Experimental and TheoreticalStudy of MulticomponentBatch DistillationHOUSAM BINOUS,1 MAMDOUH A. AL-HARTHI,1 AHMED BELLAGI2

1Department of Chemical Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

2D�epartement de G�enie Energ�etique (Energy Engineering Department), Ecole Nationale d’Ing�enieurs de Monastir,

University of Monastir, Tunisia

Received 20 December 2014; accepted 8 March 2015

ABSTRACT: We present in this pedagogical paper several important aspects of multi-component batch

distillation. These later include: (1) Experimental implementation and results of a simple multi-component batch

distillation; (2) Theoretical prediction of temperature and composition evolution with time; and (3) Liquid-vapor

equilibrium calculations of ternary mixtures, including residue curves computations. Such study material can be

easily introduced in undergraduate chemical engineering laboratory including the senior-level laboratory, called

CHE 409 at King Fahd University of Petroleum & Minerals (KFUPM). The selected multi-component mixture is

composed of chloroform, acetone, and methanol. This mixture is particularly interesting because it has several

distillation boundaries and up to four azeotropes. During the simple batch distillation experiment, temperature of

the vapor phase is measured and both the initial and final compositions are determined using Nuclear Magnetic

Resonance (NMR). These experimental results are then compared to the theoretical calculations based on two

approaches: (i) mass and energy balance approach (i.e, MESH equations) and (ii) residue curve. The authors share

at the final section of the paper their experience teaching the senior-level chemical engineering laboratory at

KFUPM. � 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 9999:1–11, 2015; View this article online atwileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21652

Keywords: batch distillation; nuclear magnetic resonance; residue curves; Mathematica

INTRODUCTION

Distillation is the most ubiquitous separation method in thechemical industry. Thus, a good grasp of this technique is essentialto both chemical engineering students and professionals. Solvingchemical engineering separation problems requires being able toapply what is taught in the chemical thermodynamics course(named CHE 303 at King Fahd University of Petroleum &Minerals (KFUPM)) as well as the chemical engineeringseparation course (named CHE 306 at KFUPM). Both of thesedistillation aspects are addressed in the present paper. We furthertouch an important aspect of chemical metrology—H1NMRanalysis technique—and give some basic information in Appendix1 on how a H1NMR spectrum can be exploited to obtain themixture molar compositions.

The objective of the reported investigations is the separationby distillation of a three-component mixture composed of

chloroform, acetone and methanol, labeled component 1, 2, and3, respectively in the text thereafter. This mixture is selectedbecause it presents a wealth of interesting behavior in distillationsince it has three binary azeotropes and a ternary azeotrope asindicated in Table 1 [1].

The initial ternary mixture is fed to a simple batch distillationapparatus. During the distillation process the temperature of theexiting vapor is continuously measured. Initial and finalcompositions of the treated mixture are also determined. Thesemeasurements are then compared to the predictions of a theoreticaltreatment of the batch distillation operation involving mass andenergy balance equations (MESH equations), liquid–vaporequilibrium calculations and computation of residue curves.

All calculations as well as graphical representations ofexperimental data are performed using just one software,Mathematica©. The resolution of the large equations systemcomposing the mathematical model of the batch distillationprocess is made possible based on the built-in Mathematica©

function NDSolve, which allows the solution of systems ofdifferential and algebraic equations or DAEs.

Although there are many pedagogical publications on thecomputational aspects of distillation [2–6] and applied

Correspondence to H. Binous (binoushousam@yahoo.com)

© 2015 Wiley Periodicals, Inc.

1

thermodynamics using various software [7–9], to our knowledge,only few present a comparison between experimental data andtheoretical predictions [3]. Another motivation to perform thepresent investigation is the need for the chemical engineeringfaculties at KFUPM to come up with new experiments on appliedthermodynamics. Such experiments had to be simple enough to beconducted in a three-hour lab session, yet allowing interestingtheoretical calculations to be presented in the bi-weekly report thatCHE 409 students have to submit.

