基于双向时序差分记忆网络的批次过程质量预测方法OA

To address the challenges of capturing non-stationary characteristics of quality indicators and insufficient long-term dependency modeling accuracy in batch production processes,this study proposes a Bidirectional Temporal Difference Memory Network(BiTDMN),which integrates local dynamic feature extraction and global temporal modeling capabilities.Three-dimensional batch data are reconstructed using sliding windows;key variables are selected via mutual information;non-stationary feature extraction is enhanced through forward-backward difference operations and a bidirectional recursive structure is employed to achieve collaborative modeling of historical and future information.An incremental online update mechanism is introduced to fine-tune the model every 20 hours using offline detection data,suppressing multi-step prediction bias accumulation.Validation on industrial penicillin fermentation data shows that BiDRNN achieves a mean absolute error(MAE)of 0.103,root mean square error(RMSE)of 0.131,and coefficient of determination(R2)of 0.972.Long-period prediction results demonstrate a significant reduction in fitting errors for concentration peaks,with a 60%reduction in cumulative deviation for multi-batch endpoint predictions.The online update strategy achieves near-zero error in inflection point time prediction,verifying the model's effectiveness in non-stationary dynamic modeling and real-time correction.This method provides an efficient solution for online quality monitoring in batch processes across industries such as fine chemicals and biopharmaceuticals.