The Effect of Energy Input on the Efficiencies of a

Corning 20-liter Distillation Unit

First report of Project #1

10/14/2011

Group B

Arvind Ravichandran

Ganesh Swaminathan

Christian Parrish

Colleen Quinlan

1

Abstract After acquiring an old plant from Quack pharmaceuticals, WasteNot

Chemicals found drums filled with dye mixtures in other volatile components. As

the pure dyes had a greater market value, WasteNot Chemicals wanted to

extract these valuable dyes using a distillation column. Hence, they required our

team to evaluate and recommend operating conditions for the Corning 20-liter

Distillation unit, a pilot-scale batch distillation column. Our team sought to determine the best energy input to run the column,

such that it would be most efficient in terms of cost and energy. To this end, we

ran the column using a water and ethanol mixture, without producing any

distillate i.e. total reflux. We took samples from each stage and used the

refractometer to measure its refractive index. Prior to this, we set up a standard

curve to correlate refractive index of the sample to the ethanol composition of the

sample. By computing the composition of ethanol at each stage using the

standard curve, our team conducted a McCabe Thiele analysis.

Through this analysis we determined the overall and individual tray

efficiencies (Murphree vapor efficiency) of the column at three different energy

inputs: 1.6 kW, 2.0 kW and 2.4 kW. This showed us how much, in general, the

column deviated from theory and enabled us to determine the condition and

performance of each tray.

We discovered that the column performed best at the 1.6 kW energy input,

where it had an overall efficiency of 0.469. We specified that the trays 3 and 5

were performing relatively poorly compared to other trays at various energy

inputs, by using the Murphree vapor efficiency. We also specified conditions that

should be avoided so as to prevent the improper functioning of certain stages of

the distillation column known as weeping and flooding.

Since the column efficiencies that we computed is applicable to all

systems, with our analysis and framework for future experiments, WasteNot

Chemicals can purify their dyes dissolved in any solvent.

2

Table of Contents

ABSTRACT   1  

DEFINITION  OF  SYMBOLS   3  

INTRODUCTION   4  

BACKGROUND   4  

EXPERIMENTAL  METHODS   13  

RESULTS   18  

DISCUSSION   25  

CONCLUSION  AND  RECOMMENDATIONS   28  

LITERATURE  CITED   29  

3

Definition of Symbols

P Pressure (atm)

T Temperature (K)

F degrees of freedom

π Number of phases

N Number of different components

xA Molar composition of component A in the liquid phase

yA Molar composition of component A in the vapor phase

L Liquid flowrate (kg/s)

V Vapor flowrate (kg/s)

Eo Overall efficiency

EMV Murphree vapor efficiency

%wt Weight fraction

%mol Mole fraction

4

Introduction This project was carried out to measure the efficiency of the Cornell 20-

liter Distillation Unit. Upon acquiring an old plant from Quack pharmaceuticals,

WasteNot Chemicals discovered many drums filled with dye mixtures in other

volatile components. However, these dyes were dissolved in either ethanol and

water or methanol and water or 1-propanol and water or benzene and toluene.

Since the pure dyes had a greater market value, WasteNot Chemicals wanted to

extract these valuable dyes from the volatile solvents using a distillation column.

To this end, we used the Cornell 20-liter Distillation Unit. As the column

did not have any documentation explaining its features, our team conducted

experiments to understand the unit and evaluate the conditions to operate the

column. Specifically, we analyzed how the overall efficiency of the unit and the

efficiencies of each tray varied with the power input. Using this information, we

defined the optimum power input for operation and provided recommendation on

the performance of each tray.

Background

Phase  Behavior    

In our distillation process we analyze the interaction between the liquid

and the vapor phase. We define a phase as a homogeneous region of matter.1

When a vapor and a liquid are in contact with each other, liquid molecules

continually vaporize, while vapor molecules continually condense. When two

chemical species, such as ethanol and water, are present in a mixture they will

condense and vaporize at different rates. At equilibrium, the temperatures,

pressures and fractions (mass or mole fraction) of the vapor and liquid phases

cease to change.2 This is known as vapor-liquid equilibrium (VLE). Note that the

equilibrium can only be reached, when the system is at steady state. For a

system at steady state, quantities related to the process are time invariant.

Hence, in our experiment when we observe constant temperature and

5

composition over time, we can estimate that the system has reached steady

state and is also in equilibrium.

It is possible to determine the state of a pure homogenous fluid when two

intensive thermodynamic properties are set at specific values. This is expressed

in the Gibbs’ phase rule.3 (See Equation 1.)

€

F = 2−π + N [Equation 1]

In Equation 1, F is the degrees of freedom,

€

π is the number of phases and N is

the number of components in the system. According to Equation 1, for a two-

phase, two-component system, we will have two degrees of freedom. Hence,

specifying temperature and pressure we can obtain the composition of ethanol

and water at equilibrium. This is known as VLE data and it can be represented

graphically through means of a temperature-composition diagram at a specified

pressure. (See Figure 1.)

Figure 1: The vapor-liquid equilibrium mixture of ethanol and water at 1 atm. There are two

graphs, of which, one is temperature vs. xEth (liquid), and the other is temperature vs. yEth (above).

