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Design and optimization of an ethanol dehydration process using stochastic methods Article  in  Separation and Purification Technology · February 2013 DOI: 10.1016/j.seppur.2012.12.002

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1

Design and optimization of an ethanol dehydration process using

2

stochastic methods

3 4

María Vázquez-Ojeda1, Juan Gabriel Segovia-Hernández1,*, Salvador Hernández1,

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Arturo Hernández-Aguirre2, Anton Alexandru Kiss3 1

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División de Ciencias Naturales y Exactas, Universidad de Guanajuato.

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Noria Alta s/n, Cp. 36050, Guanajuato, Gto., México. 2

CIMAT, A.C., Departamento de Ciencias Computacionales, Callejón de Jalisco s/n, 36240, Mineral de

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Valenciana, Guanajuato, Gto., México. 3

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Alumnus of University of Amsterdam, The Netherlands.

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Abstract

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Due to the increasing demand for renewable fuels that are economically attractive, as well as part of the

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quest for energy alternatives to replace carbon-based fuels, the purification of ethanol plays a key role.

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This paper presents the design and optimization of a dehydration process for ethanol, using two separa-

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tion sequences: a conventional arrangement and an alternative arrangement based on liquid-liquid

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extraction. Both sequences were optimized using a stochastic global optimization algorithm (differential

18

evolution) implemented in Mathworks Matlab and coupled to rigorous process simulations carried out in

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Aspen Plus. The economic feasibility of the two configurations was studied by changing the ethanol-

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water composition in the analyzed feed stream. The results clearly demonstrate that significant savings

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are possible by extraction when the ethanol content in the feed stream exceeds 10% mol (22 wt%).

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Keywords: energy savings, process optimization, bioethanol dehydration, L-L extraction, distillation

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* Author to whom all correspondence should be addressed, e-mail: [email protected], Phone: +52

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(473) 732-0006 ext 8142.

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1. Introduction

2

Ethanol is by far the most promising sustainable biofuel, with major advantages over all other fuel

3

alternatives (such as hydrogen) – as it can be easily integrated in the existing fuel systems as a 5-85%

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mixture with gasoline that does not need any modification of the current engines. Brazil and United

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States are major users and producers of bioethanol, and as such both countries together were responsible

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for 88% of the world's ethanol fuel production in 2010. Remarkable, bioethanol is an environmentally-

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friendly fuel with less greenhouse gases emissions than gasoline, but with similar energy power [1].

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The bioethanol production at industrial scale relies on several processes, such as: corn-to-ethanol,

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sugarcane-to-ethanol, basic and integrated lignocellulosic biomass-to-ethanol. However, according to

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Pimentel [2], the corn and other food crops should be used for food as priority and not for ethanol

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production. The ethanol production is claimed to increase the degradation of the environment as the corn

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production causes more soil erosion than any other crop. Moreover, it can take more than 29% of energy

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to produce one gallon of ethanol as compared to the energy content of a gallon of ethanol. According to

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these and also other factors, it was concluded that ethanol production from subsidized U.S. corn is in

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fact not a renewable energy source. Nigam and Singh [3] performed a review of all the available

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literature up to date concerning liquid biofuels. Although biofuels seem not to be yet an economical

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alternative to large-scale biofuel supply, there is still an urgent need to perform extensive research in

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order to introduce more efficient processes by developing the technology, reducing the energy

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requirements, decreasing emissions and production costs, and ultimately establishing biofuels as an

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alternative for the future. Anhydrous ethanol is widely used in the chemical industry as a raw material in

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chemical synthesis of esters and ethers, and as solvent in production of cosmetics, sprays, perfumery,

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paints, medicines and food, among others. The most popular processes used in ethanol dehydration are:

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heterogeneous azeotropic distillation using solvents such as benzene, pentane, iso-octane and

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cyclohexane; extractive distillation with solvents and salts as entrainers; adsorption with molecular

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sieves; and, processes that use pervaporation membranes [1]. The large-scale production is still

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dominated by the extractive and azeotropic distillation despite recent advances in pervaporation and

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adsorption with molecular sieves [4]. The large-scale production of bioethanol fuel requires energy

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demanding distillation steps to concentrate the diluted streams from the fermentation step and to

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overcome the azeotropic behavior of the ethanol-water mixture. The conventional separation sequence

