Energy‐Efficient and Sustainable Design Optimization for Ternary Mixture Separation: A Comparative Analysis of Sequential Iterative Optimization and Non‐Dominated Sorting Genetic Algorithm II

ABSTRACT This study presents a comparative analysis of two optimization approaches: Sequential Iterative Optimization (SIO) and Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) applied to the separation of ternary mixtures (1‐butanol, isobutanol, and 2‐butanol) using Dual‐Column Distillation (DCD) and Dividing‐Wall Column (DWC) processes. Through a multi‐objective assessment considering total annual cost (TAC), CO 2 emissions, and entropy generation, the NSGA‐II optimized DCD and DWC processes demonstrated significantly better performance than the SIO approach in terms of energy efficiency, environmental impact, and economic viability. After NSGA‐II optimization, the DWC process reduced TAC by 20.97%, CO 2 emissions by 45.58%, and entropy generation by 39.43% compared to the DCD process. These findings demonstrate DWC technology's dual superiority in enhancing separation efficiency while significantly reducing energy consumption, particularly when optimized with the NSGA‐II.