The paper is structured as follows:

- Description of apparatus and procedure for the experimentalbatch distillation operation,

- presentation of experimental results for two case studies(named case 1 and case 2 in the text thereafter),

- presentation of the theoretical model for the batchdistillation process (Mass and energy balances, equationsfor residue curve calculation),

- background thermodynamic information and method for thecomputation of VLE and enthalpies,

- presentation and discussion of the theoretical results for thetwo case studies, and

- conclusion with some final remarks regarding the relevanceof these experiments and calculations in the chemicalengineering curriculum.

Finally, a brief presentation of H1NMR analysis techniqueand interpretation of spectra is given in the Appendix 1.

THE EXPERIMENTAL SET-UP, CONDITIONS, ANDRESULTS

Figure 1 shows a sketch of the experimental apparatus used for ourinvestigations. This set-up is composed of an electrical heater(magnetic hot plate HS40, Torrey Pines Scientific, CA,USA), an oilbath, a still (250ml roundbottomflask), a thermocouple (Digi-SensetypeK thermocouple thermometer, Cole-Parmer, IL,USA), awater-cooled refrigerant and a distillate receiver. The chemicals usedwereas follows: (i) Chloroform HPLC grade from Sigma–Aldrich(99.8%), (ii) Acetone AR grade from Alpha (99.5%), and (iii)Methanol laboratory grade from Fisher (99.5%). It should be notethat all experiments were conducted under a fume hood since (i)chloroform and methanol are toxic, and (ii) methanol and acetoneare flammable.

This set-up is characterized by its simplicity, robustness, andlowcost if compared tomany of the lab equipment that KFUPMhasrecently purchased for example for evaporation and distillation. Anobvious improvement of our set-up would be to use an automateddata acquisition system connected to a dedicated PC.

Two case studies are considered in the present paper. Thedesign of both cases builds upon the insight obtained from residue

curve computation, which will be described in the sectionthat discusses theoretical results. Indeed, in the first case study (orcase 1), one can take an initial composition so that the final drop inthe still is pure methanol. In the second case study (or case 2), thefinal drop in the still is the binary azeotrope between chloroformand acetone labeled A3 in Table 1. Thus, no methanol is left in thestill at the very end of this experiment (case 2).

Case Study 1. The still is initially filled with 75 g of the mixtureof chloroform–acetone–methanol with the composition 14.75chloroform, 30.31 acetone, and 54.94mole % methanol. This isreadily achieved by mixing 25 g of each one of the threecomponents since the molecular weights of chloroform, acetoneand methanol are equal to 119.38, 58.08, and 32.04 in g/mol,respectively. We set the temperature of the heater at around 80°C.After a short heating period of few minutes, the first drop ofdistillate falls in the receiver. Temperature, given by thethermocouple, is then manually recorded every 10 s until the stillis empty.

Case Study 2. Again the still is initially filled with roughly 75 g ofthe components in such proportions to achieve the desiredcomposition of 32.06 mole% chloroform, 39.21 mole% acetoneand 28.72 mole% methanol. The initial pressure during bothexperiments (cases 1 and 2) is atmospheric.

The duration of the experiment was approximately 64minfor the first test and 69min for the second test. Figures 2 and 3 givethe measured temperature (red dots) versus time for cases 1 and 2,respectively. H1NMR analysis results of the initial and finalcompositions for both cases are given in Tables 2 and 3. It is worthnoting that gas chromatography could be as an excellentalternative to H1NMR. Indeed, gas chromatography equipmentis much cheaper and far more common in both academia andindustry. The authors have chosen H1NMR because (i) theequipment is available in our chemistry department at KFUPM,

Table 1 Boiling Point and Composition of All Four Azeotropes for

the Chloroform–Acetone–Methanol Mixture at P¼ 101.325 kPa.

Mole %

Chloroform Acetone Methanol BP in °C

A1 65.52 — 34.48 53.82A2 — 79.07 20.93 55.35A3 66.13 33.87 — 64.45A4 22.90 33.21 43.89 57.32

Figure 1 Experimental set-up for simple batch distillation (1: heater, 2:oil bath, 3: still, 4: thermocouple, 5: refrigerant, and 6: distillate receiver).[Color figure can be viewed in the online issue, which is available atwileyonlinelibrary.com.]

