In this study, we developed a machine learning–based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM128 was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R2 of 0.924.
|Number of pages||6|
|Journal||Applied Chemistry for Engineering|
|Publication status||Published - 2020 Oct|
Bibliographical notePublisher Copyright:
© 2020 The Korean Society of Industrial and Engineering Chemistry. All rights reserved.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)