华南理工大学学报(自然科学版) ›› 2009, Vol. 37 ›› Issue (2): 152-157.

• 动力与电气工程 • 上一篇    下一篇

混合遗传算法在制粉系统优化中的应用

廖艳芬 马晓茜   

  1. 华南理工大学 电力学院, 广东 广州 510640
  • 收稿日期:2007-11-21 修回日期:2007-12-28 出版日期:2009-02-25 发布日期:2009-02-25
  • 通信作者: 廖艳芬(1976-),女,副教授,博士,主要从事电站优化运行和洁净燃烧研究. E-mail:yfliao@scut.edu.cn
  • 作者简介:廖艳芬(1976-),女,副教授,博士,主要从事电站优化运行和洁净燃烧研究.
  • 基金资助:

    中国科学院可再生能源与天然气水合物重点实验室开放基金资助项目(0807K2)

Application of Hybrid Genetic Algorithm to Optimization of Pulverizing System

Liao Yan-fen  Ma Xiao-qian   

  1.  School of Electric Power, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2007-11-21 Revised:2007-12-28 Online:2009-02-25 Published:2009-02-25
  • Contact: 廖艳芬(1976-),女,副教授,博士,主要从事电站优化运行和洁净燃烧研究. E-mail:yfliao@scut.edu.cn
  • About author:廖艳芬(1976-),女,副教授,博士,主要从事电站优化运行和洁净燃烧研究.
  • Supported by:

    中国科学院可再生能源与天然气水合物重点实验室开放基金资助项目(0807K2)

摘要: 提出一种混合遗传算法对电站多煤种掺烧制粉系统的运行进行寻优.采用模糊神经网络评估混煤的结焦特性,利用径向基神经网络预测掺烧煤的煤粉细度和磨出口温度.在此基础上,以磨煤机安全性和炉内燃烧稳定性为约束,以混煤煤价和制粉单耗最低为目标进行遗传算法寻优,并采用组合结构基因加速运算.仿真结果表明,该算法对电站多煤种掺烧制粉系统的运行优化具有较好的实用性和适应性.

关键词: 制粉系统, 混合遗传算法, 模糊神经网络

Abstract:

This paper proposes a hybrid genetic algorithm (GA) to optimize the operation of pulverizing system in admixing combustion conditions. In this algorithm, a fuzzy neural network is adopted to evaluate the slag property of admixed coal, and a Radial Basis Function neural network is used to predict the fineness of pulverized coal and the outlet temperature of the mill. Then, the pulverizing system is optimized, with the mill safety and the furnace com- bustion stability as the restraints, and with the lowest blended coal price and pulverizing unit cost as the targets. Moreover, a kind of hierarchical gene is designed to fasten the searching process. Simulated results indicate that the proposed algorithm is practicable and feasible in optimizing the operation of pulverizing system in admixing combus- tion conditions.

Key words: pulverizing system, hybrid genetic algorithm, fuzzy neural network