油田管式加热炉出口温度控制技术研究

Research on the outlet temperature control technology of tubular heating furnace inoilfield

针对热力消耗在地面集输系统中能耗占比较大的问题,需优化加热炉出口温度,降低不同因素对出口温度的影响。在收集加热炉运行状态数据后,对异常值进行检测和修复,通过BP神经网络模型和遗传算法实现加热炉出口温度的自动控制。结果表明,基于神经网络-遗传算法的参数整定方法可以将加热炉全年出口温度控制在相对平稳的区间内,所需的燃料气流量和加热炉出口温度均有所降低,每月可节约燃料费用12366~55155元,节能效果显著。

Due to the problem that the heat consumption accounts for a large proportion of energy consumption in the ground gathering and transportation system,the outlet temperature of heating fur-nace is optimized and the influence of different factors on the outlet temperature is reduced.After col-lecting the running state data of the heating furnace,the abnormal value has been detected and re-paired,and the automobile control from the outlet temperature of heating furnace is realized by BP neural network model and genetic algorithm.The results show that the parameter setting method,based on neural network and genetic algorithm,can be controlled the annual outlet temperature of the heating furnace within a relatively stable range.The required fuel gas flow and the outlet temperature of the heating furnace are reduced,and the fuel cost can be saved from 12366 yuan~55155 yuan,which makes energy conservation effect remarkable.