These curves represent the saturated liquid and the saturated vapor lines. Any point below the

saturated liquid curve represents a subcooled liquid and any point above the saturated vapor

curve represents a superheated vapor. Points between the two saturation curves represent

streams consisting of both liquid and vapor.1

6

The isotherm shown in Figure 1 is called a tie line. For a system at

equilibrium, the liquid to vapor ratio can be determined using the lever rule, given

by Equation 2.

€

nlnv

=vl

[Equation 2]

In Equation 2, nl and nv are the number of moles of liquid and vapor respectively.

l and v represent the length of the segments of the tie line as shown in Figure 1.

Dynamics  of  Distillation  

When a liquid mixture of a certain composition in the pot is heated it boils

and produces vapor. (Denoted by a in Figure 2.) This vapor, denoted by b, is in

equilibrium with the mixture at the pot. It enters the stage above it. Due to a lower

temperature at this stage, the vapor condenses into a liquid, denoted by c. The

liquid becomes richer in the more volatile component of the original mixture, as

can be seen in Figure 2.

This process continues on, giving an enriched mixture of the volatile

component in the distillate and an enriched mixture of the less volatile

component in the still pot.

However, proceeding further brings the system to point d where the

condensed vapor results in a distillate that has the same composition as the

solution in the pot. Such a distillate is known as an azeotrope.4 After this point,

further separation is no longer possible, and the solution boils at a constant

temperature. For the ethanol-water mixture, Figure 1 shows that the saturated

liquid and vapor curves merge at a certain temperature. This is known as the

minimum-boiling azeotrope i.e., the liquid and vapor are of exactly the same

composition at a mole fraction ethanol of 0.8943.4

7

Figure 2: Temperature-composition diagram. This illustrates a minimum boiling point

azeotrope. The mixture in the pot, denoted by a, is heated and it boils to produce vapor. This

vapor, denoted by b, is in equilibrium with the mixture at the pot. It enters the stage above it. The

temperature at this stage is lower than that of the pot, due to a lack of energy input at this stage.

The vapor condenses into a liquid, denoted by c. The liquid becomes richer in the more volatile

component of the original mixture.5

McCabe  Thiele  Analysis  

The McCabe-Thiele diagram is very useful in the analysis of fluid

dynamics in a distillation column. The diagram is a plot of the vapor phase

composition against the liquid phase composition of the more volatile component

(ethanol in our project). It includes the VLE plot and the operating line.

The operating line relates liquid and vapor flows between trays, also

known as passing streams. The operating line is derived from a mass balance.

(See Figure 3.) For the batch distillation case, the equation of the operating line

can be obtained by solving a total mass balance and a component balance

across 2 stages. This is expressed in Equation 3.

€

y j+1 =LVx j + (1− L

V)xd [Equation 3]

8

In Equation 3, yj+1 is the composition of ethanol in the vapor phase in the stage

below the jth stage. xj and xd are the compositions of the ethanol in the liquid

phase in the jth stage and the vapor composition in the distillate respectively.1

(See Figure 4A.)

The reflux ratio of a column is the ratio of the amount of condensate being

refluxed to the amount being withdrawn as product. It is given by L/V in Equation

3. At total reflux, L/V = 1. The operating line reduces to the y = x line.1 (See

Figure 3.)

Note that the vapor from the (j+1)th stage and the liquid from the jth are

called passing streams. L and V are the flow rates of the mixture through the

downcomers and bubble caps respectively. L and V remain constant throughout

the column, and that this equation implies a critical assumption.

Figure 3: McCabe Thiele Diagram showing the VLE line, x = y line and the operating line. The operating line is defined by Equation 3 and has a non-zero reflux ratio. At total reflux, the

operating line is given by the y = x line.

9

This analysis is conducted with the assumption of Constant Molal

Overflow (CMO)1. CMO consists of the following:

• The molar heats of vaporization of the feed components are equal

• For every mole of liquid vaporized, a mole of vapor is condensed

• Heat effects such as heats of solution and heat transfer to and from the

distillation column are negligible.

Characteristics  of  a  Distillation  Column  

The distillation column that was used in the project contained 6 stages.

These stages enable the liquid and vapor phases of the mixture to interact and

approach equilibrium at different, decreasing temperatures, up the column.

A theoretical plate or stage is a hypothetical zone or stage in which the

liquid and vapor phases establish equilibrium with each other. Any physical

device that provides good contact between the vapor and liquid phases present

in the distillation columns constitutes a plate or tray. Since an actual, physical

plate is rarely a 100% efficient equilibrium stage, the number of actual plates is

more than the required theoretical plates.6 (See Equation 4.)

The flow of the vapor into stages above is made possible through bubble

caps. (See Figure 6.) These are caps with slots or holes that enable the vapor to

bubble through them1. Downcomers, enable the mixture in the liquid phase to

flow downwards to the stage below. (See Figure 7.) The bubble caps and

downcomers are essential for the distribution of the mixture between stages and

to attain a state close to equilibrium for the purpose of effective separation.1

Efficiencies  

To compare the performance of an actual stage to an equilibrium stage,

we use the measure of efficiency.1 For the purpose of this project we will be

using the concepts of overall efficiency and the Murphree vapor efficiency.

The overall efficiency,

€

Eo, is given by Equation 4, where

€

Nequil is the

number of theoretical plates required for the separation and

€

Nactual is the actual

number of stages required.

