Thermodynamic Insight for the Design and Optimization of Extractive Distillation of 1.0-1a Class Separation

Nous étudions la distillation extractive continue de mélanges azéotropiques à temperature de bulle minimale avec un entraineur lourd (classe 1.0-1a) avec comme exemples les mélanges acétone-méthanol avec l’eau et DIPE-IPA avec le 2-méthoxyethanol. Le procédé inclut les colonnes de distillation extractive et de régénération de l’entraineur en boucle ouverte et en boucle fermée. Une première stratégie d’optimisation consiste à minimiser la fonction objectif OF en cherchant les valeurs optimales du débit d’entraineur FE, les positions des alimentations en entraineur et en mélange NFE, NFAB, NFReg, les taux de reflux R1, R2 et les débits de distillat de chaque colonne D1, D2. OF décrit la demande en énergie par quantité de distillat et tient compte des différences de prix entre les utilités chaudes et froides et entre les deux produits. La deuxième stratégie est une optimisation multiobjectif qui minimise OF, le coût total annualisé (TAC) et maximise deux nouveaux indicateurs thermodynamiques d’efficacité de séparation extractive totale Eext et par plateau eext. Ils décrivent la capacité de la section extractive à séparer le produit entre le haut et le bas de la section extractive. L’analyse thermodynamique des réseaux de courbes de résidu ternaires RCM et des courbes d’isovolatilité montre l’intérêt de réduire la pression opératoire dans la colonne extractive pour les séparations de mélanges 1.0-1a. Une pression réduite diminue la quantité minimale d’entraineur et accroît la volatilité relative du mélange binaire azéotropique dans la région d’opération de la colonne extractive. Cela permet d’utiliser un taux de reflux plus faible et diminue la demande énergétique. La première stratégie d’optimisation est conduite avec des contraintes sur la pureté des produits avec les algorithmes SQP dans les simulateurs Aspen Plus ou Prosim Plus en boucle ouverte. Les variables continues optimisées sont : R1, R2 et FE (étape 1). Une étude de sensibilité permet de trouver les valeurs de D1, D2 (étape 2) et NFE, NFAB, NFReg (étape 3), tandis l’étape 1 est faite pour chaque jeu de variables discrètes. Enfin le procédé est resimulé en boucle fermée et TAC, Eext et eext sont calculés (étape 4). Les bilans matières expliquent l’interdépendance des débits de distillats et des puretés des produits. Cette optimisation permet de concevoir des procédés avec des gains proches de 20% en énergie et en coût. Les nouveaux procédés montrent une amélioration des indicateurs Eext et eext. Afin d’évaluer l’influence de Eext et eext sur la solution optimale, la seconde optimisation multiobjectif est conduite. L’algorithme génétique est peu sensible à l’initialisation, permet d’optimiser les variables discrètes N1, N2 et utilise directement le shéma de procédé en boucle fermée. L’analyse du front de Pareto des solutions met en évidence l’effet de FE/F et R1 sur TAC et Eext. Il existe un Eext maximum (resp. R1 minimum) pour un R1 donné (resp. Eext). Il existe aussi un indicateur optimal Eext,opt pour le procédé optimal avec le plus faible TAC. Eext,opt ne peut pas être utilisé comme seule fonction objectif d’optimisation mais en complément des autres fonctions OF et TAC. L’analyse des réseaux de profils de composition extractive explique la frontière du front de Pareto et pourquoi Eext augmente lorsque FE diminue et R1 augmente, le tout en lien avec le nombre d’étage. Visant à réduire encore TAC et la demande énergétique nous étudions des procédés avec intégration énergétique double effet (TEHI) ou avec des pompes à chaleur (MHP). En TEHI, un nouveau schéma avec une intégration énergétique partielle PHI réduit le plus la demande énergétique. En MHP, la recompression partielle des vapeurs VRC et bottom flash partiel BF améliorent les performances de 60% et 40% respectivement. Au final, le procédé PHI est le moins