Research on Nuclear Power Plant Equipment Layout Method Based on Genetic Algorithm
Abstract Based on the process of nuclear power plant equipment layout, this paper proposes a design method for automatic layout of nuclear power plant equipment in the form of solving a two-dimensional box packing problem based on genetic algorithm. By considering factors and requirements such as the overall dimensions of the nuclear power plant equipment, the process system to which it belongs, the maintenance space of the equipment, radiation levels, etc., a numerical model of the equipment process room is established. Taking factors such as the transportation path of the nuclear power plant equipment layout plan as the main indicators, establish an evaluation system for the nuclear power plant equipment layout plan. Based on the solution to the two-dimensional box packing problem, the nuclear power plant equipment layout process is simplified and a mathematical model for nuclear power plant equipment layout optimization is constructed. The advantage of genetic algorithm in solving optimization problems is used to realize the automatic layout of nuclear power plant equipment and obtain the optimal layout plan. Programming simulation is carried out according to the above method and flow, and under the set equipment layout constraints and genetic algorithm parameters, the automatic layout scheme design of nuclear power plant equipment is carried out and the equipment layout scheme is provided. The relative position of the equipment and the direction and location of the main channels reflected in the plan can provide a design basis for subsequent plan design.
Authors
- Hao Dong (ORCID: https://orcid.org/0000-0001-7611-3341)
- Jincheng Su (ORCID: https://orcid.org/0000-0002-9059-4353)
- Xiaopan Jia (ORCID: https://orcid.org/0009-0000-1941-0518)
- Xiaoshan Zhao
Publication Details
- Published
- 2024-08-04
- DOI
- https://doi.org/10.1115/icone31-135851
- Citations
- 1
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.35