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consists of three distillation columns performing several tasks with high energy penalties: first a column

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for the pre-concentration of ethanol, second a column for extractive distillation and a third column for

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solvent recovery (Fig. 1). Considering the high costs of the ethanol dehydration, an optimization of the

1

process is performed – where the designs of distillation and extractive distillation are usually

2

characterized as being of large size problems, since the significant number of strongly nonlinear

3

equations. Note that typically ethanol is pre-concentrated using a conventional distillation column to

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values close to the azeotrope of ethanol and water (about 95 wt% ethanol), followed by an extractive

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distillation column where the desired purity (over 99.8 wt% according to the standards) is achieved for

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ethanol, and a solvent recovery column.

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Huang et al. [5] provide a review of separation process and technologies related to biorefining including

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pre-extraction of hemicellulose and other value-added chemicals, detoxification of fermentation

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hydrolyzates, and ethanol product separation and dehydration. With respect to the azeotropic nature of

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ethanol–water mixture they conclude that desired processes with low energy consumption are the

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extractive distillation with ionic liquids and hyperbranched polymers, adsorption with molecular sieve

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and bio-based adsorbents. New configurations have been studied for the separation process of

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bioethanol, such as arrangements based on thermally coupled columns and column sections

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recombination to obtain sequences with significant savings in the capital cost compared to the classic

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arrangement proposed in the literature [6]. It is also possible to reduce costs by energy integration,

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studies carried out in extractive distillation column using a partial condenser (the steam is fed to the

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second column) thus reducing the usage of steam and cooling water [7]. The alternative proposed here is

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to replace the distillation column used conventionally by a liquid-liquid extraction column, followed by

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a distillation column to recover the solvent employed. In this work we designed and optimized the

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process of bioethanol dehydration using the conventional process (Fig. 1) as well as the alternative

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separation process based on liquid-liquid extraction (Fig. 2). Moreover, different solvents [8-9] were

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also evaluated in the liquid-liquid extraction column, while ethylene glycol was used as extractive agent

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in the extractive distillation (ED). The design and optimization was carried out using, as a design tool, a

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differential evolution algorithm with restrictions coupled with the process simulator Aspen Plus, for the

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evaluation of the objective function, ensuring that all results obtained are rigorous. In this context,

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stochastic optimization methods are playing an important role because they are generally robust

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numerical tools that present a reasonable computational effort in the optimization of multivariable

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functions; they are also applicable to unknown structure problems, requiring only calculations of the

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objective function, and can be used with all models without problem reformulation [10, 11].

30

1

2. Approach and methodology

2

Recently, the bioethanol dehydration has been studied for concentrations of 5-10% wt of ethanol from

3

the fermentation step, by extractive and azeotropic distillation in dividing-wall columns that are able to

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concentrate and dehydrate bioethanol in a single unit [4,12]. These sequences were optimized using

5

sequential quadratic programming (SQP) method. The results show energy savings around 10 and 20%

6

[4,12]. Ahmetovic et al. [13] presented the optimization of a plant producing corn-based ethanol, aimed

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to reduce the energy requirements, the fresh water consumption and the discharge of wastewater. The

8

optimization was performed using mathematical programming techniques to optimize the energy

9

requirements and the network for the process water. Čuček et al. [14] carried out the integration of

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different technologies, raw materials and energy for the dry milling process to produce ethanol from

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corn and stover. The optimization was performed using mathematical programming techniques using the

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process simulator MIPSYN. In terms of extractive distillation several authors have studied the vapor-

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liquid equilibrium in a ternary mixture of ethanol-water and a solvent. Wang et al. [15] measured the

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vapor pressure in water-ethanol mixtures, water-methanol and ethanol-methanol in the presence of an

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ionic liquid. García-Herreros et al. [16] have recently reported the optimization of the extractive

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distillation of ethanol using glycerol as an alternative solvent – the problem being formulated as mixed

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integer nonlinear programming (MINLP). That work determined the number of stages in the columns

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and the feed stages as discrete variables along with continuous variables, design variables and balance

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variables of the model. The optimization problem was achieved by combining stochastic and

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deterministic algorithms. Some of these methods can provide useful results; however, it would be

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desirable to incorporate rigorous models in the synthesis procedures, in order to increase their industrial

