Experimental and Theoretical Study of Multicomponent Batch Distillation

Transcription

1 Experimental and Theoretical Study of Multicomponent Batch Distillation HOUSAM BINOUS, 1 MAMDOUH A. AL-HARTHI, 1 AHMED BELLAGI 2 1 Department of Chemical Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia 2 Departement de Genie Energetique (Energy Engineering Department), Ecole Nationale d Ingenieurs 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. ß 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 9999:1 11, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI /cae Keywords: batch distillation; nuclear magnetic resonance; residue curves; Mathematica INTRODUCTION Distillation is the most ubiquitous separation method in the chemical industry. Thus, a good grasp of this technique is essential to both chemical engineering students and professionals. Solving chemical engineering separation problems requires being able to apply what is taught in the chemical thermodynamics course (named CHE 303 at King Fahd University of Petroleum & Minerals (KFUPM)) as well as the chemical engineering separation course (named CHE 306 at KFUPM). Both of these distillation aspects are addressed in the present paper. We further touch an important aspect of chemical metrology H 1 NMR analysis technique and give some basic information in Appendix 1 on how a H 1 NMR spectrum can be exploited to obtain the mixture molar compositions. The objective of the reported investigations is the separation by distillation of a three-component mixture composed of Correspondence to H. Binous (binoushousam@yahoo.com) 2015 Wiley Periodicals, Inc. chloroform, acetone and methanol, labeled component 1, 2, and 3, respectively in the text thereafter. This mixture is selected because it presents a wealth of interesting behavior in distillation since it has three binary azeotropes and a ternary azeotrope as indicated in Table 1 [1]. The initial ternary mixture is fed to a simple batch distillation apparatus. During the distillation process the temperature of the exiting vapor is continuously measured. Initial and final compositions of the treated mixture are also determined. These measurements are then compared to the predictions of a theoretical treatment of the batch distillation operation involving mass and energy balance equations (MESH equations), liquid vapor equilibrium calculations and computation of residue curves. All calculations as well as graphical representations of experimental data are performed using just one software, Mathematica. The resolution of the large equations system composing the mathematical model of the batch distillation process is made possible based on the built-in Mathematica function NDSolve, which allows the solution of systems of differential and algebraic equations or DAEs. Although there are many pedagogical publications on the computational aspects of distillation [2 6] and applied 1

2 2 BINOUS ET AL. Table 1 Boiling Point and Composition of All Four Azeotropes for the Chloroform Acetone Methanol Mixture at P ¼ kpa. Mole % Chloroform Acetone Methanol BP in C A A A A thermodynamics using various software [7 9], to our knowledge, only few present a comparison between experimental data and theoretical predictions [3]. Another motivation to perform the present investigation is the need for the chemical engineering faculties at KFUPM to come up with new experiments on applied thermodynamics. Such experiments had to be simple enough to be conducted in a three-hour lab session, yet allowing interesting theoretical calculations to be presented in the bi-weekly report that CHE 409 students have to submit. The paper is structured as follows: - Description of apparatus and procedure for the experimental batch 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 batch distillation process (Mass and energy balances, equations for residue curve calculation), - background thermodynamic information and method for the computation of VLE and enthalpies, - presentation and discussion of the theoretical results for the two case studies, and - conclusion with some final remarks regarding the relevance of these experiments and calculations in the chemical engineering curriculum. Finally, a brief presentation of H 1 NMR analysis technique and interpretation of spectra is given in the Appendix 1. THE EXPERIMENTAL SET-UP, CONDITIONS, AND RESULTS Figure 1 shows a sketch of the experimental apparatus used for our investigations. This set-up is composed of an electrical heater (magnetic hot plate HS40, Torrey Pines Scientific, CA, USA), an oil bath, a still (250 ml round bottom flask), a thermocouple (Digi-Sense type K thermocouple thermometer, Cole-Parmer, IL, USA), a watercooled refrigerant and a distillate receiver. The chemicals used were as 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 note that all experiments were conducted under a fume hood since (i) chloroform and methanol are toxic, and (ii) methanol and acetone are flammable. This set-up is characterized by its simplicity, robustness, and low cost if compared to many of the lab equipment that KFUPM has recently purchased for example for evaporation and distillation. An obvious improvement of our set-up would be to use an automated data acquisition system connected to a dedicated PC. Two case studies are considered in the present paper. The design of both cases builds upon the insight obtained from residue 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 at wileyonlinelibrary.com.] curve computation, which will be described in the section that discusses theoretical results. Indeed, in the first case study (or case 1), one can take an initial composition so that the final drop in the still is pure methanol. In the second case study (or case 2), the final drop in the still is the binary azeotrope between chloroform and acetone labeled A 3 in Table 1. Thus, no methanol is left in the still at the very end of this experiment (case 2). Case Study 1. The still is initially filled with 75 g of the mixture of chloroform acetone methanol with the composition chloroform, acetone, and mole % methanol. This is readily achieved by mixing 25 g of each one of the three components since the molecular weights of chloroform, acetone and methanol are equal to , 58.08, and 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 of distillate falls in the receiver. Temperature, given by the thermocouple, is then manually recorded every 10 s until the still is empty. Case Study 2. Again the still is initially filled with roughly 75 g of the components in such proportions to achieve the desired composition of mole% chloroform, mole% acetone and mole% methanol. The initial pressure during both experiments (cases 1 and 2) is atmospheric. The duration of the experiment was approximately 64 min for the first test and 69 min for the second test. Figures 2 and 3 give the measured temperature (red dots) versus time for cases 1 and 2, respectively. H 1 NMR analysis results of the initial and final compositions for both cases are given in Tables 2 and 3. It is worth noting that gas chromatography could be as an excellent alternative to H 1 NMR. Indeed, gas chromatography equipment is much cheaper and far more common in both academia and industry. The authors have chosen H 1 NMR because (i) the equipment is available in our chemistry department at KFUPM,

