热轧过程中加热炉炉温优化控制方法

为了有效地解决当前加热炉能源消耗高、控制精度差、控制滞后等问题,针对步进式加热炉的工艺特点,提出了加热炉燃烧过程的智能控制策略,即模糊RBF网络自学习和自寻优功能,并结合动态PID反馈补偿策略。经试验表明,该系统不仅保证了在工况波动下的炉温控制精度,提高升降温速度,减少吨钢燃耗、电耗和钢坯烧损,而且提高了加热炉的生产能力。

Optimizing Control Method of Reheating Furnace Temperature in Hot Rolling Process

In order to solve the problems efficiently in the combustion process of reheating furnace such as the high consumption of energy, the poor precision and time-delay of the temperature control. etc., an intelligent control strategy of the combustion process of reheating furnace was proposed, according to the technical characteristics of walking beam reheating furnace, namely Fuzzy-RBF networks self-learning and the optimization function, and the dynamic PID feedback compensation strategy of reheating furnace。As experience shows, the system not only ensure the accuracy of the furnace temperature control, increases the speed of temperature adjustment and reduces the energy consume, electricity consumption and the billet combustion loss, but also can effectively improve production capacity.