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relevance and scope of application, particularly for nonideal mixtures. These rigorous MINLP synthesis

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models exhibit significant computational difficulties, such as the introduction of equations that can

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become singular, the solution of many redundant equations, and the requirement of good initialization

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points. The high levels of nonlinearities and nonconvexities in the MESH equations, thermodynamic

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equilibrium equations, and convergence difficulties are common problems. These difficulties translate

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into high computational times and the requirement of good initial guesses and bounds on the variables to

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achieve convergence. Also, these approaches just deal with one objective, total annual cost in most of

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the cases. Although Messac et al. [17] present good alternatives for the solution of this problem with a

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multiobjective perspective using non-linear programming optimizers, clear limitations in the case of

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solving bi-objective problems are shown by Martínez et al. [18]. On the other hand, stochastic

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optimizers deal with multi-modal and non-convex problems in a very effective way to solve these

1

limitations. Stochastic optimization algorithms are capable of solving, robustly and efficiently;, the

2

challenging multi-modal optimization problems, and they appear to be a suitable alternative for the

3

design and optimization of complex separation schemes [19]. Several heuristic techniques for global

4

optimization mimicking biological evolution have emerged in the literature. Among the most

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representative algorithms, we can mention the genetic algorithms (GA) [20], particle swarm

6

optimization (PSO) [21], and a new class of evolutionary methods called differential evolution (DE)

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algorithms [22]. For different theoretical and practical problems, comparative studies have shown that

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the performance of DE algorithms is clearly better than that of the other two algorithms (GA and PSO);

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see, for example, Ali and Torn [23], Xu and Li [24], Panduco and Brizuela [25] and Sacco et al. [26].

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Differential evolution is a stochastic global optimization algorithm developed in 1995, whose operations

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and search principles are common to evolutionary algorithms [27]. DE works with a population of

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vectors or individuals which undergo reproduction and mutation operations during several iterations or

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generations. At the end of every generation the individuals with best fitness value are chosen to populate

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the next generation. The first step of the DE algorithm is to create the initial population (called parents)

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and to evaluate the fitness function value of all members (calculated on the function or problem being

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optimized). Then, the main loop performs the following steps:

17

•

For each parent a “mutant” is produced by applying the reproduction and mutation operators;

18

•

The mutant fitness function is computed;

19

•

The fitness value of the mutant and the parent are compared, and the best of the two (with better

20 21

fitness value) is copied into a temporary file; •

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Once all parents are visited, the new population is created by replacing the current parents with the members of the temporary file;

•

The process is iterated until the stopping criteria is met. At termination the best solution is found in temporary file.

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The creation of a ‘mutant’ encompasses the following operations: for every dimension d of the problem,

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three individuals are chosen at random (with no repetition), and a difference vector, hence the name

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differential, is found by subtracting the second random from the first random vector. This difference is

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added to the third random and some value K is obtained. Whether this value is inserted into the current

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dimension d is decided via the crossover probability, CR. If the random number is smaller than CR then

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insert the value K into the current dimension d of the mutant being generated. Otherwise, insert into the

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current dimension d of the mutant a copy of the value at dimension d of the current parent. The mutation

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and reproduction operations accomplish two goals: first, to maintain the diversity and exploration of the

1

population, and second, achieve convergence to the optimal through adaptive mutations. Upon

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completion DE provides the best solution vector.

3

Numerous investigations have been performed using the differential evolution algorithm to solve

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different problems in diverse areas of chemical engineering; some resent works include those presented

5

by Yerramsetty and Murty [28], Kheawhom [29], Bonilla-Petriciolet et al. [30], Kumar et al. [31], Vakili

6

et al. [32], Bhattacharya et al. [33], among many others. Their studies demonstrate how the differential

7

evolution algorithm enables to optimize different kinds of problems robustly and efficiently.

8

In this work we use the conventional ethylene glycol solvent for the extractive distillation. As described

9

hereafter, the design and optimization of conventional separation sequence (CSS) and the optional

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separation sequence (OSS-I) were investigated using the differential evolution algorithm coupled to

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Aspen Plus process simulator (Fig. 3). Once obtaining the best designs for each sequence, the energy

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requirements and the total annual cost were determined.

