化工学报2025，Vol.76Issue(12)：6486-6496,11.DOI:10.11949/0438-1157.20250798
融合即时学习的深层扩展VAE软测量建模方法
Soft sensor development based on deep extended variational autoencoder with just-in-time learning
摘要
Abstract
Traditional deep learning-based soft sensor modeling methods lack online updating mechanisms,and are susceptible to information redundancy as the network depth increases,thereby limiting further improvement in model prediction performance.To address these issues,a deep extended variational autoencoder with just-in-time learning(JITL-DE-VAE)is proposed,which consists of an offline training stage and an online updating stage.First,to mitigate the accumulation of reconstruction errors in multi-layer VAEs during the offline phase and impaired prediction performance caused by excessive invalid information in feature extraction,a key variable-guided feature constraint mechanism is introduced and an extended variational autoencoder(E-VAE)is constructed to improve feature extraction accuracy.Second,a deep extended variational autoencoder(DE-VAE)is proposed on the basis of E-VAE,which utilizes both the input and hidden features from the previous layer as inputs to the next layer,significantly enhancing the feature utilization efficiency through cross-layer information integration strategy.Moreover,a just-in-time learning strategy is introduced during the online updating stage to enhance model adaptability to time-varying processes,which calculates the weighted Euclidean distance metric based on the maximum information coefficient to retrieve similar samples from the historical database,and updates the model via a dynamically weighted loss function according to sample similarity.Finally,ablation experiments and comparative experiments were conducted using data from an industrial butane removal tower and sulfur recovery process.The results validate the effectiveness and superiority of the proposed method.关键词
动态建模/神经网络/预测/软测量/变分自编码器/即时学习/模型更新Key words
dynamic modeling/neural networks/prediction/soft sensor/variational autoencoder/just-in-time learning/model adaptation分类
信息技术与安全科学引用本文复制引用
熊晟杰,谢莉,徐梁,曹余庆,杨慧中..融合即时学习的深层扩展VAE软测量建模方法[J].化工学报,2025,76(12):6486-6496,11.基金项目
国家重点研发计划项目(2022YFC3401302) （2022YFC3401302）
中国博士后科学基金项目(2021M691276) （2021M691276）
