This study was conducted to enhance the efficiency of chemical process systems and address the limitations of conventional methods through hyperparameter optimization. Chemical processes are inherently continuous and nonlinear, making stable operation challenging. The efficiency of processes often varies significantly with the operator’s level of expertise, as most tasks rely on experience. To move beyond the constraints of traditional simulation approaches, a new machine learning‐based simulation model was developed. This model utilizes a recurrent neural network (RNN) algorithm, which is ideal for analyzing time‐series data from chemical process systems, presenting new possibilities for applications in systems with special chemical reactions or those that are continuous and complex. Hyperparameters were optimized using a grid search method, and optimal results were confirmed when the model was applied to an actual distillation process system. By proposing a methodology that utilizes machine learning for the optimization of chemical process systems, this research contributes to solving new problems that were previously unaddressed. Based on these results, the study demonstrates that a machine learning simulation model can be effectively applied to continuous chemical process systems. This application enables the derivation of unique hyperparameters tailored to the specificities of a limited control volume system.

Cover Caption: The cover image is based on the Research Article Development and application of machine learning-based prediction model for distillation column by Hyukwon Kwon et al., https://doi.org/10.1002/int.22368.

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.