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作 者:罗国鹏 董益斌 孙殿伟 黄群星[2] 高峰 朱燕华 时丕伟 王松 汪守康 LUO Guopeng;DONG Yibin;SUN Dianwei;HUANG Qunxing;GAO Feng;ZHU Yanhua;SHI Piwei;WANG Song;WANG Shoukang(Everbright Environmental(China)Co.,Ltd,Shenzhen 518000,Guangdong Province,China;State Key Laboratory of Clean Energy Utilization(Zhejiang University),Hangzhou 310027,Zhejiang Province,China;Everbright Environmental Energy(Hangzhou Fuyang)Co.,Ltd,Hangzhou 311400,Zhejiang Province,China)
机构地区:[1]光大环保(中国)有限公司,广东省深圳市518000 [2]能源清洁利用国家重点实验室(浙江大学),浙江省杭州市310027 [3]光大环境能源(杭州富阳)有限公司,浙江省杭州市311400
出 处:《中国电机工程学报》2024年第20期8146-8156,I0019,共12页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING
摘 要:提出一种视觉分析与智能控制技术融合的大规模垃圾焚烧炉炉自动控制策略。该控制策略以人工操作的控制逻辑为基础,通过机器视觉技术获取图像火线信息,结合模型预测控制、模糊控制等智能控制技术,对配风系统以及炉排系统实行自动化控制。结果表明,该种机器视觉技术提取火线的吻合度高于80%,将该控制策略投入后,有效改善了炉内关键参数,包括主汽流量、炉膛温度、锅炉出口氧量等的波动情况,三者较之设定值的平均偏差分别降低了92.8%、27.2%、62.2%,而三者的标准差也分别降低了42.8%、26.5%、21.8%。同时该锅炉的CO排放均值也下降了23.9%。说明该控制策略具有较好的实践意义,可为未来大型生活垃圾焚烧炉的自动化改造提供参考。An automatic control strategy of waste incineration grate based on machine vision and intelligent control is proposed in this paper.The strategy is based on the control logic of manual operation,through the machine vision method to obtain flame information in the image,combined with the model predictive control(MPC)and fuzzy control(FC)to implement automatic control of the air distribution system and grate system.The results show that the accuracy rate of flame information extraction by this machine vision method is higher than 80%.This control strategy can effectively reduce the fluctuation of the key parameters in the furnace,including main steam flow,furnace temperature,boiler outlet oxygen,etc.The average deviation of the three compared with the set value is reduced by 92.8%,27.2%and 62.2%,and the standard deviation is also reduced by 42.8%,26.5%and 21.8%.In addition,the average CO emission of the boiler is decreased by 23.9%.This control strategy has good practical significance and provides a reference for the automatic transformation of large-scale waste incinerators in the future.
分 类 号:TM621.6[电气工程—电力系统及自动化]
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