SERI/STR-231-3136

UC Category: 61

DE87001192

Fuel Grade Ethanol by

Solvent Extraction

Final Subcontract Report

D. W. Tedder

Georgia Institute of Technology

Atlanta, GA

April 1987

SERI Technical Monitor:

J. D. Wright

Prepared under Subcontract No. XX-4-04076-01

Solar Energy Research Institute

A Division of Midwest Research Institute

1617 Cole Boulevard

Golden, Colorado 80401-3393

Prepared for the

U.S. Department of Energy

Contract No. DE-AC02-83CH 10093
NOTICE

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legal liability or responsibility for the accuracy. completeness or usefulness of any information. apparatus.

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ii

TABLE OF CONTENTS

List of Tables •••••••••••••••••••••••••••••••••••••••••••••••••••••• vi

List of Figures••• •••••••• •••••••••••••••••••••••••••••••••••••••• viii

1. Executi ve Summary ••••••••••••••••••••••••••••••••••••••••••••••• 1

2. Economic Analysis and Design Summary •••••••••••••••••••••••••••• 5

Basis••••••••••••••••••••••••••••••••••••••••••••••• 5

Reference~ ••••••••••••••••••••••••••••••••••••••••••••••••• 11

.......................

2.1

2.2

2.3

2.4

Design

Computer

Economic

S1mulation ••••••••• •••••••

Comparisons••••••••••••••••

••••••••••••••••••••••• 6

6

3. Experimental Equipment and Materials•••••••••••••••••••••••••••• 13

Solvents•••••••••••••••••••••••••••••••••••••••

Mea sureme nts••••••••••••••••••••••••••••••••••••••••

Pumping •••••••••••.••.•••••••••••.•••••••••••••••••.•••••••

References •••••••••••••••••••••••••••••••••••.••••••••••

13

13

14

14

14

14

14

14

17

17

19

....................

Extraction Column •••••••••••••••••

Sath .•.••.•••.••••.••••••.•.•••.••.•..

Measurements•••••••••••••••••••••••••••••••••

1•.........................••.........•.......

2•••••••••••••••••••••••••••••••••••••••••••••

Appara tus•••••••••••••••••

Measurements•••••••••••••••••••

Concen trati on

Constant Temperature

Karr Reciprocating Plate

Temperature

R.P.M.

Vapor-Liquid

3.7.1 Setup

3.7.2 Setup

Organic

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4. Experimental Methods and Results•••••••••••••••••••••••••••••••• 21

Vapor-Liquid

4.1.1 Vapor

4.1

4.2

Equilibrium Measurements••••••••••••••••••••.• 21

Pressure Measurements••••••••••••••••••••••••• 21

4.1.2 Isobaric Binary VLE Measurements•••••••••••••••••••• 21

Liquid-Liquid Equilibrium Measurements••••••••••••••••••••• 21
iii

4.3 Tie Line and Distribution Coefficient Determinations••••••• 22

4.3.1 Graphical Method •••••••••••••••••••••••••••••••••••• 22

4.3.2 Analytical Method ••••••••••••••••••••••••••••••••••• 23

4.4 The Effect of Temperature and Dextrose on the Distribution

Coefficients••••••••••••••••••••••••••••••.•••••••••••••••• 26

4.5 Countercurrent Extgraction Using a Reciprocating I ..

P1a te Co1urnn ••••••••••••••••••••••••••••••••••••••••••••••• 28

4.6 Continuous Extraction/Extractive Disstillation Tests••••••• 28

4.7 References••.•••••..••••••..•••••.••••••.•••..••.•...•..••. 29

5. Characteristics of Immobilized Yeast Reactors ••••••••••••••••••• 31

5.1 Summary •••••••••.•.••••••..••••.•••.•••••.•.•...••..••...•. 31

5.2 I~troduction•••••••••••••••••••••••••••••••••••••••••• • •••• 31

5.3 Materials and Methods•••••••••••••••••••••••••••••••••••••• 34

5.3.1 The Fermentation Apparatus•••••••••••••••••••••••••• 34

5.3.2 Organism. Medium and Fermentation Conditions•••••••• 34

5.3.3 Surface Immobilization Carriers••••••••••••••••••••• 35

5.3.4 Immobilization Procedures••••••••••••••••••••••••••• 38

5.3.5 Residence Time Distribution ••••••••••••••••••••••••• 38

5.3.6 Analytical Procedures••••••••••••••••••••••••••••••• 39

5.4 Results and Discussion ••••••••••••••••••••••••••••••••••••• 40

5.4.1 Carrier Screening for Surface Immobilization •••••••• 40

5.4.2 Particle Size Effects on Performance •••••••••••••••• 40

5.4.3 Determination of Cell Dry Weight•••••••••••••••••••• 45

5.4.4 Residence Time Distribution ••••••••••••••••••••••••• 47

5.4.5 Kinetic ~1odel ••..••..•••.••••.••..•••..•.•...•.••... 49

5.5 Conclusions••••••••••••••••••••••••••••••••••••••••••••••.• 53

5.6 References•..•.............................••.......... 0 ••• 54

6. Liquid-Liquid Extraction Correlations••••••••••••••••••••••••••• 57

6.1 Correlations for Liquid-Liquid Equilibrium ••••••••••••••••• 57

6.1.1 Two Parameter Correlations•••••••••••••••••••••••••• 57

6.1.2 Temperature Effects••••••••••••••••••••••••••••••••• S9

6.2 Dextrose Effects.••..•.••...•...••.•••••.•..••..•....•..... 63
;v

6.3 Prediction of the r~utual Solubility Curve with the

UN IQUAC Mode1•••••••••••••• ••••••••••••••••••••••••••••••• 64

6.4 References••••••••••••••••••••••••••••••••••••••••••••••••• 68

7. Experimental Results and Discussion ••••••••••••••••••••••••••••• 69

7.1 Vapor-Liquid Equilibrium••••••••-••••••••••••••••••••••••••• 69

7.1.1 Estimation of Antoine Vapor Pressure Parameters ••••• 69

7.2 Thermodynamic Consistency Tests•••••••••••••••••••••••••••• 71

7.3 UNIQUAC Liquid Activity Coefficient Model •••••••••••••••••• 73

7.4 References ••••••••••••••••••••••••••••••••••••••••••••••••• 77

8. The Use of Pervaporation in Ethanol Recovery from Dilute

Aqueou s Mi xture s•••••••••••••••••,••••••••••••••••••••••••••••••• 79

8.1

8.2

8.3

8.4

8.5

8.6

8.7

8.8

8.9

8.10

8.11

Summary •••••••••••••••••••••• ••••••••••••••••••••••••••••• 79

Introducti on ••••••••••••••••••••••••••••••••••••••••••••••• 79

Pervaporation Theory ••••••••••••••••••••••••••••••••••••••• 80

Experimental Apparatus and Materials••••••••••••••••••••••• 81

Membranes Tested ••••••••••••••••••••••••••••••••••••••••••• 83

Data Analyses•••••••••••••••••••••••••••••••••••••••••••••• 83

8.6.1 Se1ect i vi ty ••••••••••••••••••••••••••••••••••••••••• 83

8.6.2 Flux...•...................•.....•.......••.•....... 84

Evaluation Criteria •••••••••••••••••••••••••••••••••••••••• 84

8.7.1 Selectivity ••••••••••••••••••••••••••••••••••••••••• 84

8.7.2 Flux....••...•........•.............•......•.••.••.• 85

8. 7. 3 Campa t i bi 1i ty • ••• ••••••••••• •• •••• ••••• ••••••••• •• •• 85

8.7.4 Handling••••••••••••••• •••••••••••••••••••••••••••• 85

Pervaporation Experimental Results and Discussion •••••••••• 85

Design and Economics••••••••••••••••••••••••••••••••••••••• 90

8.9.1 Process Description ••••••••••••••••••••••••••••••••• 90

8.9.2 Design •••••••••••••••••••••••••••••••••••••••••••••• 93

8.9.3 Results of Feasibility Design Calculations•••••••••• 93

Conclusions •••••••••••••••••••••••••••••••••••••••••••••••• 99

References••••••••••••••••••••••••••••••••••••••••••••••••• 99
v

9. Reciprocating Plate Column •••••••••••••••••••••••••••••••••••••• 101

9.1 Empirical Correlations for HETS •••••••••••••••••••••••••••• 101

9.2 References••••••.•••••••.•.••••.•••••••.•.•....•••.•..••••• 108

P . . i .. St d· 113 10. SEED rocess Opt1m zat10n u 1es•••••••••••••••••••••••••••••••