10

€

Eo =Nequil

Nactual

[Equation 4]

The overall efficiency lumps together everything that happens in the column.1

The number of actual equilibrium stages is determined through the plotting of the

pseudo-equilibrium curve. (See Figure 4B.)

Stage efficiencies are defined for each stage and may vary from stage to

stage. The Murphree vapor efficiency is defined as given in Equation 5.

€

EMV =y j − y j+1y j* − y j+1

[Equation 5]

In Equation 4,

€

y j is the vapor mole fraction in tray j,

€

y j* is the vapor mole fraction

in equilibrium with actual liquid mole fraction

€

x j . (See Figure 3A.)

Figure 4: Murphree efficiency, (A) stage nomenclature, (B) McCabe-Thiele diagram for EMV.

8

Graphically, Murphree vapor efficiency can be calculated by taking the ratio of

the distance from the operating line to the pseudo-equilibrium curve and the

11

operating line and the equilibrium curve. (See Figure 4B.) This is given by

Equation 6 as shown in Figure 4B.

€

EMV =ABAC

[Equation 6]

Flooding  

When liquid is held-up in the column, or in a particular stage, it is known

as flooding.7 When the vapor rising up the column is of great enough pressure to

cause liquid hold-up the liquid in the stage cannot flow through the downcomer to

the stage below and it is collected.

Refractometry  

To fulfill the objective of the project, we had to take the composition of the

mixtures at the various stages of the distillation column. For this, we used a

refractometer.

We can determine the concentration of the solution by determining the

refractive index (RI) of the solution. The RI of a medium is the ratio of the speed

of light traveling in that medium to the speed of light traveling in a vacuum.9

Since, this ratio is directly affected by the polarity of a medium, with

increasing ethanol composition, the polarity and the RI of the mixture increases.10

However, temperature also affects the RI of a solution significantly.11 (See Figure

5.)

12

Figure 5: Plot of refractive index, n, against T. The change in refractive index with temperature

is roughly constant with increasing ethanol concentration. And between the 22.5°C to 27.5°C the

refractive index of 11.91mol% water drops from 1.363 to 1.361.11

Our team had to necessarily monitor the temperature of the readings in

order to obtain reliable results. This relationship means that through the

measurement of the RI of the solution, the composition of ethanol in the mixture

can be back calculated, through the use of a standard curve.

We obtained the standard curve through the measurement of RI of known

mass compositions of ethanol in water. However, this relationship between

composition and RI is not always a one-to-one mapping. (See Figure 6.)

Note that the difference in RI is smaller at the 0.3 to 1 mole fraction region

compared to the 0 to 0.2 mole fraction region.

13

Figure 6: Plot of the refractive index of a water-ethanol mixture against ethanol mole

fraction at 25°C (from literature).12 Note that beyond a certain composition (around 70 mol%),

the RI decreases with increasing water concentration. Also, the differences in RI are small

beyond the 0.4 mole fraction region, relative to the 0 to 0.2 mole fraction region.12

Note that the difference in RI is smaller at the 0.3 to 1 mole fraction region

compared to the 0 to 0.2 mole fraction region.

Experimental Materials and Methods

Distillation  Column  

We used a bubble cap version of the Corning 20-liter Distillation Unit with

six identical plates, each holding a single 3-inch bubble cap in the project. A

schematic of the distillation column is shown below. (See Figure 5.)

14

Figure 7: A Schematic of the Distillation Column used in the experiments. Note that the

placement of thermocouples at each stage. There were also sampling taps present at each

stage but are not represented in the schematic.

In the bubble-cap there is a riser, or weir, around each hole in the tray. A

cap with slots or holes is placed over this riser, and the vapor bubbles through

these holes. A photograph of the bubble-cap is shown. (See Figure 6.)

15

Figure 8: A photograph of the bubble-cap in the Cornell Distillation Unit

A photograph of the downcomer in the column used is shown. (See Figure 7.)

Figure 9: A photograph of the downcomer in the Cornell Distillation Unit.

The still (boiling pot) was a 20-liter reboiler vessel fitted with a 2.4 kW

electric heating mantle with three separate heating bands. We adjusted them to

vary the energy inputs into the boiling pot.

Choice  of  ethanol-­‐water  mixture  

In comparison to the other mixtures, we chose the ethanol-water mixture

for this pilot project due to concerns over safety and cost. Our reasons for not

using the other mixtures are explained below. (See Table 1.)

Table 1: Reasons why other mixtures were not chosen

Mixtures Safety issue

Methanol+water Poisonous, methanol is known to cause blindness

1-Propanol+water Propanol is inflammable

Benzene+Toluene Both chemicals are known to be highly carcinogenic

16

Column  Initializing  

We mixed 10 liters of an ethanol-water mixture at 4.5 mole%, 1.6 liters of

pure ethanol and 0.82 liters of pure water and charged the reboiler vessel with

this mixture. We determined the ethanol-water mixture to be approximately 7

mole%. We made sure that the liquid level was above the top heating band, and

approximately half of the 20 liter pot was filled with the still. Sample calculations

of the still-pot composition are provided in Appendix A.