coûteux tandis que la recompression totale des vapeurs est la moins énergivore. ABSTRACT : We study the continuous extractive distillation of minimum boiling azeotropic mixtures with a heavy entrainer (class 1.0-1a) for the acetone-methanol with water and DIPE-IPA with 2-methoxyethanol systems. The process includes both the extractive and the regeneration columns in open loop flowsheet and closed loop flowsheet where the solvent is recycled to the first column. The first optimization strategy minimizes OF and seeks suitable values of the entrainer flowrate FE, entrainer and azeotrope feed locations NFE, NFAB, NFReg, reflux ratios R1, R2 and both distillates D1, D2. OF describes the energy demand at the reboiler and condenser in both columns per product flow rate. It accounts for the price differences in heating and cooling energy and in product sales. The second strategy relies upon the use of a multi-objective genetic algorithm that minimizes OF, total annualized cost (TAC) and maximizes two novel extractive thermodynamic efficiency indicators: total Eext and per tray eext. They describe the ability of the extractive section to discriminate the product between the top and to bottom of the extractive section. Thermodynamic insight from the analysis of the ternary RCM and isovolatility curves shows the benefit of lowering the operating pressure of the extractive column for 1.0-1a class separations. A lower pressure reduces the minimal amount of entrainer and increases the relative volatility of original azeotropic mixture for the composition in the distillation region where the extractive column operates, leading to the decrease of the minimal reflux ratio and energy consumption. The first optimization strategy is conducted in four steps under distillation purity specifications: Aspen Plus or Prosim Plus simulator built-in SQP method is used for the optimization of the continuous variables: R1, R2 and FE by minimizing OF in open loop flowsheet (step 1). Then, a sensitivity analysis is performed to find optimal values of D1, D2 (step 2) and NFE, NFAB, NFReg (step 3), while step 1 is done for each set of discrete variables. Finally the design is simulated in closed loop flowsheet, and we calculate TAC and Eext and eext (step 4). We also derive from mass balance the non-linear relationships between the two distillates and how they relate product purities and recoveries. The results show that double digit savings can be achieved over designs published in the literature thanks to the improving of Eext and eext. Then, we study the influence of the Eext and eext on the optimal solution, and we run the second multiobjective optimization strategy. The genetic algorithm is usually not sensitive to initialization. It allows finding optimal total tray numbers N1, N2 values and is directly used with the closed loop flow sheet. Within Pareto front, the effects of main variables FE/F and R1 on TAC and Eext are shown. There is a maximum Eext (resp. minimum R1) for a given R1 (resp. Eext). There exists an optimal efficiency indicator Eext,opt which corresponds to the optimal design with the lowest TAC. Eext,opt can be used as a complementary criterion for the evaluation of different designs. Through the analysis of extractive profile map, we explain why Eext increases following the decrease of FE and the increase of R1 and we relate them to the tray numbers. With the sake of further savings of TAC and increase of the environmental performance, double-effect heat integration (TEHI) and mechanical heat pump (MHP) techniques are studied. In TEHI, we propose a novel optimal partial HI process aiming at the most energy saving. In MHP, we propose the partial VRC and partial BF heat pump processes for which the coefficients of performance increase by 60% and 40%. Overall, optimal partial HI process is preferred from the economical view while full VRC is the choice from the environmental perspective

