基于残氧-模糊神经网络的加热炉燃烧系统的应用  

Application of heating furnace combustion system based on residual oxygen-fuzzy neural network

作　　者：滕克勇 Teng Keyong(Guangxi Gaofeng Wuzhou Wood-Based Panel Co.,Ltd.)

机构地区：[1]广西高峰五洲人造板有限公司

出　　处：《冶金能源》2025年第2期66-70,共5页Energy For Metallurgical Industry

基　　金：广西省科技示范项目(2023GXLK04)。

摘　　要：为了实现燃烧精确控制,降低氧化烧损,文章设计了基于残氧—模糊神经控制的燃烧系统,主要包括多点的自动化残氧监测、炉温数学控制模型和模糊逻辑技术。将该系统应用于工程实践中,氧化烧损率由1.011%降低至0.896%,吨钢单耗由94.4 m^(3)/t降至91.715 m^(3)/t,综合节能率2.84%;同时加热炉的整体温度进一步靠近标准温度,钢坯加热温度均匀性更好。In order to achieve accurate combustion control and reduce oxidation burning loss,a combustion system based on residual oxygen-fuzzy neural control is designed,which mainly includes multi-point automatic residual oxygen monitoring,furnace temperature mathematical control model and fuzzy logic technology.The system is applied to engineering practice,the oxidation burn rate is reduced from 1.011%to 0.896%,the unit consumption of ton steel is reduced from 94.4 m^(3)/t to 91.715 m^(3)/t,and the comprehensive energy saving rate is 2.84%.At the same time,the overall temperature of the heating furnace is closer to the standard temperature,and the heating temperature uniformity of the billet is better.

关 键 词：残氧监测 模糊逻辑技术 氧化烧损率 标准温度 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TG307[自动化与计算机技术—控制科学与工程]