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http://dx.doi.org/10.14478/ace.2020.1057

Development of Machine Learning Model for Predicting Distillation Column Temperature  

Kwon, Hyukwon (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology)
Oh, Kwang Cheol (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology)
Chung, Yongchul G. (School of Chemical & Biomolecular Engineering, Pusan National University)
Cho, Hyungtae (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology)
Kim, Junghwan (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology)
Publication Information
Applied Chemistry for Engineering / v.31, no.5, 2020 , pp. 520-525 More about this Journal
Abstract
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.
Keywords
Machine learning; Distillation column; LSTM; Prediction model; Adam optimizer;
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Times Cited By KSCI : 3  (Citation Analysis)
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