水泥分解炉出口温度LSTM分步预测方法研究  

Research on step-by-step prediction method of cement decomposition furnace outlet temperature based on LSTM

作　　者：曹伟 何非 CAO Wei;HE Fei(Department of Industrial Engineering,School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院工业工程系,江苏南京210094

出　　处：《中国测试》2023年第5期23-30,共8页China Measurement & Test

摘　　要：分解炉出口温度是水泥分解工艺的重要指标,温度是否合理对于水泥产品质量有重要意义。为对水泥分解炉出口温度进行预测,结合质量影响因素分析选取的工艺参数,基于LSTM算法建立水泥分解炉出口温度预测模型,模型分为直接预测模型及分步预测模型。在验证集上采用直接预测模型进行预测并与BP神经网络模型进行对比,在实际工况的测试集上将基于状态变量预测的分步预测模型与采用近似值的直接预测模型进行对比,结果表明,分步预测模型针对实际工况有更好的泛化性能,预测误差为0.42℃,误差率仅为0.05%。该模型的建立可以为后续分解工艺参数优化及分解炉出口温度控制提供研究基础。Decomposition furnace outlet temperature is an important indicator of the cement decomposition process,and whether the temperature is reasonable is of great significance to the quality of the cement preduct.In order to predict and control the cement decomposition furnace outlet temperature,combined with the selected process parameters of quality influencing factors analysis,the LSTM algorithm is used to establish a cement decomposition furnace outlet temperature prediction model.This model is divided into a direct prediction model and a step-by-step prediction model.The direct prediction model is used on the validation set to predict and compare with the BP neural network model.The step-by-step prediction model based on state variable prediction is compared with the direct prediction model using approximate values on the test set that simulates actual working conditions.The results show that the step-by-step prediction model has better generalization performance for actual working conditions,the error of prediction is 0.42℃,and the error rate is only 0.05%.The establishment of this model can provide a research basis for the subsequent optimization of decomposition process parameters and the control of the outlet temperature of the decomposition furnace.

关 键 词：温度预测模型 分步预测方法 LSTM循环神经网络 时间序列数据 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]