3 MULTICOMPONENT BATCH DISTILLATION 3 Figure 2 Measured (red dots) and predicted (blue curve) temperatures versus time for case 1. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] and (ii) the results are obtained in less than a minute. Comparisons of experimental findings with theoretical predictions are given after basic distillation and thermodynamics equations are presented. THEORETICAL BACKGROUND Governing Equations for Simple Batch Distillation The overall mass balance equation is given by: dn dt ¼ V ð1þ where n is the number of moles or molar hold-up in the still and V the vapor boil-up rate expressed in mol/s. The component mass balance equations are as follows: dðnx i Þ ¼ Vy dt i for i ¼ 1; 3 ð2þ where x i and y i are the liquid-phase and vapor-phase mole fractions of component i. Equations (1) and (2) are not independent so that only three of the four equations are needed. The energy balance equation is given by: dðnhþ ¼ VH þ Q ð3þ dt where h and H are the liquid-phase and vapor-phase molar enthalpies (in kj/mol) and Q is the heat rate (in kw) supplied by the electric heater. It is worth listing the assumptions behind Equation (3): (i) the holdup in the vapor phase is negligible and (ii) the molar enthalpy and molar internal energy are equal (i.e., contribution of the P V term to the enthalpy is negligible). One must add to the above differential equations an algebraic equation, which allows the calculation of the bubble temperature: X 3 i¼1 P sat i g i x i ¼ P: ð4þ In this relation P is the total pressure (in bar), P sat i, the vapor pressure, and g i, the activity coefficient of component i in the liquid mixture. Thus, the nature of the equations system that governs simple batch distillation process is both differential and algebraic. This system of DAEs is readily solved using the Mathematica built-in command NDSolve. Governing Equations for Residue Curves A residue curve (RC) is obtained by solving the following equations: dx i dj ¼ x i y i for i ¼ 1 to Nc ð5þ where x i and y i are the liquid mole fraction of component i in the still and the equilibrium vapor mole fraction and Nc ¼ 3 for ternary systems. j is the warped time defined by: dj ¼ n V dt where t is the clock time. These equations are obtained from the overall material balance and the (Nc 1) independent component balances written 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 is essential in order to obtain reliable simulation results for the batch distillation on one hand and for the computation of the residue curves on the other. In the following we present more details about this important matter, which is ubiquitous in chemical separation science. The vapor pressure of a pure fluid, a function of temperature only, is usually estimated using empirical correlations such as the Modified Antoine equation: Figure 3 Measured (red dots) and predicted (blue curve) temperatures versus time for case 2. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] ln P sat i ¼ Ai þ B i C i þ T þ D ilnðtþþe i T Fi ð6þ

