An intelligent moisture prediction method for tobacco drying process using a multi-hierarchical convolutional neural network

The moisture content of tobacco, as an important characteristic which should be kept at a desired level to maintain consistent product quality in drying process, is difficult to perform the direct measurement and anomaly detection due to its large delay in actual process. Therefore, an intelligent real-time detection method is an urgent and challenging task in ensuring the product quality. This paper proposes a time-domain raw data conversion method along with a novel deep learning architecture called multi-hierarchical convolutional neural network (MHCNN) for moisture prediction, in which the proposed architecture automatically learns multi-hierarchical features from transformed image-like data and simultaneously performs online prediction. Experiments are conducted on the real production data from the cigarette factory and the presented model performs well on overall testing dataset. Specifically, the MAE, RMSE and R2 of normal production batch can reach to 0.0131, 0.0244, and 0.9721 respectively, which are far superior to the estimation of experience and other alternatives. It demonstrates that the proposed online prediction strategy can simultaneously perform multi-hierarchical feature extraction and moisture online prediction with high precise to eliminate the detection delay for process optimization and control.