论文部分内容阅读
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。
In order to solve the problems such as high energy consumption, poor control accuracy and control lag in the current heating furnace, the intelligent control strategy of the heating process of the heating furnace is proposed according to the technological characteristics of the step heating furnace, that is, fuzzy RBF network self-learning and self-searching Excellent function, combined with dynamic PID feedback compensation strategy. The test shows that the system not only guarantees the accuracy of furnace temperature control under conditions of fluctuation, but also raises the heating and cooling rate, reduces the fuel consumption per ton steel, electricity consumption and billet burning, and improves the heating furnace production capacity.