4 4 BINOUS ET AL. Table 2 H 1 NMR Initial and Final Compositions for Case 1 Group Area Area/No. H-atom Initial mixture composition at t ¼ 0 (mole %) Area Area/No. H-atom Mixture composition at t ¼ 3730 (mole %) H {CHCl 3 } H {HO-CH 3 } H {CH 3 -CO-CH 3 } Table 3 H 1 NMR Initial and Final Compositions for Case 2 Group Area Area/No. H-atom Initial mixture composition at t ¼ 0 (mole %) Area Area/No. H-atom Mixture composition at t ¼ 3995 (mole %) H {CHCl 3 } H {HO-CH 3 } H {CH 3 -CO-CH 3 } Table 4 Antoine s Constants for the Components: Chloroform Acetone Methanol A B C D E F Chloroform e-6 2 Acetone e-6 2 Methanol e-6 2 where A i, B i, C i, D i, E i, and F i are fluid specific constants. Table 4 gives the modified Antoine s constants used in the present study for 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 the case at low to moderate pressures), the vapor phase is approximately an ideal gas. The vapor liquid equilibrium is then described by equation (7): y i P ¼ x i P sat i g i ð7þ For the calculation of the activity coefficient g i of component i in the liquid phase the Wilson model [11,12] can be used:! lnðg k Þ ¼ ln XNc x j A kj þ 1 XNc j¼1 i¼1 x i A ik X Nc j¼1 x ja ij A ij is the binary interaction parameter, which depends on the molar volumes (n i and n j ) and the energy terms l ii and l ij, A ij ¼ v j exp l ij l ii : ð9þ v i RT Table 5 Binary Interaction Parameters for the Components: Chloroform Acetone Methanol (in kcal/mol) Chloroform Acetone Methanol Chloroform Acetone Methanol ð8þ This model allows good prediction of vapor liquid equilibria as long as no immiscible or partially miscible liquid phases are present. Tables 5 and 6 give the constants of the Wilson model for our ternary system. Liquid and vapor enthalpies of each pure component i, labeled h i, and H i, respectively, are expressed in this study solely as functions of temperature T by the following equations: h i ¼ a i;l þ b i;l T þ c i;l T 2 þ d i;l T 3 þ e i;l T 4 H i ¼ a i;v þ b i;v T þ c i;v T 2 þ d i;v T 3 þ e i;v T 4 ð10þ ð11þ The constants a i,l, b i,l, c i,l, d i,l, e i,l, a i,v, b i,v, c i,v, d i,v, and e i,v for chloroform, acetone, and methanol are given in Tables 7 and 8. It should be note that the heat of vaporization is incorporated in the expression of the vapor enthalpies given by Equation 11. All data in Tables 4 8 are retrieved from Aspen-HYSYS 1 thermodynamic properties database ( To test the developed thermodynamic model, all three binary VLE of the ternary system are calculated. As Figure 4 shows, the Table 6 Molar Volumes for the Components: Chloroform Acetone Methanol (in cm 3 /mol) n Chloroform 80.7 Acetone 74.0 Methanol 40.7

5 MULTICOMPONENT BATCH DISTILLATION 5 Table 7 Constants for the Liquid-phase Enthalpy for the Components: Chloroform Acetone Methanol a L b L c L d L e L Chloroform e e e-14 Acetone e e e-7 Methanol e e e-13 Table 8 Constants for the Vapor-Phase Enthalpy for the Components: Chloroform Acetone Methanol a v b v c v d v e v Chloroform e e-7 Acetone e e-7 Methanol e e-7 Figure 4 VLE data for the three binary mixtures: (a) acetone-chloroform, (b) acetone-methanol, and (c) chloroformmethanol at P ¼ 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.]

