Optimisation of heterogeneous batch extractive distillation

Abstract

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in: http://oatao.univ-toulouse.fr/ Eprints ID: 4157Any correspondence concerning this service should be sent to the repository Abstract This paper considers the optimisation of batch extractive distillation, using heterogeneous entrainers for the first time. The objective function includes the maximum of overall profit and the optimisation variables are the entrainer flowrate and the reflux ratio that is an optimal combination of both decanted phases. Simulation and optimization is performed within MATLAB, by using a genetic algorithm coupled to a short-cut model of the distillation column. The performance of the optimisation scheme is illustrated through the separation of chloroform -methanol mixture with water considering either a constant or a piecewise constant policy for both optimization variables.

Evolutionary Algorithm Based Multiobjective Optimization of Vapor Recompressed Batch Extractive Distillation: Assessing Economic Potential and Environmental Impact

“…20−26 Almost all the papers on BED optimization in the open literature are of SOO type. Only one conference article 27 reported the multiobjective optimization (MOO) of BED, where simultaneous maximization of economic profit and minimization of environmental impact for the separation of chloroform and methanol with water as the entrainer are considered; but, in that study, design variables are not considered in the optimization. While designing a BED, conflicting objectives are inevitable.…”

Multi-objective optimization of three-phase batch extractive distillation

“…Definition of "α" is not straightforward because it has a controversial influence over the economical and environmental optimization of HEBD. Previous optimization of HEBD was done by using the short-cut modeling [3]. In the present paper, we couple rigorous simulation with optimization tools to handle variables related to energy consumption such as the entrainer temperature (T FE ).…”