Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process

Coal preparation is the most effective and economical technique to reduce impurities and improve the product quality for run-of-mine coal. The timely and accurate prediction for key quality characteristics of separated coal plays a significant role in condition monitoring and production control. However, these quality characteristics are usually difficult to directly measure online in industrial practices Although some computation intelligence based soft sensor modeling methods have been developed and reported in existing research for these quality variables estimation, some problems still exist, i.e., manual feature extraction, considerable unlabeled data, temporal dynamic behavior in data, which will influence the accuracy and efficiency for established soft sensor model. To address above-mentioned problem and develop an more excellent quality prediction model for coal preparation process, a novel deep learning based semi-supervised soft sensor modeling approach is proposed which combining the advantage of unsupervised deep learning technique (i.e., Stacked Auto-Encoder (SAE)) with the advantage of supervised deep bidirectional recurrent learner (i.e., Bidirectional Long Short-Term Memory (BLSTM)). More specifically, the unsupervised SAE networks are implemented to learn the representative features hidden in all available input data (labeled and unlabeled samples) and store them as context vector. Then, partial context vector with corresponding labels and the quality variable measure value at previous time are concatenated to form a new merged input feature vector. After that, the temporal and dynamic features are further extracted from the new merged input feature vector via BLSTM networks. Subsequently, the fully connected layers (FCs) are exploited to learn the higher-level features from the last hidden layer of the BLSTM. Lastly, the learned output features by FCs are fed into a supervised liner regression layer to predict the coal quality metrics. Meanwhile, to avoid over-fitting, some regularization techniques are utilized and discussed in proposed network. The application in ash content estimation for a real dense medium coal preparation process and some comparison experiment result demonstrate that the effectiveness and priority of proposed soft sensor modeling approach.