机电工程技术2025，Vol.54Issue(19)：35-40,114,7.DOI:10.3969/j.issn.1009-9492.2025.00003
基于Transformer-LSTM的高炉炼铁硅含量软测量方法
Soft Measurement Method of Silicon Content in Blast Furnace Ironmaking Based on Transformer-LSTM
摘要
Abstract
Silicon content indicators that are difficult to measure in real-time during blast furnace ironmaking process.It is of great significance to study the rapid prediction method to guide the follow-up operation of the process and guarantee the final quality.Therefore,a new soft measurement framework for silicon content in blast furnace ironmaking is designed based on deep learning algorithm.Considering the impact of high temperature and pressure production environment on the sensor,the collected data mostly contains outliers and noise.The abnormal data filtering module based on One-class SVM and the mixed feature noise reduction module based on Fourier Transform and Gaussian Smoothing are designed.Considering the strong nonlinearity and temporal dependence of industrial data,a regression module based on the improved Transformer-LSTM model is designed.Finally,using the actual industrial process data set of blast furnace ironmaking,the mean square error of the model is reduced by 1.1%and 16.07%by the anomaly filtering module and the noise reduction module through the multi-model comparison experiment and the ablation experiment of model functions.The prediction error of Transformer LSTM model is0.63%,4.7%and 2.4%lower than that of LSTM,GRU and TCN,respectively.And the number of stacking layers of the recurrent neural network is effectively reduced.关键词
软测量/高炉炼铁/质量预测/特征降噪/变换/LSTMKey words
soft measurement/blast-furnace ironmaking/quality prediction/feature noise reduction/transformer/LSTM分类
信息技术与安全科学引用本文复制引用
钱子豪,王磊,靳晟..基于Transformer-LSTM的高炉炼铁硅含量软测量方法[J].机电工程技术,2025,54(19):35-40,114,7.基金项目
中央引导地方科技发展资金项目(ZYYD2024JD28) （ZYYD2024JD28）
