A new soft sensing method based on serial-parallel GRU with self-attention mechanism for complex multi-unit industrial processes.

Abstract

With the deep digital transformation of traditional manufacturing industry and the continuous automation level improvement of production lines, it is more important to predict the Key Performance Indicators (KPIs) of processes in a timely and accurate manner. The traditional laboratory destructive test method for obtaining KPIs consumes a large amount of time and incurs high costs, which not only fails to provide timely and effective guidance for production processes but also results in significant losses for manufacturing enterprises. To address these issues, an online prediction soft sensor model for KPIs based on a serial-parallel gated recurrent unit with self-attention mechanism (SPGRU-SA) soft sensor model is proposed. This model achieves accurate online prediction of KPIs by considering both the dynamic features of multi-unit processes and the static features of process setups. First, a serial-parallel gated recurrent unit model is designed to extract multi-unit dynamic features. Second, based on the self-attention mechanism, the attention weights of static features and dynamic features are calculated, which can reflect the correlation of the performance indicators. Then, the fully connected layers output the result. Finally, the comparative experimental results based on the hot rolling strip mill process and the Tennessee Eastman process show that SPGRU-SA can accurately predict the KPIs of complex multi-unit industrial processes.