Solving method of optimization model of gas collection master station

Abstract

The invention discloses a solving method of an optimization model of a gas collection master station. The method includes: (a) first, determining a matrix at the shortest distance between vertexes; (b) then, according to the determined matrix at the shortest distance among each vertex pair, summing weighted distances from one vertex to the other vertexes; (c) finding out the smallest value from the sums of the distances, wherein the vertex corresponding to the sum of the weighted distances is a weighting center of a corresponding graph; (d) writing down the serial number of the vertex of the weighting center; and (e) according to the selected weighting center and a minimum spanning tree, converting a undirected graph to a directed tree, and determining the layout of a piping network preliminarily. The solving method of the optimization model of the gas collection master station can complete the solution of the optimization model of the gas collection master station fast and output the result, the output result is accurate, the solving steps are simple, and the required cost is reduced.

Description

technical field [0001] The invention relates to a method for solving an optimization model of a gas gathering terminal. Background technique [0002] At present, natural gas pipelines are increasingly developing towards long-distance, large-diameter, high-pressure, multi-user, large-scale networks, and extending to the polar region (sea). Coupled with the increasing complexity of gas wells, traditional pipeline optimization technology can no longer meet the requirements. The needs of the current pipeline development, which makes the layout optimization design of the entire pipeline network system more difficult. In addition, the topology optimization problem of the gathering pipeline network is a complex combinatorial optimization problem involving a large number of discrete variables. From the perspective of optimization, this optimization problem involves many optimization theories such as discrete optimization and nonlinear optimization. From the point of view, it belong...

AI Technical Summary

Problems solved by technology

[0011] In the previous process of well group division, due to the limitations of the algorithm used and the sequential calculation of computer programming, there are often certain disadvantages in the process of assigning gas wells to each gas gathering station

The optimal division of well groups is a large-scale, nonlinear, mixed integer programming problem. Since the problem is multi-variable and multi-constrained, it cannot guarantee that the objective function is a convex function, let alone its differentiability

Traditional optimization theoretical analysis methods such as linear programming, nonlinear programming, mixed integer programming, sensitivity analysis, interior point method, etc. have continuous and differentiable requirements on the objective function and constraint conditions, and the results generally obtained are often local optimal solutions. , the global optimum cannot be guaranteed

In recent years, various intelligent algorithms, especially genetic algorithms, have strong adaptability to problems, are suitable for dealing with combinatorial optimization problems with integer variables, and have the characteristics of global convergence in theory. Certain progress, but there are still problems such as slow calculation speed, poor local convergence and easy to fall into local optimum when dealing with large-scale problems, which prompts it to be improved in combination with other intelligent methods