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Complex pipeline operation control method based on deep learning

A deep learning and operation control technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of short reliable operation time, high energy consumption, frequent safety problems, etc., and achieve long reliable operation time , the effect of reduced energy consumption and fewer safety issues

Active Publication Date: 2020-07-31
NINGBO UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The present invention provides a complex pipeline operation control method based on deep learning to solve the problems that the existing technology cannot effectively and reasonably control complex pipeline operation, resulting in frequent safety problems, high energy consumption, and short reliable operation time

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  • Complex pipeline operation control method based on deep learning
  • Complex pipeline operation control method based on deep learning
  • Complex pipeline operation control method based on deep learning

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specific Embodiment approach 1

[0029] Specific implementation mode one: combine figure 1 , figure 2 , image 3 This embodiment is described. The complex pipeline operation control method based on deep learning given in this embodiment is as follows: image 3 As shown, the specific steps are as follows:

[0030] Step 1. For each section of the pipeline in the pipeline network, a control-oriented complex pipeline model is established in the SCADA system. A complex pipeline network usually consists of water source 1 (reservoir), flow meter 2, pressure gauge 5, pump 4 (pumping station), valve 3 and so on. Flow meter 2, pressure gauge 5, pump 4 (pump station), valve 3 are connected to the central control room through wired or wireless; the central control room dispatches and monitors the entire pipeline network in real time through SCADA system and powerful numerical calculation, information fusion, etc. information (a schematic diagram of a complex pipeline network such as figure 1 , figure 2 shown);

...

specific Embodiment approach 2

[0046] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in step 3, the specific steps for determining whether to adopt the complex pipeline open-loop control strategy based on the deep learning method or to adopt the complex pipeline closed-loop control strategy based on the deep learning method include: :

[0047] Define the following toggle function:

[0048]

[0049] Among them, P e1 , P e2 is the switching function weight, is the flow value at the preset pipeline position or the flow value in the pipeline section, is the pressure value at the preset pipeline position or the pressure value in the pipeline section, q i Indicates the flow value measured by the i-th flowmeter in the pipe network, i∈[1,M], p j Indicates the pressure value measured by the jth pressure gauge in the pipe network, j∈[1,N], M is the number of flowmeters in the pipe network, and N is the number of pressure gauges in the pipe network;

[0050] Generally speaking...

specific Embodiment approach 3

[0052] Specific implementation mode three: as Figure 4 As shown, the difference between this embodiment and the specific embodiment 1 is that in step 41, the specific steps of the complex pipeline open-loop control strategy include:

[0053] A1. Starting from the mechanism model of complex pipelines, establish a control-oriented space-time evolution model, give boundary valves, and write them into the pipeline space-time evolution module;

[0054] A2. Set the performance index of complex pipeline open-loop control and write it into the performance index module of open-loop control;

[0055] A3. Introduce the Lagrangian functions λ(l,t) and μ(l,t) into the co-state module of the open-loop control to obtain the extended open-loop control performance index and obtain the open-loop control performance by the variational method Co-state model, write the co-state model of open-loop control into the co-state module of open-loop control, and write the obtained gradient form into the...

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Abstract

The invention provides a method for controlling the operation of a complex pipeline based on deep learning, belongs to the technical field of pipeline transportation, and especially relates to a method for controlling the operation of a complex pipeline. First, a control-oriented complex pipeline model is built in an SCADA system; the position or section needing operation control is determined according to the pipe network information monitored in real time and the user or industrial demand for the pipeline; then, whether to adopt a complex pipeline open-loop control strategy based on a deep learning method or a complex pipeline closed-loop control strategy based on a deep learning method is decided according to the obtained flow and pressure information of the position or section needingoperation control, and a strategy is performed according to the decision result; and finally, information is fused, and the operation control of the complex pipeline network is completed in a coordinated manner. In the prior art, the operation of a complex pipeline cannot be controlled effectively and reasonably, which leads to frequent occurrence of safety problems, high energy consumption and short reliable running time. The problem is solved by using the method of the invention. The method of the invention can be used in pipeline transportation.

Description

technical field [0001] The invention belongs to the technical field of pipeline transportation, in particular to a complex pipeline operation control method. Background technique [0002] A pipeline is a device used to transport gas, liquid or fluid with solid particles connected by pipes, pipe connectors and valves. In actual engineering, according to the layout and connection of pipelines, pipelines are divided into two types: simple pipelines and complex pipelines; simple pipelines refer to pipelines whose diameter and flow do not change along the way (equal-diameter pipelines without branches); complex pipelines refer to pipelines Pipelines whose diameter and flow rate change along the way (a pipeline system composed of more than two pipelines), complex pipelines can be divided into series, parallel pipelines, branched and ring pipe networks, etc. Worldwide, complex pipelines are widely used for the supply and transportation of materials such as water, petroleum, liquef...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 陈特欢蔡振宇
Owner NINGBO UNIV
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