13 14

3. Results and discussion

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Four feeds are considered by changing the molar composition ethanol/water with a feed flowrate of

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45.36 kmol/h (equivalent to a production rate of 0.34-2.60 ktpy bioethanol), at 25 °C and 1 atm. Table 1

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shows the molar and mass compositions of the feed stream. Ethylene glycol is used as extractive

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distillation agent, at same conditions. Additionally, three extraction solvents were evaluated for the

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optional design (OSS-I): octanoic acid, octanol and iso-octanol (ethylhexanol). Although other solvents

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were proposed by Offeman et al. [8-9], the three solvents chosen in this work showed the best results.

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Note that the design objective is specified for the ethanol mass fraction of 0.999 or more, while for the

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water, the chosen solvent, and ethylene glycol the objective is a mass fraction of 0.99 or more.

23 24

3.1. Optimization strategy

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In the conventional separation sequence the goal is to minimize the total operating cost, meaning the

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cost of steam plus the cooling water for each column. The minimization of the goal is subject to the

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requirements of purity and recovery of ethanol, water and ethylene glycol – as follows:

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Subject to

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y m ≥ xm

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min(CU) = f (SI, RRI, RDI, SFI, FS, SII, RRII, RDII, SFS, SFII, SIII, RRIII, RDIII, SFIII) (1)

1

where CU is the cost of utilities i.e. the cost of steam plus the cost of the cooling water of the sequence, S

2

is the number of stages in each column, RR and RD are reflux and flow of distillate in each of the three

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columns that integrate the sequence , SF is the feed streams of each of the columns, FS is the flow of

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solvent in the extractive distillation column, SFS is the solvent feed stream to the column, finally ym and

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xm are the vectors of the purities and recoveries obtained and required of the m components respectively.

6

A total of 14 variables (7 continuous and 7 integer variables) were manipulated.

7

Just as in the conventional sequence, in the optional separation sequence (OSS-I), the goal is to

8

minimize the total operating cost. The minimization of the goal is subject to the requirements of purity

9

and recovery of ethanol, water, ethylene glycol and the solvent used in the liquid-liquid extraction.

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min(CU) = f (C, SEX, FA, SI, RRI, RDI, SFI, FS, SII, RRII, RDII, SFS, SFII, SIII, RRIII, RDIII, SFIII)

13

y m ≥ xm

Subject to

(2)

14 15

where CU is the cost of utilities of the sequence, C is the acid or alcohol chosen and FA flow of this acid

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or alcohol, SEX is the number of steps to perform the liquid-liquid extraction, S is the number of stages in

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each column, RR and RD are reflux and flow of distillate in each of the three columns, SF is the feed

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streams of each of the columns, FS is the flow of solvent into the column extractive distillation, SFS is the

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solvent feed stream to the column, finally ym and xm are the vectors of the purities and recoveries

20

obtained and required of the m components respectively. A total of 17 variables (8 continuous and 9

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integer variables) were manipulated. Table 2 shows the integer variables and search range, while Table 3

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shows the search ranges of continuous variables of the sequences studied. The search ranges are taken

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based on practical considerations for equipment design as is referenced by Couper et al. [34], among

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others.

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The optimization of the proposed sequences CSS and OSS-I is performed using the differential

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evolution algorithm (DE) coupled with constraints AspenONE Aspen Plus. This connection allows

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obtaining rigorous optimal designs meeting the requirements of purity and recovery. The algorithm used

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in this work is differential evolution. Fig. 3 shows the flow diagram of the algorithm coupled to the

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Aspen Plus process simulator, while Fig. 4 shows the connection of Mathworks Matlab with AspenTech

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Aspen Plus via MS Excel, including the flow of data between these programs. During the evolution of

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the DE, the vector values of decision variables (Vx) are sent from Matlab to Microsoft Excel using DDE

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(dynamic data exchange) by COM technology. These values are attributed in Excel to the corresponding

1

process variables (Vp) and then sent to Aspen Plus by a similar interface. Note that using the COM

2

technology it is possible to add code such that the applications behave as an Object Linking and

3

Embedding (OLE) automation server. After running the rigorous simulation, Aspen Plus returns to MS

4

Excel the vector of results (Vr). Finally, Excel returns the objective function (FOB) value to Matlab for

5

the DE procedure. Note that the CPU time is high for each optimization step due to the long

6

convergence time required by the Aspen Plus simulator that ensures rigorous process simulation results.

