摘要
为满足环保排放标准,降低冶炼机组脱硫成本,针对冶炼企业存在的烧结烟气污染问题,对传统石灰石—石膏湿法脱硫工艺,设计了自动喷氨和燃烧加热技术,对脱硫反应环节和热风炉燃烧供热过程进行建模,预测并控制烟气出口SO;的浓度和反应器温度,将干扰因素的扰动降至最低水平。国内多个冶炼企业改造后的实际运行结果表明,所提出的基于模型预测的智慧化控制系统的应用,使得每年氨水节约成本10万元,煤气节省120万元,生产效率提高25%,当设备入口烟气中SO_(2)的平均浓度为422.43 mg/m^(3)(标准状态)时,出口烟气中污染物的浓度分别低于28 mg/m^(3),能够满足预期的排放要求,喷氨系统的控制效果得到了改善。
In order to meet the requirement of environmental emission standards,reduce desulfurization cost of smelting units,and aim at the problem of sintering flue gas pollution in smelting enterprises,automatic ammonia injection and combustion heating in traditional limestone+gypsum wet desulfurization process was designed.The model predictive control technology was used to model the desulfurization reaction link and combustion and heating process of hot blast stove,predict and control SO;concentration at flue gas outlet and reactor temperature,and reduce the disturbance of interference factors to the lowest level.The actual operation results show that application of the intelligent control system based on model prediction proposed saves 100000 Yuan of ammonia cost,1.2 million Yuan of gas cost,and 25% of production efficiency.When the average concentration of SO_(2) in flue gas at the equipment inlet is 422.43 mg/m^(3)(Standard state),the concentration of pollutants in outlet flue gas is lower than 28 mg/m^(3) respectively,which can meet the expected emission requirements,and the control effect of ammonia injection system has been improved.
作者
胡岩
魏军
于显池
李春雷
赵洪华
HU Yan;WEI Jun;YU Xian-chi;LI Chun-lei;ZHAO Hong-hua(School of Electrical Engineering,University of Ji'nan,Ji'nan 250022,China;Shandong Research Institute of Intelligent Robots Application Technology,Jining 273500,Shandong,China;Shandong Cancer Hospital,Ji'nan 250017,China;Shanxin Software Company Ltd.,Ji'nan 250101,China;School of Mechanical Engineering,University of Ji'nan,Ji'nan 250022,China)
出处
《有色金属（冶炼部分）》
CAS
北大核心
2022年第2期114-119,共6页
Nonferrous Metals（Extractive Metallurgy)
基金
国家重点研发计划项目(2018YFF01013402)
山东省重大科技创新工程资助项目(2019JZZY010435)。
关键词
石灰石—石膏湿法
模型预测
烟气脱硫
智慧管理
冶炼
limestone-gypsum wet desulfurization
model predict control
flue gas desulfurization
intelligent management
smelting
