Multi-objective optimization of vapor recompressed distillation column in batch processing: Improving energy and cost savings

Abstract

This works aims at formulating a multi-objective optimization (MOO) strategy to improve the energetic and economic potential of a batch distillation through vapor recompression. The optimization strategy is developed based on elitist non-dominated sorting genetic algorithm along with the selection of an optimal point implementing the technique for order of preference by similarity to ideal solution method by using entropy information for weighting. The factorial design methodology is incorporated to find the dominating variables, which are further utilized for the formulation of MOO problem. Process optimization involves two or more objectives, which are often conflicting in nature that leads to many equally-good optimal solutions from the perspective of the given objectives. Here, two conflicting performance criteria, i.e., total annual cost and total annual production are proposed as two objective functions. With this, we first optimize a conventional batch distillation (CBD) followed by its retrofitted scheme with vapor recompression. Then, we propose an optimal vapor recompressed batch distillation keeping in mind the case of setting up a new plant. Finally, the energetic and economic potential of the vapor recompression based schemes are evaluated with reference to the CBD by simulating and optimizing a nonreactive and a reactive example system.