In this contribution, a unique multi-objective mixed-integer dynamic optimization problem considering two conflicting objectives, namely, maximization of amount of product per dollar while minimizing CO₂ emission is formulated and solved using the elitist non-dominated genetic algorithm for both conventional batch distillation (CBD) and vapor recompressed batch distillation (VRBD) operating at constant reflux mode. Here, selection of an optimal solution from the Pareto-optimal front is performed by 10 Pareto ranking methods along with entropy weighting. A wide boiling separating system (i.e., acetone and water) is adopted for illustrating the proposed multi-objective optimization of batch distillation. Two separate optimization studies for CBD and VRBD are conducted with the target of either improving an existing plant or setting up a new plant. Results obtained show that most of the popular Pareto ranking methods select same optimal solution for each of these problems. Finally, a comparative analysis is performed to find the benefits of vapor recompression over the conventional scheme.

在此贡献中，一个独特的多目标混合整数动态优化问题考虑了两个相互矛盾的目标，即最大化每美元产品量同时最小化CO ₂对于常规间歇蒸馏（CBD）和以恒定回流模式运行的蒸气再压缩间歇蒸馏（VRBD），使用精英非主导遗传算法来确定并解决排放。在此，通过10种Pareto排序方法以及熵权重，从Pareto最优前沿中选择最优解。采用宽沸点分离系统（即丙酮和水）来说明分批蒸馏的多目标优化方案。针对CBD和VRBD进行了两个单独的优化研究，目标是改善现有工厂或建立新工厂。获得的结果表明，大多数流行的Pareto排序方法针对这些问题中的每一个都选择了相同的最佳解决方案。最后，