10.1 Economic Optimization•••••••••••••••••••••••••••••••••••••• 113

10.2 References•••••••••••••.•.•••.••••.•.•••••••••••••••.••.•.• 122

APPENDICES

A. Pure Component and Vapor-Liquid Data •••••••••• •••••••••••••••••123

B. Liquid-Liquid Equilibrium Data •••••••••••••••••••••••••••••••••• 131

C. Reciprocating Plate Column Data ••••••••••••••••••••••••••••••••• 142
vi

LIST OF TABLES

2.1 Major equipment items and costs for SEED Process········.· ••• 9

2.2 Total c~pital inv~stment for ethanol extraction via SEE~ •••• lO

2.3 Manufacturing costs for ethanol extraction via SEE~ ••••••••• 10

2.4 Total capital investment for optimized distillat;o~ ••••••••• 10

2.5 Manufacturing costs for optimized distillation.· ••• •• •••••••• 10

5.1 Summary of reported literature productivitie~••••••••••••••• 32

5.2 Literature review of carriers· cell loading~•••••••••••••••• 33

5.3 Literature review of long-term performance stabilit~ •••••••• 35

5.4 Surface immobilization carriers tested •••••••••••••••••••••• 36

5.5 Characteristics of the reactors ••••••••••••••••••••••••••••• 37

5.6 Characteristics of the four chamber reactor ••••••••••••••••• 39

5.7 Analysis of reactors 3-8 for carrier screenin~•••••••••••••• 41

5.8 Performances of reactors 2. 9-12 •••••••••••••••••••••••••••• 46

5.9 Average cell dry weight in reactors using bric~ ••••••••••••• 47

5.10 Dry cell weight in four chamber reacto~ ••••••••••••••••••••• 48

5.11 Apparent fit of alternative reactor models •••••••••••••••••• 52

6.1 Two parameter distribution coefficient models ••••••••••••••• 58

6.2 Distribution coefficients in solvents with polar group~ ••••• 60

6.3 Distribution coefficients in alkanes •••••••••••••••••••••••• 60

6.4 Distribution coefficients in tridecyl alcohol blends •••••••• 61

6.5 Three parameter models for distribution coefficients •••••••• 62

6.6 Six parameter models for distribution coefficients •••••••••• 65

6.7 Six parameter models for distribution coefficients •••••••••• 65

6.8 Uniquac parameters for binary systems ••••••••••••••••••••••• 67

7.1 Antoine vapor pressure model parameters ••••••••••••••••••••• 70

7.2 Legendre coefficients for consistency tests ••••••••••••••••• 76

7.3 Uniquac interaction parameters based on binary VLE data ••••• 76

8.1 List of membranes studied ••••••••••••••••••••••••••••••••••• 83

8.2 Results of general pervaporation studies •••••••••••••••••••• 86

8.3 Results of pervaporation studies with GE MEM-101 membrane ••• 8?

8.4 Estimated installed cost of pervaporation unit•••••••••••••• 94
vii

8.5 Estimated installed cost for pervaporation and adsorptian•••••• 95

8.6 Estimated installed cost for distillation system •••••••••••••• 94

8.7 Estimated annual gross profits••••••••••••••••••••••••••••••••• 96

8.8 Energy use for ethanol recovery•••••••••••••••••••••••••••••••• 9?

8.9 Net production costs for ethanol recovery•••••••••••••••••••••• 98

A.I Ethanol :pure com~~nent vapor pressures •••••••••••••••••••••••• 123

A.2Tridecyl alcohol vapor pressures •••••••••••••••••••••••••••••• 124

A.3 Oiisopropyl ketone vapor pressures •••••••••••••••••••••••••••• 124

A.4 Isopar-M Initial BP vapor pressure~ ••••••••••••••••••••••••••• 125

A.s Tri-n-butyl phosphate vapor pressure~ ••••••••••••••••••••••••• 125

A.6 Methyl ester, CE-1218 vapor pressures ••••••••••••••••••••••••• 126

A.7 Vapor pressures for 2-ethylhexanol••••••••••••••••••••••••••••• 126

A.8 Tridecyl acetate vapor pressures •••••••••••••••••••••••••••••• 126

A.9 Isobaric ethanol/water VLE at 380 mm Hg ••••••••••••••••••••••• 127

A.10 Isobaric ethanol/tridecyl alcohol VLE at 380 mm Hg •••••••••••• 128

A.11 Isobaric ethanol/lsopar-M VLE at 380 mm Hg •••••••••••••••••••• 129

A.12 Isobaric Isopar-M/tridecyl alcohol VLE at 188 mm Hg ••••••••••• 130

B.1 Ethanol/water distribution into 2-ethyl hexanol ••••••••••••••• 132

B.2 Ethanol/water distribution into Isopar-L •••••••••••••••••••••• 133

B.3 Ethanol/water distribution into dimethyl heptanone •••••••••••• 134

B.4 Ethanol/water distribution into 20~ tridecyl alcohol •••••••••• 135

B.5 Ethanol/water estraction in TOOH/lsopar-M and dextrose •••••••• 136

B.6 Ethanol/water extraction in TDOH/Isopar-M and dextrose •••••••• 137

B.7 Ethanol/wate extraction in CE-12l8 and dextrose ••••••••••••••• 138

B.8 Equilibrium data for ethanol/water/Z-ethylhexanol ••••••••••••• 139

B.9 Equilibrium data for ethanol/water/lsopar-M ••••••••••••••••••• 140

B.10 Equilibrium data for ethanol/water/CE-1218 •••••••••••••••••••• 141

C.l Data on 1 inch diameter reciprocating plate columns ••••••••••• 143

C.2 Data on 3 foot diameter reciprocating plate columns ••••••••••• 144

C.3 Data on 1 inch diameter reciprocating plate columns ••••••••••• 145
viii

LIST OF FIGURES

2.1 Conceptual flowsheet of the SEED Process ••••••••••••••••••••• 7

2.2 Estimated energy requirements for ethanol recovery•••••••••••• 8

2.3 Cooling water requirements for ethanol recovery •••••••••••••• 8

2.4 Effect of feed composition on recovery costs ••••••••••••••••• 9

3.1 Reciprocating plate column ••••••••••••••••••••••••••••••••••• 15

3.2 VLE apparatus (setup I) •••••••••••••••••••••••••••••••••••••• 16

3.3 VLE apparatus (setup 2) •••••••••••••••••••••••••••••••••••••• 18

4.1 Liquid/liquid data for ethanol/water/2-ethylhexanol •••••••••• 24

4.2 Liquid/liquid data for ethanol/water/dimethyl heptanone •••,••• 24

4.3 Liquid/liquid data for ethanol1water/Norpar-12 ••••••••••••••• 25

4.4 Liquid/liquid data for ethanol/water/lsopar-L •••••••••••••••• 25

5.1 Pore size distribution of brick and pine wood •••••••••••••••• 36

5.2 Carrier screening •••••••••••••••••••••••••••••••••••••••••••• 42

5.3 Ethanol versus time for reactor 2 •••••••••••••••••••••••••••• 43

5.4 Productivity of reactor 2 versus time •••••••••••••••••••••••• 43

5.5 Maximum productivity vs residence time for various pellets ••• 44

5.6 Comparison of encapsulation reactor with literature data ••••• 45

5.7 Cell dry weight profile in four chamber reactor •••••••••••••• 48

6.1 Liquid/liquid region for ethanol/water and 10% TDOH •••••••••• 62.1

6.2 Liquid/liquid region for ethanol, water, and 20% TDOH·•••••••• 62.1

6.3 Liquid/liquid region for ethanol, water, and 30% TDOH •••••••• 62.2

6.4 Liquid/liquid region for ethanol, water, and 50% TDOH •••••••• 62.2

7.1 Ethanol/water consistency test••••••••••••••••••••••••••••••• 74

7.2 Ethanol/Isopar-M consistency test•••••••••••••••••••••••••••• 74

7.3 Ethanol/tridecyl alcohol consistency test•••••••••••••••••••• 75

7.4 Isopar-M/tridecyl alcohol consistency test••••••••••••••••••• 75

8.1 Schematic of transport through nonporous membrane •••••••••••• 80

8.2 Pervaporation and ultrafiltration apparatus••••••••••••••••••82

8.3 Comparison of distillation with GE M81 101 membrane•••••••••• 89

8.4 Conceptual flow diagram for pervaporation unit••••••••••••••• 91

8.5 Conceptual flow diagram with pervaporation and adsorption ••• 91

8.6 Conceptual flow diagram using distillation •••••••••••••••••• 92
ix

8.7 Effect of membrane cost on return on investment ••••••••••••••••••98

8.8 Effect of membrane flux on return on investment •••••••••••••••••• 98

9.1 Effect of reciprocation speed on HETS •••••••••••••••••••••••••••• 105

9.2 Effect of linear velocity on HETS •••••••••••••••••••••••••••••••• 105

9.3 Effect of surface tension on HET~ •••••••••••••••••••••••••••••••• 106

9.4 Effect of extraction factor on HETS •••••••••••••••••••••••••••••• 106

9.5 Experimental .vs predi~~d HETS/O values •••••••••••••••••••••••••• 107

10.1 Composition node model of the SEEO process structure ••••••••••••• 115

10.2 Energy requirements for the SEED process based on RUNOPT ••••••••• 118

10.3 Ethanol production costs using the SEEO process based on RUNOPT •• 118
1

CHAPTER 1

EXECUTIVE SUMMARY

(D. W. Tedder)

This report summarizes final results for ethanol recovery by

solvent extraction and extractive distillation. Earlier reports and

theses describing various aspects of this work include:

D. W. Tedder (ed), Fuel-Grade Ethanol Recovery by Solvent

Extraction: Final Report, Georgia Institute of Technology, Atlanta,

GA (August 1984).

D. W. Tedder (ed), Fuel-Grade Ethanol Recovery by Solvent

Extraction: Technical Progress Report for Period September 15,

1981 through September 15. 1982, Georgia. Institute of

Technology, Atlanta, GA (September 1982).

D. W. Tedder (ed), Fuel-Grade Ethanol Recovery by Solvent

Extraction: Technical Progress Report for Period September 15.

1980 through September 15. 1981, Georgia Institute of

Technology, Atlanta, GA (September 1981).

M. Anselme. Immobilized Yeast Extractor for Ethanol

Production, M.S. Thesis, School of Chemical Engineering, Georgia

Institute of Technology, Atlanta, GA (June 1985).

W. Y. Tawfik, Design of Optimal Fuel-Grade Ethanol

Recovery System Using Solvent Extraction, Ph.D. Thesis, School

of Chemical Engineering, Georgia Institute of Technology, Atlanta,

GA (1986).

L. M. Sroka, Membrane Use in Ethanol Recoverv

Processes, M.S. Thesis, School of Chemical Engineering, Georgia

Institute of Technology, Atlanta, GA (August 1984).

N. Griffin, Vapor-Liquid Equilibria of Selected Ethanol

Systems, M.S. Thesis, School of Chemical Engineering, Georgia

Institute of Technology, Atlanta, GA (December 1983).

K. B. Garg, Design of Liquid-Liquid Extractants for the

Recovery of Fuel-Grade Ethanol, M.S. Thesis, School of Chemical

Engineering, Georgia Institute of Technology, Atlanta, GA (March

1982).

W. Y. Tawfik, Efficiency of Ethanol Extraction from Aaueous

Mixtures, M.S. Thesis, School of Chemical Engineering, Georgia

Institute of Technology, Atlanta, GA (1982).

D. R. Sommerville, Diffusion of Ethanol in Organic Solvents,

M.S. Thesis, School of Chemical Engineering, Georgia Institute of

Technology, Atlanta, GA (April 1985).
2

L. H. Krosnowski, The Measurement of the Diffusion

Coefficients of Ethanol in Oraanic Solvents, M.S. Thesis, School

of Chemical Engineering, Georgia Institute of Technology, Atlanta,

GA (June 1983).

A. J. Eckles, Modeling of Ethanol Extraction in the Karr

Reciprocating Plate Column, M.S. Thesis, School of Chemical

Engineering, Georgia Institute of Technology, Atlanta, GA (1984). /

The present report discusses the comparative economics of

distillation versus ethanol recovery by solvent extraction (Chapter

2).

Chapters 3 and 4 summarize experimental methods and

procedures. Equilibrium data is presented in Chapters 6-7 and

Appendices A and B. Empirical correlations are also presented

in Chapters 6-7. Much of this data was presented in earlier

reports, but it is recapitulated here for reader convenience.

Preliminary results on immobilized cell fermentation in upflow

reactors are summarized in Chapter 5. Chapter 8 presents the

results of pervaporation studies in solvent regeneration. Chapter 9

summarizes the results of mass transfer studies in reciprocating

plate columns for liquid/liquid systems of interest. Additional

experimental data for the reciprocating plate column are

summarized in Appendix C.

Chapter 10 summarizes the economic optimization analyses

for the SEED process (Solvent Extraction and Extractive

Distillation). It was learned through the optimization that solvent

selectivity in the SEeD process is less important economically

than solvent loading. That is, the optimal solvent composition was

100% tridecyl alcohol. Future studies should, therefore, attempt to

identify other high boiling solvents with similar properties, but

higher loadings and lower selectivities. .

The conclusion of this work can be summarized as follows.

Ethanol dehydration and recovery dilute fermentates is feasible

using liquid/liquid extraction and extractive distillation. Compared to

distillation, the economics are more attractive for more dilute

feeds (e.g. less than 5 wtOfo ethanol initially). However, an

economic bias in favor of SEED appears to exist even for 10

wt% feeds.

It is of particular interest to consider the group extraction of

ethanol and acetic acid followed by conversion to a mixture of

ethanol and ethyl acetate. The latter species is a more valuable

commodity and group extraction of inhibitory species is one

feature of liquid/liquid extraction that is not easily accomodated

using distillation. Upflow immobilized reactors offer the possibility

of achieving high substrate conversion while also maintaining low

metabolite concentrations. However, many questions remain to be

answered with such a concept.
3

Although the use of the SEED process for the recovery of

ethanol enhances the economics of gasahol production using

fermentation, the benefits do not appear sufficient to enable

market entry at this time. Basically. the feedstock costs dominate

the economics. A producer needs feed material without cost.

Alternatively, the value of ethanol must rise to about $2.00/gal on

the commodities market before significant production via the

fermentation route will become attractive.

However, Solvent Extraction and Extractive Distillation (SEED)

is technically feasible. The solvent must have a vapor pressure

that is significantly less than water or ethanol. Then the extract

can be dehydrated in an extractive distillation column (EDC) and

the dry product recovered using a subsequent solvent

regeneration column (SRC). The process in energy efficient

because relatively little water or solvent is distilled. and ethanol

is only evaporated once. Sensible heat requirements are

minimized through heat exchanger matching.

5

CHAPTER 2

ECONOMIC ANALYSIS AND DESIGN SUMMARY:

THE SEED PROCESS VERSUS DISTILLATION

(D. W. Tedder)

2.1 Design Basis

The research effort focused on the use of solvent extraction

and extractive distillation together (the SEED Process) to achieve

the design goals. A laboratory system consisting of a 1 inch

diameter Karr Reciprocating Plate column and two glass bubble

cap columns was constructed and tested. The two bubble cap

columns were also tested while using a 16 stage mini

mixer-settler rather than the reciprocating plate column for the

liquid/liquid cascade.

The reciprocating plate column consisted ~f 92 stainless

steel reciprocating plates with an active contact height of 244

cm. The glass column inner diameter is 2.54 ern, Model KC 1-8

sold by the Chern Pro Corp., New Jersey.

The two bubble cap columns had 7.6 cm diameters. The

extractive distillation column included three bubble cap trays

above the feed and three below. The solvent regeneration column

consisted of tour bubble cap trays below the feed and two

below. Each tray had one bubble cap with liquid flowing across

the tray.

In all cases, total solvent recycle was achieved and steady

state was approached. Beer extract was produced and

dehydrated by extractive distillation through the selective removal

of coextracted water. The dehydrated extract was then passed to

a solvent regeneration bubble cap column for ethanol recovery.

Regenerated solvent was also recycled continuously to the top of

the extractive distillation column.

Liquids were transported between the units using either

laboratory scale metering pumps from FMI tnc., Oyster Bay, MA

or a low-frequency bellows pump sold by Fischer. The latter

pump was used to return cooled solvent from the solvent

regeneration column to atmospheric pressure without cavitation.

(Commercial. units will likely require the use of a barometric leg

at that point.)

Initial tests with the solvent 1 + 1 by volume tridecyl alcohol

and Isopar-M were completed. Ethanol was extracted from a

sucrose beer containing about 5 MOJo ethanol. The feasibility of

the EDC concept was proven since it was possible to maintain

the ethanol concentrations below 1 ~/o in the EDC distillate.

Subsequent tests used ethanol/water mixtures containing from

3 to 9 wr'/o ethanol and either the reciprocating plate column or

the mini mixer-settler bank. The solvent consisted either of pure
6

tridecyl alcohol or tridecyl aicohollisopar-M mixtures. Product

recovered from the EDC distillate typically contained 1 to 20 wt%

ethanol. Product recovered from the SAC column ranged from 85

to 98 wt% ethanol.

In order to achieve the desired results, the feed

temperatures ,'for both,' the EDC and SAC columns must be

carefully controlled. Solvent foaming problems can be controlled

by starting up the EDC and SCA columns with total solvent

recycle without sending extract to the EDC. It helps to have the

solvent at the correct temperature on each stage in the EDC

column before extract is introduced.

2.2 Computer Simulation

Detailed equilibrium stage calculations based upon

experimental equilibrium data, UNIQUAC and UNIFAC models, and

the SimSci PAOCESS flowsheet model, indicate that the basic

concept is correct. Moreover, the model predicts that a 99 mole

% recovery of ethanol can be achieved from beers as dilute as

0.5 wt% ethanol. Conditions in the EDC and SRC columns must

be adjusted, however, with the beer quality to achieve a 99

mole % ethanol product. Solvent carryover, a problem in the lab,

is controllable using a partial condenser in the SRC column.

2.3 Economic COmparisons

Cost analysis was completed in which three beers (5.15%,

1.9%, and 0.57 wt%) were treated using the SEED process.

Figure 2.1 shows the conceptual SEED flowsheet. Comparisons

were then made with the Berkeley optimized distillation (1 )

concept using their net energy balances, cooling water, and

theoretical tray estimates. Azeotropic distillation costs were

modelled using the data provided by Black(2). All cases

assumed a 99% ethanol recovery and purity(mole basis).

Using the LBL concept, heat is introduced into the beer

stripper and passed to the beer concentrator by matching the

stripper condenser with the concentrator reboiler. Hence, the

concentrator operates at reduced pressures. By heat exchanger

matching, the energy requirements for the azeotropic distillation

are satisfied from the stripper duty.

Two fuel prices were used for natural gas, $4.64 and

$6.47/MBTU, based upon a recent study by Breuer (3). Capital

investment was estimated using correlations derived from Guthrie

(4). Although a premium fuel was assumed, the resulting internal

stream prices ($6.39 and S8.51/MBTU) are comparable with

estimates by Breuer for coal and only slightly affect the resulting

conclusions.




