期刊文献+

基于智能集成策略的烧结块残硫软测量模型 被引量:16

Soft-sensing model of sulfur content in agglomerate based on intelligent integrated strategy
下载PDF
导出
摘要 针对铅锌冶炼烧结过程烧结块残硫估计问题,提出了一个基于智能集成策略的软测量模型,主要包括数学模型、专家规则模型和智能协调器几部分.其中数学模型通过物料平衡方程计算烧结块残硫,方程中的部分不可解参数由神经网络估计给出.专家规则模型对残硫与主要影响因素之间的关系进行了描述.基于模糊逻辑的智能协调器根据生产条件的情况综合各模型的输出作为估计结果.工业实际数据验证表明,智能集成模型的残硫估计误差平均值仅为7.5%,而且真实反映了烧结块残硫的变化趋势,可以为生产操作提供有益的指导. The parameter estimation in sintering process of plumbum and zinc smelting is a concerned challenge for the complexity in chemical reactions. The idea of intelligent integrated modeling strategy was to combine multiple modeling techniques to acquire all kinds of information from the plant. The study of estimation of sulfur content in agglomerate was vital for operation optimization. An estimation model for sulfur estimation based on intelligent integrated strategy was put forward, in which the mathematical model calculates the sulfur content in agglomerate following material balance equation with some unsolvable parameters estimated by neural network method, while the expert rule model describe the relationship between sulfur quantity and key factors. An intelligent coordinator based on fuzzy logic is proposed to synthesize the output of the models. The estimation model was tested by industrial practical data, its average error is 7.5%. So this model could be used as a guide in practical operation in sintering process.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2004年第1期75-80,共6页 Control Theory & Applications
基金 国家十五863计划项目(2001AA411040 2001AA414240) 国家973重点基础研究发展规划项目(2002CB312203).
关键词 铅锌 冶炼 烧结过程 烧结块 残硫 软测量模型 智能集成策略 数学模型 soft-sensing model neural networks material balance intelligent coordination expert rules
  • 相关文献

参考文献6

  • 1[2]GOMM J B, YU D L. Selecting radial basis function network centers with recursive orthogonal least squares training [J]. IEEE Trans on Neural Networks, 2000, 11(2): 306-314.
  • 2[3]RUANO A E, FERREIRA P M, CABRITA C, et al. Training neural networks and neural-fuzzzy systems: a unified view [C]∥Proc of the 15th IFAC. Barcelona: Elsevier Science, 2002.
  • 3[6]KODKINEN J, YLINIEMI L, LEIVISK K. Fuzzy modeling of a rotary dryer [C]∥Preprints of the IFAC Workshop. Finland: Elsevier Science, 2000:166-171.
  • 4[7]WU M, NAKANO M, SHE J H. A model-based expert control strategy using neural networks for the coal blending process in an iron and steel plant [J]. Expert Systems with Applications, 1999, 6(16): 271-281.
  • 5[8]HAGAN M T, DEMOUTH H B, BEALE M H. Neural Network Design [M]. Boston: PWS Publishing Company, 1996.
  • 6[9]HAGAN M T, MENHAJ M. Training feed forward network with the Marquardt algorithm [J]. IEEE Trans on Neural Networks, 1994, 5(6): 989-993.

同被引文献147

引证文献16

二级引证文献72

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部