Improved Design and Efficiency of the Extractive Distillation Process for Acetone–Methanol with Water

We show how thermodynamic insight can be used to improve the design of a homogeneous extractive distillation process, and we define an extractive efficiency indicator to compare the optimality of different designs. The case study is related to the separation of the acetone–methanol minimum boiling azeotrope with water. The process flow sheet includes both the extractive distillation column and the entrainer regeneration column. Insight from analysis of the ternary residue curve map and isovolatility curves shows that a lower pressure reduces the minimal amount of entrainer needed and increases the relative volatility of acetone–methanol in the extractive column. A 0.6 atm pressure is selected to enable the use of cheap cooling water in the condenser. We optimize the entrainer flow rate, adjusting both column reflux ratios and feed locations, by minimizing the total energy consumption per product unit. The total annualized cost (TAC) is calculated for all processes. Double-digit savings in energy consumption and in TAC are achieved compared to literature values. We then propose a novel efficiency indicator that describes the ability per tray of extractive section to discriminate the desired product between the top and the bottom of the extractive section. Shifting the feed trays’ locations improves the efficiency of the separation, even when less entrainer is used

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

Double-effect heat integration and mechanical heat pump technique are investigated for the extractive distillation process of the acetone–methanol minimum boiling azeotropic mixture with entrainer water and compared from the economical view by the total annual cost (TAC) and environmental aspect by CO2 emissions. Firstly, A novel optimal partial heat integration (OPHI) process is proposed and optimized through the minimization of a newly defined objective function called OF2 that describes the energy consumption used per product unit flow rate and allows comparison with the literature direct partial and full heat integration processes. We find that the minimum TAC is not achieved by the full heat integration process as intuition, but by the new OPHI process. Secondly, the vapour recompression (VRC) and bottom flash (BF) mechanical heat pump processes are evaluated with respect to energy and CO2 emissions. We proposed a new partial VRC and a new partial BF process in order to reduce the high initial capital cost of compressors. Overall the results show that compared to the conventional extractive distillation process the proposed OPHI process gives a 32.2% reduction in energy cost and a 24.4% saving in TAC while the full BF process has the best performance in environmental aspect (CO2 emissions reduce by 7.3 times)

Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol

We show how reducing pressure can improve the design of a 1.0-1a mixture homogeneous extractive distillation process and we use extractive efficiency indicators to compare the optimality of different designs. The case study concerns the separation of the diisopropyl ether (DIPE)–isopropyl alcohol (IPA) minimum boiling azeotrope with heavy entrainer 2-methoxyethanol. We first explain that the unexpected energy cost OF decrease following an increase of the distillate outputs is due to the interrelation of the two distillate flow rates and purities and the entrainer recycling through mass balance when considering both the extractive distillation column and the entrainer regeneration column. Then, we find that for the studied case a lower pressure reduces the usage of entrainer and increases the relative volatility of DIPE–IPA for the same entrainer content in the extractive column. A 0.4 atm operating pressure is selected to enable the use of cheap cooling water in the condenser. We run an optimization of the entrainer flow rate, both columns reflux ratios, distillates and feed locations by minimizing the total energy consumption per product unit. Double digit savings in energy consumption are achieved while TAC is reduced significantly. An extractive efficiency indicator that describes the ability of the extractive section to discriminate the desired product between the top and the bottom of the extractive section of the extractive section is calculated for comparing and explaining the benefit of lowering pressure on the basis of thermodynamic insight

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

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

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

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 Eext and eext 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 Eext at a given reflux ratio, and there is minimum reflux ratio for a given Eext. There is an optimal efficiency indicator Eext,opt which corresponds to the minimum TAC taken as the best design. In other words, Eext,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

Energy-Saving Reduced-Pressure Extractive Distillation with Heat Integration for Separating the Biazeotropic Ternary Mixture Tetrahydrofuran–Methanol–Water

There is rich literature on the separation of binary azeotropic mixtures, whereas few studies exist on the separation of biazeotropic ternary mixtures. In this work, we propose a systematic approach for energy-efficient extractive distillation processes for the separation of a biazeotropic mixture that involves thermodynamic insights via residue curve maps and the univolatility line to find the optimal entrainer and operating pressure, global optimization based on a proposed two-step optimization procedure, and double-effect heat integration to achieve further saving of energy consumption. An energy-saving reduced-pressure extractive distillation (RPED) with a heat integration flowsheet is then proposed to achieve the minimum total annual cost (TAC). The results show that the TAC, energy consumption, and exergy loss of the proposed RPED with heat integration are reduced by 75.2%, 80.5%, and 85.8% compared with literature designs

Optimal design of extractive distillation for acetic acid dehydration with N-methyl acetamide

A distinctive strategy for entrainer recycling is proposed in this work for acetic acid (AA) dehydration by extractive distillation by using N methyl acetamide (NMA). The use of standard entrainers such as DMF or DMSO has the main drawback of forming an azeotrope with acetic acid. However, the vapour liquid equilibrium AA – NMA exhibits a tangential pinch point at NMA end composition. The new strategy rises from the thermodynamic analysis of the ternary diagram that which involves no azeotrope. As a result, acetic acid with high purity can be obtained by the recycling of the entrainer with a relaxed constraint in its purity. Optimization studies are discussed by using two approaches: two-step optimization method with Sequential Quadratic Programming (TSOM case) and the multi-objective genetic algorithm. The multi-objective genetic algorithm allowed the computation of the optimal acetic acid dehydration with an impurity of 3% in the recycled entrainer. Significant cost savings are achieved thanks to the optimization of both columns together. Energy consumption is reduced by 12.8% and 56.9% whereas TAC is saved by 28.4% and 56.3% compared with optimal case TSOM (impurity content 1%) and a published “Case Ref” (impurity content 0.01%), respectively

