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.