Optimization of Pressure-Swing Distillation for Anhydrous Ethanol Purification by the Simulated Annealing Algorithm

Abstract

The present study addresses the novel application of the simulated annealing algorithm (SAA) to optimize the pressure-swing distillation (PSD) process for anhydrous ethanol purification. Three different softwares (Aspen Plus ® , Excel ® and Matlab ®) were integrated to simultaneously optimize seven design and operational variables. The configuration with the best TAC represented a 40.2% saving per year in comparison to the non-optimized PSD. Such reduction was achieved by using the higher acceptance probability and the slower temperature decrement. This saving is mainly related to operational cost reductions, a fact that evidences the viability of using the herein described optimization methodology to improve the PSD design.

Better Performance in C2-Conversion to Aromatics by Optimized Feed and Catalysts

“…In this picture, a stoichiometry of 0.5 equiv of water would correspond to a pure diethyl ether feed, 1 equivalent of water would correspond to a pure ethanol feed, and 1.5 equiv of water is equivalent to an ethanol feed that contains 15 wt % water. This represents a lower ethanol content (66%) than required as the azeotropic point of the water/ethanol mixture at 1 atm has an ethanol content of 87.2% …”

Distributed Control Strategy with Smith's Predictor in a Pilot‐Scale Diabatic Distillation Unit

“…Important advances have also been made in new control strategies for advanced distillation configurations. Pressure-swing distillation has stood out in recent years for the good capacity of separating azeotropic systems with energy and environmental advantages [45]. Yang et al [46] proposed an innovative, robust, plant-wide control strategy of feedforward and dual-temperature difference control to reduce deviations and offsets of products purities.…”

A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems

“…Moreover, stochastic algorithms require a large number of fitness evaluations due to the combinatorial nature of sampling multidimensional space, leading to long computational time. For example, one simulation run for a pressure swing distillation case (Battisti et al, 2019) using simulated annealing algorithm can be up to 1 h. Multiple runs have to be conducted with difference parameters in order to find the closest proximity to the global optimum. This may get much worse for solving MINLP problems since the presence of integer variables results in a combinatorial explosion of solutions.…”