LAUSR

The data collected from complex process industries are usually time series with considerable nonlinearities and dynamics, as well as excessive redundancy. Moreover, there are temporal and spatial correlations between input variables and key performance variables. These characteristics bring great difficulties to data-driven modeling of the key performance variables. To overcome the problems, a new regularized spatiotemporal attention (STA)-based long short-term memory (LSTM) was developed. First, a standard LSTM network with an STA module was trained to capture the dynamic relationship between input and target variables. Second, the least absolute shrinkage and selection operator was introduced to optimize the STA module. Third, the hyperparameter representing the regularization strength of the algorithm was determined using a moving window cross-validation strategy. Finally, the proposed algorithm was compared to other state-of-the-art algorithms using artificial data, and then it was used to predict the nitrogen oxide emissions of a selective catalytic reduction denitration system. Simulation results showed that the proposed algorithm achieved more accurate predictions than the other algorithms. Furthermore, the statistics and analysis of the importance of the variables are consistent with known chemical-reaction mechanisms and observations of field experts. Thus, the proposed method can provide technical support for the predictive control and optimization of such systems.