7

For the optimization of the sequences, a population of 100 individuals and 300 generations – amounting

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30,000 function evaluations in total – were used with the parameters of crossover and mutation of 0.80

9

and 0.40 respectively [27]. The optimization was carried out on a Dell computer with Intel Core i7CPU

10

930 processor at 2.80 GHz, 6.00 GB of RAM, Windows 7 Ultimate, Matlab v7.8.0.347 (R2009a) and

11

Aspen One Aspen Plus V7.0.

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3.2 Process comparison

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The total annual costs (TAC) were calculated using the method of Guthrie (1968). For this work, a series

15

of general equations were used for the purchase cost of equipment and cost of utilities based on the

16

method of Guthrie presented by Turton et al. [35]. The installation cost is also updated by using the

17

CEPCI index of 556.4 (Jun 2010). The capital cost – purchase plus installation cost – is annualized over

18

a period, which is often referred to as the plant lifetime:

19 20

Annual capital cost = Capital cost / Plant life time

(3)

21

TAC = Annual operating cost + Annual capital cost

(4)

22 23

The operating costs were calculated based only on the utility costs (steam 3.17 $/GJ when the bottom

24

temperature is less than 150 °C and cooling water 0.16 $/GJ – values taken from Turton et al. [35]),

25

while the plant lifetime was considered ten years, with an operating time of 8400 hrs/yr.

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The design objectives specified for the purity of ethanol, purity of water, solvent selected in the liquid-

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liquid extraction stage and flowrate of ethylene glycol were achieved. Tables 4 and 5 show the purities

28

in all streams in mass fraction, while Tables 6 and 7 show the design parameters of the separation

29

sequences. Note that all sequences meet the target design specifications for both recovery and purity (see

30

the product streams). Analyzing the feed stream FI, it can be observed a decrease in the total investment

31

cost (18%) and in the total annual cost (14%) in the optional sequence (OSS-I) and for this arrangement,

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lower energy requirements were obtained using octanol as solvent in the liquid-liquid extraction column.

1

The energy requirements of the CSS and OSS-I are presented in terms of utilities: steam and cooling

2

water in the reboilers and condensers, respectively. Analyzing the feed stream FII (see Table 6), it can

3

be observed a decrease in total investment cost and in the total operating cost of 34 and 32%

4

respectively (OSS-I). For this arrangement, lower energy requirements were obtained using octanol as

5

solvent in the liquid-liquid extraction column.

6 7

Although there are no energy savings for the optional sequence when working with the feed stream FI

8

and FII, there is a significant reduction in the cost of equipment, as well as the total annual cost when

9

compared to the conventional sequence. However, by decreasing the fraction of ethanol in the feed

10

stream, there is a clear opposite behavior for the FIII. The sequence OSS-I has higher costs for both

11

capital investment and annual operating cost. For the feed stream FIV the same behaviour is observed,

12

the arrangement OSS-I has higher costs with respect to the conventional sequence.

13

Not surprisingly the designs found have many stages and low reflux to minimize energy costs. By

14

reducing the reflux, the energy consumption decreases at the cost of increasing the number of stages in

15

the design. An alternative to be considered in the optimization process is to add a penalty function to

16

limit the maximum number of stages in the column when the change in the objective function is not

17

significant. In this way, it is possible to find solutions with minimal energy consumption with a smaller

18

or similar size to the processes implemented in the industry.

19

The results obtained for cases FIII and FIV are consistent with the experimental results presented by

20

Offeman et al. [8-9] for compositions with presence of significant amounts of ethanol in the ethanol /

21

water mixture, obtained in stage laboratory for liquid-liquid extraction in one step using different

22

extractants (which we tested in this work to a liquid-liquid extraction multistage). The results generated

23

indicate that the viability of the new process, compared with traditional dehydration of ethanol in two

24

distillation systems, depend of the extractant used in the liquid-liquid extraction step.

25 26

4. Conclusion

27

Stochastic global optimization methods – such as differential evolution algorithm – were successfully

28

used for optimizing the separation of ethanol and water, to obtain optimal process designs using the

29

rigorous Aspen Plus process simulator. The conventional sequence was optimized and a novel optional

30

sequence using liquid-liquid extraction systems was proposed and investigated.