2 BINOUS ET AL.

and (ii) the results are obtained in less than a minute. Comparisonsof experimental findings with theoretical predictions are givenafter basic distillation and thermodynamics equations arepresented.

THEORETICAL BACKGROUND

Governing Equations for Simple Batch Distillation

The overall mass balance equation is given by:

dndt

¼ �V ð1Þ

where n is the number of moles or molar hold-up in the still and Vthe vapor boil-up rate expressed in mol/s.

The component mass balance equations are as follows:

dðnxiÞdt

¼ �Vyi for i ¼ 1; 3 ð2Þ

where xi and yi are the liquid-phase and vapor-phasemole fractionsof component i. Equations (1) and (2) are not independent so thatonly three of the four equations are needed.

The energy balance equation is given by:

dðnhÞdt

¼ �VH þ Q ð3Þ

where h and H are the liquid-phase and vapor-phase molarenthalpies (in kJ/mol) and Q is the heat rate (in kW) suppliedby the electric heater. It is worth listing the assumptionsbehind Equation (3): (i) the holdup in the vapor phase is negligibleand (ii) the molar enthalpy and molar internal energy areequal (i.e., contribution of the P�V term to the enthalpy isnegligible).

One must add to the above differential equations an algebraicequation, which allows the calculation of the bubble temperature:

X3i¼1

Psati g ixi ¼ P: ð4Þ

In this relation P is the total pressure (in bar), Psati , the vapor

pressure, and gi, the activity coefficient of component i in theliquid mixture.

Thus, the nature of the equations system that governs simplebatch distillation process is both differential and algebraic. Thissystem of DAEs is readily solved using the Mathematica© built-incommand NDSolve.

Governing Equations for Residue Curves

A residue curve (RC) is obtained by solving the followingequations:

dxidj

¼ xi � yi for i ¼ 1 to Nc ð5Þ

where xi and yi are the liquid mole fraction of component i in thestill and the equilibrium vapor mole fraction and Nc¼ 3 forternary systems. j is the warped time defined by: dj ¼ n

V dtwhere tis the clock time. These equations are obtained from the overallmaterial balance and the (Nc�1) independent component balanceswritten for the simple batch distillation. Doherty and Malone [10]give a full derivation of these equations.

Thermodynamic Background and Data

It is clear that good vapor–liquid equilibrium prediction isessential in order to obtain reliable simulation results for the batchdistillation on one hand and for the computation of the residuecurves on the other. In the following we present more details aboutthis important matter, which is ubiquitous in chemical separationscience.

The vapor pressure of a pure fluid, a function of temperatureonly, is usually estimated using empirical correlations such as theModified Antoine equation:

ln Psati

� � ¼ Ai þ Bi

Ci þ Tþ Diln Tð Þ þ EiT

Fi ð6Þ

Figure 2 Measured (red dots) and predicted (blue curve) temperaturesversus time for case 1. [Color figure can be viewed in the online issue,which is available at wileyonlinelibrary.com.]

Figure 3 Measured (red dots) and predicted (blue curve) temperaturesversus time for case 2. [Color figure can be viewed in the online issue,which is available at wileyonlinelibrary.com.]

MULTICOMPONENT BATCH DISTILLATION 3

where Ai, Bi, Ci, Di, Ei, and Fi are fluid specific constants. Table 4gives the modified Antoine’s constants used in the present studyfor temperature in Kelvin and pressure in kPa.