We switched off the reflux divider and at start up only provided a 2kW heat

input using the potter and the middle heater and we turned the powerstat to the

“zero” position. We obtained the temperatures that we monitored through the

thermocouples located at each stage, the boiler pot and the distillate. (See Figure

7.)

Flooding  

During the running of the experiment, we observed flooding on the third

stage of the column. At start up, the energy input was at maximum. We saw

vigorous bubbling at this stage prior to flooding. The decrease in power input had

little effect on the bubbling, and we observed liquid rise to nearly three quarters

up the stage. The potter temperature was at 89.6°C and the temperature at the

third stage was 82.0°C.

We shut off the power input at the pot, and the column was allowed to cool

down. We turned the column back on, when all the excess liquid in the stage had

drained out.

Taking  measurements  

We started a timer at the start up of the column and recorded temperature

at approximately five to ten minute intervals for the duration of the experiment.

We operated the column at a total of three different power inputs: 1.2, 2, and the

maximum power input of 2.4kW.

We collected liquid samples from each stage after ensuring that steady

state was reached after each change of the power setting. We determined that

17

the column had reached steady state by observing that the temperature readings

remained fairly constant.

We tried to obtain three different samples for each power setting to ensure

that the readings were accurate. We also made sure that the 1.5 ml Eppendorf®

tubes were filled up completely so that the composition of the sample would stay

constant until measurements were taken at the refractometer. We believed that

some of the mixture in the liquid phase would evaporate and cause inaccuracies

if the tubes were not filled up completely.

We also ensured that we avoided taking readings too often, because this

would lead to a decrease in the volume of fluid in a stage, and thus manifest as a

disturbance in the steady state of the system.

We ensured that the valves were open for a short time to get rid of

residual liquid, before beginning sampling.

Refractometry  

We used the Reichert AR6 Series refractometer to measure the

composition of ethanol in the samples. We measured the RI of each sample only

once. We set up the water bath at 25°C and performed a one-point calibration

with water. After cleaning the prism surface with a Kim wipe, we placed samples

on the surface and took RI measurements.

To prepare a standard curve, we prepared samples at different

compositions by mass of ethanol. We prepared samples of composition by mass

from 5% to 60%, at steps of roughly 5%. Upon plotting their RI against the

composition by mole of ethanol, we obtained the standard curve. (See Figure

10.)

Dilution  

To obtain accurate RI readings that would tolerate small inaccuracies in

ethanol composition, we carried out a 1:3 dilution with water for all our samples.

This ensured that the measured ethanol composition was in a viable region

between 0 and 30 mole% as described in Figure 6. Beyond 40 mole% we

18

observe in both Figure 6 and Figure 10 that large differences in composition only

leads to small changes in RI.

Moreover, we immersed the samples in a water bath at 25°C to ensure

that the temperature of the samples were constant before their RI was measured.

(See Figure 5.)

Safety

Although the chemicals that we worked with were not very harmful, it was

critical to take certain safety precautions. We wore nitrile gloves when we

handled the column or the ethanol-water mixture. We wore safety goggles at all

times when the experiment was conducted to prevent splashes of harmful

chemicals from entering the eye. We also took precautions when climbing the

ladder to observe the column, so as to not knock over thermocouples that were

sometimes in the way. We also did not make any contact with the still-pot during

the experiment.

Results The purpose of the project was to calculate the overall efficiency and

individual tray efficiencies of the column. We did this by determining how much

the column deviated from theoretical equilibrium conditions. Using this we

determined the best heat input to operate the column.

Calibration  Curve  

The calibration curve for the refractometer was essential in our analysis of

the column because we used it to calculate the composition of the mixtures from

the various RI values. (See Figure 10.) We prepared three samples at each

weight fraction of ethanol and measured the RI of each of them. We took the

average of these RI values and used them to prepare Curve 2 on Figure 10.

Unlike those of Curve 2, the points on Curve 1 represent single data points at

different weight fractions from Curve 2. We appended it in Figure 10 for

completion.

19

Figure 10: Standard Curve for Refractometry. Curve 1 contains single data points at different weight fractions from Curve 2. Curve 1 is plotted here because of the remarkable correlation with Curve 2 and to complete the missing data points that Curve 2 didn’t cover. Curve 2 represents the average of three data points. The solid curve is fitted only with the points on Curve 2. The equation of the Curve 2 is y = -0.0592x2 + 0.0875x + 1.3314 and the R2 value of the fit is 0.9994. We will use this equation to compute compositions of our samples.

The error bars in Figure 10 are very small and are not visible because the

average standard deviation of the three points for the many weight fractions was

approximately around 0.000126.

Determination  of  sample  composition  

We computed the composition of the samples using the formula given in

Figure 10, once the RI was measured. However, to ensure that the data points

fell in the 1.33 to 1.35 region, we carried out 1:3 dilutions on the samples. We

back calculated the original composition of the mixture using literature values of

density for the dilute mixtures at 25°C. We performed a sample calculation of this

in Appendix B.

y  =  -­‐0.058x2  +  0.087x  +  1.331  R²  =  0.998  

1.32500  

1.33000  

1.33500  

1.34000  

1.34500  

1.35000  

1.35500  

1.36000  

1.36500  

1.37000  

0   0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8   0.9   1  

Refractive  Index  

weight  fraction  of  ethanol  

Curve  1  Curve  2  

20

McCabe  Thiele  Analysis  

In the McCabe Thiele Analysis for our system at total reflux, L/V from

Equation 3 is equal to 1. Hence, we obtain Equation 7.

