A Transferable Adversarial Network for Dimethyl Oxalate Synthesis Process

This research introduces the TA-informer, a novel model developed to enhance the prediction of key quality indicators in the Dimethyl Oxalate Synthesis(DMOS) process, where reliance on costly and time-consuming offline laboratory analysis often creates significant bottlenecks, particularly in Few-Shot Learning scenarios. The TA-informer combines diverse self-attention and cross-attention mechanisms to concurrently capture long-term dependencies and relationships among multiple time series. It also integrates an adversarial learning strategy utilizing minimax games to interpret complex data distributions effectively. Moreover, the model incorporates a deep transfer learning framework designed to utilize extensive datasets from distributed control systems and online analyzers, thus facilitating efficient information transfer to the target domain with limited OLA data. Empirical comparisons with several advanced methods demonstrate the TA-informer's superior capabilities within the DMOS process in Few-Shot settings, showcasing its potential to mitigate existing analytical challenges significantly.