【作者】 刘振焘； 吴敏； 曹卫华； 何勇；

【Author】 Liu Zhentao,Wu Min,Cao Weihua,He Yong School of Information Science and Engineering,Central South University,Changsha 410083,P.R.China

【机构】 中南大学信息科学与工程学院；

【摘要】 本文提出一种基于信息融合的高炉料面温度场建模方法,充分利用高炉炉喉温度检测信息,以基于两点法的温度动态定标方法作为基准定标方法,采用基于遗传算法的BP神经网络技术实现对温度动态定标的非线性误差校正。该方法具有良好的鲁棒性及非线性逼近能力,克服了传统温度定标方法的不足,获得了更为准确的料面温度场模型。仿真实验和工业应用结果表明,基于信息融合的高炉料面温度场模型更为准确地反映了料面温度场分布情况,为了解高炉煤气流分布、指导高炉布料提供了一条有效途径。更多还原

【Abstract】 A novel method for modeling burden surface temperature field in blast furnace(BF) is presented in this paper.It is based on information fusion and makes full use of temperature information detected from the throat of BF.A dynamic temperature calibration method based on two-point method is taken as the benchmark calibration method,in which the nonlinear error involved is adjusted by an improved BP neural network based on Genetic Algorithm(GA) .Due to the high robustness and better nonlinear approximation ability,the proposed method overcomes the shortages of conventional calibration methods,and gets the distribution model of temperature field with more details.The simulation results and industrial implementation show that,this model of burden surface temperature field can depict distribution of temperature field more exactly,so that it is more effective to understand the distribution of gas flow and instruct the operation of burden distribution.更多还原