A novel data-driven soft sensor model was proposed that integrates Transformer encoder and convolutional neural network(CNN)to predict coal feeding rate in a CFB boiler because of the lack of real-time monitoring of coal feeding in an industrial boiler of Shanxi.The CFB boiler operating data was utilized as the model input.Firstly,the operating data was mapped into the high-dimensional space by Transformer encoder,where attention technique was employed to extract relevant features from the data.Secondly,the extracted high-dimensional features were embedded into a low-dimensional space by CNN.Finally,a fully connected layer was utilized to predict the coal feeding rate.The proposed model not only compared the feature extraction performance with Transformer encoder and CNN,but also analyzed the effect of the model hyperparameters on the prediction performance.The results showed that the determination coefficient(R2)of this integrated method was 0.969 6,demonstrating that the integration of Transformer encoder and CNN could effectively predict the coal feeding rate.
关键词
循环流化床锅炉/软测量/给煤预测/卷积神经网络
Key words
circulating fluidized bed boiler/soft sensor/coal feeding prediction/CNN
维普
万方数据