基于工况知识引导注意力时间卷积网络的烧结终点预测
Burning through point prediction based on working condition know-ledge guided attention mechanism temporal convolutional network
方怡静 1蒋朝辉 2桂卫华 2潘冬2
作者信息
- 1. 中南大学自动化学院,湖南长沙 410083;南洋理工大学化学化工与生物工程学院,新加坡 637459
- 2. 中南大学自动化学院,湖南长沙 410083
- 折叠
摘要
烧结终点位置的实时准确预测对于优化烧结工艺具有重要的意义.针对烧结过程中强非线性和动态时变性造成烧结终点高精度预测难的问题,本文提出了一种基于工况知识引导注意力时间卷积网络(AM-TCN)模型.首先,构建堆叠的时间卷积模块用于充分提取烧结过程数据中深层次的非线性特征;其次,将历史工况知识引入注意力机制,引导模型在保留过程数据时序特征的同时区分不同特征的重要性;最后,构建预测模型用于烧结终点位置在线预测.工业数据实验表明,所提AM-TCN模型具有较好的烧结终点预测精度,对提升烧结过程热状态稳定性具有重要意义.
Abstract
The precise and real-time prediction of the burning through point(BTP)is essential for optimizing process operation.However,due to the strong nonlinearity and dynamic time-varying characteristics of the sintering process,high-precision prediction of the BTP has been challenging.In this paper,an attention mechanism temporal convolutional network(AM-TCN)model is proposed based on the knowledge of working conditions.First,stacked temporal convolution blocks are developed to extract deep nonlinear features in the sintering process data.Second,the attention mechanism incorporates the knowledge of historical working conditions,allowing the model to identify the significance of various extracted features while preserving the time series features of the process data.Finally,an online BTP prediction model can be established.The results of experiments using industrial data illustrate that the proposed AM-TCN model has good BTP prediction accuracy,which is critical in improving the stability of the thermal state during the sintering process.
关键词
注意力机制/时间卷积网络/工况知识/烧结终点/预测Key words
attention mechanism/temporal convolutional network/condition knowledge/burn through point/prediction引用本文复制引用
基金项目
国家重大科研仪器研制项目(61927803)
国家自然科学基金(61773406)
中央高校基本科研业务费专项中南大学项目(2020zzts572)
出版年
2024
国家科技期刊平台
NSTL
万方数据