Burning through point prediction based on working condition know-ledge guided attention mechanism temporal convolutional network
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.
attention mechanismtemporal convolutional networkcondition knowledgeburn through pointprediction
国家科技期刊平台
NSTL
万方数据
