changes in multiple modes of oil refining process, etc.

Pending Publication Date: 2022-03-25
EAST CHINA UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, according to the current device operating data, it is a challenging job to judge the level of the device's operating status, adjust the process parameters in time, and realize the optimized operation of the production process.
[0004] At present, the process operation mode based on the state of a single time point and the single factor curve within a time period can no longer meet the real-time monitoring of the changes of multiple modes in the refining process

Method used

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  • Oil refining process pattern recognition and optimization method based on big data
  • Oil refining process pattern recognition and optimization method based on big data
  • Oil refining process pattern recognition and optimization method based on big data

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Embodiment 1

[0310] In this embodiment, the big data-based refinery process model optimization method of the present invention is applied to the catalytic reforming (CCR) process. figure 1 The flow chart of catalytic reforming process is given. The catalytic reforming process consists of a pre-hydrogenation unit, a reforming unit, and a catalyst regeneration system. For the purpose of producing aromatics, it also includes aromatics extraction and rectification units. The pretreated raw material enters the reforming section, is mixed with circulating hydrogen and heated, and then enters the reactor. There are 3 to 4 reactors connected in series, and a heating furnace is installed between them to compensate for the heat absorbed by the reaction. The material leaving the reactor enters the separator to separate the hydrogen cycle gas (the excess part is discharged), and the obtained liquid is used as reformed gasoline after removing light components from the stabilization tower, which is a hi...

Embodiment 2

[0377] In this embodiment, according to a certain 1.8 million tons / year industrial catalytic cracking production historical data, as shown in Table 4, select 88 device independent variable data points as model input variables and 20 device dependent variable data points as the output variables corresponding to the model , to collect and preprocess production history data.

[0378] In this embodiment, a data-driven catalytic cracking unit operating state identification and optimization method of the present invention is used to identify and optimize the catalytic cracking unit operating state, such as Figure 10 shown, including the following steps:

[0379] 1. Collect the actual production history data of the device, see Table 4 for the names of related variables. The sample set Z=[z 1 ,z 2 ,...,z i ,...,z n ]∈R m×n , where z i =[z 1i ,z 2i ,...,z mi ] T represents the m samples of the ith measured variable.

[0380] Table 4: Catalytic unit model variable names

...

Embodiment 3

[0431] In this embodiment, according to a certain 600,000 tons / year industrial sulfur recovery production history data, as shown in Table 5, select 75 device data points, wherein 55 are model input variables, 20 are as output variables, and the production history data are processed acquisition and preprocessing.

[0432] In this embodiment, a method for identifying and optimizing the operating state of a sulfur recovery device based on data-driven methods of the present invention is used to identify and optimize the operating state of the sulfur recovery device, such as Figure 16 shown, including the following steps:

[0433] 1. Collect the actual production history data of the sulfur recovery unit, see Table 5 for the names of some independent variables. The sample set Z=[z 1 ,z 2 ,...,z i ,...,z n ]∈R m×n , where z i =[z 1i ,z 2i ,...,z mi ] T represents the m samples of the ith measured variable.

[0434] Table 5: Sulfur Plant Variable Names

[0435]

[043...

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Abstract

The invention relates to an oil refining process mode recognition and optimization method based on big data, and the method comprises the following steps: carrying out the preprocessing of historical data collected in an oil refining process, and obtaining standardized data; processing the standardized data by using a principal component analysis method, establishing a model by using a score matrix, and drawing a confidence ellipse; calculating a score matrix for a new sample acquired in real time, and projecting the score matrix to a confidence ellipse; the sample projected into the ellipse is a normal point and can be added into historical data to establish a new model, so that adaptive updating of the model is realized; samples projected out of the ellipse are abnormal points, and fault tracing can be carried out according to the fault contribution rate; furthermore, according to the original variables corresponding to the points in the confidence ellipse, an improved path optimization algorithm is adopted to solve the operation variable adjustment mode and path.

Description

technical field [0001] The invention belongs to the technical field of oil refining process monitoring, and in particular relates to a big data-based oil refining process pattern recognition and optimization method. Background technique [0002] With the continuous improvement of modern information technology, the data acquisition system can collect a large amount of data in the oil refining process through various measuring instruments. The changes of these data are often related to different production modes of the process, so effective monitoring of these data is of great significance to improve the production efficiency of the refining process and ensure its production safety. [0003] The production processes such as catalytic reforming, catalytic cracking, sulfur recovery, residue hydrogenation, atmospheric and vacuum, and hydrocracking in the refining process have the characteristics of multi-variable, strong interference, large hysteresis, and strong coupling, and ar...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06K9/62G16C20/10
CPCG06F30/20G16C20/10G06F18/2135G06F18/214
Inventor 钟伟民钱锋杜文莉李智杨明磊隆建范琛
Owner EAST CHINA UNIV OF SCI & TECH
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