€

y j+1 = x j [Equation 7]

In other words, the ethanol composition liquid in a particular stage has the

same composition as the ethanol composition in vapor in the stage below it. This

means that the passing streams have the same compositions. Also the operating

line is given by the y=x line. (See Figure 3.) Using this knowledge, we found out

the respective vapor compositions in each stage, because this was just equal to

the liquid composition in the downcomer on that stage. (See Figure 4A.)

Analyzing the liquid sample from stage j gave us the mole fraction of ethanol in

the liquid phase, xj. (See Table 2.) To obtain the pseudo-equilibrium curve, we

corresponded x1 and y1, where y1= x2. Hence we got the point (0.30926,

0.42942). Similarly, by doing this for the other values we obtained 6 points to plot

a best-fit line, which gave us the pseudo-equilibrium curve. (Figure 11.) We were

able to use the distillate composition to begin stepping off stages and finally end

at the bottoms composition.

Table 2: Composition of ethanol by mole fraction for 1.2kW power input. The x1 and y1

values correspond, x2 and y2 values correspond and so forth. These points are used to generate

the pseudo equilibrium curve in Figure 11.

x Sample A Sample B Sample C Average Std Dev Std Error y

1 0.18605 0.36844 0.37329 0.30926 0.10673 0.06162 0

2 0.27073 0.51517 0.50236 0.42942 0.13758 0.07943 1

3 0.34029 0.56359 0.57950 0.49446 0.13375 0.07722 2

4 0.33025 0.60945 0.62742 0.52237 0.16663 0.09620 3

5 0.57918 0.72470 0.62108 0.64165 0.07491 0.04325 4

6 0.72011 0.63432 0.63050 0.66165 0.05067 0.02925 5

D 0.66401 0.86615 0.63915 0.72310 0.12451 0.07188 6

21

Figure 11: McCabe Thiele diagram for 1.2kW power input. The equilibrium line is the line

above with equation: y = 305.9x5 - 345.0x4 + 193.3x3 - 56.40x2 + 8.501x. And quadratic best fit for

the pseudo equilibrium line is below with equation: y = 12.24x3 - 8.803x2 + 3.190x and R2 = 0.978.

Three samples were taken from each tray within approximately 7-minute time intervals to obtain

the six points giving the pseudo equilibrium line. The line that steps off the pseudo equilibrium

curve shows the actual step off giving six stages. Using the bottoms and distillate composition

obtained from the analysis, the equilibrium step off is constructed. This gives the number of

equilibrium stages to be three.

In Figure 11, the still-pot concentration can be determined by stepping

down to the corresponding xeth from the tray closest to the still. For the 1.2kW

power input, we found that the still-pot composition of ethanol by mole was about

0.35 from Figure 11.

Similarly, we used readings from the 2kW power input experiment to

populate Table 3.

-­‐0.2  

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

-­‐0.2   0   0.2   0.4   0.6   0.8   1   1.2  

mol  fraction  ethanol  in  vapor  

mol  fraction  ethanol  in  liquid  

Equilibrium  Line  

Pseudo-­‐Equilibrium  Line  

22

Table 3: Composition of ethanol by mole fraction for 2kW power input. Points obtained from

this table are used to generate the pseudo equilibrium curve in Figure 12.

x   Sample  A   Sample  B   Sample  C   average   std  dev   std  error   y  1   0.35337   0.32365   0.35727   0.34477   0.01839   0.01062   0  2   0.52549   0.47243   0.44985   0.48259   0.03883   0.02242   1  3   0.51286   0.62108   0.84421   0.65938   0.16896   0.09755   2  4   1.40830   0.68167   0.18234   0.75743   0.61648   0.35593   3  5   0.60820   1.20095   0.96660   0.92525   0.29853   0.17236   4  6   0.62577   0.74107   1.36409   0.91031   0.39719   0.22932   5  7   0.89990   0.17702   0.64894   0.57528   0.36703   0.21190   6  

Like the 1.6 kW input, we used the same analysis to plot the pseudo-equilibrium

curve and performed the McCabe Thiele Analysis. (See Figure 12.)

Figure 12: McCabe Thiele Diagram for 2kW power input. The equation of the equilibrium

curve is identical to that in Figure 11. The pseudo-equilibrium curve in the figure has the equation:

y = 1.15x3 - 0.497x2 + 1.494x with an R2=0.549. There are 2 equilibrium stages and 6 actual

stages. 3 samples were taken from each tray within approximately 7-minute time intervals to

obtain the 6 points seen above.

-­‐0.2  

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

-­‐0.2   0   0.2   0.4   0.6   0.8   1   1.2  

mole  fraction  ethanol  in  vapor  

mole  fraction  ethanol  in  liquid  

Equilibrium  Line  

Pseudo-­‐Equilibrium  Line  

23

Only two samples were obtained for the 2.4 kW power input. (See Table 4.)

