(543c) Improved Long-Short Term Memory Model for Dynamic and Multimodal Processes Based on K-Means Clustering: Application to an Industrial 2, 3-Bdo Distillation Process
AIChE Annual Meeting
Wednesday, November 16, 2022 - 4:08pm to 4:27pm
To handle this challenge, in this work, we developed a new LSTM network utilizing clustered features relevant to normal operating conditions. First, we collect raw data representing dynamic, multimodal, and normal operating conditions of a process. Next, a clustering model that classifies the data based on its operating conditions is identified by applying K-means clustering to the collected raw data. We use Silhouette method to select an optimal number of clusters for enhanced clustering accuracy. Through the identified clustering model, we obtain training data corresponding to normal operating conditions. This data is further used to develop an LSTM network, which is used for process prediction in real-time. Additionally, we execute feature selection and noise filtering techniques to further improve the performance of LSTM. Data from the bio 2,3-BDO distillation process of GS Caltex, South Korea is modified and utilized to formulate a hypothetical case study to demonstrate the proposed method. This distillation process is dynamic with unexpected disturbances and operates under multimodal conditions. In this case study, we predicted the bottom product temperature using the proposed method. The results obtained proved the superior performance and stability of the developed LSTM as compared to the LSTM identified using raw data (without clustering-based feature selection).
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