锅炉尾部受热面子系统的遗传优化设计(英文)  

Subsystem Optimization of Tail Boiler Heating Surface via Genetic Algorithm

作　　者：吴燕玲[1] 钟崴[1] 童水光[1] 

机构地区：[1]浙江大学,浙江省杭州市310027

出　　处：《中国电机工程学报》2010年第8期56-62,共7页Proceedings of the CSEE

基　　金："十一五"国家科技支撑计划项目(2006BAF01A46)~~

摘　　要：为实现由多个受热面组成的锅炉尾部子系统的优化,提出基于遗传算法的优化模型。该子系统优化设计过程繁琐复杂,涉及热力计算、汽水阻力计算及烟风阻力计算等多个性能计算。建立了以热力计算为基础的优化计算模型,其他性能计算被简化为性能约束条件,与几何约束、速度约束、温度约束共同组成遗传算法的约束条件以保证每个个体均满足设计要求。该方法简化了计算过程,也保证了准确性。遗传优化过程从随机产生的初始群体开始,经过交叉、变异产生新个体,并引进"家族竞争"促使新群体朝着优化方向发展。通过例题证明该遗传优化方法能够有效减少子系统的传热面积并且降低了对设计经验的依赖性。This paper developed a model of area estimating for the heating surfaces located in the tail of boiler which were regarded as subsystem of boiler via genetic algorithm （GA）. The estimation was very complex for several performance calculations, including thermal calculation, hydraulic resistance calculation and fuel-gas ＆ air resistance calculation, should all be done. The model of the subsystem was developed based on thermal calculation while other performance calculations were simplified as constraints, which were introduced with geometric constraints, velocity constraints and temperature constraints to insure that the candidate design schemes satisfied to all the design specifications. A genetic based algorithm was developed, programmed, and applied to estimate the optimum value of the total area of the subsystem. In the algorithm, starting from a randomly generated population, crossover and mutation were used to produce new individuals and ＂family competition＂ was introduced to improve the GA efficiency. A case study was presented showing that significant area reduction is feasible and the designer＇s experience is less relied on.

关 键 词：锅炉 受热面 子系统 遗传算法 优化 家族竞争 

分 类 号：TK212[动力工程及工程热物理—动力机械及工程]