Vapor recompressed batch distillation: Optimizing reflux ratio at variable mode

Abstract

This work aims at optimizing a vapor recompressed batch distillation that runs at variable reflux mode by employing a multi-objective optimization (MOO) strategy. This involves the formulation of optimization problem using factorial analysis for identifying dominating variables followed by solving the optimization problem using the elitist non-dominated sorting genetic algorithm. The selection of an optimal point is made by employing the technique for order of preference by similarity to ideal solution (TOPSIS) method with entropy information for weighting of objective functions. Here, two conflicting performance indicators, i.e., total annual cost (TAC) and total annual production (TAP) are considered as objective functions. At first, for the existing plant scenario, the conventional batch distillation column operated at variable reflux ratio mode is optimized and then its retrofit is proposed by employing an external thermal arrangement under vapor recompression framework. Subsequently, the optimal vapor recompressed batch distillation is separately developed for setting up a new plant. Finally, the proposed vapor recompressed schemes are demonstrated with an example system and their performances are quantified in terms of energy savings, TAC and TAP.