31

Furthermore, the ethanol dehydration was studied for four different concentrations from the fermentation

32

step, ranging from 5 to 32 wt% ethanol. The results show considerable savings in total annual cost (32 %

1

approximately) for the proposed alternative systems, for a feed stream with 10% mol (22% wt) of

2

ethanol. When the ethanol in feed stream has a lower concentration, the optimized SSC sequence is

3

actually a better economic alternative. Until an improved extracting agent is found for the liquid–liquid

4

extraction column, this alternative process is limited when the concentrations of ethanol are 5-12% and

5

32 wt% in the feed stream, by its ability to trap the product of interest and it is directly related to the

6

content of the product of interest in the feed stream.

7

Finally the optional sequence optimizes the solvent employed in the liquid-liquid extraction column,

8

thus allowing to optimize not only its design and operating variables but also the use of different

9

compounds to perform the separation The same can be applied to the extractive distillation column,

10

where it is possible to analyze the behavior of different solvents.

11 12 13

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ethanol using glycerol as entrainer, Computers and Chemical Engineering 39 (2012) 129-142. [2]

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1

Table 1. Feeds of ethanol-water mixture. Mol flow [kmol/h] Ethanol Water Molar composition (%) Ethanol Water Mass composition (%) Ethanol Water

FI 6.80 38.56

FII 4.54 40.82

FIII 2.27 43.09

FIV 0.91 44.45

15 85

10 90

5 95

2 98

31 69

22 78

12 88

5 95

2 3 4 5

Table 2. Search range for integer variables Sequence CSS (FI-FIV)

Variable

Range of search

S SF/SFS

5-100 2-100

C SEX/S SF/SFS

1-3 5-100 2-100

OSS (FI-FIV)

6

1 2

Table 3. Search range for continuous variables Sequence CSS (feed stream)

Variable

Range search (low limit-upper limit) FII FIII 0.1-36 7.26-10.89 4.66-6.99 2.33-3.50

0.93-1.40

5.40-8.10 0.73-1.59 6.80-13.61 FI

FI

RR RDI [kmol/h] RDII [kmol/h] RDIII kmol/h] FS [kmol/h] OSS (feed stream) RR RDI [kmol/h] RDII [kmol/h] RDIII kmol/h] FS [kmol/h] FA [kmol/h]

FIV

1.80-2.70 0.43-0.64 4.22-6.33 FIII

0.72-1.08 0.17-0.26 1.69-2.53 FIV

11.79-18.60

3.60-5.40 0.86-1.28 8.44-12.66 FII 0.1-36 7.89-12.34

4.54-6.82

2.54-4.13

5.39-8.08 6.21-10.44 3.27-5.44 3.63-5.44

3.59-5.39 4.45-6.94 2.54-3.92 3.63-5.99

1.80-2.69 2.73-4.14 1.27-2.18 3.97-5.96

0.72-1.08 1.72-3.05 1.09-2.18 3.63-6.26

3 4 5 6 7 8

9

Table 4. Mass fraction for all streams for conventional separation sequence (CSS). STREAM

FEED I

DIST-CI

WATER

MAKEUP SOLV

Purity [wt%] Ethanol Water Ethylene glycol (solv)

0.3110 0.6890 0.0000

0.0000 1.0000 0.0000

0.9188 0.0812 0.0000

0.0000 0.0000 1.0000

Ethanol Water Ethylene glycol (solv)

0.2213 0.7787 0.0000

0.9346 0.0654 0.0000

0.0000 1.0000 0.0000

0.0000 0.0000 1.0000

Ethanol Water Ethylene glycol (solv)

0.1186 0.8814 0.0000

0.9008 0.0992 0.0000

0.0000 1.0000 0.0000

0.0000 0.0000 1.0000

Ethanol Water Ethylene glycol (solv)

0.0496 0.9504 0.0000

0.9123 0.0877 0.0000

0.0000 1.0000 0.0000

0.0000 0.0000 1.0000

SOLV FI 0.0000 0.0040 0.9960 FII 0.0000 0.0049 0.9951 FIII 0.0000 0.0092 0.9908 FIV 0.0000 0.0089 0.9911

ETHANOL

BOTT-CII

DIST-CII

1.0000 0.0000 0.0000

0.0077 0.0611 0.9312

0.1178 0.8822 0.0000

0.9990 0.0010 0.0000

0.0063 0.0266 0.9671

0.1409 0.4922 0.3669

1.0000 0.0000 0.0000

0.0052 0.0389 0.9558

0.1102 0.6362 0.2536

0.9996 0.0004 0.0000

0.0052 0.0343 0.9605

0.1329 0.6572 0.2099

1 2

Table 5. Mass fraction for all streams for optional separation sequence (OSS-I). STREAM

