控制理论与应用2026，Vol.43Issue(4)：843-852,10.DOI:10.7641/CTA.2024.40246
面向空气分离过程的动态集成增量学习软测量算法研究
Research on dynamic ensemble incremental learning soft sensor algorithms for an air separation process
摘要
Abstract
To address the problems of multi-modality,multi-variable,large time delay and existence of measurement outliers in an actual air separation process,a dynamic incremental learning soft sensor algorithm based on a Lipschitz recurrent neural network(LRNN)is proposed.Firstly,the LRNN with easy convergence and excellent stability is used to deal with the nonlinearity and time delay in the process data.Secondly,a novel LRNN loss function is designed by combining the robustness to outliers of the Log-Cosh estimation and the sparsity of the L1 regularization,which is effective in dealing with the problems of measurement outliers and model redundancy of the process.Further,the adaptability to the modal changes of the production process is improved by the dynamic weighted ensemble of different robust LRNN base models.Finally,the proposed algorithm is applied to the soft sensor modeling of the O2 concentration at the outlet of the low-pressure tower in an actual air separation process in a steel plant,and the effectiveness and superiority of the proposed algorithm is verified by comparing with other state-of-the-art algorithms.关键词
循环神经网络/空气分离过程/Log-Cosh估计/L1正则化/增量学习/软测量Key words
recurrent neural networks/air separation process/Log-Cosh estimation/L1 regularization/incremental learning/soft sensor引用本文复制引用
吴修粮,赵磊,曹茂永,孙凯..面向空气分离过程的动态集成增量学习软测量算法研究[J].控制理论与应用,2026,43(4):843-852,10.基金项目
山东省自然科学基金项目(ZR2021MF022),山东省科技型中小企业创新能力提升工程项目(2023TSGC0399)资助. Supported by the Shandong Provincial Natural Science Foundation of China(ZR2021MF022)and the Shandong Provincial Innovation Capability Enhancement Engineering Project of Technology-Based Small and Medium-Sized Enterprises(2023TSGC0399). （ZR2021MF022）