Table 4: Composition of ethanol by mole fraction for 2.4kW power input. Points obtained

from this table are used to generate the pseudo equilibrium curve in Figure 13.

x   Sample  A   Sample  B   average   std  dev   std  error   y  1   0.34523   0.39659   0.37091   0.03632   0.02097   0  2   0.45745   0.68167   0.56956   0.15855   0.09154   1  3   0.54240   0.52398   0.53319   0.01303   0.00752   2  4   0.56410   0.60820   0.58615   0.03118   0.01800   3  5   0.61922   0.71107   0.66514   0.06495   0.03750   4  6   1.03144   0.72701   0.87922   0.21526   0.12428   5  7   0.63241   0.74825   0.69033   0.08191   0.04729   6  

Plotting the points as per before we obtain the following McCabe Thiele graph.

(See Figure 13.)

Figure 13: McCabe Thiele Diagram for 2.4kW (maximum power input). The equation of the pseudo equilibrium curve is given by: y = - 0.352x2 + 1.556x with R2=0.305. For this power input, we only took two samples from each tray at an approximate 7-minute time interval.

-­‐0.2  

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

-­‐0.2   0   0.2   0.4   0.6   0.8   1   1.2  

mole  fraction  ethanol  in  vapor  

mole  fraction  ethanol  in  liquid  

Equilibrium  Line  

Pseudo-­‐Equilibrium  Line  

24

Efficiency  

We calculated the Murphree efficiency of each stage using the

composition of the sample points and Equation 6. The technique described in

Figure 4B was used for each of the three cases for all six stages. Our final

evaluation of the efficiency of each stage at different power inputs is as follows in

Table 5.

Table 5: Table of Murphree efficiency for each stage at different power inputs. This is our

final evaluation of the efficiency of each stage at different power inputs is as follows in Table 3.

    Power  input  (kW)       1.2   2.0   2.4  

1   0.539   0.568   0.863  2   0.516   1.037   -­‐0.328  3   0.176   1.702   0.388  4   0.641   3.819   0.795  5   0.304   0.180   3.871  

Stages  

6   1.084   8.982   -­‐9.053  

With the distillate composition and the still-pot composition, the number of

equilibrium stages required was graphically determined from Figure 11 to 13. For

instance, from Figure 11, we know that for the 1.2kW input we have 2 complete

equilibrium stages and a 0.815 fraction appears at the end. Hence, the total

number of equilibrium stages is calculated to be 2.815. Since we knew that the

actual number of stages is six, using Equation 4, we calculated the overall

efficiency of the column at each power input, Eo, to be 0.469. We carried out the

same analysis for the other two cases. (See Table 6.)

Table 6: Table showing Eo values at different power inputs. This is our final evaluation of the

overall efficiency of the column for different power inputs.

Power input (kW) Equilibrium Stages Actual Stages Eo

1.2 2.815 6 0.469

2.0 1.686 6 0.281

2.4 2.683 6 0.447

25

Discussion Our analysis suggested that there is no apparent correlation between

power input and the overall and tray efficiencies of the column. However, it must

be noted that at 1.2kW, the column operates at the best overall efficiency

compared to the other two power inputs.

Significance  of  efficiencies  

With the understanding that the overall efficiency lumps together

everything that happens in the column, we could better appreciate the results

that we obtained from the experiment.

The hydrodynamic flow properties such as viscosity and gas flow rate

affect the flow regime in the column. The mass transfer rate, which is affected by

the diffusivity, in turn affects efficiency.1 Hence, by finding out which power input

had the best efficiency, we took into account the many different variables that

affect the dynamics of the distillation unit.

Moreover, the tray efficiency told us how each tray performed at different

power inputs. A recurring pattern of low tray efficiency at a particular tray would

have pointed out that something was faulty with the stage, possibly with the

bubble caps or the downcomers.

Ultimately, by carrying out this analysis of the column, our team wanted to

point out which stages were faulty using stage efficiency data and also specify

the best power input to run the column based on overall stage efficiency. We also

sought to notice conditions, which caused flooding and weeping so that they can

be avoided in the future.

Data  Analysis  

Tray 3 and tray 5 have been in general exhibiting lower tray efficiencies

compared to other trays. For the 1.2 kW and 2.4 kW energy inputs, tray 3 has

been exhibiting some of the lowest efficiencies. For the 1.2 kW and 2 kW energy

input, tray 5 has been showing low tray efficiencies.

26

From the overall efficiency data, we can see that the column exhibits best

overall efficiency at the 1.2 kW energy input. However, the 2.4 kW energy input

the Eo is only 4.7% smaller than that at the 1.2 kW energy input.

It must be noted that the standard curve for the refractometeter proved to

be reproducible and reliable. Moreover, these results also corresponded closely

to literature values12. This meant that the ethanol compositions that we had

obtained through the use of the refractometer were reliable.

We also noticed flooding during sustained levels of very high power input

at start up. The column should be closely monitored during and shortly after start

up. The power input should be turned down or switched off completely when

signs of vigorous bubbling are observed to avoid flooding.

The results that we computed for the Murphree tray efficiencies do not

follow a pattern as we had expected. Certain trays did not show a consistent

pattern of performance, and the efficiencies continually varied for each tray for

the different energy inputs. In some cases, unfeasible efficiencies were

computed. For instance, an efficiency of -9.053 was calculated for tray 6 for the

2.4 kW energy input and some values were above 1. This was because we used

the raw measured composition values from each tray (instead of using the

pseudo-equilibrium curve) and compared it with the equilibrium data. Although

this was done to present a representative analysis of the column, we had

measured unfeasible composition values in many cases. Namely, we computed

a Murphree vapor tray efficiency to be greater than 1 whenever the measured

ethanol composition was greater than that of the equilibrium curve.