FEED I

MAKEUP ACID/ ALCOHOL

ACID/ ALCOHOL

WATER

BOTTEX

Purity [wt%] Ethanol Water Ethylene glycol solv) Octanol

0.3110 0.6890 0.0000 0.0000

0.0000 0.0000 0.0000 1.0000

0.0007 0.0089 0.0000 0.9904

0.0000 0.9981 0.0000 0.0019

0.2811 0.1286 0.0000 0.5904

Ethanol Water Ethylene glycol solv) Octanol

0.2213 0.7787 0.0000 0.0000

0.0000 0.0000 0.0000 1.0000

0.0003 0.0068 0.0000 0.9930

0.0001 0.9980 0.0000 0.0019

0.2468 0.1145 0.0000 0.6387

Ethanol Water Ethylene glycol solv) Iso-octanol

0.1186 0.8814 0.0000 0.0000

0.0000 0.0000 0.0000 1.0000

0.0007 0.0049 0.0000 0.9944

0.0009 0.9973 0.0000 0.0019

0.1405 0.0802 0.0000 0.7793

Ethanol Water Ethylene glycol solv) Octanoic acid

0.0496 0.9504 0.0000 0.0000

0.0000 0.0000 0.0000 1.0000

0.0000 0.0094 0.0000 0.9906

0.0008 0.9952 0.0000 0.0040

0.0626 0.0634 0.0000 0.8739

DISTCI FI 0.6824 0.2997 0.0000 0.0179 FII 0.6784 0.3031 0.0000 0.0185 FIII 0.6197 0.3384 0.0000 0.0419 FIV 0.5288 0.4656 0.0000 0.0056

MAKEUP SOLV

SOLV

ETHANOL

BOTTCII

DISTCIII

0.0000 0.0000 1.0000 0.0000

0.0000 0.0068 0.9932 0.0000

1.0000 0.0000 0.0000 0.0000

0.0028 0.3251 0.6527 0.0194

0.0082 0.9258 0.0099 0.0561

0.0000 0.0000 1.0000 0.0000

0.0000 0.0035 0.9964 0.0001

0.9999 0.0001 0.0000 0.0000

0.0067 0.3078 0.6667 0.0188

0.0196 0.8997 0.0256 0.0551

0.0000 0.0000 1.0000 0.0000

0.0000 0.0091 0.9908 0.0001

0.9994 0.0006 0.0000 0.0000

0.0019 0.2836 0.6793 0.0352

0.0059 0.8824 0.0000 0.1117

0.0000 0.0000 1.0000 0.0000

0.0000 0.0004 0.9951 0.0045

1.0000 0.0000 0.0000 0.0000

0.0016 0.3474 0.6468 0.0042

0.0044 0.9920 0.0000 0.0035

1

Table 6. Design parameters for the separation sequences for feed I and II. Column Number of stage Stage of feed

CI 59 21

Specifications Distillate rate [kmol/h] Bottoms rate [kmol/h] Reflux ratio

8.34 37.02 1.62

Feed stream Acid/Alcohol flowrate [kmol/h] Solvent flowrate [kmol/h] Stage of feed solvent Product streams Ethanol flowrate [kmol/h] Ethanol purity [wt%] Water flowrate [kmol/h] Water purity [wt%] Acid/Alcohol flowrate [kmol/h] Acid/Alcohol purity [wt%] Solvent flowrate [kmol/h] Solvent purity [wt%] Energy Requirements Reboiler duty [kW] Condenser duty [kW]

