基于GA-BP的烧结风箱阀门预测模型  

GA-BP Based Sintering Airbox Valve Prediction Model

作　　者：张学锋 朱梅 汤亚玲 储岳中 ZHANG Xuefeng;ZHU Mei;TANG Yaling;CHU Yuezhong(School of Electrical and Information Engineering,Anhui University of Technology,Ma’anshan 243000 China)

机构地区：[1]安徽工业大学计算机科学与技术学院,安徽马鞍山243000

出　　处：《西华大学学报(自然科学版)》2025年第2期113-119,共7页Journal of Xihua University:Natural Science Edition

基　　金：安徽高校自然科学研究项目(KJ2019A0063);安徽高校自然科学研究项目(2022AH050290)。

摘　　要：在烧结点火过程中,风箱压力应维持在目标范围内,使炉膛保持微负压状态。在实际烧结生产中,目标范围内的风箱压力所需的阀门开度难以预测,错误的调节风箱阀门常常使风箱内部压力不符合烧结生产要求,难以达到烧结微负压点火的要求。为此,文章使用遗传算法(GA)对BP神经网络进行优化,提出了一种烧结风箱阀门预测模型,用钢厂的实际烧结数据对其进行训练,并将几种预测模型的预测结果进行对比。实验结果表明,GA-BP预测模型的预测效果优于传统的BP预测模型以及拟合预测模型,该模型可以较为准确地预测风箱压力达到目标范围所需的阀门开度,为烧结现场的风箱阀门开度智能控制提供了可靠的理论支持,能够很好地满足生产需求。During the sintering ignition process,the airbox pressure needs to be maintained within the target range to keep the furnace chamber at a slightly negative pressure.In actual sintering production,the valve opening required for the blast box pressure within the target range is difficult to predict,and incorrect adjustment of the blast box valve often makes the internal pressure of the blast box not meet the sintering production requirements and makes it difficult to achieve the sintering micro-negative pressure ignition.To address this problem,a sintering bellows valve prediction model was proposed using genetic algorithm(GA)to optimize the BP neural network,which was trained with actual sintering data from a steel mill,and the prediction results of several prediction models were compared.The experimental results show that the prediction effect of the GA-BP prediction model is better than that of the traditional BP prediction model and the fitted prediction model,and the model can predict the valve opening required for the blast box pressure to reach the target range more accurately,which provides reliable theoretical support for the intelligent control of the blast box valve opening at the sintering site and can well meet the production requirements.

关 键 词：BP神经网络 遗传算法 阀门预测 烧结点火 微负压 

分 类 号：TH134[机械工程—机械制造及自动化] TF325.1[冶金工程—冶金机械及自动化]