Draining pipeline network optimizing method based on generalized reverse learning difference algorithm

A differential algorithm and drainage pipe network technology, applied in the sewer system, calculation, waterway system, etc., can solve the problems of complex solution process and large preprocessing workload, so as to solve optimization problems, improve utilization capacity, and facilitate efficient optimization. effect of the problem

Active Publication Date: 2014-07-02
BEIJING UNIV OF TECH
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Problems solved by technology

[0006] Aiming at the problems of large preprocessing workload and complex solution process in the optimization of drainage pipe network,

Method used

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  • Draining pipeline network optimizing method based on generalized reverse learning difference algorithm
  • Draining pipeline network optimizing method based on generalized reverse learning difference algorithm
  • Draining pipeline network optimizing method based on generalized reverse learning difference algorithm

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Embodiment Construction

[0046] The experimental data adopted in the embodiment comes from the simplified model of the sewage pipe network in the residential area of ​​the first district of Dongying Oilfield, Shandong, as attached figure 2 As shown, the model consists of 79 pipe segments and 80 nodes, with a drainage area of ​​2.6km 2 , more than 10,000 people. At the same time, 24 available pipe diameters are provided: {0.2,0.25,0.3,0.35,0.38,0.4,0.45,0.5,0.53,0.6,0.7,0.8,0.9,1,1.05,1.2,1.35,1.4,1.5,1.6, 1.8,2,2.2,2.4} meters.

[0047] The flow chart of the drainage network optimization method based on the generalized reverse learning difference algorithm is as follows: figure 1 As shown, the specific steps are as follows:

[0048] (1) Obtain the ground elevation, pipe section length and design flow rate of each node of the pipe network, as shown in Table 1.

[0049] (2) Use the algorithm to generate the initialization population: each pipe section randomly selects a pipe diameter from 24 option...

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Abstract

The invention relates to a draining pipeline network optimizing method based on a generalized reverse learning difference algorithm and aims to solve the problems that draining pipeline optimization is complex in calculation and low in calculation precision, and unreasonable and infeasible schemes cannot be excluded thoroughly. The improved self-adaption difference algorithm is utilized to obtain the optimal value of a pipeline manufacturing cost function. The optimal value includes hydraulic parameters such as flow rate, slope and burial depth, satisfying a hydraulic constraint condition, of each pipeline section. A reserve learning method is used for initializing the self-adaption algorithm, the initialized population of the algorithm is processed before variation, probability theory knowledge is utilized to refine initial solution while population number is not increased, population variety is increased, and probability for finding global optimal solution is increased. In addition, few variables are related to the algorithm, variation strategies are selected through self-adaption, and simple algorithm operation, fast convergence and high optimization precision are achieved.

Description

technical field [0001] The invention belongs to the field of drainage pipe network optimization, and relates to a drainage pipe network optimization method based on a generalized reverse learning difference algorithm. The generalized reverse learning method is used to improve the self-adaptive difference algorithm, which is used for the cost optimization design of the drainage pipe network. Background technique [0002] The drainage system usually consists of a drainage network and a sewage treatment plant. The drainage network is a facility for collecting and transporting domestic sewage, industrial wastewater, and rain and snow water, and is responsible for transporting these wastes to sewage plants or other water outlets. As an indispensable part of urban infrastructure construction, the system is mainly composed of drainage pipes, drainage pumping stations, inspection wells and other engineering facilities. In order to meet the material needs of the people and further im...

Claims

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Application Information

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IPC IPC(8): G06F17/50E03F3/00
Inventor 乔俊飞刘昌芬韩红桂武利王超李瑞祥
Owner BEIJING UNIV OF TECH
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