11

The SEED process energy requirements (see Fig. 2.2)

compare favorably with optimized distillation. For the 50/0, 20/0 and

0.5 MOJo beers, the estimates are 9,700, 25,900, and 55,000

BTU/gal respective. This corresponds to energy saving of about

3,600, 10,000. and 19,000 BTU/gal respectively when compared to

optimized distillation. Since the solvent extraction cases have not

been optimized, further improvements can be expected.

Cooling 'water requirements (see Fig. 2.3) for the SEED

process are also much lower than for optimized distillation. The

solvent extraction process offers new opportunities for heat

exchanger matching and, more importantly, facilitates beer preheat

with significantly reduced transfer area requirements.

Cost analysis for the three beer cases (see Fig. 2.4)

suggests that solvent extraction becomes more economically

attractive as the beer quality decreases. The capital investment

requirements are also less than for optimized distillation,

especially for lower grade beers. Some of the economic

differences are summarized in Tables 2.1-2.5 for the 2 MOJo beer

case. Estimated ethanol recovery costs ranges from

$0.25-$0.90/gal for solvent extraction. Compared to optimized

distillation, the estimated savings were about $0.12, $0.34, and

$O.44/gal for the three beer cases respectively.

These initial results for ethanol recovery from dilute beers

suggest that solvent extraction will eventually displace distillation

technology for solute recovery from low-grade fermentates.

Additional savings are possible from optimization as discussed in

Chapter 10.

2.4 References

1. T. K. Murphy, H. W. Blanch, and C. R. Wilke, Recovery of

Fermentation Products from Dilute Aqueous Solution,

LBL-17979, Lawrence Berkeley Laboratory (April 1984).

2. C. Black. CEP(September 1980) 78.

3. C. T. Bruer, Chern. Eng. (September 17, 1984) 97.

4. K. M. Guthrie, Modern Cost Engineering Techniques,

McGraw-Hili; New York (1970) 80.
12
13

CHAPTER 3

EXPERIMENTAL EQUIPMENT AND MATERIALS

rH. Y. Tawfik)

3.1 Concentration Measurements

Equilibrium data were obtained by concentration

measurements in both phases. The concentration analyses were

made using a gas/liquid chromatograph, a Hewlett Packard type

5710A, with a 4-ft. x 1/8-inch diameter, Porapak Q 80/100 mesh

packed column. Helium was used as the carrier gas, obtained

from the' Alabama Oxygen Co., lnc., Bessmer. Alabama. The gas

chromatograph was operated at oven temperature of 150°C and

injection port temperature of 250°C with a thermal conductivity

detector which was also operated at 250°0. The peaks were

. integrated using a Hewlett Packard 3390A peak integrator..

The output from the integrator was in the form of area

percentages of those sample components which chromatographed.

The area percentages were then converted to the corresponding

weight percentages using calibration curves that were obtained by

analyzing samples of known compositions and plotting the

integrated ratio of area percentages versus the ratio of weight

percentages. A similar calibration curve was used for analyzing

the organic phases using reagent grade propanol from Fischer

as a reference peak. In the latter case, this analysis yielded the

ethanol and water concentrations in the organic phase as well

as the weight fraction solvent. Solvent concentrations in the

aqueous phases were also measured using propanol spiking.

3.2 Constant Temperature Bath

The effect of temperature on ethanol and water equilibria

was studied using a circulating heating bath model HAAKE-L

equipped with a heating element and a heat controller model

HAAKE-01. The bath is operated with temperature accuracy of

±0.1°C. The heated samples were carried on glass bottles with

capacity of 15 cc. An equilibration time of about 30 minutes was

needed to achieve uniform temperatures in the samples. For

insuring good temperature measurements. reference bottles

containing samples similar to the ones being tested were used

to measure temperatures inside the sample.

Another type of heating bath was used to provide heated

aqueous feed and organic solvent to the insulated extraction

column in order to operate it at higher temperatures. This bath

consisted of a small rectangular glass tank with a capacity of

approximately five and a half gallons. The temperature in the

bath was regulated by a Model 73 Immersion Circulator. obtained
14

from Fisher Scientific Company. The control of temperature was

to ±0.01°C.

3.3 Karr Reciprocating Plate Extraction Column

This column consists of 92 stainless steel perforated plates

mounted on a central ..shaft which can be reciprocated by means

of a drive mechanism located above the column. The main

portion of the column is a borosilicate glass pipe. The frequency

of reciprocation can be varied from 0-400 strokes per minute

(Fig. 3.1). The amplitude is also variable.

The model that was used is the KC 1-8 purchased from

the Chem Pro Corp.. New Jersey. It has an overall height of

152 inches. a diameter of 1 inch, a plate stack height of 96

inches. a base length of 24 inches, a base width of 15 inches

and a plate spacing of 2 inches. This column was wrapped with

heating tape purchased from Fisher Scientific for temperature

control. Solvents and aqueous feeds entering the column were

preheated as required.

3.4 Temperature Measurements

A type K thermocouple (nickel chromium/nickel aluminum)

was used to measure the operating temperatures of both the

mixer-settler and the reciprocating plate column. The

thermocouple was connected to a digital multimeter type Simpson

460 Series 4, made by Simpson Electronic Co., Elgin, Illinois,

which has a nominal accuracy of ±0.2 MV. The reference

thermocouple junction was maintained at a temperature of O°C.

3.5 Pumping

Positive displacement pumps, purchased from Fluid Metering,

Inc., Oyster Bay, were used to provide a uniform flow of

aqueous feed and organic solvent to the extractors. These flow

rates range from 0-120 ml per minute. Tygon tubing with 1/4-inch

outside diameter was used for piping.

3.6 R.P.M. Measurements

The rotational frequencies of the centrifuges and the mixers

were measured using a tachometer Model C-871 made by Power

Instruments, Inc.

3.7 Vapor-Uquid Apparatus

3.7.1 Setup 1.

.The liqUid-vapor .equilibrium chamber consisted of three

principal devices:· glassware for distilling with total reflux, an

acetone/dry ice cooling system, and a pressure regulated vacuum

system. Figure 3.2 illustrates the general layout of the apparatus.


17

For a given liquid phase composition and system pressure, both

the vapor phase composition and the system temperature are

fixed.

The distillation glassware and heater consisted of a heating

mantle with rheostat (0-110V), a SOO-ml boiling flask, a distillation

head with finger condenser, a side-mounted needle value for

sampling refluxed condensate, a sampling cow with four S-ml

fingers, and vacuum attachments. A thermometer (typically -10°C

to 250°C) was also used. Ideally, the 500 ml boiling flask should

be three-necked, supporting the use of a septum for withdrawing

liquid samples without breaking the vacuum, the thermometer, and

an additional funnel for introducing the various components. More

importantly, the system was insulated from the boiling flask to

condenser to prevent premature condensation of the vapors.

The acetone cooling system included a positive displacement

cooling pump (F.M.I.), a dry ice/acetone bath, and connections to

the condenser.

The vacuum system included a cold trap, a Gilmont

C··2200-0 Manostat, a mercury trap, manometer, three-way valve,

air bleed needle valve, and a suitable high-vacuum pump.

3.7.2 Setup 2.

For more complicated systems, the previous set-up failed to

insure constant pressures for isobaric experimental data. A more

sophisticated device (Fig. 3.3) was used to achieve better

accuracy.

The equilibrium cell was a Stage-Muller dynamic equilibrium

still, manufactured by Fischer Labor und Verlahrenstechnik, West

Germany. Good circulation for both liquid and vapor phases was

achieved by means of Cottrell pumps, which insured good

contact of the liquid and vapor phases with the temperature

sensing element.

Thermal insulation for the· vapor phase was provided by a

silver-plated vacuum jacket surrounding the equilibrium chamber,

preventing partial condensation of the vapor phase. Simultaneous

sampling for liquid and vapor phases in equilibrium was done

through two magnetic sampling valves. The system pressure was

maintained constant using a Fischer VKH 100 pressure controller.

The features of this still were described in detail by Stage and

Fischer's (1, 2).

3.8 Organic Solvents

The following solvents were used as received from their

respective vendors:

19

2 Ethyl-1-hexanol (2EHOHl. This technical grade organic

solvent has a boiling range of 183 - 185°C and was purchased

from the Fisher Scientific Company, New Jersey.

2.6 Dimenthyl-4-heptanone (DMH). This practical grade

organic has an initial boiling point of 169°e and was purchased

from the Fisher Scientific Company, New Jersey.

Isopar-L. This is a heavy narrow-cut, isoparaffinic solvent

composed of C12 mixtures of branched alkanes. It has a specific

gravity of 0.767 at 15.6°C, a viscosity of 1.99 c.p. at 25°C and

a boiling range of 177 - , 97°C. The solvent is a refinery

product obtained from Exxon Refining.

Isopar-M. This is a heavy isoparafinic narrow refinery cut,

composed mainly of mixtures of C12 and C13, with a boiling point

range of 20r> to 2540C and specific gravity of 0.78 at 25°C. The

solvent is a product of Exxon.

Methyl Ester CE-1218. This is a Proctor and Gamble

product with saponification value of 238 and specific gravity of

0.866 at 25°C. The solvent contained 55% C12 , 22% C14 , 10%

C16 , and 13% C18 • .

Norpar.' 2. This is a narrow-cut, normal parrafinic solvent

composed primarily of C11 and C12 mixtures of alkanes. It has a

specific gravity of 0.751 at 15.6°C, a viscosity of 1.26 c.p. at

25°C, and a boiling range of 188 - 219°C. The solvent is a

refinery product obtained from Exxon.

Tridecyl Alcohol. This is a distilled product, consisting of

isomeric primarily alcohols, predominately C13 • It has a specific

gravity of 0.838 at 20°C, a viscosity of 18.9 c.p. at 25°C and a

boiling range of 253 - 266°C.

Tri-n-butyl phosphate. This is a technical grade solvent from

Fisher. It has a normal boiling point of 289°C with decomposition

and a specific gravity of 0.976 at 25°C.

The aqueous solutions were prepared by mixing different

quantities of absolute reagent grade ethanol (99.5% pure) with

distilled water. Gas chromatography was used to determine the

weight percentages of ethanol in the aqueous solutions.

Apparatuses were calibrated and checked for accuracy using the

ethanol/water binary system and literature data (3).

3.9 References

t. H. Stage and W.G. Fischer, "Improved LABODEST

Circulation Apparatus for Measurement of Vapor-Liquid

Equilibria" t G.I.T. Special Periodical, vol. 11, (1968).

2. H. Stage and W.G. Fischer, "Experimental Experience with
· 20

and Improved LABODEST Circulation Apparatus", J. Chem.

Eng., vol. 7, (1973).

3. J.A. Larkin and R.C. Pemberton, Thermodynamic Properties

of Mixtures of Water and Ethanol, Division of Chemical

Standards, England, (1976).
21

CHAPTER 4

EXPERIMENTAL METHODS AND RESULTS

rn. Y. Tawfik)

4.1 Vapor-Liquid Equilibrium Measurements

4.1.1 Vapor Pressure .Measurement.

About 100 ml of the pure liquid were placed in the boiling

flask. Heat was provided using a heating mantel. Continuous

stirring was provided using a magnetic stirrer to insure

homogeneous temperature in the liquid phase. When Setup 2

(Chapter 3) was used, good circulation was provided using the

Cottrell pumps for both phases in equilibrium. When Setup 1

(Chapter 3) was used, the system was operated at total reflux,

providing more time to achieve thermal equilibrium. While the use

of the manostat resulted in some pressure fluctuations, the VKH

'00 pressure controller in Setup 2 minimized the pressure

disturbance to acceptable levels (to.' mm Hg). The cooling

media for the vapor condenser was acetone and dry ice.

Equilibrium was achieved when the monitored temperatures

remained constant for 30 minutes at the given pressure. The

previous procedure was repeated at different pressures and the

temperatures and the corresponding vapor pressures were

recorded. The experimental vapor pressure data for ethanol and

seven different solvents are summarized in Appendix A (Tables

A' through AS).

4.1.2 Isobaric Binary VLE Measurements.

It was found by experience that the optimized procedure

should start with , 00 ml of the less volatile component of the

binary to avoid thermal instabilities. Incremental amounts of the

more volatile component were added to the boiling flask, and

good mixing provided before heating the system to insure

homogeneous mixtures. The previous procedure was repeated at

the given pressure for each liqUid composition; however, more

time (about 45 minutes) was needed to achieve thermal

equilibrium. Simultaneous samples of liquid and vapor were then

drawn, and the temperature recorded at the given pressure.

Composition analysis was carried out using the HP gas

chromatograph as described earlier.

The previous procedure was repeated until the entire

composition range of the binary system was covered. The test

system used for both setups was the ethanol-water binary

system. Good agreement with the literature data of Larkin and

Pembertom (36) was found for this test system. The experimental

values .for the isobaric binary VLE systems of interest are

summarized in Appendix A (Tables A9 through A11).

4.2 Uquid/Liquid Equilibrium Measurements
22

tnitlal studies (1 through 12) were concerned with gathering

equilibrium data about ethanol-water-organic solvent systems using

different techniques such as tie-line measurements, solubility curve

titrations, batch equilibrations and solvent stripping tests. The

experimental data obtained by these experiments classified the

organic solvents into two broad categories. Drying solvents have

higher selectivities for extracting ethanol from water, but their

ethanol distribution coefficients are relatively low. These solvents

usually are rlefinery products, composed mainly from mixtures of

heavy molecular weight alkanes such as Isopar-L and Norpar-12.

Recovery solvents have higher ethanol distribution coefficients, but

their selectivities for ethanol are lower. These solvents are

systems like branched alcohols and ketones, such as

2-ethyl-1-hexanol and 2.6-dimethyl-4-heptanone.

