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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.

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This study considers the possibility of utilizing agri-byproducts as energy sources via pelletization and torrefaction. Pellets were placed in a capsule and torrefied in an electrical furnace. Subsequently, they were cooled for 30 min, and their mass loss was measured. To investigate the resulting changes in fuel characteristics, ultimate and proximate analyses were performed, and calorific values were measured. To estimate the water absorption of the pellets, hygroscopicity evaluations were conducted. Based on the experimental results, the energy yield, lower heating value, and exergy were calculated to determine the optimum conditions for torrefaction. The calculation was performed by utilizing the useful exergy and standards applied to biomass power plants. We determined that torrefaction for agro-pellets should be conducted under low-to-intermediate temperatures (210–250 °C) within a period of 50 min. Under these conditions, 7–55% mass reductions were observed, the higher heating value increased from 4110 to 6880 kcal kg−1, and the lower heating value changed from 3780 to 6520 kcal kg−1 owing to reduced hygroscopicity. So, Agro-byproducts can contribute to the practical application by improving the heating value through torrefaction as an alternative to wood pellets.