6 6 BINOUS ET AL. predicted vapor-liquid equilibria are consistent with the published experimentally measured data [13 15]. COMPARISON OF EXPERIMENTAL DATA WITH THEORETICAL PREDICTIONS Case Study 1: Pure Methanol as Residue The residue curve for this case is represented in red in the ternary diagram of Figure 5. This curve goes through the blue circle with coordinates (0.1475, , and ) corresponding to the molar composition of the initial mixture fed to the still. Further, the direction of increasing warped time, j, is shown using the two gray arrows on the red residue curve. The distillation boundaries or distillation frontiers are indicated by the dotted curves. These boundary calculations are based on a simple trial-and-error algorithm thoroughly described by A. Lucia [16]. Considering this Figure, we note that Methanol is a stable node since it is the point where this residue curve ends up. The binary azeotrope between acetone and methanol, indicated by A 2, is an unstable node since the computed residue curve diverges from this point. There is only one saddle point in the region colored in green: the unique ternary azeotrope labeled A 4. As expected, the residue curve remains in the region defined by the green pane and does not cross any of the boundaries. To test our batch distillation model, we compare in Figure 2 the experimentally measured and computed temperatures for this case. 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 have access to their actual values experimentally and evaluated to 94 kpa and kw, respectively. Hence, only reasonable agreement is observed between the theoretical and experimental temperature evolution versus time in the still. It should be noted that the temperature measurement inherent to the type K thermocouple is 0.5 C. The blue curve (i.e, the theoretical temperature) falls close to the region of uncertainty shown in green in Figure 2. In Figures 6 and 7 we show the effect of these two parameters (P and Q) on the temperature evolution. Increasing the total pressure, P, will shift up the temperature profile because the boiling temperature of the ternary mixture increases with total pressure. On the other hand, increasing the heat rate, Q, will shift the temperature versus time curve to the left (i.e., the distillation process is conducted more rapidly). As expected, the vapor boil-up rate, V, will also become higher (see Figure 8). Another reason for the observed small discrepancy between experimental and theoretical results can be invoked. The theoretical predictions are calculated by assuming that thermal and thermodynamic equilibrium are reached instantaneously, i.e., heat and mass transfer processes are very fast. This might not be the case during the experiment. Contrary to the binary case, it is not possible to infer composition from temperature measurements in ternary systems. However, the initial and final compositions were measured experimentally using H 1 NMR. Figure 9 shows the predicted evolution of liquid and vapor phase mole fractions of all three components by solid and dotted curves, respectively. The H 1 NMR results, for all three component as well as for the initial and final compositions, superimpose nicely in this plot on the calculate curves. Figure 5 Residue curve that goes through the point (0.14, 0.30). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

7 MULTICOMPONENT BATCH DISTILLATION 7 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 in the online issue, which is available at wileyonlinelibrary.com.] Finally, Figure 10 shows how the molar hold up in the still varies with time. It is evident that n(t) starts from an initial value given by nt¼ ð 0Þ ¼ m MW ¼ 75g X 3 ¼ 1:420 mole M i¼1 ix i;0 where M i and x i,0 are the molecular weight and the initial mole fraction of component i, respectively. The final value of n(t) equals to zero (i.e., no more liquid is left in the still at the end of the experiment). Case Study 2: Binary Azeotrope as a Residue The residue curve for this case is represented in red in the ternary diagram of Figure 11. It goes through the blue circle with 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.] coordinates (0.3206, , and ) corresponding to the molar composition of the initial mixture in the still. Further, the direction of increasing j is shown by the gray arrow on the residue curve. The distillation boundaries or distillation frontiers are indicated by the dotted curves. By considering Figure 11 we can make some remarks as fallows The binary chloroform-acetone azeotrope (point A 3 on the Figure) is a stable node since it is the point where this residue curve ends up. The binary chloroform-methanol azeotrope indicated by A 1 is an unstable node since the computed residue curve diverges 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 9 Liquid (solid curve) and vapor (dashed curve) phase compositions versus time for case 1 (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 at wileyonlinelibrary.com.]

8 8 BINOUS ET AL. Figure 10 Molar hold up, n(t), in the still versus time for case 1. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] There are two saddle points in the region colored in yellow: the unique ternary azeotrope labeled A 4 and pure chloroform. As expected the residue curve remains in the region defined by the yellow pane and does not cross any of the boundaries. Figure 3 compares the measured and calculated temperature profiles for this case. Good agreement is observed. Again, the blue curve (i.e, the theoretical temperature) falls within the region of Figure 12 Liquid (solid curve) and vapor (dashed curve) phase compositions 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 at wileyonlinelibrary.com.] uncertainty shown in green in Figure 3. Like in the first case the total pressure, P, and heating rate, Q, are deduced by regression and found to be 99 kpa and 8.05 kw, respectively. Figure 12 shows the predicted liquid and vapor phase mole fractions of all three components by solid and dotted curves, respectively. As can be noticed, the NMR measurements of all compositions (initial and final) are in good agreement with their calculated values. Figure 11 Residue curve that goes through the point (0.32, 0.39). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

