Surrogate Model-Based Optimization of Pressure-Swing Distillation Sequences with Variable Feed Composition

Pressure-swing distillation (PSD) is a frequently applied method to separate pressure-sensitive azeotropic mixtures; however, its energy demand is very high. In continuous mode, PSD is performed in a system consisting of a high- and a low-pressure column. If the composition of the feed is between the azeotropic compositions at the two pressures, it can be introduced into any of the columns, leading to two possible column sequences. Depending on the feed composition, one of the sequences is optimal whether in terms of energy demand or total annual cost (TAC). In the present work, surrogate model-based optimization is applied to determine the optimal TAC value as a function of the feed composition between the azeotropic ones. As a first step, the column sequence with feeding into the high-pressure column is studied here. The mixture to be separated consists of water and ethylenediamine, which form a maximum-boiling azeotrope. The columns are modeled separately and a large number of simulations are performed in points generated by Latin hypercube sampling, then, algebraic surrogate models are fitted to the simulation results. The resulting surrogate optimization problem is solved for different feed composition values and the accuracy of the models is verified by rigorous simulations, as well. The energy cost and TAC has a maximum as a function of the composition. The error of the estimation of TAC remains under 6 % except at the lowest water concentration studied.