基于双向多步预测的炉管温度场重构方法  

A Temperature Field Reconstruction Method of Furnace Tube Based on Bidirectional Multistep Prediction

作　　者：林启钊 彭志平 郭棉 崔得龙 LIN Qi-zhao;PENG Zhi-ping;GUO Mian;CUI De-long(College of Computer and Electronic Information,Guangdong University of Petrochemical Technology,Maoming 525000,China;College of Computer,Guangdong University of Technology,Guangzhou 510006,China;College of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou 510665,China)

机构地区：[1]广东石油化工学院计算机与电子信息学院,广东茂名525000 [2]广东工业大学计算机学院,广东广州510006 [3]广东技术师范大学电子与信息学院,广东广州510665

出　　处：《计算机与现代化》2024年第1期53-58,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(62273109);广东省自然科学基金资助项目(2021A1515012252,2022A1515012022)。

摘　　要：针对高温封闭的乙烯裂解环境下裂解炉炉管温度感知难的问题,提出一种融合机理和长短记忆神经网络(Long Short-Term Memory,LSTM)的裂解炉炉管表面温度场重构方法。该方法首先基于计算流体力学仿真平台fluent构建乙烯裂解反应机理模型,用来描述裂解反应与炉管温度的数学关系,然后利用工业现场数据对机理模型进行数值矫正和过程参数求解,进一步基于皮尔逊相关系数确定适用性强的主要过程参数,在此基础上,设计卷积块注意力模块(Convolu tional Block Attention Module,CBAM)对反映裂解反应与炉管温度关系的主要过程参数的特征进行提取,最后基于遗传算法和LSTM网络设计双向多步预测模型(GA-BMLSTM)对炉管温度分布进行预测。实验结果表明该方法对炉管温度场的重构有较高的准确率和较强的适用性。Aiming at the difficulty of sensing the tube temperature in cracking furnace under high temperature closed ethylene cracking environment,a method of surface temperature field reconstruction of cracking furnace tube based on fusion mechanism and Long Short-Term Memory(LSTM)is proposed.Firstly,the mechanism model of ethylene cracking reaction is constructed based on fluent,a computational fluid dynamics simulation platform,which is used to describe the mathematical relationship be tween cracking reaction and furnace tube temperature.Then,the mechanism model is numerically corrected and the process pa rameters are solved using the industrial field data.Major process parameters with strong applicability are determined based on Pearson correlation coefficient.Based on this,a convolutional block attention module(CBAM)is designed to extract the charac teristics of the main process parameters reflecting the relationship between the cracking reaction and the temperature of the fur nace tube.Finally,a bidirectional multistep prediction model(GA-BMLSTM)is designed based on genetic algorithm and long and short memory neural network to predict the temperature distribution of furnace tubes.Experimental results show that this method has high accuracy and applicability to the reconstruction of temperature field of furnace tube.

关 键 词：乙烯裂解炉 温度场重构 计算流体力学 注意力机制 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]