For mixtures, if the pressure effects can be neglected (i.e.,when the fugacity can be replaced by the pressure as it is often thecase at low to moderate pressures), the vapor phase isapproximately an ideal gas. The vapor–liquid equilibrium isthen described by equation (7):

yiP ¼ xiPsati g i ð7Þ

For the calculation of the activity coefficient gi of componenti in the liquid phase the Wilson model [11,12] can be used:

ln gkð Þ ¼ �lnXNc

j¼1

xjAkj

!þ 1�

XNc

i¼1

xiAikXNc

j¼1xjAij

ð8Þ

Aij is the binary interaction parameter, which depends on the molarvolumes (ni and nj) and the energy terms lii and lij,

Aij ¼ vjviexp � lij � lii

RT

� �: ð9Þ

This model allows good prediction of vapor–liquid equilibriaas long as no immiscible or partially miscible liquid phases arepresent. Tables 5 and 6 give the constants of the Wilson model forour ternary system.

Liquid and vapor enthalpies of each pure component i,labeled hi, and Hi, respectively, are expressed in this study solelyas functions of temperature T by the following equations:

hi ¼ ai;L þ bi;LT þ ci;LT2 þ di;LT

3 þ ei;LT4 ð10Þ

Hi ¼ ai;V þ bi;VT þ ci;VT2 þ di;VT

3 þ ei;VT4 ð11Þ

The constants ai,L, bi,L, ci,L, di,L, ei,L, ai,V, bi,V, ci,V, di,V, and ei,Vfor chloroform, acetone, and methanol are given in Tables 7 and 8.It should be note that the heat of vaporization is incorporated in theexpression of the vapor enthalpies given by Equation 11.

All data in Tables 4–8 are retrieved from Aspen-HYSYS1

thermodynamic properties database (http://www.aspentech.com/).To test the developed thermodynamic model, all three binary

VLE of the ternary system are calculated. As Figure 4 shows, the

Table 2 H1NMR Initial and Final Compositions for Case 1

Group AreaArea/No.H-atom

Initial mixturecomposition att¼ 0 (mole %) Area

Area/No.H-atom

Mixture composition att¼ 3730 (mole %)

H {CHCl3} 0.23 0.23 0.1328 0.01 0.01 0.0097H {HO-CH3} 1.00 1.00 0.5775 1.00 1.00 0.9662H {CH3-CO-CH3} 3.01 0.5017 0.2897 0.15 0.03 0.0242

Table 3 H1NMR Initial and Final Compositions for Case 2

Group AreaArea/No.H-atom

Initial mixturecomposition att¼ 0 (mole %) Area

Area/No.H-atom

Mixture composition att¼ 3995 (mole %)

H {CHCl3} 1.145 1.145 0.3212 1.00 1.00 0.5134H {HO-CH3} 1.00 1.00 0.2805 0.08 0.0267 0.0137H {CH3-CO-CH3} 8.519 1.419 0.3983 5.52 0.92 0.4726

Table 4 Antoine’s Constants for the Components: Chloroform–Acetone–Methanol

A B C D E F

Chloroform 73.7058 �6055.60 0 �8.9189 7.74407 e-6 2Acetone 71.3031 �5952.00 0 �8.53128 7.82393 e-6 2Methanol 59.8373 �6282.89 0 �6.37873 4.61746 e-6 2

Table 5 Binary Interaction Parameters for the Components:

Chloroform–Acetone–Methanol (in kcal/mol)

Chloroform Acetone Methanol

Chloroform 0 116.1171 1694.0240Acetone �506.8518 0 551.4545Methanol �361.7944 �124.9328 0

Table 6 Molar Volumes for the Components: Chloroform–Acetone–

Methanol (in cm3/mol)

n

Chloroform 80.7Acetone 74.0Methanol 40.7

4 BINOUS ET AL.

Table 7 Constants for the Liquid-phase Enthalpy for the Components: Chloroform–Acetone–Methanol

aL bL cL dL eL

Chloroform 0 108.14 �2.680 e-2 8.267 e-5 �2.734 e-14Acetone 0 135.60 �8.850 e-2 9.457 e-5 1.722 e-7Methanol 0 105.80 �1.811 e-1 3.126 e-4 3.639 e-13

Table 8 Constants for the Vapor-Phase Enthalpy for the Components: Chloroform–Acetone–Methanol

av bv cv dv ev

Chloroform 38581.32 129.0917 �0.3174863 7.54837 e-4 �6.97207 e-7Acetone 41714.86 115.8302 �0.1686754 2.83902 e-4 �1.42898 e-7Methanol 50918.36 93.9760 �0.3743472 7.93734 e-4 �6.26927 e-7