6.73 8.42 0.81

1.52 6.90 0.70

9 5

OSS-I (FI) CII CIII 94 20 86 3

14.49 5.33 0.11

6.77 12.17 2.06

CI

EX 21

7.64 4.53 0.1

CI 79 25

5.35 40.00 3.12

CSS (FII) CII CIII 69 81 59 25

4.47 9.32 0.28

0.88 8.44 0.34

CI 21 16

9.75 4.31 0.11

OSS-I (FII) CII CIII 78 16 70 14

4.41 8.52 2.10

4.16*

6.80 19

4.45 22

8.44 35

3.25 34

6.73 100

6.77 100

4.46 99.9

4.49 100

37.02 100

30.60 99.97

40 100

35.44 99.8

4.99

4.11

99.04

99.3

6.80 99.6

312 -240

Economic evaluation Total operating cost [k$/year] Total investment cost [k$] Total annual cost [k$/year] Key performance indicators Energy requirements [kWh/ton Ethanol] octanol*, iso-octanol **, octanoic acid***

171 132 527 -402

44 -30

4.42 99.32

260 -182

245 -224

113 -97

EX 48

5.27 3.25 0.1

5.06*

Total reboiler duty [kW] Total cooling duty [kW]

2

CSS (FI) CII CIII 98 9 79 7

8.30 99.5

312 -241

117 -62

25 -15

3.21 99.6

188 123

165 150

79 -67

618 -503

454 -319

432 -341

80

99

70

69

8,863 966

7,276 827

11,237 1,194

7,384 808

1700

1981

2205

2084

1

Table 7. Design parameters for the separation sequences for feed III and IV. Column Number of stage Stage of feed Specifications Distillate rate [kmol/h] Bottoms rate [kmol/h] Reflux ratio

CI 50 20

Energy Requirements Reboiler duty [kW] Condenser duty [kW] Total reboiler duty [kW] Total cooling duty [kW] Economic evaluation Total operating cost [k$/year] Total Investment Cost [k$] Total annual cost [k$/year] Key performance indicators Energy requirements [kWh/ton Ethanol]

2

CI 10 4

OSS-I (FIII) CII CIII 98 34 70 31

EX 35

CI 80 35

CSS (FIV) CII CIII 86 13 63 4

CI 17 2

OSS-I (FIV) CII CIII 95 54 76 46

2.91

2.23

0.59

5.43

2.24

3.12

1.13

0.89

0.20

2.91

0.89

2.02

42.45

5.23

4.64

4.53

5.37

2.25

44.23

2.04

1.85

4.29

3.11

1.09

1.11

0.41

0.30

0.12

1.96

0.18

1.46

0.31

0.42

0.11

2.27

0.10

Feed stream Acid/Alcohol flowrate [kmol/h] Solvent flowrate [kmol/h] Stage of feed solvent Product streams Ethanol flowrate [kmol/h] Ethanol purity [wt%] Water flowrate [kmol/h] Water purity [wt%] Acid/Alcohol flowrate [kmol/h] Acid/Alcohol purity [wt%] Solvent flowrate [kmol/h] Solvent purity [wt%]

CSS (FIII) CII CIII 77 26 58 19

4.43** 2.18

1.81

1.09

10

13

27

6

2.23

2.24

0.89

0.89

100

99.94

100

100

100

138 -68

4.01***

4.55

42.45

62 -34

39.80

44.23

99.72

100

42.13 99.52

4.37

3.99

99.45

99.06

4.50

2.18

1.79

1.09

99.08

99.08

99.11

99.50

15 -10

132 -69

81 -72

50 -43

101 -31

24 -13

5 -3

86 -37

36 -32

30 -25

215

263

130

152

-111

-184

-47

-94

33

42

19

24

6,662

8,226

8,421

12,566

700

865

862

1,281

2093

2549

3171

3707

octanol*, iso-octanol **, octanoic acid ***

EX 97

1 2

Figure 1. Conventional separation sequence (CSS).

3 4 5 6

7 8

Figure 2. Optional separation sequence (OSS-I).

DE Initial design

Codification of variables

no

Maximum number of generation reached

Generate new population

The parent and the mutant are compared and the best individual is selected to populate the next generation

Generation of initial population and evaluation of objective and constraints function using Aspen Plus

Generate subpopulations of N mutant individuals by mutation and crossover operations. Then evaluate objective and constraint functions using Aspen Plus

yes Best point that has been explored over all the generations End

1 2

Figure 3. Flowchart for the differential evolution algorithm.

3 4 5 6 7 8 9 10 11 12 13

14 15

Figure 4. Connection of Mathworks Matlab with AspenTech Aspen Plus via MS Excel

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