Stochastic-Based Optimization Strategy of Dimethyl Oxalate Hydrogenation for Ethylene Glycol Production

The multi-objective optimization (MOO) of ethylene glycol (EG) production in a hydrogenation tubular reactor focuses on two main objectives: increasing yield and reducing energy cost. A model-based optimization approach using the ASPEN Plus simulator was employed to simulate the reactions. In addition, an inequality constraint was imposed on the reactor temperature to prevent a runaway condition. To solve the optimization problems, three multi-objective stochastic optimization algorithms, which are the multi-objective stochastic paint optimizer (MOSPO), multi-objective slime mold algorithm (MOSMA), and multi-objective dragonfly algorithm (MODA), were utilized along with MATLAB and ASPEN Plus simulator. In addition, performance metrics including hypervolume (H), pure diversity (PD), and spacing (S) were employed to evaluate and decide the most effective MOO approach. The results show that the most effective MOO approach for EG production in a hydrogenation tubular reactor is MODA. Its solution set provides precise, diverse, and well-distributed allocation of ND points along the Pareto Front (PF). Also, the results indicate that the highest productivity, lowest energy cost, and highest yield achieved are RM41.3499 million/year, RM0.1667 million/year, and 95.5249%, respectively. Furthermore, the plots of decision variables demonstrate that the reactor pressure highly impacts the optimal solution.