In this work, the effects of temperature on the equilibrium

were also examined using some of the systems which had been

studied earlier. by Tedder, ~. (13) and Tawfik (14), at room

temperature. These temperature studies resulted from a need to

reduce the tendency of some solvents to form a stable emulsion

with the aqueous phase. Higher temperatures did not help much

in solving the emulsion problem; however, in analyzing some of

the organic and the aqueous phases of the heated samples, it

was observed that both ethanol distribution coefficients and the

solvent selectivities had been increased.

4.3 ne Une and Distribution Coefficient Determinations

Tie lines and distribution coefficients for any specific solvent

were determined by equilibrating a known weight of aqueous

solution of ethanol and water (A) with a known weight of the

given pure solvent (OJ). The concentrations of ethanol in the

aqueous phase before and after equilibration with the solvent (Xei,

Xe, respectively) were determined by GC analysis. In these cases,

the GC analysis was based on the area percentages for the

integrated peaks of the ethanol and water species only (Le., on

a solvent-free basis). These area percentages were then

converted to the corresponding weight fractions using a

calibration curve. However, for a complete organic phase analysis

one of the following two techniques were used depending upon

the nature of the solvent

4.3.1 Graphical Method.

First the system was titrated volumetrically. Starting with a

known weight of either solvent or water, successive additions of

the remaining two species were titrated into the mixture to

repeatedly form and remove the liquid/liquid cloud. Using the

titrant volumes and pure species densities, the mutual solubility

curve was then constructed. The initial aqueous composition was

plotted on a ternary diagram with the mutual solubility curve.

After equilibrating known weights of aqueous and solvent mixtures,

the aqueous composition after mixing was plotted on a ternary
23

diagram with the mutual solubility curve. Gas chromatography

gave the precise ethanol/water weight ratio in the equilibrated

aqueous phase. Connecting the two points by a straight line

resulted in an intersection with the mutual solubility curve and

the equilibrium aqueous composition and yielded one tie line for

the system. The intersection between the tie line and the organic

phase side of the solubility curve yielded the equilibrium organic

compositions (Ye. v-. Ys).

This method was suitable for relatively wet systems like

2-ethyl hexanol and dimethyl heptanone because of the ease in

determination of the water weight fraction in the organic phase

from the solubility curve (Figs. 4.1, 4.2). On the other hand, for

relatively dry systems like Norpar-12 and Isopar-L (Figs. 4.3, 4.4)

in which the organic phase side of the solubility curve almost

coincides with the solvent ethanol edge of the ternary diagram, it

was difficult to accurately measure the water concentration in the

organic phase. In these latter cases the following analytical

technique was used. This method is described in detail

elsewhere (4-8).

4.3.2 Analytical Method.

This method is based on solving the mass balance

equations for the equilibrium phases using. the GC analysis of

the phase before and after equilibration. In the absence of the

solubility curve, one assumption is necessary; namely, that the

solubility of the solvent in the aqueous phase within the range of

interest is negligible, t.e,

x, =0 (4.1)

Alternatively, the two phases may be analyzed by the GC using

propanol spiking to estimate x, and Ys. However, assuming

component mass balance for ethanol and water, the component

mass balance equations yield:

where:

ZeF • xeA =YeO (4.2)

(4.3)

Zj = weight fraction of component i in the feed

Xj = weight fraction of component i in the equilibrated aqueous phase

Yi = weight fraction of component i in the equilibrated organic phase


26

A =equilibrated aqueous phase weight, gm

a =equilibrated organic phase weight. gm

F =a + A =weight of aqueous and organic phases, gm

Dividing Eqn. 4.2 by 4.3 gives:

since

(Y. I Yw) = Cf. I Vw) (on a solvent-free basis)

(4.4)

(4.5)

where Vi = weight fraction of component i in the organic phase

in solvent free basis.

Rearranging Eqn. 4.4 and solving for A:

A =F [CfelYw) Zw:z.] I [CfelYw) xw-x.]

and:

Rearranging Eqns. 4.2 and 4.3 :

Y. = (z. * F - X.* A)/O

Yw =(zw * F - Xw * A)/O

Ys = 1 - Y. - Yw

De = Y. I X.

Dw =Yw I x,

S = D. I o,

where:

(4.6)

(4.7)

(4.8)

(4.9)

(4.10)

(4.11 )

(4.12)

(4.13)

OJ = distribution coefficient of component i in weight basis

S = selectivity of component i in weight basis

4.4 The Effect of Temperature and Dextrose on the Distribution

Coefficients

The same procedures described above for tie-line

determination were repeated for the same solvents at higher

temperatures rather than at room temperature using a constant

temperature bath. An equilibration time of 30 minutes was

provided for "the test samples to achieve equilibrium. To insure
27

accurate measurements, similar reference samples were placed in

the same bath and a mercury thermometer was used to

measure their temperatures.

The resulting distribution coefficients were correlated initially

with the temperatures at the given initial aqueous feed

concentrations in the form of:

In Oi = a' + b'tT (4.14)

This type of correlation was not quite suitable to express

the effect of temperature on the distribution coefficients since any

change in their values must be accompanied with a change in

the ethanol concentrations in the equilibrated aqueous phase and,

clearly, that a general correlation must have the distribution

coefficients as a function of both variables. Tables 81 through

B4 (Appendix 8) summarize the measured distribution coefficients

for ethanol and water using 2-ethyl-hexanol, Isopar-L,

dimethylheptanone, and 200/0 TDOH in Norpar- 12 as solvents,

respectively, at different temperatures.

The correlation that was chosen has the form:

(4.15)

in which a·, b' and c· are constants for the given components

and solvents over the range of interest.

Actual fermentation broth was found to have residuals of

sugars (mainly sucrose). Moreover, it was found that sucrose has

a significant effect on the distribution coefficients for both ethanol

and water. The effect resulted in higher ethanol recovery and

drier product for a given solvent and is large enough to justify,

in some cases, the maintenance of higher levels of sucrose in

the aqueous phases through the process. Dextrose was used to

test the effect of sugar on the experimental distribution coefficient

for ethanol and water. In the presence of dextrose, the

experimental values of the distribution coefficients for both ethanol

and water are summarized in Tables 85 through 87 (Appendix

B) for three different solvents. On the other hand, the distribution

coefficient dependence on the ethanol weight fraction was no

longer linear, and the best correlation was found to be:

(4.16)

where:

Xi = weight fraction of component i in the equilibrated aqueous phase

'm =volume fraction of modifier in the solvent

T = temperature, OK
28

The resultant parameters for this correlation are summarized in

the next chapter.

4.5 Countercurrent Extraction Using a Reciprocating Plate Column

This column is a continous extraction device which can be

used for countercurrent extraction as described by Karr (15) in

detail. The aqueous feed solution was metered into the column

via the nozzle above the reciprocating plates. Similarly, the

extracting solvent enters the column at a controlled rate via the

nozzel below the extractor plates. Before the final adjustment of

the reciprocating speed is made, the interface is established at

approximately the midpoint of the upper disengaging section.

Thus, nearly the entire column will be filled with the organic

phase and only a few inches of the aqueous phase will be

present at the bottom of the column. The interface is established

by setting the control valve in the bottom discharge line through

which the aqueous phase exits from the column. Subsequently, a

final adjustment of the reciprocation speed of the plates is

made. . The reciprocation speed should be such that in the

vicinity of the plates the average diameter of the dispersed

phase droplets is one millimeter or less.

The interfacial area between the continuous and dispersed

phase is the principle factor controlling the rate of mass transfer

between them. However, the time needed to achieve steady state

conditions is also a function of the total feed flow rates and the

reciprocating speed. Usually, at least one and a half hours were

needed to achieve steady state under the present operating

conditions. Analysis of the final extract and raffinate compositions

was done using gas chromatography.

The experimental data for 22 runs is summarized in Table

C1 (Appendix C) for ethanol-water-Isopar-M systems.

Additional data for a 3-ft column and 1-in column obtained

from Karr (16) and Karr and Lo (15, 17) are summarized in

Appendix C.

4.6 Continuous Extraction/Extractive Distillation Tests

Tests were carried out using the Karr column for extraction

and a custom-fabricated pyrex column for solvent regeneration.

Solvent was continuously regenerated and recycled to the Karr

column. The solvent regeneration column in this case was about

2 inches in diameter and 3 feet tall. It was equipped with a

glass wool demister pad and condenser assembly at the top,

Extraction tests using dilute ethanol mixtures were carried

out using either the Karr column or the mixer-settler bank to

provide extract. Extract was then processed theough a coalescer,

an extract preheater, and passed into an extractive distillation
29

column consisting of 6 bubble cap trays (3 above and 3 below

the feed). Regenerated solvent was continuously pumped to the

top of this column (about 3 feet tall) which was equipped with a

condenser assembly and a , -uter triple neck reboiler which was

jacketed with a heating mantel. Dehydrated extract was passed

by pressure differential from the bottom of the extractive

distillation column into the solvent regeneration column. A

stainless steel' valve ln the line was adjusted to achieve steady

flowrates. The latter column also consisted of 6 bubble cap trays

(2 above and 4 below the feed). It had a condenser assembly

and heating mantel similar to the extractive distillation column.

Solvent transfer from the bottom of the solvent regeneration

column back to atmospheric pressure was accomplished using a

low frequency bellows pump obtained from Fisher. Prior to

recovering the solvent, it was necessary to cool it using an ice

bath to prevent damage to the bellows pump. It was also found

convenient to provide feedback control loops. In particular, the

Karr column was operated with 'on/off control on the liquid/liquid

interface at the bottom of the column (l.e, organic continuous).

The temperatures of the extractive distillation column feeds

were controlled using a heating mantel. triple neck flask and

on/off controllers connected to Variacs. Finally, the pressures in

the extractive distillation and solvent regeneration columns were

controlled using two additional on/off feedback controllers.

Pressures in the extractive distillation and' solvent regeneration

columns were maintained at reduced values by a common

vacuum pump. In all cases, the controllers used conductivity

probes which were simply clamped to the parameter of interest

(i.e., either an interface let, a mercury thermometer, or a

mercury manometer). These techniques were found to be highly

effective and could maintain the process to within 1°C and 2mm

Hg.

4.7 References

1. D.W. Tedder, C.L. Liotta, F.M. Williams and M. Spanbauer,

Fuel-Grade Ethanol Recovery by Solvent Extraction:

Technical Progress Report for the Period September 15.

'980 thru March 31, 1981, School of Chemical Engineering,

Georgia Institute of Technology, Atlanta, GA (April, 1981).

2. D.W. Tedder and C.L. Liotta, Fuel-Grade Ethanol Recovery

by Solvent Extraction, Schools of Chemical Engineering and

Chemistry, Georgia Institute of Technology, Atlanta, GA

(June 30, 1979).

3. D.W. Tedder, et al., Fuel"Grade Ethanol Recovery by

Solvent Extraction: Technical Progress Report for the Period

September 15, 1981, Georgia Institute of Technology,

Atlanta, GA (October 1981).
30

4. D.F. Othmer, R.F. White, and E. Trueger, "Liquid-Liquid

Extraction Data" , Industrial and Engineering Chemistry, vol.

33, no. 10, (1941), pg. 1240.

5. A.V. Brancker, T.G. Hunter, "Tie Lines in Two-Liquid Phase

Systems", Ind. and Eng. Chemistry, vol. 32, no. 1, (1940),

pg. 35.

6. I. Bachman, "Tie Lines in Ternary Liquid Systems", !m1

and Eng. Chemistry. vol. 32, no. 1, (1940), pg. 35.

7. J.L. Schweepe and J.R. Lorah, "Ternary System Ethyl

Alcohol-n-Heptane-Water at 30°", Ind. and Eng. Chemistry.

vol. 46, no. 11, (1941), pg. 2391.

8. C.M. Qualline and Van Winkle, "Ternary Liquid Systems",

Ind. and Eng. Chemistry, vol. 44, no. 2, (1952), pg. 1668.

9. D.F. Othmer and P.E. Tobias, "liquid-liquid Extraction

Data", Ind. and Eng. Chemistry, vol. 34, no. 6, (1942), pg.

696.

10. J.W. Roddy and C.F. Coleman, "Distribution and Miscibility

Limits in the System Ethanol-Water-TBP", Oak Ridge

National Laboratory, (1981 ).

11. J.W. Roddy, "Distribution of Ethanol Water Mixtures to

Organic Liquids", Ind. Eng. Chem. Process Des. Dev., vol.

20, (1981), pps. 104-108.

12. J.W. Roddy and C.F. Coleman, "Distribution of Ethanol-Water

Mixtures to Normal Alkanes from Ce to C16 " , Ind. Eng.

Chem. Fundam., vol. 20, (1981), pps. 250-254.

13. D.W. Tedder, et at., Fuel Grade Ethanol Recovery by

Solvent Extraction, Technical Progress Report, Georgia

Institute of Technology, (October 1982).

14. W.Y. Tawfik, "Efficiency of Ethanol Extraction from Aqueous

Mixtures", Thesis, Georgia Institute of Technology, (1982).

15. T.C. La and A.E. Karr, "Development of a Laboratory Scale

Reciprocating Plate Extraction Column", Ind. Eng. Chem.

Proc. Des. Dev., vol. 11, no. 4, (1972).

16. D.S. Abrams and J.M. Prausnitz, AIChE J., vol. 21, (1975),

pg. 116.