Figure 4 VLE data for the three binary mixtures: (a) acetone-chloroform, (b) acetone-methanol, and (c) chloroform-methanol at P¼ 101.325 kPa (predictions using Wilson model shown by solid lines and experimental data from Refs. 13,14, and 15 shown by &). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

MULTICOMPONENT BATCH DISTILLATION 5

predicted vapor-liquid equilibria are consistent with the publishedexperimentally measured data [13–15].

COMPARISON OF EXPERIMENTAL DATA WITHTHEORETICAL PREDICTIONS

Case Study 1: Pure Methanol as Residue

The residue curve for this case is represented in red in the ternarydiagram of Figure 5. This curve goes through the blue circle withcoordinates (0.1475, 0.3031, and 0.5494) corresponding to themolar composition of the initial mixture fed to the still. Further, thedirection of increasing warped time, j, is shown using the two grayarrows on the red residue curve. The distillation boundaries ordistillation frontiers are indicated by the dotted curves. Theseboundary calculations are based on a simple trial-and-erroralgorithm thoroughly described by A. Lucia [16].

Considering this Figure, we note that

� Methanol is a stable node since it is the point where thisresidue curve ends up.

� The binary azeotrope between acetone and methanol,indicated by A2, is an unstable node since the computedresidue curve diverges from this point.

� There is only one saddle point in the region colored in green:the unique ternary azeotrope labeled A4.

� As expected, the residue curve remains in the region definedby the green pane and does not cross any of the boundaries.

To test our batch distillation model, we compare in Figure 2the experimentally measured and computed temperatures for thiscase. In order for the theoretical prediction to closely match

the experimental data, the total pressure, P, and heating rate, Q,were considered as adjustable parameters—since we did not haveaccess to their actual values experimentally—and evaluated to94 kPa and 12.45 kW, respectively. Hence, only reasonableagreement is observed between the theoretical and experimentaltemperature evolution versus time in the still. It should be notedthat the temperature measurement inherent to the type Kthermocouple is �0.5°C. The blue curve (i.e, the theoreticaltemperature) falls close to the region of uncertainty shown in greenin Figure 2. In Figures 6 and 7 we show the effect of these twoparameters (P andQ) on the temperature evolution. Increasing thetotal pressure, P, will shift up the temperature profile because theboiling temperature of the ternary mixture increases with totalpressure. On the other hand, increasing the heat rate, Q, will shiftthe temperature versus time curve to the left (i.e., the distillationprocess is conductedmore rapidly). As expected, the vapor boil-uprate, V, will also become higher (see Figure 8).

Another reason for the observed small discrepancy betweenexperimental and theoretical results can be invoked. Thetheoretical predictions are calculated by assuming that thermaland thermodynamic equilibrium are reached instantaneously, i.e.,heat and mass transfer processes are very fast. This might not bethe case during the experiment.

Contrary to the binary case, it is not possible to infercomposition from temperature measurements in ternary systems.However, the initial and final compositions were measuredexperimentally using H1NMR. Figure 9 shows the predictedevolution of liquid and vapor phase mole fractions of all threecomponents by solid and dotted curves, respectively. The H1NMRresults, for all three component as well as for the initial and finalcompositions, superimpose nicely in this plot on the calculatecurves.

Figure 5 Residue curve that goes through the point (0.14, 0.30). [Color figure can be viewed in the online issue, which isavailable at wileyonlinelibrary.com.]

6 BINOUS ET AL.

Finally, Figure 10 shows how the molar hold up in the stillvaries with time. It is evident that n(t) starts from an initial valuegiven by

n t ¼ 0ð Þ ¼ mMW

¼ 75gX3

i¼1Mixi;0

¼ 1:420mole

where Mi and xi,0 are the molecular weight and the initial molefraction of component i, respectively. The final value of n(t) equalsto zero (i.e., no more liquid is left in the still at the end of theexperiment).

