A novel vapor recompressed batch extractive distillation: Design and retrofitting

Abstract

This work deals with the formulation of a mixed-integer nonlinear multi-objective optimization (MOO) problem having five objectives to optimize the design of a conventional batch extractive distillation (BED). Of them, three primary objectives are to minimize total annual cost (TAC) and CO2 emissions while maximizing the total annual production of the desired component, and the two secondary objectives are total annual solvent (entrainer) recovery and total annual production of the undesired component. The MOO problem with five objectives is solved using the elitist non-dominant sorting genetic algorithm (NSGA-II) to obtain Pareto-optimal solutions. Two strategies, both using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) along with entropy weights, are examined for selecting one of the Pareto-optimal solutions. The selected optimal solutions by these strategies are analyzed in terms of performance indicators such as TAC savings, CO2 emissions reduction, amount of product per dollar, etc. Separation of isopropanol and water with ethylene glycol as the homogeneous entrainer is used to illustrate the above procedure for BED optimization. In the next phase, the retrofit of the optimal conventional BED using vapor recompression is studied. Here, a novel limited vapor recompressed BED (L-VRBED) is proposed and compared with the traditional vapor-recompressed BED (T-VRBED) using several performance indicators. The results show that retrofitting CBED to L-VRBED is justifiable for reducing CO2 emissions by 45% despite a 2.5% increase in TAC.