摘要
研究提出了烧结全过程节能减排大气污染物治理的模式和方法,根据物质流和能源流节点特征把烧结工序分成烧结源头系统、过程系统、末端处理系统。采用"双平衡"约束方法进行深度置信网络进行配料-成矿预测模型的训练,实现源头配料的智能控制,通过调整优化燃料配比,从源头上降低燃料消耗及污染物排放。基于深度神经网络进行了风箱负压和温度预测耦合模型的构建,降低了烧结过的程电能消耗。根据源头和过程污染物产生的信息调节末端脱硝还原剂使用量,最终建立了烧结全过程的节能减排智能辅助诊断决策系统。该系统在应用中固体燃料消耗降低18.9%,电能消耗减少21.9%,NO_(x)减少排放43.6%,SO2减少排放14.0%,颗粒物减少20.1%,节能减排效果显著。
This study proposed the model and method for emission reduction of air pollutants and energy saving in the whole sintering process.According to the node characteristics of material flow and energy flow,the sintering process was divided into sintering source system,process system,and end treatment system.Based on the deep belief network,the"dual-balance"constraint method was used to train the ingredients-mineralization prediction model for the intelligent control of the source ingredients.By optimizing the fuel ratio,fuel consumption and pollutant emissions can be reduced from the beginning.Based on the deep neural network,the wind box negative pressure and temperature prediction coupling model was constructed,which reduced the electrical energy consumption during the sintering process.Reduction agent for terminal denitrification was adjusted according to the information from the pollutants at the beginning and during the sintering process.Finally,an intelligent auxiliary diagnosis and decision-making system for energy saving and emission reduction in the whole sintering process was established.In the application of this system,solid fuel consumption,electricity consumption,NO_(x)emission,SO2emission and particulate matter were reduced by 18.9%,21.9%,43.6%,14.0%,and 20.1%,respectively.The effect of this system on energy saving and emission reduction was determined significant.
作者
于晗
赵满坤
潘志成
于宏兵
曹玉鑫
李英杰
YU Han;ZHAO Man-kun;PAN Zhi-cheng;YU Hong-bing;CAO Yu-xin;LI Ying-jie(School of Environmental Science and Engineering,Nankai University,Tianjin 300350,China;National Enterprise Technology Center,Haitian Water Group Co.,Ltd.,Chengdu 610095,Sichuan,China;College of Intelligence and Computing,Tianjin University,Tianjin 300350,China)
出处
《中国冶金》
CAS
北大核心
2020年第12期112-118,共7页
China Metallurgy
基金
天津市科技计划项目科技重大专项与工程资助项目(15ZXGTSF00020)
国家重点研发计划资助项目(2016YFC0209301)
天津市科技计划资助项目(17PTGCCX00250)。
关键词
烧结
智能化
全过程
多变量
节能减排
sintering
intelligence
whole process
multivariable
energy saving and emission reduction