Case Study 2: Binary Azeotrope as a Residue

The residue curve for this case is represented in red in the ternarydiagram of Figure 11. It goes through the blue circle with

coordinates (0.3206, 0.3921, and 0.2872) corresponding to themolar composition of the initial mixture in the still. Further,the direction of increasing j is shown by the gray arrow on theresidue curve. The distillation boundaries or distillation frontiersare indicated by the dotted curves.

By considering Figure 11 we can make some remarks asfallows

� The binary chloroform-acetone azeotrope (point A3 on theFigure) is a stable node since it is the point where this residuecurve ends up.

� The binary chloroform-methanol azeotrope indicated by A1

is an unstable node since the computed residue curvediverges from this point.

Figure 7 Effect of heat rate on the temperature profile (Blue: 17 kw, Red:12 kw, and Green: 7 kw). [Color figure can be viewed in the online issue,which is available at wileyonlinelibrary.com.]

Figure 6 Effect of total pressure on the temperature profile (Blue:108 kPa, Red: 100 kPa, and Green: 92 kPa). [Color figure can be viewed inthe online issue, which is available at wileyonlinelibrary.com.]

Figure 9 Liquid (solid curve) and vapor (dashed curve) phasecompositions versus time for case 1 (Red: methanol, Blue: acetone, andGreen: chloroform, ^: NMR final composition and *: NMR initialcomposition). [Color figure can be viewed in the online issue, which isavailable at wileyonlinelibrary.com.]

Figure 8 Effect of heat rate on the vapor boil-up rate (Blue: 17 kw, Red:12 kw, and Green: 7 kw). [Color figure can be viewed in the online issue,which is available at wileyonlinelibrary.com.]

MULTICOMPONENT BATCH DISTILLATION 7

� There are two saddle points in the region colored inyellow: the unique ternary azeotrope labeled A4 and purechloroform.

� Asexpected the residuecurve remains in the regiondefinedbythe yellow pane and does not cross any of the boundaries.

Figure 3 compares the measured and calculated temperatureprofiles for this case. Good agreement is observed. Again, the bluecurve (i.e, the theoretical temperature) falls within the region of

uncertainty shown in green in Figure 3. Like in the first case thetotal pressure, P, and heating rate, Q, are deduced by regressionand found to be 99 kPa and 8.05 kW, respectively. Figure 12 showsthe predicted liquid and vapor phase mole fractions of all threecomponents by solid and dotted curves, respectively. As canbe noticed, the NMR measurements of all compositions (initialand final) are in good agreement with their calculated values.

Figure 10 Molar hold up, n(t), in the still versus time for case 1. [Colorfigure can be viewed in the online issue, which is available atwileyonlinelibrary.com.]

Figure 11 Residue curve that goes through the point (0.32, 0.39). [Color figure can be viewed in the online issue, whichis available at wileyonlinelibrary.com.]

Figure 12 Liquid (solid curve) and vapor (dashed curve) phasecompositions for case 2 (Red: methanol, Blue: acetone, and Green:chloroform,^: NMR final composition and*: NMR initial composition).[Color figure can be viewed in the online issue, which is available atwileyonlinelibrary.com.]

8 BINOUS ET AL.

It is clear that the final product of this batch distillation isthe binary azeotrope A3, since the liquid-phase and vapor-phase compositions intersect at that point (i.e., xi¼ yi for i¼ 1–3) and the methanol mole fractions are equal to zero (i.e.,x3¼ y3¼ 0 at the end of the calculation when the molar hold-upin the still is zero). The authors obtained results showing similartrends to those presented in Figures 6, 7, 8, and 10 for case 2.These results are not shown to keep the article as concise aspossible.