17. A.E. Karr and T.C. Lo, "Performance and Scale Up of

Reciprocating Plate Extraction Column", Proc. of ISEC,

Society of Chem. Ind., London, vol. 218, (1971).
31

CHARACTERISTICS OF IMMOBILIZED YEAST REACTORS PRODUCING

ETHANOL FROM GLUCOSE

(M.J. Anselme and D.W. Tedder)

5.1 SUlllllary

The evol uti onary performance of upflow reactors are affected by

the cell immobilization matrix and the matrix particle size

distribution. Higher productivities are obtained using a low-density

brick with a particle size of about 400 to 1400~. A medium condition

favoring growth quickly leads to large biomass gradients within the

reactors and, eventually. reductions in average productivities due to

bed plugging. These systems can be accurately modeled using Monod

kinetics when dispersion and the biomass gradient are considered. The

productivity was apparently not controlled by substrate diffusion in

these ca sese

5.2 Introduction

Bioreactor design can, in principle, be used to increase

productivities, achieve higher conversions, and reduce fermentation

costs. Many studies (1-28) have focused on ethanol production from

sugar, for example, using a variety of designs. Table 5.1 summarizes

reported maximum productivities. A continuous stirred tank reactor

(CSTR) without cell recycle represents a lower value reported while a

vacuum CSTR with cell recycle is an upper value. Reported values for

immobilized cell reactors (packed beds) fall in between.

Immobilized cell reactors offer the possibility of higher cell

concentrations than are easily maintained in CSTRs. Typical reported

values are summarized in Table 5.2 either in terms of cell dry

weight/unit weight of carrier or estimated cells/unit weight of

carrier. These values should reflect average properties for the

reactors, but not necessarily the availabli cell concentrations. That

is, growing colonies may become isolated through bed plugging during

operation and cell activity is more difficult to measure directly than

in CSTR.

Also, such reactors are typically operated to maintain cell

growth and, therefore. the active cell population on the immobilized

carrier increases during initial operation until the bed is

effectively saturated. At that point cell debris can often be

observed and pl uggi ng beg; ns to cause a decrease in average

produc ttvt t'[es, A measure of such effects is provided in the long

term bed stability. Literature values are summarized in Table 5.3.
..

32

TABLE S.l SUMMARY OF REPORTED LITERATURE PRODUCTIYITIES

Maltl_

Producth1'ty

Reactor TJ1)e Given In the Ref.

Article (6/L/Hr)

Continuous stirred tank without 7.0 20

ce11 recycle

Continuous stirred tank with 29.0. 18.3 20

cell recycle

Batch reactor (free cells) 2~2 20

Yacuu. Fer-entor 82.0 20

with cell recycle

Packed bed of birch wood chips 55.S0" S

Packed bed of beech wood chips 21.8* 17

HorizontaI packed bed 40.0" 20

of pectin gel beads

Pacted bed of carrageenan 43.S* 31

gel beads

Pacted bed of CI alginate 50.0" 10

gel beads

Stirred tank with ca alginate 15.0 10

gel beads

Fluidized bed of CI alginate 20.0 19

gel beads

Packed ca alginate beads 26.77 9

reinforced with pol~r

Packed gelatin beads 15.9* 28

crosslinked with glutaraldehyde

Packed bed of photocrosslintable 11.0 18

resin beads

Three stage fluidized bed 8.4 18

of Al alginate gel beads

Means that the productiYity is based on liquid yolu.e as

opposed to total reactor Yolu.e~
33

TABLE 5.2 LITERATURE REVIEW Of CARRIERS' CELL LOADING

carrier Loading With Unit Ref.

Pouzzolane (brick) 3.6 9 Of cell Ow/Kg

Of carrier

11

11

17

17

14

14

14

14

14

14

•

•

•

•

•

•

•

•

16.2

2.1 X 107 cellslg of

carrier

5.0

188.0

43 X 106

2.7 X 106

7.0 X 106

4.9 X 106

23 X 106

Dowex 50 V X8 (resin)

Beech wood chips

Cera.ic

Cordierite. 3 .'crons

(ayerage pore size)

tordierite. 10 .icrons

Frited glass. 3.5 .ic.

Fritted glass. 3.5 .ic.

fritted glass. 40 .ic.

Zirconia cera.ic

19 .icrons

Borosilicate glass

non porous

1.9 X 106 • 14

Polyvinyl chloride 8.5 G of cell Dw/L

of Carrier

11

Polyvinyl chloride

.-ixed wi th wood

12.4 • 11

PolYYinyl chloride

flakes 5.3 • 11

Raschig ring (5-7 .. ad)

Raschig ring (8-10 .. ad)

Porous bricket

(Rover France)

7.25

8.7

12.3

•

•

•

6

6

6

Carrier A (patented)

tarrier B (patented)

Birch wood chips

20.7

18.5

26.1

•

•

G of Cell Dw/L

of Reactor

6

6

5

Ca alginate beads

(estiMted) 150.0 • 19

Continuous stirred tlnk

Continuous stirred tlnk

with cell recycle

12.0

SO.O

•

•

20

20

Vacuum fer"'llentor

with cell ~cycle 124.0 • 20
34

. Cell immobilization is generally achieved by either encapsulation

(e.g. Ca alginate beads) or by attachment to a surface (e.g. beech

wood chips). In this study, calcium alginate beads were used as a

calibration tool to check experimental methods, and the primary

emphasi s was gi ven to characteri zi ng surfaces for ce 11

immobilization. In the case of highly porous matrices (e.g. low

density bricks), cells could attach either to the outer edges of bed

particles or within the pores of these particles.

This paper first describes a carrier screening test for surface

immobilized yeast reactors producing ethanol from glucose. The

carrier which yielded the best reactor performance was then studied to

determi ne the effect of the carrier parti cle si ze, Subsequently, the

reactor with the best carrier at the optimum particle size was studied

to determine the cell concentration gradient. With these results, the

performance was modelled as a plug flow reactor with dispersion by

integrating the appropriate equations.

5.3 Materials and Methods

5.3.1 The Fermentation Apparatus

The fermentation vessels used in the work were simple jacketed

glass cylinders. The inside diameter of the glass tubing was

nominally 18 mm, and the single section reactors were all

approximately 30 cm high. A four section reactor was also built to

measure dry cell weight as a function of reactor height. In this

reactor, each jacketed section was 18 mm in diameter and 8 cm high.

The reactors were supplied with feed using a four head peristalic

pump. The pump was 1-100 rpm variable speed Masterflex model number

7520-30, the four heads were Masterflex model number 7013-20, all from

the Cole Parmer Instrument Co. This system allowed the operation of

four upflow reactors simultaneously at approximately the same rates.

The reactors were kept at 300C by circulating water from a constant

temperature bath through the reactors' jackets.

5.3.2 Organism, Medium and Fermentation Conditions

The organism used was Saccharomyces cerevisiae, ATCC 4126.

The liquid medium used throughout this work was:

--100 g/l of glucose

(0 glucose anhydrous from Aldrich Chemical Co.)

--7.5 g/l of yeast extract

(from BBL Microbiology System)

--7.S g/l of peptone

(from Fluka Cie, Switzerland)

The pH of the medium was adjusted to 4 wi th concentra ted

sulfuric acid (6M).
~5

5.3.3 Surface Immobilization Carriers

Screening procedures were developed to choose between alternative

immobilization matrices and to measure the effect of particle size

distribution. In these tests, several matrices (see Table 5.4) were

examined using the procedures outlined below. Carriers giving better

performance as measured in terms of average productivities were

subjected to additional testing and characterization.

Figure 5.1 shows the pore size distribution for the brick (data

provided by Corning Glass corp.) and pine. Table 5.5 summarizes the

characteristics of reactors 2,3,4,5,6,7 and 8 in which various

packings were used.

TABLE 5.3 LITERATURE REYIEW Of LONG TE~REACTOR PERfORMANCE STABILITY

Reactor Type Stability Ref.

Period (days)

fluidized bed 90 18

of Al alginate beads

Packed bed of 90 18

photocrosslinkable resin beads

Packed bed of Ca alginate beads 20 10

Horizontal packed bed of pectin 20 20

gel

Packed bed of agar beads treated 60 9

with polyacrylamide

fluidized Ca alginate beads 166 19

regenerated continuously

Packed bed of patented carrier 75 6

(surface i.mobilization)

Yacuua fermenter 11 20

with cell recycle
TABLE 5.4 SURfACE INHOBILIZATION CARRIERS TESTED

•

~,

:~ i.• • • •

! '. ! \ :8::I~ti • • • • • •

·• • • • • • : q · ,." ,\ I , ' ,

·· ,,',., , : 13 \ • 't ,

: ,l " · , . ;I rf.. r ..•.

"

.: -,.. b--.. 'O",

" ~\',

:: ".., : : ,,, · , \ ' ..

· . ·: D, "''' \ .... , .. : :

" ...... · . ....', .';' '0

: p '<, '? •••.•

o J

c:i~---r--.,.--.,.--"--"--'--'-~

trOll New

Zealind

13.1 II pores

frolll Johns

Hanvt l1e

Ac t hUed type

4A frllD 8llhr

Type B-365 trOll

fisher

Type IRC 50

froll Resinous

Products Ind

CheMlcah

CHARACTERISTICS

20 II ftbers

SIZE

20 II fibers

3 III partfcles

2 mm (8-12 Mesh)

4 .. particles

Restn

steves

ftre Brick

BoHeezers

AIIIber11te

Cotton

11001

HATRU

"olecular

lihUe Ptne 8 .. particles 3 II pores

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 "0.0

pore diameter. r in microns

ffgure 5.1 Pore Size Dfstrfbutfon

of the Brfck Ind 'fne lIood
37

TABLE 5.5 CHARACTERISTICS Of THE R£ACTORS

Reactor Total Packing Packing Mass of Liq. Void

, Volume Nature Size Pac. Vol. Frac.

(Ml) (Microns) (G) (Ml) (S)

1 67.4 ca a191 nate JOOO 48.10 24.25 36.00

beads

2 67.0 brick 1981 17.49 62.60 89.40

3962

3 64.0 molecular Mesh 47.07 42.85 66.90

sieve 11-12

4 65.4 bofleezers 4000 63.17 40.07 61.3

5 66.0 resin 3000 32.44 36.11 54.70

6 63.0 cotton dfam. 3.8 61.50 97.60

20

1 67.0 wool dfam. 3.8 65.50 97.60

20

8 65.0 white pine 8000 16.16 38.00 58.50

9 69.0 brick 250 28.76 54.70 79.28

425

10 64.0 brick 1397 19.19 56.42 88.16

1981

11 65.4 brick 425 23.04 57.66 88.16

1397

12 66.0 brick 3962 16.36 59.51 90.17

5613

13 66.5 brick 425 24.6 58.69 88.25

1397

14 68.8 brick 425 26.23 60.68 88.20

1397
38

To show the effect of parti cl e si ze on the performance of a

surface immobilization reactor, the brick was crushed and sieved into

five size categories. Each size was then packed in a separate bed.

The characteristics of the beds are reported in Table 5.5 A series of

U.S. standard sieves of 250, 425, 1397, 1981, 3962 and 5613 microns of

openi ng were used to si eve the crushed bri ck,

5.3.4 Immobilization Procedure

5.3.4.1 Preparation of .the Surface Immobilization Carriers

Once the reactors were packed by pouri ng the carri er into the

glass tubes, absolute ethanol was passed upflow through them for one

hour with the feed pump. This technique was used to get rid of any

colonies of living organisms and also to clean the carriers of any

ethanol soluble impurities. After the ethanol wash, tap water (which

pH has been set to 4 with sulfuric acid) was passed upflow through the

reactors. In this way the carriers desorbed the ethanol and allowed a

more efficient cell immobilization. Experiments showed that if the

reactors were not rinsed with water after the ethanol wash, live cell

would not be immobilized on the carrier during the insemination

peri od,

After these precautions were taken, a two day old yeast cul ture

was passed upflow through the reactors in closed circuit for 12

hours. During this time, the yeast culture tank was gently stirred to

prevent yeast deposition and the pump was set at 11 rpm. This rate

represents an average flowrate of 0.4 ml/min.

5.3.4.2 Cellular Growth on the Carriers

After the insemination period for the surface immobilization

reactors, the reactors were fed with sterilized medium. Air was

sparged for five minutes every hour using the feed line itself.

Prior to use, the medium was sterilized at 120°C for twenty

minutes in an autoclave to prevent any biological acitivity in the

feed tank that would perturb the actual sugar concentration in the

inlet flow of the reactors. However, sterilization of the feed was

not suffi ci ent to assure asepti c condi ti ons in the reactors because

the insemination procedure had not been done under aseptic conditions.

5.3.5 Residence Time Distribution

To study the residence time distribution. reactor 14 (see Table

5.5) was used. After the inseminations, the feed pump was set at 11

rpm (0.358 ml/min) which corresponds to 3.2 hrs of residence time

based on total volume. The reactor was sparged for five minutes every

ten hours with the feed pump at 100 rpm. When complete conversion of

the glucose was achieved, the reactor was rinsed and the reactor

response to a step change in ethanol concentration was measured in the

effluent. The ethanol weight percent in the outlet of the reactor was

recorded versus time. The. data was then analyzed using standard

procedures (30) to compute axial dispersion.
39

5.3.6 Analytical Procedures

5.3.6.1 Ethanol Concentration Measurement

The ethanol content was determined by gas chromatography. The

gas chromatograph was a model 5710 A from Hewlett Packard. The column

was six feet long, packed with Poropak Q mesh size 80/100 from

Supelco. The carrier gas was helium at a flowrate of 2.43 ml/s, the

injection port and the detector of the chromatograph were 250oC.

5.3.6.2 Measurement of'Immobilized Cell Dry Weight

The following procedures were used to determine the dry weight of

the cells immobilized in a reactor, or in each of the four modules

that make up the fermentor designed for the cell density profile

determination (see characteristics of the chambers in Table 5.6).

