摘要
由于电石冶炼过程存在较大的滞后性,当预判错误或操作延迟,容易出现电流波动,会影响生产效率,损害电石炉。基于采集的某厂电石炉的大量现场数据,利用极限学习算法建立该电石炉的系统模型。基于现场操作经验,利用模糊迭代学习控制设计针对该电石炉的电极控制策略。
The large hysteresis in calcium carbide smelting and the misjudgment or operation delay easily causes current fluctuation and may affect the production efficiency or brings damage to the calcium carbide furnaces. In this paper, basing on a large number of data of calcium carbide furnace collected in a factory, the extreme learning algorithm was adopted to establish the model of calcium carbide furnace system, including having the experience of field operation based and the fuzzy iterative learning control used to design an electrode control strategy for the calcium carbide furnace.
作者
孙浩翔
黎英
SUN Hao-xiang;LI Ying(Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
出处
《化工自动化及仪表》
CAS
2019年第6期436-441,共6页
Control and Instruments in Chemical Industry
关键词
电极调节系统
电石炉智能控制
模糊迭代控制
控制极限学习机
电极控制策略
electrode regulating system
smart control over calcium carbide furnace
fuzzy iterative learning control
extreme learning machine
electrode control strategy
