Input Variable Selection and Structure Optimization for LSTM-Based Soft Sensor with A Dual Nonnegative Garrote Approach

Soft sensor, as a significant intelligent inspection technology, has been widely used in modern process industries to achieve effective monitoring and prediction of product quality. However, the redundancy of model inputs and structure in practical industrial process modeling usually results in increased modeling complexity and decreased model performance. In this study, an input variable selection and structure optimization algorithm for long short-term memory (LSTM)-based soft sensor with a dual nonnegative garrote (DNNG) approach was proposed. Firstly, a well-trained initial LSTM model is constructed using the process dataset to capture the temporal dynamic behavior of the industrial process. Secondly, the DNNG algorithm is integrated into the LSTM to reduce the redundancy of input variables and hidden nodes. The strategy efficiently selects the most consequential input variables for the model, and simultaneously simplifies the LSTM structure by eliminating redundant recurrent hidden nodes to reduce model overfitting risk. Moreover, the hyperparameters of the model are determined by combining grid search with blocked cross-validation. Finally, the developed algorithm is compared to other state-of-the-art algorithms using a numerical case and employed to forecast the SO 2 concentration in the net flue gas emissions of a coal-fired power plant desulphurization system. Comparative results show that the proposed algorithm effectively eliminates redundant variables and streamlines the model structure while presenting better prediction performance than other algorithms.