首页|基于遗传算法的按需通风系统能耗优化研究

基于遗传算法的按需通风系统能耗优化研究

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针对阵地工程运维通风系统分支风量优化问题,即满足各分支按需通风的同时,以通风总功率最小为目标,开展了模型构建及能耗优化方法研究,建立了基于图数据库和图算法的通风模型,设计了采用遗传算法分步骤通风能耗优化方法.首先,对按需通风条件下通风网络能耗优化问题进行了描述,建立了优化目标函数;其次,构建了通风系统的图数据库模型,采用图查询语言和Prim算法分析模型内组件关系进行迭代计算,生成了独立回路及余树弦集;再次,对照优化目标函数,设计了以余树弦参数为对象的遗传算法分步骤通风能耗优化方法,设计编码规则求解余树弦风量及调压参数,然后进行独立回路的风量分配及调压解算,并将解算结果对应目标函数评价.最后,以某工程通风系统为案例进行了能耗优化分析,研究表明优化算法比常用算法节能 6.62%,充分体现了图数据库构建通风系统模型的高效性和能耗优化算法的科学性.
Genetic algorithm-based energy optimization for on-demand ventilation systems
In view of the problem of branch airflow optimization in the operation and maintenance of position en-gineering ventilation systems(that is to minimize the total ventilation power consumption while satisfying the on-demand ventilation requirements of each branch),this study constructed a ventilation model based on graph data-bases and graph algorithms,and designed a step-by-step optimization method for ventilation energy consumption by using genetic algorithms.First,it described the energy consumption optimization problem of the ventilation network under on-demand ventilation conditions,and established a target function for optimization.Secondly,a graph database model of the ventilation system was constructed to address the nonlinear constrained optimization issue of the hybrid ventilation system,and the graph query language and Prim′s algorithm were used to analyze,the relationships within the model′s components and to generate independent circuits.A genetic algorithm solution process was then proposed for the residual tree string parameters,involving the design of encoding rules to solve for airflow and pressure regulation parameters,followed by the allocation of airflow and pressure regulation calcu-lations for the independent circuits.The results were then evaluated against the target function.Finally,an opti-mization analysis was conducted by using a specific engineering case,which showed that the optimized ventilation system had a total power consumption reduction of 6.62%,and proved the high efficiency of the model and the rationality of the algorithm.

on-demand ventilationgraph databasegenetic algorithmnetwork optimization

陈都、安瑞楠、李刚、史雷、赵川、何品杰

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军事科学院国防工程研究院

清华大学

火箭军研究院

联勤保障部队工程质量监督站

中国电建集团水电十四局有限公司

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按需通风 图数据库 遗传算法 网络优化

中国水利水电第十四工程局有限公司基金资助项目

XL-YSFD-FW-2023-006

2024

防护工程
总参谋部工程兵科研三所

防护工程

影响因子:0.2
ISSN:
年,卷(期):2024.46(4)
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