9 MULTICOMPONENT BATCH DISTILLATION 9 Figure 13 Mixture bubble temperature distribution at P ¼ kpa. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] It is clear that the final product of this batch distillation is the binary azeotrope A3, since the liquid-phase and vaporphase compositions intersect at that point (i.e., xi ¼ yi for i ¼ 1 3) and the methanol mole fractions are equal to zero (i.e., x3 ¼ y3 ¼ 0 at the end of the calculation when the molar hold-up in the still is zero). The authors obtained results showing similar trends to those presented in Figures 6, 7, 8, and 10 for case 2. These results are not shown to keep the article as concise as possible. It is to be noted that any batch distillation experiment starting from a ternary mixture composed of chloroform acetone methanol gives as residue either methanol (case 1) or the 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 or unstable nodes. This become clearer if one considers Figure 13 where the bubble temperature distribution is plotted. Indeed, stable nodes correspond to the points located in the pinkmagenta region with high mixture bubble-temperatures. The saddle point are located in the green region, which corresponds to intermediate mixture bubble temperatures. Finally, the unstable nodes are located in the orange yellow region of the diagram where low mixture bubble temperatures are to be found. CONCLUDING REMARKS The authors of this paper have performed both experimental and theoretical calculations of a multi-component batch distillation of a ternary mixture composed of chloroform, acetone, and methanol. This mixture is particularly interesting because it has several distillation boundaries and up to four azeotropes. The residue curves of this mixture, however, indicate that only two cases are possible: the residue is either pure methanol or the binary azeotrope between acetone and chloroform. This simple and inexpensive experiment is rich in its theoretical implications and teachings. Further, it is one of the simplest chemical engineering examples of an unsteady-state process. For this reason, the authors encourage other chemical engineering departments to adopt batch distillation experiments. Calculations with Mathematica take a fraction of a second on a PC i5 2.7 GHz processor. Finally, all the Mathematica codes used in the presented theoretical calculations are available upon request from the corresponding author. Nomenclature h H n P liquid-phase enthalpy kj/mole vapor-phase enthalpy kj/mole molar hold-up (mol) total pressure (bar)

