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Chemical processes are inherently continuous and nonlinear, making stable operation challenging. The efficiency of processes often varies significantly with the operator\u2019s level of expertise, as most tasks rely on experience. To move beyond the constraints of traditional simulation approaches, a new machine learning\u2010based simulation model was developed. This model utilizes a recurrent neural network (RNN) algorithm, which is ideal for analyzing time\u2010series 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. 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