面向空气分离过程的动态集成增量学习软测量算法研究OA

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.