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有色金属(冶炼部分):2022,(2):114-119
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烧结烟气中脱硫无人智慧化控制系统研究
胡岩1,2, 魏军3, 于显池4, 李春雷5, 赵洪华6
(1.济南大学自动化与电气工程学院;2.山东智能机器人应用技术研究院;3.济南大学 自动化与电气工程学院;4.山东省肿瘤医院;5.山信软件股份有限公司;6.济南大学机械工程学院)
Research on Intelligent Control of Denitrification of Sintering Flue Gas Based on Model Predict Control
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投稿时间:2021-10-14    修订日期:2021-11-10
中文摘要: 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SO2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SO2的平均浓度为422.43 mg/m3(标准状态)时,出口烟气中污染物的浓度分别低于28 mg/m3,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。
Abstract: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 SO2 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 100 000 Yuan of ammonia cost, 1.2 million Yuan of gas cost, and 25% of production efficiency. When the average concentration of SO2 in flue gas at the equipment inlet is 422.43 mg/m3(Standard state), the concentration of pollutants in outlet flue gas is lower than 28 mg/m3 respectively, which can meet the expected emission requirements, and the control effect of ammonia injection system has been improved.
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基金项目: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)
引用文本:
胡岩,魏军,于显池,李春雷,赵洪华.烧结烟气中脱硫无人智慧化控制系统研究[J].有色金属(冶炼部分),2022(2):114-119.
HU Yan,WEI Jun,YU Xian-chi,LI Chun-lei,ZHAO Hong-hua.Research on Intelligent Control of Denitrification of Sintering Flue Gas Based on Model Predict Control[J].Nonferrous Metals (Extractive Metallurgy),2022(2):114-119.

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