摘要
为克服模式识别优化方法在过程优化中应用的困难,根据特征权重与梯度思想,有针对性地提出样本权重和类间斜率概念,改进模式识别优化方法.对非线性、多变量、强耦合系统的仿真结果和与自适应优化的对比证明:改进后的方法在优化效果上有较大改善.在一套年产8万吨的氨合成塔上对氨净值实施在线优化,改善了氨合成塔的操作条件,氨净值提高0.38%,取得明显的经济效益.
Aiming at difficulties in applying pattern recognition optimization method to the process optimization, the concepts of sample weight and class gradient are proposed to improve the existing algorithms according to the feature weight and gradient theory. Simulation results in a nonlinear, multi-variable and strongly coupled system illustrate that the proposed method outperforms the conventional pattern recognition optimization method and adaptive optimization method . The proposed method has been applied in the practical on-line operation condition optimization of an ammonia synthesizer （80,000 tons per year） and the net value of ammonia has raised 0. 38% with considerable economic benefit.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第3期342-348,共7页
Pattern Recognition and Artificial Intelligence
基金
中国科学院知识创新工程重大项目(No.KGCX-SW-15)
安徽省优秀青年科技基金项目(No.04042046)
关键词
模式识别
过程优化
氨合成塔
氨净值
Pattern Recognition , Process Optimization , Ammonia Reactor , Net Value of Ammonia
