Wu Xiuliang, Sun Kai, Cao Maoyong
College of Electrical Engineering and Automation, Shandong University of Science and Technology (SDUST), Qingdao 266590, China.
School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
ACS Omega. 2023 Mar 30;8(14):12853-12864. doi: 10.1021/acsomega.2c08205. eCollection 2023 Apr 11.
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.
从复杂过程工业中收集的数据通常是具有相当大的非线性和动态性以及过多冗余的时间序列。此外，输入变量和关键性能变量之间存在时间和空间相关性。这些特性给关键性能变量的数据驱动建模带来了很大困难。为克服这些问题，开发了一种基于正则化时空注意力（STA）的新型长短期记忆（LSTM）。首先，训练一个带有STA模块的标准LSTM网络来捕捉输入变量和目标变量之间的动态关系。其次，引入最小绝对收缩和选择算子来优化STA模块。第三，使用移动窗口交叉验证策略确定表示算法正则化强度的超参数。最后，使用人工数据将所提出的算法与其他先进算法进行比较，然后将其用于预测选择性催化还原脱硝系统的氮氧化物排放。仿真结果表明，所提出的算法比其他算法实现了更准确的预测。此外，变量重要性的统计和分析与已知的化学反应机理以及现场专家的观察结果一致。因此，所提出的方法可为这类系统的预测控制和优化提供技术支持。
