Plant Layout Clustering Using Graph Modularity
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Solution Overview
Problem
Existing methods for arranging devices in processing plants do not effectively quantify the connection relationships between devices and pipes, leading to increased pipe lengths and material usage without clear grouping criteria.
Innovation Solution
A clustering method that represents devices and pipes as a graph model, using algorithms like Girvan-Newman to optimize grouping based on modularity, considering pipe costs, device areas, and branching, to rationally evaluate and adjust device arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If devices are disorderly arranged, then arrangement flexibility is improved, but pipe material usage increases
Solution Approach 1:
The patent segments devices into clusters based on connection relationships, allowing flexible arrangement within clusters while reducing overall pipe material usage. The clustering algorithm divides the device network into meaningful groups that can be arranged collectively, resolving the contradiction between arrangement flexibility and material efficiency.
Solution Approach 2:
The patent changes the arrangement parameter from individual device positioning to cluster-level positioning. By transforming the problem into cluster formation based on connection relationship parameters, the system achieves both flexibility in device arrangement and optimization of pipe material usage through quantitative evaluation of cluster configurations.
2Loss of substance
If devices are divided into groups, then pipe material usage is reduced, but grouping methodology becomes complex
Solution Approach 1:
The patent replaces complex manual grouping methodology with a computational clustering algorithm. The Girvan-Newman algorithm automatically identifies optimal device groupings based on connection relationships, substituting complex human judgment with a systematic computational approach that reduces pipe material usage without increasing operational complexity.
Solution Approach 2:
The clustering algorithm performs self-service by automatically analyzing connection relationships and forming optimal groups without external intervention. The system evaluates connection patterns and independently determines device groupings that minimize pipe material usage, eliminating the need for complex manual grouping methodologies.
3Productivity
If process content is considered for grouping, then processing efficiency is improved, but connection relationship analysis is lost
Solution Approach 1:
The patent makes the clustering algorithm universal by enabling it to handle both process content relationships and connection relationship data. The same computational framework can analyze different types of device relationships, allowing simultaneous consideration of processing efficiency and connection patterns without losing either aspect of analysis.
Solution Approach 2:
The patent changes the analysis parameter from purely process-content based to connection-relationship based clustering. By transforming the grouping criterion to focus on connection relationships while maintaining process efficiency considerations, the system achieves both objectives without losing connection relationship analysis.
Data Source
AI summary
A clustering method includes the steps of: creating a plant model in which connection relationships between a plurality of devices (31) forming a processing plant (1) and pipes (4) are represented by a graph showing a linking relationship between a node (51) and an edge (52); setting, regarding the plant model, a cluster division condition; and performing, by a computer, the cluster division of the plant model by searching for an edge between the clusters in which modularity is further increased while the cluster division condition is satisfied.