A spatiotemporal‐distributed deep‐learning framework for KPI estimation of chemical processes with cascaded reactors

Abstract

AbstractOnline and accurate estimation of key performance indicators (KPI) is the foundation for operational optimization of a chemical process. However, a chemical process usually consists of multiple reactors, and the factors influencing KPI are spatially distributed in the long process flow. In addition, due to the distinct time lags between KPI and each reactor, temporal relationships among KPI and its influence factors are a mixture of short‐term and long‐term relationships. In this regard, a deep distributed KPI estimator with a self‐attention mechanism is proposed in this paper. First, considering the process topology, a cascaded long short‐term memory network is developed to simulate the process topology and capture the short‐term effects. Then, to extract the long‐term dependencies, a de‐noise self‐attention layer is employed to model interactions of all the influence factors explicitly and dynamically. Lastly, the proposed method is compared with typical state‐of‐the‐art methods using real industrial data. The comparison results illustrate the performance and effectiveness of the proposed KPI estimation method.