It is to be noted that any batch distillation experimentstarting from a ternary mixture composed of chloroform –acetone–methanol gives as residue either methanol (case 1) orthe binary azeotrope between chloroform and acetone (case 2).Indeed, these are the only two stable nodes. All other points (i.e.,pure components and azeotropes) are either saddle points orunstable nodes. This become clearer if one considers Figure 13where the bubble temperature distribution is plotted. Indeed,stable nodes correspond to the points located in the pink-magenta region with high mixture bubble-temperatures. Thesaddle point are located in the green region, which correspondsto intermediate mixture bubble–temperatures. Finally, theunstable nodes are located in the orange–yellow region of thediagram where low mixture bubble–temperatures are to befound.

CONCLUDING REMARKS

The authors of this paper have performed both experimental andtheoretical calculations of a multi-component batch distillation ofa ternarymixture composed of chloroform, acetone, andmethanol.This mixture is particularly interesting because it has severaldistillation boundaries and up to four azeotropes. The residuecurves of this mixture, however, indicate that only two cases arepossible: the residue is either pure methanol or the binaryazeotrope between acetone and chloroform. This simple andinexpensive experiment is rich in its theoretical implications andteachings. Further, it is one of the simplest chemical engineeringexamples of an unsteady-state process. For this reason, the authorsencourage other chemical engineering departments to adopt batchdistillation experiments. Calculations with Mathematica© take afraction of a second on a PC i5 2.7GHz processor. Finally, all theMathematica© codes used in the presented theoretical calculationsare available upon request from the corresponding author.

Nomenclatureh liquid-phase enthalpy kJ/moleH vapor-phase enthalpy kJ/molen molar hold-up (mol)P total pressure (bar)

Figure 13 Mixture bubble–temperature distribution at P¼ 101.325 kPa. [Color figure can be viewed in the online issue,which is available at wileyonlinelibrary.com.]

MULTICOMPONENT BATCH DISTILLATION 9

Pi partial pressure (bar)Psati vapor pressure (bar)

t time (s)T temperature (K)n molar volume (cm3/mole)V vapor boil-up rate (mole/s)x liquid-phase composition (mole fraction)y vapor-phase composition (mole fraction)

Greek Letters

g activity coefficientj warped time

ACKNOWLEDGMENT

The support of King Fahd University of Petroleum & Minerals isduly acknowledged.

REFERENCES

[1] H. Binous, A. Wakad, and S. Ben Achour, Residue curve mapcalculation of a ternary mixture, Comput Educa J 16 (2006), 32–35.

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[4] T. Castrell�on, D. C. Bot�ıa, R. G�omez, G. Orozco, and I. D. Gil, Usingprocess simulators in the study, design, and control of distillationcolumns for undergraduate chemical engineering courses, ComputAppl Eng Educ 19 (2011), 621–630.

[5] J. F. Granjo, M. G. Rasteiro, L. M. Gando-Ferreira, F. P. Bernardo,M. G. Carvalho, and A. G. Ferreira, A virtual platform to teachseparation processes, Comput Appl Eng Educ 20 (2012), 175–186.

[6] S. X. Liu and M. Peng, The simulation of the simple batch distillationof multiple-component mixtures via Rayleigh’s equation, ComputAppl Eng Educ 15 (2007), 198–204.

[7] R. Baur, J. Bailey, B. Brol, A. Tatusko, andR. Taylor,Maple and the artof thermodynamics, Comput Appl Eng Educ 6 (1998), 223–234.

[8] F. Cruz-Perag�on, J. M. Palomar, E. Torres-Jimenez, and R. Dorado,Spreadsheet for teaching reciprocating engine cycles, Comput ApplEng Educ 20 (2012), 681–691.

[9] Y. Liu, Development of instructional courseware in thermodynamicseducation, Comput Appl Eng Educ 19 (2011), 115–124.

[10] M. F. Doherty and M. F. Malone, Conceptual Design of DistillationSystems. McGraw-Hill, New York, 2001.

[11] S. I. Sandler, Chemical Engineering Thermodynamics, 3rd ed., JohnWiley & Sons, NewYork, 1999.

[12] G. M. Wilson, Vapor-liquid equilibrium XI: a new expression forthe excess free energy of mixing, J Am Chem Soc 86 (1964), 127–130.

