Optimization of pre-concentration, entrainer recycle and pressure selection for the extractive distillation of acetonitrile-water with ethylene glycol

Abstract

We optimize the extractive distillation process for separating the acetonitrile – water azeotropic mixture with ethylene glycol by using a multi-objective genetic algorithm for minimizing under purity constraints the total cost, the energy consumption and the separation efficiency. For the first time we have shown the interest of five aspects by considering them simultaneously (1) the pre-concentration column has been included and (2) there is no need to set a distillate composition constraint (like being at the azeotropic composition) in the pre-concentration column. (3) The operating pressure should be lower than 1 atm because it enhances the relative volatility for 1.0-1a class system. (4) A closed loop optimization must be run, to handle the effect of impurity in the entrainer recycle since too much impurity limits the main product recovery and purity from the extractive column. (5) All three columns process must be optimized together rather than sequentially and with multiple objectives. The studied system belongs to class 1.0-1a and the impurity of the recycled entrainer has strong effect on the purity of acetonitrile product. Overall, 17 variables are optimized; column trays, all feed locations, refluxes, entrainer flow rate and all distillate products; under purity constraints for the acetonitrile and water product and for the entrainer recycle impurity. Among nearly 400 designs satisfying the purity specifications, the design case 3 shows an energy consumption and TAC reduced by more than 20% than a literature reference case, thanks to smaller entrainer flow rate, a reduction of 32 trays and lower operating pressures. The best design is a trade-off between first a feasibility governed by thermodynamics through composition profiles and relative volatility maps and second process cost and energy demands.