基于卷积长短期记忆网络的加热炉温度场实时预测方法

REAL-TIME PREDICTION METHOD OF FURNACE TEMPERATURE FIELD BASED ON CONVOLUTIONAL LONG SHORT-TERM MEMORY NETWORK

易结焦加热炉由于燃烧过程不稳定,会引起炉管局部超温,导致其破坏失效,因此实际工程中需要对加热炉各处的温度进行测量。目前采用计算流体力学(CFD)数值方法进行直接实时温度场预测,虽然精度高,但是非常耗时,且现有计算能力无法实现。为解决这个难题,文章提出一种基于卷积长短期记忆网络的加热炉温度场实时预测方法,该方法完全由数据驱动,使用ConvLSTM动态地提取时间序列的内部关系进行温度场预测,可实现工业加热炉温度场的软测量,预测温度场的平均绝对误差MAE为31.7 K。

Due to the unstable combustion process,local over-temperature may occur on the tube of the heating furnace which is prone to coking,leading to its failure.Therefore,it is necessary to measure the temperature of the heating furnace in the actual project.At present,computational fluid dynamics(CFD)numerical method is used for direct real-time temperature field prediction.Although the method is of high accuracy,it is very time-consuming and the prediction cannot be achieved with the existing calculation capability.To solve this problem,this paper proposes a real-time prediction method for the temperature field of heating furnaces based on a convolutional long short-term memory network.The method is completely driven by data and uses ConvLSTM to extract the internal relationship of the time sequence dynamically for temperature field prediction.This can realize the soft sensor of the temperature field of industrial heating furnace and the MAE(mean absolute error)of the predicted temperature field is 31.7 K.