Investigation of Separation Efficiency Indicator for the Optimization of the Acetone–Methanol Extractive Distillation with Water

Abstract

A multiobjective genetic algorithm optimization of the extractive distillation process of acetone−methanol minimum azeotropic mixture with heavy entrainer water is investigated. The process includes the extractive and entrainer regeneration columns, and the optimization minimizes the energy cost objective function (OF) and total annual cost (TAC) and maximizes efficiency indicators E ext and e ext that describe the ability of the extractive section to discriminate the product between the top and the bottom of that section. Earlier work (You et al. Ind. Eng. Chem. Res. 2015, 54, 491) found that improvement of some designs in the literature led to an increase in those indicators. A two-step optimization strategy for extractive distillation is conducted to find suitable values of the entrainer feed flow rate, entrainer and azeotropic mixture feed locations, total number of trays, two reflux ratios, and two distillates in both the extractive column and the entrainer regeneration column. The first step relies upon the use of a nonsorted genetic algorithm (NSGA) with the four aforementioned criteria. Second, the best design taken from the GA Pareto front is further optimized focusing on decreasing the energy cost by using a sequential quadratic programming (SQP) method. In this way, the most suitable design with optimal efficiency indicators, low energy consumption, and low cost are obtained. Analyzed with respect to thermodynamic insights underlying the extractive section composition profile map, the Pareto front results show that there is maximum E ext at a given reflux ratio, and there is minimum reflux ratio for a given E ext. There is an optimal efficiency indicator E ext,opt which corresponds to the minimum TAC taken as the best design. In other words, E ext,opt can be a criterion for the comparison between different designs for the same separating system. A SQP-based design is only <1% better in TAC than the best NSGA design, showing that this later method is able to find a consistent design for the extractive process concerning the 1.0-1a class mixture.

Reducing process cost and CO2 emissions for extractive distillation by double-effect heat integration and mechanical heat pump

“…3. It is an extractive distillation process optimized in our previous works by focusing on the energy saving of the process itself for the separation of the minimum azeotropic mixture acetonemethanol with heavy entrainer water [36]. That typical 1.0-1a class extractive separation [37] is favored in energy cost at low operating pressure of the extractive column and the P = 0.6 atm is chosen in order to use cooling water for the condenser [13].…”

Review of extractive distillation. Process design, operation, optimization and control

“…Fig. 18 also displays the composition profile for the optimal design in You et al (2015b). It is noticed that the liquid composition on the entrainer feed tray contains very little methanol (B), in agreement with thermodynamic insight prescribing that composition should match the SN extr one, very close to the ternary diagram edge A-E. A sufficient number of trays in the extractive section is recommended for that purpose.…”

“…If an external optimizer is used, the simulation is treated as a black box model. Therefore, metaheuristic methods, such as genetic algorithm (Leboreiro and Acevedo, 2004;Bravo-Bravo et al, 2010;Medina-Herrera et al, 2014;Modla and Lang, 2012;Modla, 2013;You et al, 2015b) and other evolutionary algorithms are frequently applied, where only the values of the objective function and constraints are required. Sensitivity analysis of the process variables (Gil et al, 2009;Hilal et al, 2002;Langston et al, 2005;Luyben, 2008b) can also be used, independently of the modelling approach.…”

“…Evidently the reflux ratio should be taken into account at the same time to get a more realistic range of feasibility conditions as shown in Fig. 21 later (You et al, 2015b).…”

CAMD for entrainer screening of extractive distillation process based on new thermodynamic criteria

“…We discuss the process operation first. According to the common practice, the best entrainer is deemed to carry out the separation with either lowest energy consumption or total annual cost (TAC) or both, although both criteria do not always imply the same optimum (You et al, 2015b). Energy consumption and TAC are primarily related to the minimum entrainer flow rate and the reflux ratio at the top of the extractive distillation column and the entrainer recovery column.…”