First one stopped pumping fermentation medium and began pumping fresh

water at the same flowrate. The superficial velocity being the same

as during fermentation, no additional cell washout was observed. When

essentially pure water began to flow from the reactor (after two

residence times, approximately) the contents of the reactor, or of

each chamber, were poured in beakers. The beakers were then placed in

an oven at 60°C for three days. After three days of dryi ng, the

pack i ng were wei ghed. By di fference wi th the wei ght of the carr; er

before it was packed in the reactor, one determi ned the dry wei ght of

cells immobilized on the carrier. To be sure that the packing was

really dry, it was put a fourth day in the oven and weighted again to

make sure that the weight did not change.

TABlE 5.6 CHARACTERISTICS Of TltE FOUR CHAMBER REACTOR

Total Vol. Mass of Lfq. Vol. Void

(Ml) Carrier (Ml) Fraction

(G) (S)

Chamber 11 14.4 4.B 12.7 88.0

(bottom)

Chamber 12 15.0 5.48 13.2 B8.0

Chamber 13 15.2 4.B7 13.4 88.0

Chamber '4 15.6 5.4 13.7 88.0

( top)

whole reac, 60.2 20.55 53.0 88.0

Note: The four chamber reactor was packed with the brick. The brick

particle size was between 425 and 1397 mic.
5.4 Results and Discussion

5.4.1 Carrier Screening for Surface Immobilization

In order to find a surface immobilization carrier which will

improve the reactor performance, reactors 2,3,4,5,7, and 8 were

operated. The ethanol content of the outlet medium and the C02

product; on ra te were recorded versus time duri ng reactor opera ti on.

Except for the spargi ng peri ods the feed pump was set at 8 rpm;

however, only reactors 2,3,4, and 5 were sparged with air. Air was

spa rged in these reactors a fter each samp1i ng for fi ve mi nutes with

the feed pump at 100 rpm (4.55 ml/h). During the sparging time no

medium was fed in the reactors.

The ethanol concentration in the outlet versus time for the

various reactors are reported in Figure 5.2 and Table 5.7. The

productivities in Table 5.7 are on a total reactor volume basis.

From the results which are obtained for residence times close

enough to allow comparison, it appears that reactor 2 (firebrick) has

the best performance. It is the only reactor to reach total glucose

conversion (94:) of theoretical) at a residence time of approximately

4.5 Hrs.

5.4.2 Particle Size Influence on the Reactor Performance

5.4.2.1 RESULTS

The reactors 2, 9, 10, 11 and 12 were i nsemi nated and then the

cells were grown using the medium with the pump at 8 rpm. Air was

sparged using the feed pump for five minutes at 100 rpm after each

sampl ing. This corresponds approximately to five minutes of sparging

every twenty hours. After each spargi ng peri od, the ethanol

concentration in the outlet increased until it reached a maximum.

This maximum ethanol concentration is characteristic of each reactor

for the residence time corresponding to 8 rpm.

Once the maximum theoretical ethanol concentration was reached

the pump was set 11 rpm. After the ethanol concentrati on stabil i zed

this process was repeated at feedrates of 14 and 17 rpm.

Thi s procedure resul ted in a slowly dri fting system where the

biomass gradually accumulated on the bed as can be inferred from

examination of Figures 5.3 and 5.4. Because growth conditions were

maintained in the reactor, the overall average productivity (Fig. 5.4)

gradually increased until the adverse effects of plugging began to

predominate. In each instance, however, a gradual increase in

productivity resulted until a maximum was achieved.
41

TABLE 5.7 ANAlYSIS Of REACTOR 3, 4, 5, 6, 7 AND 8 FOR CARRIER SCREENING

Time CO2 ETOH Feed Res. ETOH Produc.

(Hrs) (HicLlS) Area S (Ml/Mn) Time Wt S (G/L/Hr)

(Hrs)

Reactor 12, brick 1981-3962 microns

0.00 0.00 0.00 0.24 4.75 0.00 0.00

22.50 57.00 2.58 0.24 4.75 3.44 7.25

43.67 54.80 3.18 0.24 4.75 4.25 8.94

56.67 59.90 3.01 0.24 4.75 4.02 8.46

69.75 62.10 3.56 0.24 4.75 4.75 10.00

91.30 54.10 3.63 0.24 4.75 4.84 10.19

Reactor '3, molecular sieve

22.50 4.70 0.39 0.24 4.54 0.52 1.15

43.67 5.30 0.39 0.24 4.54 0.52 1.16 56~67 8.96 0.69 0.24 4.54 0.91 2.01

69.75 10.10 0.79 0.24 4.54 LOS 2.32

91.50 12.10 0.95 0.24 4.54 1.26 2.78

Reactor '4, boileezers

22.50 9.60 0.40 0.24 4.64 0.54 1.16

43.67 12.80 0.54 0.24 4.64 0.72 1.56

56.67 20.10 1.22 0.24 4.64 1.62 3.50

69.75 23.10 1.52 0.24 4.64 2.03 4.37

91.50 24.30 1.81 0.24 4.64 2.41 5.20

Reactor 15, resin

22.50 10.00 0.00 0.24 4.64 0.00 0.00

43.67 13.40 0.71 0.24 4.64 0.94 2.03

56.67 21.70 2.00 0.24 4.64 2.67 5.75

69.75 19.60 1.52 0.24 4.64 2.02 4.36

91.50 16.80 1.55 0.24 4.64 2.07 4.47

Reactor 16, cotton

19.0 10.5 1.22 0.26 4.04 1.63 4.03

43.5 11.2 1.21 0.26 4.04 1.62 4.00

52.57 10.6 1.15 0.26 4.04 1.54 3.80

Reactor 17, wool

19.0 6.58 0.98 0.25 4.41 1.31 2.97

43.5 6.51 1.00 0.25 4.41 1.34 3.03

52.5 6.20 0.91 0.25 4.41 1.22 2.76

Reactor '8, white pine

19.0 13.40 1.50 0.28 3.89 2.00 5.14

43.5 11.11 1.25 0.28 3.89 1.67 3.86

52.4 11.10 1.23 0.28 3.89 1.64 3.86
42

The maximum ethanol concentrations observed in the outlet

corresponding to each residence time (on a total volume basis) are

reported for various particle size in Table 5.8. These data are

characteristic of the packing type and the particle size

di stribution. Figure 5.5 shows the maximum productivi tes versus the

various residence times for the different brick reactors. A datum

using calcium alginate beads (2) is also shown.

5.4.2.2 Interpretat;on..

Table 5.8 and Figure 5.5 suggest several comments. First, the

size of the brick particles has an important influence on the

reactor1s performance.

This observation can be interpreted in the following way. The

yeast cells are more numerous near the surface of the particle than

deep in the pores (electron micrographs also support this observation

(31» and this cell density gradient in the pores can be due to

several factors. First, the pores natural tortuosity or the pore

plugging by cell growth can make it harder for yeast cells to grow

deep in the pores. Secondly, a glucose or oxygen concentra~ion

gradient in the pores due to the metabolization by the cells

immobilized in the pores can slow down the cell growth rate near the

pore mouth. Any, or a combination of, these phenomena would limit the

useable volume for cell immobilization in the particle.

boileezers

nisin

i

'00.0

cotton

wool

bnc

mol.

pine

I

80.0

•

/ -, .0 . ,.

t-.. I .. 0 _·-e -. '

,/0 _.::.:::'.~ 0'-

l!;- - - IJ./-A .0-__-------0 :1/ .' .0----

In /" ... /0" .... o I:' »:.0--7·;.-----0'

o -" 0,,'-"'" /' , Ie I i

0.0 20.0 40.0 60.0

time in hours

Fig. 5.2 Carrier screening

_..o

CII')

cP . ~..,

cPo. .., -..ell')

0').

04) N

~o

ON

Cll') o . ..r:;-

-cPo..

It)

--

.. J

.....

If, however, the influence of the particle size is due primarily

to a limitation of the cell penetration in the particle, then why

would a reactor packed with 337 micron diameter particles (reactor 9)

have an overall productivity lower than the one of a reactor packed

with 911 micron diameter particles (reactor 11)1 This result can be

due to the fact that the interstitial gas holdup tends to increase

with decreasing particle size. Thus, the effective liquid residence

times are actually decreased for very small particle size

di stri.butions due to this effect.

One can al so' think of macroscopic limitation to the access of

glucose to some particles by channeling that would tend to decrease

the performance of the reactor when the particle size diminishes. A

positive and a negative influence of the diminishing particle size

explains the presence of an optimum particle size which represents the

best trade off situation.

Figure 5.5 indicates that the ethanol weight percent in the

outlet flow of reactor 11 (optimum particle size) is better than the

performance of the calcium alginate encapsulation reactor (Reactor 1

and Figure 5.6) which was operated to verify the accuracy of the

experimental techniques. The productivity at complete glucose

conversion of reactor 11, 23.2 g/1/hr, compares well with literature

data (2) for the encapsulated reactor.

It)";l--l!--- ;l \-,<.

II') • ..

<,s:!l ...",

<:::::"11') ... " C)"":l c <, ... - ,,, . •- 0 A 0

f~l <, ~It) ,

K~ .""

o I "-~". \ I

, / . ~l b

........ iii iiiiii

to t5 2.0 2.5 3.0 3.5 ".0 ".5 5.0

residence time (total vol. basis), hrs

Figure 5.5 Maximal Productivity ys Residence

Time for Various Pellet Size
45

5.4.3 Determination of Cell Dry Weight in the Reactors

5.4.3.1 Overall Cell Dry Weight in the Reactors

The reactors using brick as a carrier (2, 9, 10, 11 and 12) were

shut down after thirteen day runs (i .e~ the time elapsed from the end

of the insemination period until shut down). The dry weight of the

ce11 s was then determi ned. The resul ts are reported ; n Table 5.9 and

they compare well with the publ i shed results in Table 5.2. However,

they represent average dry. cell concentra ti ons tha t are not uni forml y

distributed in the reactor as is shown below.

5.4.3.2 Cell Density Profile in the Reactors

The four chamber reactor was used to determine the cell density

profile in the reactor. It was packed with the brick, the particle

size varies between 425 and 1397 microns as in reactor 11. After

insemination, the feed pump was set at 11 rpm and air was sparged

every ten hours using the feed pump at 100 rpm. Eleven rpm

corresponds to a l t Quid fl owra te of 0.375 ml / hrs and thereby to a

residence time of 2.675 Hrs.

• ."caps. (,..1. 2>

o encaps (reactor 1)

.---.-----. o /

•

/

,

•

I

o

-.",;

cII'» CP . ~~

CPo

i~J ·CLi N J

j~l Q)O..,;

II')

~lo. I iii I I i I

0.0 0.5 to 1.5 2.0 2.5 3.0 3.5 ".0

residence time in hrs (liquid basis)

Fig. 5.6 Comparison of encapsulation reactor

with published results

After five days, the ethanol area percent given by the gas

chromatograph was 3.7% which corresponds to 49.3 ethanol weight

percent (96% of theoretical). This confirms the performance of

reactor 11 (see Fig. 5.6). After the shutoff and rinsing of the

reactor, the dry weight of cells in each reactor chamber was

measured. The results are reported in Table 5.10. These results show

that for comparable performances the four chamber reactor and reactor

11 have very di fferent overa 11 dry weight of cell s immobil i zed per

unit volume of reactor. This result also suggests that after 13 days

an significant amount of unavailable (or inactive) biomass is

accumulated in reactor 11.

o

.n
46

TABLE 5.8 PERFORMAHC£S OF REACTOR 2. 9. 10. 11 AHD 12

Pump Flowrate Res. ETOH ETOH Prod.

Rpm (Ml/Mn) Time (Area S) (Wt S) G/(I.-Hrs) (Hrs)

Reactor 12. brick 1981-3962 microns

8 0.235 4.752 3.570 4.759 10.015

11 0.424 2.634 2.630 3.508 13.321

14 0.662 1.680 2.510 3.348 19.931

17 0.774 1.443 2.475 3.302 22.885

Reactor 19. brick 250-425 microns

11 0.378 2.954 3.759 5.010 16.959

14 0.524 2.131 3.419 4.558 21.389

17 0.725 1.540 2.705 3.608 23.425

Reactor 110. brick 1397-1981 microns

11 0.377 2.829 3.453 4.603 16.270

14 5.42 1.968 2.470 3.295 16.743

17 0.725 1.471 2.160 2.882 19.589

Reactor #11. brick 425-1397 microns

11 0.502 2.271 3.778 5.035 23.189

14 0.706 1.544 2.735 3.648 23.628

17 0.968 1.126 2.013 2.689 23.854

Reactor #12. brick 3962-5613 microns

11 0.436 2.523 2.382 3.178 12.596

14 0.588 1.871 1.326 1.770 9.461

17 0.835 1.317 1.173 1.566 11.885
(5.1)

The results also show that there was large ·variation in the

amount of cells immobilized with the height of the reactor. This

variation is important enough to invalidate the assumption sometimes

made (10) that the cell density is constant along the reactor

length. This phenomenon is probably due to the fact that because of

gl ucose consumpti on there is a gl ucose gradi ent in the reactor whi ch

favors growth in the bottom of the reactor. Added to thi s factor, the

bottom of the reactor consumes the dissolved oxygen and makes growth

possible even if no air is sparged. However, the dissolved oxygen

concentrations are lower at the top of the reactor. Figure 5.7 shows

the dry weight of' cells per unit volume of reactor as a function of

the posi ti on in the reactor. The posi ti on ; n the reactor is gi ven by

the volume between the bottom of the reactor and the considered

pas; ti on. The center of the four segments of Fi gure 5.7 were used as

da ta poi nts to develop a three parameter equa ti on fi tti ng the da ta.

The three parameters are determi ned us i ng the non1i near 1east squa re

method. The fitting equation is:

MOW = 110.ge-0.0984V + 4.44

where MOW is in giL and V is in m1.

The calculated values of r~DW versus V are shown in Figure 5.7.

Equation 5.1 was used below in tne development of a kinetic model.

5.4.4 Residence Time Distribution

From the analysis of the response to the ethanol concentration

step, the axial Peclet number of the reactor was calculated (30,31).

