Recently, the application of reinforcement learning in process systems engineering has attracted signifi cant attention recently.

However, the optimization of chemical processes using this approach faces various challenges related to performance and

stability. This paper presents a process optimization framework using a continuous advantage actor–critic that is modifi ed

from the existing advantage actor–critic algorithm by incorporating a normal distribution for action sampling in a continuous

space. The proposed reinforcement learning-based optimization framework was found to outperform the conventional

method in optimizing a single mixed refrigerant process with 10 variables, achieving a lower specifi c energy consumption

value of 0.294 kWh/kg compared to the value of 0.307 kWh/kg obtained using the genetic algorithm. Parametric studies

performed into the hyperparameters of the continuous advantage actor-critic algorithm, including the maximum episodes,

learning rate, maximum action value, and structures of the neural networks, are presented to investigate their impacts on the

optimization performance. The optimal specifi c energy consumption, namely 0.287 kWh/kg, was achieved by varying the

learning rate from the base case to 0.00005. These results demonstrate that reinforcement learning can be eff ectively applied

to the optimization of chemical processes.