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

This paper presents a preliminary design framework for finding suitable homogeneous entrainers E to separate minimum boiling azeotropic mixtures AB by extractive distillation. The framework incorporates techniques such as Computer Aided Molecular Design (CAMD), addressing process needs and targeted thermodynamic properties. New thermodynamic criteria are considered for the entrainer design based on both, the thermodynamic properties of the binary mixtures AE and BE and the isovolatility curves in the ternary mixture ABE. In the CAMD problem, energy related property constraints on the boiling point and the vaporization enthalpy are also considered, leading to a mixed integer non-linear programming problem. Entrainer candidates are ranked by the maximization of the driving force of separation of A and B from their respective mixtures AE and BE under constraints limiting the entrainer composition for fixed values of the relative volatility. Further process optimization is done for validating the entrainer ranking by using Aspen plus V7.3, which minimizes the energy consumption and computes the total annual cost to compare different designs. The new thermodynamic criteria perform better than selectivity alone or a combined selectivity — capacity criterion, as proposed in the literature. The framework is illustrated through an entrainer problem design for the separation of acetone–methanol. Ethylene glycol is obtained as the best design solution. Comparison with conventional entrainers water and DMSO is carried out to validate the performance of the new criteria based on optimal process design study

Approche thermodynamique pour la conception et l'optimisation de la distillation extractive de mélanges à température de bulle minimale (1.0-1a)