Thi s number was found to be 24.69 which is too large to consi der the

reactor as perfect plug flow.

TABLE 5 9 AVERAGE C£lL DRY WEIGHT 1M REACTORS

USING ·BRICK AS A CARRIER AfTER A 13 DAY RUM.

Reactor' Total Mass Ow of Ow/Mass Ow/Vol.

Vol(Ml) Of Brick Cell Of Car. Of Reac.

(G) (G) (G/G Of (G/L Of

Carrier) Reactor)

2 67.0 17.49 2.03 0.12 30.30

9 69.0 28.76 2.22 0.08 32.17

10 64.0 19.19 3.81 0.20 59.53

65.4 23.04 5.98 0.26 91.44

11

66.0 16.36 2.92 0.18 44.24

12
TABLE 5.10 DRY WEIGHT OF' CELL 1M nlE FOUR OtAMBER REACTOR

Chamber Total Mass Of 1.1q. Cell Ow/Mass Ow/Vol

I (Vo1(Ml) Brick (G) Vol. Ow(S) Of tar. Of Reac.

(MU (g/l)

11 14.4 4.80 12.16 0.85 0.177 59.03

(bottom)

12 15.0 5.48 12.89 0.26 0.047 17.33

13 15.2 4~87 12.53 0.11 0.026 7.24

14 15.6 5.40 14.07 0.08 0.015 5.12

(top)

whole 60.2 20.55 51.65 1.30 0.063 21.59

reactor

Note: The data for the whole four chamber reactor are computed in the following

manner:

--the total volume is the sume of the volume of the chambers

--the mass of carrier is the sum of the mass of carrier in each chamber

--the liquid volume is the sum of the liquid volume of each chamber

--the void fraction is the ratio of the liquid volume by the total volume

(both for the whole reactor)

c

c:i

....

mgft\'m9'iSia' fJt

.1IP.rim• mal

....---- "ow. llO.ge-O•09B4V + 4.44

c

...:

c

o -I--';~4---':"""+--";l.-~---:l_"-''''''''''.-->--....,'

0.0 10.0 20.0 30.0 <l0.0 50.0 60.0

vol. from the bottom of the recctor, ml

Figure 5.1 Cell Dry ~efght Profile in the

Four Chamber Reactor
49

5.4.4 Kinetic Model

A kinetic model was developed

performance data points of reactor 11.

following assumptions were made:

to fi t the three maximum

To develop this model the

- Hypothesis 1: the cell loading profile of reactor 11 is

proportional to the cell loading profile of

the four chamber reactor. The

proporti ona1i ty coeffi ci ent is the ra ti 0 of

the average cell dry weight in reactor 11 by

the average cell dry weight in the four

chamber reactor.

- Hypothesis 2: The active cell loading profile does not

depend on the residence time at which the

data was taken.

- Hypothesis 3: Each data point corresponds to nearly steady

state operation. That is the maximal

ethanol weight percent the ethanol

concentration profile in the reactor does

not depend on time.

- Hypothesis 4: Yl the ethanol yield (i .e, the ratio of the

mass of ethanol produced to the mass of

glucose consumed). ;s considered constant

along the reactor and equal to ; ts average

value which can be calculated using the

ethanol concentration at the reactor outlet

and the gl ucose concentra ti on at the reactor

inlet.

- Hypothesis 5: The Peclet number is constant over the range

of experimentally varied linear velocities.

With these assumptions a differential material balance on

ethanol can be written leading to:

~- _(.JL)dP + (IJ) (Rp

) = 0 A€ dz (5.2)

=

=

where

Q

A

e

= the axial dispersion coefficient, in m2/s

= the variable height in the reactor, in m

= the eJhanol concentration at height z, in

kg/m = the volumetric flowrate of the liquid. in

mj/s

the overall c~oss section of the

reactor, in m , the void fraction of the reactor
50

Rp

= the dry weight of the cell per unit

volume of react05 at height z, in kg of

cell dry weight!m of reactor.

= the rate of ethanol production. in

kg!(s-kg dry weight)

The cell dry weight in reactor 11 was estimated from the cell

density profile in the four chamber reactor using Hypothesis one.

This approach give~:

MOW =469.ge-9.061AZ + 18.81 (5.3)

where z =

A =

Ow =

the height. in m. of the considered position in

reactor 11

the average cross section area of reactor 11

the mass of cell dry weight in reactor 11. in

kg/m3.

Then the differential equation was integrated between z=O and Z

(0.3 m) for each set of experimental condi ti ons. For each of these

integrations. MOW versus z remains unchanged (Hypothesis 2).

The axial dispersion Oz was investigated using two alternative

hypotheses. In the case of laminar flow with molecular diffusion

(32) :

(5.4)

(5.6)

where is estimated from the experimental pulse test data for the

reactor.

Alternatively. over small changes in the linear velocity of the

reactor bed. one might expect the axial Peclet number (UL!OZ) to be

nearly constant. In this case (Hypothesis 5):

Oz =0Zo (U~ ) (5.5)

alternative assumptions concerning the rate

were exami ned. The models were then tested

In each instance.

limiting reaction step

using the criteria:

SSE = m~n {n~bS( P-P)

2 Iz=~ j=l

where P and P were the experimenta and predi cted sugar

concentrations at the reactor inlet. Hence. for each assumed

parameter value. numeri cal integra ti on of the reactor was compl eted

for ali data point to compute the sum of the squared errors (SSE).

The parameter value was then adjusted to minimize the residual

devi a ti ens,
51

Integrations were completed using the experimentally measured

ethanol concentration in the ractor effluent as one boundary condition

and

= 0

Z=O.3m

(5.6)

as the second boundary condition. In other words, the second

condition assumes that the free cell activity is negligible (4) at the

reactor outlet. E~perimentally. conditions were maintained well below

the cell slough off velocity.

The integration of Equation 5.2 was completed for two different

expressions for Rp and while considering or neglecting the second

order term of the differential equation. Rp was first assumed to be

of the Monod form with exponential ethanol inhibition.

(5.7)

where RLC is the ratio of available to total cells

per unit vol urne of bed. ( on a dry wei ght basis)

lJ x;s the spec; fi c ethanol producti vi ty, ; n

kWaof ethanol/{s-kg of cell dry weight),

k1 is the second Monad parameter; 0.0167 m3/kg

(Ref. 10),

k2 is the third Monod parameter; 1 kg/m2

CG is the glucose concentration, in kg/m2•

Using hypothesis four,CG is given by Eq. 5.8:

(5.8)

where <1;0 = the gjucose concentration in the feed; 100

kg/m

p = the ethanol concentration at height z, in

kg/m

Y1 = the yield at the top of the reactor (Hyp.

4) ; 0.5035 kg of ethanol produced/kg of

glucose consumed

Then Rp was assumed to be of the Monad fonn wi thout ethanol

inhibition.

(5.9)

Finally Rp was assumed to be controlled by sugar diffusion. In

this case the cell gradient is not considered

Rp = k3 CG (5.10)
52

For each of these expressions (except the last one), one found by

trial and error the value of (RU'~u ) that minimizes the sum of the

squares of the residual errO'rsma~hile predicting the ethanol

concentration at z=o given by each set of three integrations. The

results are given in Table 5.11.

I t is important to note that the equa ti on is nonhomogeneous and

numerically unstable if integrated from z=O to z=0.3 m. However it

converges quickly when integrated from z=0.3 to z=O.m.

By droppi ng the di spers! on term, the si gni fi cance of the second

order term was determined. Table 5.11 indicates the sum of the

squared errors for each of the cases studied.

TABLE 5.11 APPAREIiT FIT OF AlTERNATIVE REACTOR fIlDELS

MODEL CASE SSE

Oz ::z- OZ, -(~.y Ae d + (Ra. llmax )Mowe-k1'(<Si+C.00

d% % 2 G

2 ..IT (Udt )

Oz· + m27 1 0.0006 138

DZ • DZO{ ~J 2 0.00055 98

d2?

°z d? -(0)" -;;;:- az + CRCLIlmax) MOW fGkZ+C jG • 0

Cid )2

oZ·Ir +-. 3 0.000356 12.08

DZ \" DZO ( ~O) 4 0.000356 0.37

(~eHt- + (ROl .....1"0. ( ~. e,,)- 0 5 0.00102 8.04

d2p (..!L) dP 0-- + (Ra. Il:nax) 'G • 0 Z dZZ - AE-az

Oz • Dzo( ~. 0) 6 0.00014 2400
53

The results in (i.e. the SSE magnitudes) Table 5.11 suggest that

a Monod type kinetic expression with no ethanol inhibition fits the

data the best. They also suggest that the conversion rate is not

limited by substrate diffusion. Reference 2 (using the same yeast

strain) also reports reaction kinetics with no ethanol inhibition as

long as the ethanol concentration is below 8%. Moreover, the

assumption that the Peclet number is conserved appears more valid than

laminar flow with molecular diffusion.

Using a speci,fic pr-oductivt ty 1J1lax=O.75 g/{gs (2), the kinetic

model suggest that only 40.4% of the cells immobilized in reactor 11

were available (or active) (RCl=O.403). Also, the dispersion term

improves the data fit significiantly.

5.5 Conclusions

The experimental results suggests that the particle size of a

surface immobil i za ti on carr; er grea tly i nfl uences the performance of

the carrier and that it is possible to use a simple procedure to

determine the best particle size. They also suggest that it is

possible to find a carrier with performances that compare favorably to

encapsulation reactors. However, controlling the cell population

appears highly important. Initially, growth is desirable, but cell

gradients results probably due to oxygen and glucose gradients within

the upflow reactor. Subsequentls, con ti nued cell growth 1eads to bed

plugging and productivity losses. Under the conditions of this study,

immobilized cell reactors are not stable for extended periods. Hence

additional work in culture stabilization is required.

The cell density profile determination shows a very important

ce 11 dens; ty gradi ent in the reactor which caul d be used in further

studies to improve the fermenter design, allowing a better use of the

fermenter space and preventing early plugging.

The mathematical modelling suggests that the dispersion term

characterizing the backmixing of the fermenter cannot be neglected and

that the rate 1imiting step in these experiments was that of substrate

metabolization. (t ,e , Monod type without inhibition). Although the

hypotheses used in this analysis were not entirely valid (e.g. the

biomass concentrations were slowly drifting), the nonlinear least

squares analysis suggests that it is possible to gain insight into the

fundamental behavior of such systems using this approach.
54

5.6 References

1. M. Kierstan and sucke, "The IlMlobilization of f4icrobial Cells.

Subcellular Organelles. and Enzymes in Calcium Alginate Gels".

Biotech. Bioen. Vol. 19. page 387-397 (1977).

2. T.H. Lee. J.C. Ahn and Dewey D.Y. Ryu "Performance of an

Immobilized Yeast Reactor System for Ethanol Production".

Enzyme Microb. Techol •• 1983. Vol. 5. January.

3. M. Nagashima., M. Azima. S. Noguchi. K. Inuzuka and H. Samejima.

"Continuous Ethanol Fermentation Using Immobilized Yeast

Cells". (1983).

4. M. Moo-Young. J. Lamptey and C.W. Robinson "Immobilization of

Yeast Cells on Various Supports for Ethanol Production ll•

Biotechnology Letters. Vol. 2. number 12 (541-548).

5. S. Furusaki. M. Seki. K. Fukumura, "Reac tton Characteristics of

an Immobilized Yeast Producing Ethanol". Biotech. Bioeng •• Vol.

25. 2921-2928 (1983).

6. K. Furukawa. E. Heinzle and I.J. Dunn. uInfluence of Oxygen on

the Growth of Saccharomyces Cerevisiae in Continuous Cul ture".

Biotech. Bioeng •• Vol. 25. p. 2293-2317 (1983).

7. A. Navarro. H. Marangoni. 1. Magana Plaza. D. Callieri.

"Hor-izcntal Reactor For the Continuous Production of Ethanol by

Yeasts Immobilized in Pectinll• Biotechnology Letters. Vol. 6

number 7 p. 465-470.

8. T.K. Ghose. K.K. Bandyopadhyay. "Rapid Ethanol Fermentation in

Immobilized Yeast Cell Reac tcr", Biotech. Bioeng., Vol. 22, p.

1489-1496 (1980).

9. H.Y. Wang and D.J. Hettwer "Cell Immobilization in K-Carrageenan

with Tricalcium Phosphate". Biotech. Bioeng., Vol. 24, p, 1827-

1838 (1982).

10. M.A. Gencer. R. Mutarasan "Ethanol Fermentation in a Yeast

Immobilized Fermentor", Biotech. Bioeng., Vol. 25. p. 2243-2262

{l983}.

1L W.Y. Kuu and J.A. Polack "Improving Immobilized Biocatalysts By

Gel Phase Polymerization" Biotech. Bioeng •• Vol. 25, p. 1995-

2006 (1983).

12. Dewey D.Y. Ryu. Y.J. Kim and J.H. Kim, IIEffect of Air Supplement

on the Performance of Continuous Ethanol Fermentation System".

Biotech Bioeng., Vol. 26, p. 12-16 (1984).

13. H.. Tanaka, M. Matsumura, LA. Veliky. "Diffusion Characteristics

of Substrates In Ca-Alginate Gel Beads", Biotech. Bioeng., Vol.

26, p. 53-58 (1984).
55

14. D.C. Sitton and J.L. Gaddy, "Ethanol Production in a

Immobilized-Cell Reactorlt Biotech. Bioeng., Vol. 22, p. 1735-

1748 91980).

15. P.l. Rogers, Kye Joan Lee and O.E. Tribe, "Kinetics of Alcohol

Producti on By Zymomonas Mobil isat High Sugar Con cen tra ti ons" ,

School of Biological Technology, University of N.S.W. Sydney

Australia. ( ).

