Process parameter-driven natural gas water dew point online prediction method

A technology of process parameters and prediction methods, applied in prediction, neural learning methods, data processing applications, etc., can solve the problems of easy damage of detectors, high detection cost and impact relationship, and achieve the effect of high detection cost and high prediction accuracy

Active Publication Date: 2021-06-29
CHONGQING UNIV
View PDF6 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the disadvantages that the conventional natural gas water dew point detector is easy to be damaged, the detection cost is high, and the traditional data-driven method cannot effectively reflect the influence relationship between the natural gas water dew point and each monitoring parameter in the actual dehydration system, the invention discloses a natural gas water dew point driven by process parameters. On-line dew point prediction method, by evaluating and selecting the key parameters of natural gas water dew point, combined with NP model with flexible implementation of nonlinear prediction, realizes an online prediction of natural gas water dew point of dehydration device process parameters

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Process parameter-driven natural gas water dew point online prediction method
  • Process parameter-driven natural gas water dew point online prediction method
  • Process parameter-driven natural gas water dew point online prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0024] The realization flow chart of an online prediction method of natural gas water dew point driven by process parameters in the present invention is as follows figure 1 shown, including the following steps:

[0025] Step S1: For a dehydration system with N process monitoring parameters, each parameter is sequentially numbered as parameter 1 to parameter N, N process parameters and historical monitoring data of natural gas water dew point form the original training data set of the prediction model, and the data set has a total of P Each sample is sequentially numbered as sample 1 to sample P, the data set uses N process parameters as variables, and the natural gas water dew point as the label or target value;

[0026] For example, for a natural gas triethylene glycol dehydration device, the monitoring parameters include the absorption tower pressure of each sub-component, the circulation volume of triethylene glycol, the liquid level of the flash tank, the opening of the pr...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to the field of natural gas gathering and transportation, and discloses a process parameter-driven natural gas water dew point online prediction method aiming at the defects that a conventional natural gas water dew point detector is liable to damage and high in detection cost and a traditional data driving method cannot effectively reflect the influence relationship between the natural gas water dew point of an actual dehydration system and each monitoring parameter. According to process monitoring data of a triethylene glycol dehydration device in a production operation state, a multi-dimensional sample sequence original training data set is manufactured; by selecting key parameters for predicting the natural gas water dew point, irrelevant redundant features are eliminated, and a natural gas water dew point prediction training data set is established; an NP model is trained through the training data set to learn a multivariate regression function relationship of each process monitoring parameter of the triethylene glycol dehydration device; and real-time process monitoring data of the dehydration device is taken as target set data of the NP prediction model to realize online prediction of the water dew point of the natural gas. Compared with the prior art, the method has the beneficial effect of high accuracy.

Description

technical field [0001] The invention relates to the field of natural gas gathering and transportation, in particular to an online prediction method for natural gas water dew point driven by process parameters. Background technique [0002] Natural gas water dew point is an important technical index of natural gas quality. The accumulation of free water will lead to reduced pipeline capacity and increased corrosion, and the formation of hydrates due to high water dew points will cause pipelines and valves to block. Therefore, removing free water from natural gas is one of the important tasks in actual production. In actual production, a natural gas water dew point meter is usually used to monitor the natural gas water dew point. Due to the limitations of clogging and sensor damage of the detector in actual monitoring, the conventional method of using a natural gas dew point meter to monitor the dew point of natural gas cannot accurately and effectively monitor the dehydrati...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06Q10/04G06N3/04G06N3/08G06F17/18
CPCG06F30/27G06Q10/04G06N3/04G06N3/08G06F17/18Y02P90/02
Inventor 尹爱军谭治斌任宏基何彦霖
Owner CHONGQING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products