Development and application of machine learning‐based prediction model for distillation column. Issue 5 (14th January 2021)

• Record Type: Journal Article
• Authors: Kwon, Hyukwon; Oh, Kwang Cheol; Choi, Yeongryeol; Chung, Yongchul G.; Kim, Junghwan
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 5(2021)
• Page Start: 1970
• Page End: 1997
• Publication Date: 2021-01-14
• Subjects: Adam optimization -- distillation process -- LSTM algorithm -- machine learning -- prediction model; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22368
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Distillation is an energy‐consuming process in the chemical industry. Optimizing operating conditions can reduce the amount of energy consumed and improve the efficiency of chemical processes. Herein, we developed a machine learning‐based prediction model for a distillation process and applied the developed model to process optimization. The energy consumed in the distillation process is mainly used to control the temperature of the distillation column. We developed a model that predicted temperature according to the following procedure: (1) data collection; (2) characteristic extraction from the collected data to reduce learning time; (3) min–max normalization to improve prediction performance; and (4) a case study conducted to select the artificial neural network algorithm, optimization method, and batch size, which are the most appropriate elements for predicting production stage temperature. The result of the case study revealed that the most appropriate model was observed with a root mean squared error of 0.0791 and a coefficient of determination of 0.924 when the long short‐term memory algorithm, Adam optimization method, and batch size of 128 were applied. We calculated the amount of steam consumption required to consistently maintain the production stage temperature by utilizing the developed model. The calculation result indicated that the amount of steam consumption was expected to be reduced by approximately 14%, from an average flow rate of 2763–2374 kg/h. This study proposed a control method applying a machine learning‐based prediction model in the distillation process and confirmed that operation energy could be reduced through efficient operation.