Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

Abstract

The utilization of data-driven methods in chemical process modeling has been extensively acknowledged due to their effectiveness. However, with the increasing complexity and variability of chemical processes, predicting and warning of anomalous conditions have become challenging. Extracting valuable features and constructing relevant warning models are critical problems that require resolution. This research proposed a novel fused method that integrates K-means density-based spatial clustering of applications with noise (DBSCAN) clustering and bi-directional long short-term memory multilayer perceptron (Bi-LSTM-MLP) to enable early warning of abnormal conditions in chemical processes. The paper applied the proposed method to analyze the early warning using actual process data from Eastman Tennessee and the atmospheric pressure reduction unit as an example. In the TE model and example, the root mean square error (RMSE) of this method is 0.006855 and 0.052546, respectively, which is quite low when compared to other methods. The experimental results confirmed the effectiveness of our approach.

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

“…LSTM neural networks are excellent variants of the recurrent neural network (RNN) [25,26]. LSTM neural networks were developed to address the limitations of conventional RNNs, such as vanishing gradients and the inability to capture long-time dependencies in sequences.…”