A novel parallel feature extraction-based multibatch process quality prediction method with application to a hot rolling mill process

In a hot strip rolling mill (HSRM) process, the prediction of the steel crown is a key factor in improving the quality of the strip steel. In this paper, a new multibatch feature extraction-based method is proposed for predicting the steel crown. Different from the cascaded feature extraction-based method which cannot extract both temporal and local features well, this method parallelly captures the feature between different batches of data using a method based on the multi-channel convolution neural network (MCNN) and long short-term memory (LSTM). The feature extraction is performed in parallel by an LSTM layer fusing variable attention and temporal attention, and a Multi-channel convolutional neural network fusing channel attention and spatial attention, which are used to extract temporal and local features of the input variables, respectively. Then, an LSTM-based fusion layer is used to incorporate both features for the development of the prediction model. The proposed method is applied to a cloud–edge-end collaborative prototype system, where the actual HSRM data is integrated. Based on the fact that an HSRM process commonly runs with the steel header crown data for the model update, an adaptive prediction method is also developed and deployed in the prototype system. It can be seen from the model complexity analysis and application results that the prediction performance improves by 42.70% compared with the cascaded feature extraction-based method, and the adaptive method can ensure a realtime prediction realization.