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FUZZY NEURAL NETWORK'S APPLICATION IN FURNACE TEMPERATURE COMPENSATION BASED ON ROLLING INFORMATION FEEDBACK

机译:基于滚动信息反馈的模糊神经网络在炉温补偿中的应用

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摘要

In hot rolling process, reheating furnace and roughing mill are controlled separately in general, and the transfer of production information between the two facilities is very limited, so rolling information of roughing mill can not be fed back to reheating furnace to adjust slab's heating process dynamically, which leads to exceeding energy consumption. In this paper, fuzzy neural network (FNN) is used to deal with the feedback of rolling information, and then real-time compensation of furnace temperature setting can be obtained. Simulation results show that by using this method slab's heating process can be optimized dynamically, and energy consumption of hot rolling process can be reduced greatly, and rolling security of roughing mill can be guaranteed at the same time.
机译:在热轧过程中,加热炉和粗轧机通常是分开控制的,两个设备之间的生产信息传递非常有限,因此不能将粗轧机的轧制信息反馈给加热炉来动态地调节板坯的加热过程。 ,这会导致能耗超标。本文采用模糊神经网络(FNN)处理轧制信息的反馈,从而获得炉温设定的实时补偿。仿真结果表明,采用该方法可以动态优化板坯的加热过程,大大降低了热轧过程的能耗,同时可以保证粗轧机的轧制安全性。

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