转炉炼钢多变量神经网络预报模型  

Multivariable neural network predictive model for BOF steelmaking

作　　者：赵晓东[1] 徐生林[1] 杨成忠[1] 

机构地区：[1]杭州电子科技大学信息与控制研究所,浙江杭州310018

出　　处：《化工学报》2010年第8期2111-2115,共5页CIESC Journal

基　　金：国家自然科学基金项目(60974138; 60804010);浙江省重大科技专项(2009C14035)~~

摘　　要：终点钢水温度和碳、硫、磷等成分含量的有效控制是转炉炼钢过程的重要任务。本文基于多变量输入输出的神经网络来建立转炉炼钢的预报模型,首先对采集数据进行预处理,并采用滚动优化方法来提高模型的准确性,仿真与试验对比证实了该方法建立模型的有效性和高命中率。Effective controlling of the endpoint steel temperature and contents of carbon,sulphur,etc.is one of the main tasks of BOF steelmaking process.A multivariable neural network model was established in this paper.The input data were pretreated and standardized.Rolling optimal control method was used to increase the accuracy of the model.Simulation and experiment comparisons show that the model is validated and has high hit rate.

关 键 词：终点温度 组分含量 神经网络 命中率 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]