Similarly, we found it difficult to compute an accurate overall efficiency

because we had to fit the pseudo-equilibrium curve based on the measured

ethanol composition data. Since, some of the measured data does not conform

to the theoretical bounds of the system, it is difficult to rely on them to obtain

accurate values of efficiency.

We believe that the propagation of error in the readings of our samples

was due to both systematic and random errors. During the dilution process, it

was difficult to avoid vaporization of the sample during pipetting. Furthermore,

27

some of the sample could have also vaporized after we placed the sample in th

refractometer and waited for the temperature to equilibrate to 25°C. Vaporization

of the volatile liquids would lead to unrepresentative and inaccurate values of

composition.

We determined that the column had reached steady state through the

observation of the temperature from the thermocouples. During equilibrium, we

expected the composition of the three samples at each power input to be fairly

equal if the column had reached steady state. However, even after accounting for

random errors, the composition varied largely between each of our three samples

for the same energy input. This can be seen for the 2.0 kW energy input at the

third tray. (See Table 4.) Between samples A, B and C the composition varied

between 0.51286 and 0.62108 and 0.84421. The consistent occurrence of such a

significant difference in many of the trays for the energy input suggests that the

column had not reached steady state. Hence, using temperature fluctuations to

gauge the onset of steady state was not entirely successful.

There were also the possibility of random errors occurring due to RI

differences due to temperature variation, vaporization of the sample within the

Eppendorf® tube upon collection and inaccuracies in pipettes that need to be

accounted for in our findings. Hence, these errors have propagated in our final

efficiency computations.

Our team would have obtained more accurate data if we could observe

and analyze the data before moving onto further sampling. For instance, we

could have used the sample compositions to determine the onset of steady state

instead of temperature fluctuations. We would have preferred to take more

samples per power input and wait for a longer time for the column to reach

steady state, but were constrained with a lack of time.

We would have also liked to carry out the experiment at a more power

inputs to see exactly if there is a correlation between column efficiency and

power input.

28

Conclusion and Recommendations Our team aimed to evaluate the efficiencies of the unknown pilot

distillation column. We were meant to analyze the column and provide WasteNot

Chemicals with conditions to separate some valuable dyes from volatile

component mixtures. Through the experiments with the column, our team could

not conclusively specify the best power input necessary for such a distillation

process.

However, we were able to determine that adjusting the power input had a

significant impact on the pseudo-equilibrium curve for the column, and that the

efficiency of the column was definitely affected by the power input. To a certain

degree, we were able to show that the most efficient power input was at 1.2kW,

since that power input obtained a maximum Eo value of 0.469.

Moreover, we also pointed out that trays 3 and 5 in the distillation column

might have carried a problem as we observed from our computation of the

Murphree vapor tray efficiency. We had also set up a viable framework for future

experimentation on evaluating the performance of each tray in the distillation

column.

To move further with this goal and to completely understand the column,

we recommend running the column at many different power inputs. Furthermore,

running the column at different reflux ratios would give further information about

the column and would help in the determination of the best reflux ratio for a

distillation process. Ultimately, with further experiments on this column, we will be

able to provide specific details on power input and tray performance critical in the

separation of the dyes desired by WasteNot Chemicals.

29

Literature cited 1 Wankat, Phillip C. Separation process Engineering. Prentice Hall, 2007

2 Smith, J. M., and Ness H. C. Van. Introduction to Chemical Engineering

Thermodynamics. New York: McGraw-Hill, 1975. Print.

3 Enrico Fermi, Thermodynamics, 1956.

4 Chang, Raymond Physical Chemistry for the Chemical and Biological Sciences,

2000

5 Figure reprinted from Chang, Raymond “Phase Equilibria of Two-Component

Systems”. Physical Chemistry for the Chemical and Biological Sciences, 2000,

226, Figure 7.14(a)

6 Kister, Henry Z. Distillation Design (1st ed.) McGraw-Hill. 1992

7 Emerson Process Management. "Distillation Column Flooding Diagnostics With

Intelligent Differential Pressure Transmitter." http://www.emersonprocess.com

8 reprinted from Wankat, Phillip C. "Efficiencies" Separation Process Engineering.

Prentice Hall, 2007, 134, Figure 4-27.

9 Walker, Jearl, Halliday/Resnick Fundamentals of Physics. 2009

10 Hanson lab techniques, Refractometry

<http://www2.ups.edu/faculty/hanson/labtechniques/refractometry/interpret.htm>

11 Jiménez Riobóo, R.J. (Instituto de Ciencia de Materiales de Madrid (C.S.I.C.),

Campus de Cantoblanco, Madrid E-28049, Spain); Philipp, M.; Ramos, M.A.;

Krüger, J.K., European Physical Journal E, v 30, n 1, p 19-26, September 2009

30

12 Belda, R (Department of Thermodynamics, Faculty of Pharmacy, University of

Valencia, 46100 - Burjassot, Valencia, Spain); Herraez, J.V.; Diez, O. Source:

Physics and Chemistry of Liquids, v 43, n 1, p 91-101, February 2005

31

Appendix A

Sample  calculation  to  compute  initial  still  concentration  

To compute the initial still concentration, we followed the regime below. (See

Table 7.) We start off with an excess mixture of weight fraction 0.1315 of ethanol.