10 10 BINOUS ET AL. P i P sat i t T n V x y partial pressure (bar) vapor pressure (bar) time (s) temperature (K) molar volume (cm 3 /mole) vapor boil-up rate (mole/s) liquid-phase composition (mole fraction) vapor-phase composition (mole fraction) Greek Letters g j activity coefficient warped time ACKNOWLEDGMENT The support of King Fahd University of Petroleum & Minerals is duly acknowledged. REFERENCES [1] H. Binous, A. Wakad, and S. Ben Achour, Residue curve map calculation of a ternary mixture, Comput Educa J 16 (2006), [2] H. Binous, E. Al-Mutairi, and N. Faqir, Study of the separation of simple binary and ternary mixtures of aromatic compounds, Comput Appl Eng Educ 22 (2014), [3] H. Binous and M. A. Al-Harthi, Simple batch distillation of a binary mixture, Comput Appl Eng Educ 22 (2014), [4] T. Castrellon, D. C. Botıa, R. Gomez, G. Orozco, and I. D. Gil, Using process simulators in the study, design, and control of distillation columns for undergraduate chemical engineering courses, Comput Appl Eng Educ 19 (2011), [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 teach separation processes, Comput Appl Eng Educ 20 (2012), [6] S. X. Liu and M. Peng, The simulation of the simple batch distillation of multiple-component mixtures via Rayleigh s equation, Comput Appl Eng Educ 15 (2007), [7] R. Baur, J. Bailey, B. Brol, A. Tatusko, and R. Taylor, Maple and the art of thermodynamics, Comput Appl Eng Educ 6 (1998), [8] F. Cruz-Peragon, J. M. Palomar, E. Torres-Jimenez, and R. Dorado, Spreadsheet for teaching reciprocating engine cycles, Comput Appl Eng Educ 20 (2012), [9] Y. Liu, Development of instructional courseware in thermodynamics education, Comput Appl Eng Educ 19 (2011), [10] M. F. Doherty and M. F. Malone, Conceptual Design of Distillation Systems. McGraw-Hill, New York, [11] S. I. Sandler, Chemical Engineering Thermodynamics, 3rd ed., John Wiley & Sons, NewYork, [12] G. M. Wilson, Vapor-liquid equilibrium XI: a new expression for the excess free energy of mixing, J Am Chem Soc 86 (1964), [13] H. H. Amer, R. R. Paxton, and M. van Winkle, Methanol Ethanol Acetone Vapor-liquid equilibria, Ind Eng Chem 48 (1956), [14] I. Nagata, Isobaric Vapor-Liquid Equilibria for the Ternary System Chloroform - Methanol - Ethyl Acetate, J Chem Eng Data 7 (1962), [15] W. Reindersand and C. H. de Minjer, Vapour-liquid equilibria in ternary systems. VI. The System Water Acetone Chloroform, Recueil des Travaux Chimiques des Pays-Bas 66 (1947), [16] A. Lucia, "Distilation Tutorial V: Azeotopes and Distillation Boundaries." (accessed September 2014). APPENDIX 1 NMR analysis and spectra interpretation Two samples of about 0.3 ml each were taken for H 1 NMR analysis. The first was from the initial mixture and the second was from the residue near the end of the distillation experiment. Proton NMR spectra of these initial and final samples were taken at 23.2 C on Bruker 500 MHz spectrometer. CDCl 3 was used as NMR solvent. All chemical shifts were reported in parts per million (ppm). Chemical shifts obtained in the region of d 7.37 ppm corresponds to the 1 H-atom in the CHCl 3, d corresponds to the 3H atoms in the methanol and d 3.7 ppm corresponds to the 1H atom in the OH group of the methanol. The Chemical shift at d ppm corresponds to the 6 H atoms in the CH 3 COCH 3. The mixture composition is calculated according to the following equations: F j ¼ I j X 3 I k¼1 k for j ¼ 1::3 where F j is the molar percent of component j, I 1 ¼(Integrated area the Chloroform)/(No. of H-atoms in the Chloroform), I 2 ¼ (Integrated area of the Acetone)/(No. of H-atoms in Acetone) and I 3 ¼ Integrated area of OH group in the methanol).

11 MULTICOMPONENT BATCH DISTILLATION 11 BIOGRAPHIES Dr. Housam Binous, a visiting Associate Professor at King Fahd University Petroleum & Minerals, has been a full time faculty member at the National Institute of Applied Sciences and Technology in Tunis for eleven years. He earned a Dipl^omed ingenieur in biotechnology from the Ecole des Mines de Paris and a PhD in chemical engineering from the University of California at Davis. His research interests include the applications of computers in chemical engineering. Dr. Mamdouh A. Al-Harthi is currently an Associate Professor in the department of Chemical Engineering in King Fahd University of Petroleum & Minerals (KFUPM), Kingdom of Saudi Arabia. He has obtained both a Bachelor s and Master s degrees in Chemical Engineering from KFUPM and a PhD in Chemical Engineering from University of Waterloo, Canada in He was awarded the Gold Medal for Proficiency in Research for the best PhD thesis in 2006 by the University of Waterloo. His research interests are in the areas of polymer reaction engineering, polymer science and mathematical modeling. Dr. Al-Harthi published more than 50 papers in highly reputed journals. Ahmed Bellagi is a Professor of chemical and energy engineering at the Ecole Nationale d Ingenieurs de Monastir, University of Monastir, Tunisia since Prior to that, he has been a Professor of chemical engineering at the Ecole Nationale d Ingenieurs de Gabes. He received his PhD in 1979 from the Rheinisch- Westf alische Technische Hochschule at Aachen Germany. His research interests mainly focus on Process Thermodynamics, Absorption Refrigeration and Solar Cooling, Modeling and Simulation of Unit Operations and Processes. He has supervised 35 Master degree and 20 PhD theses. Professor Bellagi has written over 100 publications. Recently his pedagogical interests focus on the development of Demonstrations for chemical engineers using the software Mathematica 1 in collaboration with Dr. H. Binous and Professor B. G. Higgins. ( wolfram.com/author.html?author=housam+binous).