Nous étudions la distillation extractive continue de mélanges azéotropiques à temperature de bulle minimale avec un entraineur lourd (classe 1.0-1a) avec comme exemples les mélanges acétone-méthanol avec l’eau et DIPE-IPA avec le 2-méthoxyethanol. Le procédé inclut les colonnes de distillation extractive et de régénération de l’entraineur en boucle ouverte et en boucle fermée. Une première stratégie d’optimisation consiste à minimiser la fonction objectif OF en cherchant les valeurs optimales du débit d’entraineur FE, les positions des alimentations en entraineur et en mélange NFE, NFAB, NFReg, les taux de reflux R1, R2 et les débits de distillat de chaque colonne D1, D2. OF décrit la demande en énergie par quantité de distillat et tient compte des différences de prix entre les utilités chaudes et froides et entre les deux produits. La deuxième stratégie est une optimisation multiobjectif qui minimise OF, le coût total annualisé (TAC) et maximise deux nouveaux indicateurs thermodynamiques d’efficacité de séparation extractive totale Eext et par plateau eext. Ils décrivent la capacité de la section extractive à séparer le produit entre le haut et le bas de la section extractive. L’analyse thermodynamique des réseaux de courbes de résidu ternaires RCM et des courbes d’isovolatilité montre l’intérêt de réduire la pression opératoire dans la colonne extractive pour les séparations de mélanges 1.0-1a. Une pression réduite diminue la quantité minimale d’entraineur et accroît la volatilité relative du mélange binaire azéotropique dans la région d’opération de la colonne extractive. Cela permet d’utiliser un taux de reflux plus faible et diminue la demande énergétique. La première stratégie d’optimisation est conduite avec des contraintes sur la pureté des produits avec les algorithmes SQP dans les simulateurs Aspen Plus ou Prosim Plus en boucle ouverte. Les variables continues optimisées sont : R1, R2 et FE (étape 1). Une étude de sensibilité permet de trouver les valeurs de D1, D2 (étape 2) et NFE, NFAB, NFReg (étape 3), tandis l’étape 1 est faite pour chaque jeu de variables discrètes. Enfin le procédé est resimulé en boucle fermée et TAC, Eext et eext sont calculés (étape 4). Les bilans matières expliquent l’interdépendance des débits de distillats et des puretés des produits. Cette optimisation permet de concevoir des procédés avec des gains proches de 20% en énergie et en coût. Les nouveaux procédés montrent une amélioration des indicateurs Eext et eext. Afin d’évaluer l’influence de Eext et eext sur la solution optimale, la seconde optimisation multiobjectif est conduite. L’algorithme génétique est peu sensible à l’initialisation, permet d’optimiser les variables discrètes N1, N2 et utilise directement le shéma de procédé en boucle fermée. L’analyse du front de Pareto des solutions met en évidence l’effet de FE/F et R1 sur TAC et Eext. Il existe un Eext maximum (resp. R1 minimum) pour un R1 donné (resp. Eext). Il existe aussi un indicateur optimal Eext,opt pour le procédé optimal avec le plus faible TAC. Eext,opt ne peut pas être utilisé comme seule fonction objectif d’optimisation mais en complément des autres fonctions OF et TAC. L’analyse des réseaux de profils de composition extractive explique la frontière du front de Pareto et pourquoi Eext augmente lorsque FE diminue et R1 augmente, le tout en lien avec le nombre d’étage. Visant à réduire encore TAC et la demande énergétique nous étudions des procédés avec intégration énergétique double effet (TEHI) ou avec des pompes à chaleur (MHP). En TEHI, un nouveau schéma avec une intégration énergétique partielle PHI réduit le plus la demande énergétique. En MHP, la recompression partielle des vapeurs VRC et bottom flash partiel BF améliorent les performances de 60% et 40% respectivement. Au final, le procédé PHI est le moins coûteux tandis que la recompression totale des vapeurs est la moins énergivore.We study the continuous extractive distillation of minimum boiling azeotropic mixtures with a heavy entrainer (class 1.0-1a) for the acetone-methanol with water and DIPE-IPA with 2-methoxyethanol systems. The process includes both the extractive and the regeneration columns in open loop flowsheet and closed loop flowsheet where the solvent is recycled to the first column. The first optimization strategy minimizes OF and seeks suitable values of the entrainer flowrate FE, entrainer and azeotrope feed locations NFE, NFAB, NFReg, reflux ratios R1, R2 and both distillates D1, D2. OF describes the energy demand at the reboiler and condenser in both columns per product flow rate. It accounts for the price differences in heating and cooling energy and in product sales. The second strategy relies upon the use of a multi-objective genetic algorithm that minimizes OF, total annualized cost (TAC) and maximizes two novel extractive thermodynamic efficiency indicators: total Eext and per tray eext. They describe the ability of the extractive section to discriminate the product between the top and to bottom of the extractive section. Thermodynamic insight from the analysis of the ternary RCM and isovolatility curves shows the benefit of lowering the operating pressure of the extractive column for 1.0-1a class separations. A lower pressure reduces the minimal amount of entrainer and increases the relative volatility of original azeotropic mixture for the composition in the distillation region where the extractive column operates, leading to the decrease of the minimal reflux ratio and energy consumption. The first optimization strategy is conducted in four steps under distillation purity specifications: Aspen Plus or Prosim Plus simulator built-in SQP method is used for the optimization of the continuous variables: R1, R2 and FE by minimizing OF in open loop flowsheet (step 1). Then, a sensitivity analysis is performed to find optimal values of D1, D2 (step 2) and NFE, NFAB, NFReg (step 3), while step 1 is done for each set of discrete variables. Finally the design is simulated in closed loop flowsheet, and we calculate TAC and Eext and eext (step 4). We also derive from mass balance the non-linear relationships between the two distillates and how they relate product purities and recoveries. The results show that double digit savings can be achieved over designs published in the literature thanks to the improving of Eext and eext. Then, we study the influence of the Eext and eext on the optimal solution, and we run the second multiobjective optimization strategy. The genetic algorithm is usually not sensitive to initialization. It allows finding optimal total tray numbers N1, N2 values and is directly used with the closed loop flow sheet. Within Pareto front, the effects of main variables FE/F and R1 on TAC and Eext are shown. There is a maximum Eext (resp. minimum R1) for a given R1 (resp. Eext). There exists an optimal efficiency indicator Eext,opt which corresponds to the optimal design with the lowest TAC. Eext,opt can be used as a complementary criterion for the evaluation of different designs. Through the analysis of extractive profile map, we explain why Eext increases following the decrease of FE and the increase of R1 and we relate them to the tray numbers. With the sake of further savings of TAC and increase of the environmental performance, double-effect heat integration (TEHI) and mechanical heat pump (MHP) techniques are studied. In TEHI, we propose a novel optimal partial HI process aiming at the most energy saving. In MHP, we propose the partial VRC and partial BF heat pump processes for which the coefficients of performance increase by 60% and 40%. Overall, optimal partial HI process is preferred from the economical view while full VRC is the choice from the environmental perspective