[13] H. H. Amer, R. R. Paxton , and M. van Winkle, Methanol–Ethanol–Acetone Vapor-liquid equilibria, Ind Eng Chem 48 (1956), 142–146.

[14] I. Nagata, Isobaric Vapor-Liquid Equilibria for the Ternary SystemChloroform - Methanol - Ethyl Acetate, J Chem Eng Data 7 (1962),367–373.

[15] W. Reindersand and C. H. de Minjer, Vapour-liquid equilibria internary systems. VI. The System Water– Acetone–Chloroform,Recueil des Travaux Chimiques des Pays-Bas 66 (1947), 573–604.

[16] A. Lucia, "Distilation Tutorial V: Azeotopes and DistillationBoundaries." http://personal.egr.uri.edu/lucia/tutorials/tutorial5.html(accessed September 2014).

APPENDIX 1

NMR analysis and spectra interpretation

Two samples of about 0.3ml each were taken for H1NMRanalysis. The first was from the initial mixture and the second wasfrom the residue near the end of the distillation experiment. ProtonNMR spectra of these initial and final samples were taken at23.2°C on Bruker 500MHz spectrometer. CDCl3 was used asNMR solvent. All chemical shifts were reported in parts permillion (ppm). Chemical shifts obtained in the region of d

7.37 ppm corresponds to the 1 H-atom in the CHCl3, d 3.1–3.3corresponds to the 3H atoms in the methanol and d 3.7 ppmcorresponds to the 1H atom in the OH group of the methanol. TheChemical shift at d 0.9–2.2 ppm corresponds to the 6 H atoms inthe CH3COCH3.

The mixture composition is calculated according to thefollowing equations:

Fj ¼ IjX3

k¼1Ik

for j ¼ 1::3

where Fj is the molar percent of component j, I1¼(Integrated areathe Chloroform)/(No. of H-atoms in the Chloroform), I2¼(Integrated area of the Acetone)/(No. of H-atoms in Acetone)and I3¼ Integrated area of OH group in the methanol).

10 BINOUS ET AL.

BIOGRAPHIES

Dr. Housam Binous, a visiting AssociateProfessor at King Fahd University Petroleum&Minerals, has been a full time faculty memberat the National Institute of Applied Sciences andTechnology in Tunis for eleven years. He earneda Diplomed’ing�enieur in biotechnology from theEcole des Mines de Paris and a PhD in chemicalengineering from the University of California atDavis. His research interests include the appli-cations of computers in chemical engineering.

Dr. Mamdouh A. Al-Harthi is currently anAssociate Professor in the department ofChemical Engineering in King Fahd Universityof Petroleum & Minerals (KFUPM), Kingdomof Saudi Arabia. He has obtained both aBachelor’s and Master’s degrees in ChemicalEngineering from KFUPM and a PhD inChemical Engineering from University ofWaterloo, Canada in 2007. He was awardedthe Gold Medal for Proficiency in Research for

the best PhD thesis in 2006 by the University of Waterloo. His researchinterests are in the areas of polymer reaction engineering, polymer scienceand mathematical modeling. Dr. Al-Harthi published more than 50 papersin highly reputed journals.

Ahmed Bellagi is a Professor of chemical andenergy engineering at the Ecole Nationaled’Ing�enieurs de Monastir, University of Monas-tir, Tunisia since 1992. Prior to that, he hasbeen a Professor of chemical engineering at theEcole Nationale d’Ing�enieurs de Gab�es. Hereceived his PhD in 1979 from the Rheinisch-Westf€alische Technische Hochschule at Aa-chen – Germany. His research interests mainlyfocus on Process Thermodynamics, Absorption

Refrigeration and Solar Cooling, Modeling and Simulation of UnitOperations and Processes. He has supervised 35Master degree and 20 PhDtheses. Professor Bellagi has written over 100 publications. Recently hispedagogical interests focus on the development of Demonstrations forchemical engineers using the softwareMathematica1 in collaboration withDr. H. Binous and Professor B. G. Higgins. (http://demonstrations.wolfram.com/author.html?author=Housam+Binous).

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