Note that in the calculation in Table 7, we use an approximation that the density

of the mixture (983.035 g/L) can be used to determine the mass of Ethanol in the

mixture.

Table 7

Mixture Pure Ethanol

Final Mixture

Volume (L) 12 0.875 12.875

Density (g/L) 983.035 788.08

Total Mass (g) 11796.42 689.57

Ethanol Mass Composition

0.1315 1.0000 0.1795

Ethanol Mass (g) 1551.23 689.57 2240.80

Water Mass (g) 10245.19 0 10245.19

Ethanol Mol Composition

0.055934 1 0.0788

Sample  calculation  to  back  calculate  original  composition  

We obtain a sample and 0.5 ml of the sample is added to 1 ml of water to dilute

it. We run the sample in the refractometer and obtain a RI value of 1.34895 at

25°C. Using the standard curve, we obtain the weight fraction of ethanol to be

0.239. From Perry’s Handbook, after interpolation between 23 and 24% mole

fraction, we obtain the density of such a mixture at 25°C to be 0.9597 g/ml.

Density of water at the temperature is 0.9971 g/ml.  

32

Thus,

Total mass of diluted mixture = 1.5ml x 0.9597 g/ml

Mass of water added = 1 ml x 0.9971 g/ml

Original weight fraction =

Hence, original weight fraction = 0.778

33

Appendix B

Data from the Lab Notebook:

RI at 1.2 kW

         1.2kW   Refractive  Indicies   Respective  %wt  ethanol  

Sample  A   Undiluted   1:3  Dilution   Undiluted   1:3  Dilution  1   1.34563       0.18604709   0.18604709  2   1.35075       0.270733124   0.270733124  3   1.35432       0.340286277   0.340286277  4   1.35384       0.330245386   0.330245386  5   1.35645   1.34895   0.388293784   0.239322104  6   1.35645   1.35038   0.388293784   0.26410675  D   1.35631   1.34986   0.384938454   0.254947305  

                                Refractive  Indicies   Respective  %wt  ethanol  

Sample  B   1:2  Dilution   1:3  Dilution   1:2  Dilution   1:3  Dilution  1   1.35153       0.285018975      2   1.35448       0.343689908      3   1.35521       0.35960623      4   1.35582       0.373437229      5   1.35616   1.35042   0.381378387   0.264818651  6       1.34956       0.249741001  D       1.35150       0.284461217  

                                Refractive  Indicies   Respective  %wt  ethanol  

Sample  C   1:2  Dilution   1:3  Dilution   Undiluted   1:3  Dilution  1   1.35165       0.287256893      2   1.35427       0.339228584      3   1.35543       0.364535561      4   1.35604       0.378555648      5       1.34942       0.247330258  6       1.34952       0.249051007  D       1.34961       0.250604865  

34

RI at 2.0 kW

2.0  kW   Refractive  Index   Respective  %wt  ethanol  Sample  A   1:3  Dilution   1:3  Dilution  

1   1.34543   0.183000741  2   1.34828   0.228123192  3   1.34811   0.225320507  4   1.35397   0.332940278  5   1.34928   0.244931277  6   1.34947   0.248189879  D   1.35172   0.28856748  

                    Refractive  Index   Respective  %wt  ethanol  

Sample  B   1:3  Dilution   1:3  Dilution  1   1.34479   0.173362664  2   1.34753   0.215871397  3   1.34942   0.247330258  4   1.35003   0.257922553  5   1.35324   0.318030911  6   1.35056   0.267318764  D   1.34061   0.114058963  

                    Refractive  Index   Respective  %wt  ethanol  

Sample  C   1:3  Dilution   1:3  Dilution  1   1.34551   0.184217273  2   1.34718   0.210251033  3   1.35135   0.281682631  4   1.34080   0.116631986  5   1.35212   0.296130939  6   1.35383   0.330038822  D   1.34971   0.252337191  

RI at 2.4kW

2.4kW   Refractive  Index   Respective  %wt  ethanol  Sample  A   1:3  Dilution   1:3  Dilution  

1   1.34526   0.180424407  

35

2   1.34730   0.212171261  3   1.34850   0.231773182  4   1.34877   0.236288933  5   1.34940   0.246986831  6   1.35247   0.302856542  D   1.34954   0.249395883  

                    Refractive  Index   Respective  %wt  ethanol  Sample  B   1:3  Dilution   1:3  Dilution  

1   1.34627   0.195910219  2   1.35003   0.257922553  3   1.34826   0.227792667  4   1.34928   0.244931277  5   1.35030   0.262686141  6   1.35044   0.265175002  D   1.35062   0.268394307  

Graph showing Temperature change in different stages with respect to time.

0  

10  

20  

30  

40  

50  

60  

70  

80  

90  

100  

0   50   100   150   200   250  

Tem

perature  (°C)  

Time  (mins)  

Pot  

1  

2  

3  

4  

5  

6  

vapor