Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Abstract

Failure to blow ash on the heated surface of the boiler will cause a drop in heat transfer rate and even industrial safety accidents. Nowadays, the shortcomings of the fixed soot blowing operation every hour and every shift are significant, which can be improved by high-precision ash accumulation prediction. Therefore, this paper proposes a deep learning model fused with deep feature extraction. First, a dynamic fouling model and a health index-clearness factor ( CF ) of the heated surface are established. The data preprocessing method reduces unnecessary forecasting difficulty and makes the degradation trend of the CF time series more obvious. In addition, deep feature extraction is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA), which completes the multiscale analysis of time series and reduces the training time of deep learning models, and has significant contributions to improving prediction accuracy and reducing time consumption. The adaptive sliding window and the encoder–decoder based on the attention mechanism (EDA) can better mine the internal information of the time series. Compared with long short-term memory (LSTM), taking the 300 MW boiler’s various heated surface data sets as an example, multistep forward prediction and different starting point prediction experiments have verified the superiority and effectiveness of the model. Finally, under the variable working condition economizer datasets, the proposed method better completes the predictive maintenance task of the heated surface. The research results provide operational guidance for improving heat transfer rate, energy saving, and reducing consumption.