高校化学工程学报2026，Vol.40Issue(3)：521-533,13.DOI:10.3969/j.issn.1003-9015.2025.00.066
基于双向时序差分记忆网络的批次过程质量预测方法
Batch process quality prediction method based on bidirectional temporal difference memory network
摘要
Abstract
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.关键词
批次过程/质量预测/双向时序差分记忆网络/非平稳特征提取/在线更新机制Key words
batch processes/quality prediction/BiDRNN/non-stationary feature extraction/online update mechanism分类
化学化工引用本文复制引用
李文亮,纪成,孙巍,翟持..基于双向时序差分记忆网络的批次过程质量预测方法[J].高校化学工程学报,2026,40(3):521-533,13.基金项目
云南省兴滇英才支持计划(KKRD202205037). （KKRD202205037）