16. R.C. Righelato, D.Rose, A.W. Westwood, .IIKinetics of Ethanol

Production By Yeast in Continuous cul ture", Biotechnology

Letters, Vol. 3, number 1, p. 3-8 (1981).

17. C. Maignan, J.M. Navarro and G. Durand, "Activite Metabolique de

microorganismes Retenus par des Supports Solides", Oecol Plant.,

9 (4), 365-382 (1974).

18 M. Wada, J. Kato and I Chibata, "Continuous Production of

Ethanol in High Concentration Using Immobilized Growing Yeast

Cells", European J. Appl. Microbial. Biotechnol. 11, 67-71

(1981).

19. B.L, Maiorella, H.W. Blanch, and C.R. Wilke, "Biotechnology

Report. Economic Evaluation of Alternative Ethanol Fermentation

Processes", Biotech. Bioeng., Vol. 26, September 1984.

20. . G. Cysewski and C.R. Wilke, IIProcess Design and Economic Studies

of Alternative Fermentation Methods for the Production of

Ethanol", Biotech. Bioeng., Vol. 20, p. 1421-1444 (1978).

21. R.A. r4essing, R.A. Oppermann and F.B. Kolot, IIPore Dimensions

For Accumulating Biomass. II Microbes that Form Spores and

Exhibit Mycel ial Growth", Biotech and Bioeng., Vo. 21, p. 59-67

{1979}.

22. R.A. Messing and R.A. Opperman, "Pore Dimension For Accumulating

Biomass. I. Microbes that Reproduce by Fission or Budding",

Biotech. Bioeng., Vol. 21, p. 48-58 (1979).

23. R.A. Messing, "Revi ew, Support Bound Hicrobial Cells", Applied

B;ochem. Biotechnol., 6, 167-178 (1981).

24. R.A. Messing. R.A. Opperman and F.B. Kolot "Pore Dimension For

Accumulating Biomass", in A.C.S. Symposium Series No. 106,

Washington, D.C. (1979).

. 25. S. Aiba, A. Humphrey and N.F. Millis, Biochemical Engineering,

Second Edition, Academic Press, Inc., New York (1973).

Recent Topic of

in Japanese

S. Nagai. "Role of Biotechnology in Bioenergy.

Ethanol, Hydrogen and Methane Production

Institutes". Energy from Biomass (1984).

27. D.C. Sitton, "Bt otechno l oqy and Energy Production Symposium", Gatlinburg, TN~

26.
28. Biotech. Bioeng., Vol. 10, 845 (1968).

29. Peppler and Perlman, Microbial Technology, Second Edition p.

134.

30. Levenspiel, Chemical Reaction Engineering, John Wiley and Sons,

New York (1962).

31. Marc J. Anselme, "Immobilized Yeast Reactor for Ethanol

Production, MS Thesis, School of Chemical Engineering Georgia

Institute oflTechnolo~y, Atlanta, (1985).

32. J.E. Bailey and D.F. Ollis, Biochemical Engineering Fundamental s, McGraw-Hi 11, New York, 543;::"',-..,("":'1":::'9'="71"1"T"}.--...;.;..'""-----'-
57

CHAPTER 6

LIQUID/LIQUID DATA AND CORRELATIONS

(W.Y. Tawfik)

6.1 Correlations for Liquid/Liquid Equilibrium

6., ., 2-Parameter Correlation.

Initially, the research effort (t. 2, 3, 4, 5) focused on

evaluating both the distribution coefficients and selectivities for the

alternative solvents. For a quick evaluation, the tie-line data were

obtained initially at a room temperature of about 27°C ±2°C.

These single temperature tests provide good initial comparison

between the alternative solvents. The distribution coefficients

obtained from these tests were then correlated to the ethanol

weight fraction in the equilibrated aqueous phase.

where:

In Dj = a + bx, (6.')

Dj =distribution coefficient of component i in weight basis

x, =weight fraction of ethanol in equilibrated aqueous phase

Parameter estimates were generated for Eqn. 6.1 using linear

regression analysis. Table 6.1 summarizes the calculated

constants (a) and (b) for 15 different solvents obtained from

combinations of five pure solvents together with R2, the squared

correlation coefficient, which is defined as:

where:

v =number of degrees of freedom = Nebs - 2

t =value for the t-statistic distribution with 95% confidence interval and v

degrees of freedom

The experimental data are summarized elsewhere (5).

The constants of Eqn. 6.1 are specific for the given

solvents at a temperature of 27°C ±2°C and they are valid for

the values of the equilibrated aqueous concentrations less than

the plait point composition. In general the dependence of the

water distribution coefficients on the equilibrated aqueous

concentration of ethanol is much higher than the ethanol (Le.,

bETOH is less than bH20 ) . On the other hand, the constant (a)

represents the extrapolated value of the logarithm of the

distribution coefficient at zero ethanol concentration in the

equilibrated aqueous phase and the given temperature.
Table 6.1 The Parameters for the Distribution Coefficient Correlation of Ethanol and Water at

21°C with R2 the Squared Correlation Coefficient

Solventa

a

Ethanol

b a

Water

b

100S 2EUOH -0.5 1.0 0.56 -4.1 7.3 0.91

100S ONH -1.7 1.9 0.86 -5.5 6.4 0.99

100STDOH -0.72 1.3 0.81 -3.8 4.5 0.97

100% Isopar-l -7.2 4.5 0.95 -11.8 7.0 0.93

100S Norpar-12 -3.7 0 0.62 -7.9 0 0.71

lOS TOOU, 90S Norpar-12 -3.0 1.3 0.64 -6.7 3.7 0.95 tn

lY'

20% TOOU, 80S Norpar-12 -2.2 1.5 0.6 -6.0 8.3 0.88

30% TOOU, 70S Norpar-12 -2.1 1.4 0.96 -5.6 7.4 0.7

50% TOOH, 50% Norpar-12 -1.4 1.4 0.61 -5.6 9.1 0.85

50S TOOII, 50% 2EII01I -0.95 2.5 0.63 -4.0 5.9 0.97

70% TOOII , 30% 2HI01I -1.1 2.4 0.81 -4.8 1.4 0.98

50S 2EOU, 50% Isopar-l -1.1 1.25 0.82 -1.1 12.0 0.98

• 50% Isopar-l, 25% EIIOII, 25% TOOII -0.69 0 0.82 -5.8 6.3 0.93

. 35% TOotl, 30S Norpar-12, 35% 2EII01I -1.08 1.35 0.65 -4.6 5.1 0.99

100% Unleaded Gasoline -2.7 0 0.71 -6.9 6.2 0.95

a All the percentages based on volume percentages

_II
59

Table 6.1 suggests that the solvents containing alcohol or

ketone groups exhibit higher values of the constant (a) which

means that their ethanol distribution coefficients are higher than

those solvents which are composed of mixtures of alkanes even

at low ethanol concentrations in the equilibria aqueous phases.

On the other hand, alcohols and ketones have much higher

values of the. distribution coefficients of water than the alkanes.

These results agreed with the measured values of the distribution

coefficients for ethanol which were obtained from the literature by

many investigators (2, 3, 6, 7, 8).

Tables 6.2 and ·6.3 summarize the single point test for the

distribution coefficients of ethanol measured through this work and

other investigators (4, 7, 8) for both recovery solvents and drying

solvents. It is clear from Tables 6.2 and 6.3 that the ethanol

distribution coefficients for the recovery solvents which contained

alcohol or ketone groups had an average value of 0.6 ±0.3,

while those for the drying solvents which contained mixtures of

alkanes have a maximum value of about 0.06 at the same

ethanol concentration in the equilibrated aqueous phases.

Due to the differences between the values of the distribution

coefficients for ethanol of the recovery solvents and the drying

solvents, it was thought that the blending of a drying solvent with

a recovery solvent would improve the ethanol distribution

coefficient for the drying solvent. Table 6.4 summarizes the effect

of tridecyl alcohol on the equilibrium characteristics of Norpar-12.

Table 6.4 suggests that increasing the percentage of tridecyl

alcohol in Norpar-12 yields larger ethanol distribution coefficients.

On the other hand, the selectivity of the Norpar-12 is decreased

tremendously with increasing percentage of TDOH. Figures 6.1

through 6.4 also show the effect of tridecyl alcohol on the

solubility curve of ethanol-water-Norpar-12 systems.

6.1.2 The Effect of Temperature on the Equilibrium Data.

When the tie-line data was obtained at higher temperatures

than room temperature, it was clear that the temperature

represented another variable that may be exploited. The

dependence of the distribution coefficients for ethanol and water

on both the temperature and the ethanol concentration on the

equilibrated aqueous phase is shown below:

(6.2)

where:

x, = ethanol weight fraction in the equilibrated aqueous phase

T = temperature, oK
60

Tabla'.2 The Maaaurad Diacr1buciou CoefficiencS tor Solvaac. Coocaininl

lacona and Alcobol Groupa

SolftQC De ac Xe-O.05 De ac Xe-0.15 De ac lta-O. 3

100: 2-Echyl haxaooXa 0.637 0.704 0.819

100: Tridacyl alcohol 0.519 0.592 0.719

100: d1Mchyl hepcanooa 0.201 0.243 0.323

50: TIlOlI 50: 2EHOB 0.438 0.563 0.819

100: 3-Hepcanolb 0.798 0.870 0.990

100: Di-n-propyl kacona 0.639 0.693 0.784

100: n-amyl alcoholc 0.657 0.869 0.999

Tabla'.3 Tha Mea.urad Il1scr1buciOll Coafficients for Alkana.

Solvetlc De ac Xe-0.3 De at Xa-0.5 De ac Xe-o.7

100: Iso!'u-La 0.003 0.007 0.017

100: lloT!'u-12 0.008 0.016 0.024

100: Unle.dad Gasoline 0.06 0.07 0.18

100: ll-Hepcanad 0.05 0.09 0.14

100% ll-Hexanee 0.0085 0.02

100% ll-OCcatla 0.007 0.013

100: ll-Decaoa 0.006 0.01

100: Hexa-decane 0.005 0.007

adaca obcained by chi. work

bdeca obcainad by D.r.Oth_t(E»

edaca obcainad by Van Winkle <'I) 1n 1952

ddau obtained by ScINe!,,,,, <7> in 1954

·~.ta obca1ned by Roddy and Colemaoi I )in 1981
61

The constant a· represents the natural logarithm of the

extrapolated value of the distribution coefficient at zero ethanol

concentration in the equilibrated aqueous phase, while the

constant b· represents the measure of dependence on the

distribution coefficients on the .equilibrium aqueous phase

concentration of ethanol.

In general, as the. temperature increases, the solvent loading

also increases and more ethanol and water are extracted. The

temperature has a similar effect on both the recovery and the

drying solvents except that the distribution coefficients of water

are less sensitive to temperature in the case of the drying

solvents. Therefore, .their selectivities also increase with the

temperature as well as their ethanol loading. Consequently,

running the drying cycle extractor at higher temperatures results

in a dryer product and higher ethanol recovery.

The experimental data were correlated in the form of Eqn.

6.2. Table 6.5 summarizes the values of the constants a·, b· and

c·, for these four solvent systems.

Table 6. 4 The Effect of Tridecyl Alcohol on the Ethanol Distributor

Coefficients and the Selectivity of Norpar 12

Solvent D n Selectivity e w

100% Norpar-12a 0.024 0.0004 60

10% TnOH 90% Norpar-12 0.056 0.0017 33

20% TDOH 80% Norpar-12 0129 0.0056 22

30% TDOH 70% Norpar-12 OJ41 0.0077 20

50% TDOH 50% Norpar-12 0~83 O.'0()91 19

a

All the values were calculated at ethanol weight fraction in the equilibrated

aqueous phase of (0.1).
Table 6.5" Empirical Correlations for Ethanol and Water Extraction into Several Organic Solvents

b • • .a Solvent Solute 12 a b c

2-Ethyl hexanol Ethanol 2.8 1.77 -i063 0.88

Water -0.76 6.95 -973 0.95

Isopar-L Ethanol 1.86 1.44 -1922 0.92

Water -5.18 4.89 -1321 0.74

Dimethyl heptanone Ethanol 8.68 1.61 -3084 0.89

Water 4.21 5.5 -2880 0.78 0\

f>,)

20% Tridecyl alcohol Ethanol 5.4 3.19 -2310 0.95

in Norpar-12

Water -0.16 4.23 -1549 0.92

a

The applicable range of Temperature is between 200

e and 8SoC

b

n 1e correlation is applicable up to the plait point

c

n 1e correlation is applicable up to X -0.8

e d

The correlation is applicable up to X -0.5

e e

The correlation is applicable tip to X -0.7

e


63

6.2 The Effect of Dextrose on Distribution Coefficients

Simultaneous tests were made to determine the effects of

temperature, the percentage of modifier, and the percentage of

the dextrose on the distribution coefficients and the selectivities.

The experimental results suggested an increase in the activity of

the water in the aqueous phase occurs due to the presence of

the dextrose. This effect was realized for the solvents, TBP in

Isopar-M, and this increase resulted in an increase in ethanol

distribution coefficients. However, no significant increase in the

selectivity was noticed. On the other hand, the dextrose effect

was insignificant for some of the dryer solvents such as the

methyl ester (CE-1218) comparing to the temperature.

The basic model for the distribution coefficients of ethanol

and water is proposed in the following form:

OJ = f(Xe, Xo. lpm, 1IT) i = ethanol,water (6.3)

For each solvent the non-linear least square technique

(NON LS2) (9) was used to fit the parameters of the models. The

check on the statistical significance of the non-linear model was

the measure of omitting the variable interactions. The general

form of the correlation is found to be:

[ae/RT]

where i = ethanol or water and:

(6.4)

OJ =distribution coefficient of component i on weight basis

Xi = weight fraction of component i in the equilibrated aqueous phase

T =temperature, oK

lpm =volume fraction of modifier

The experimental data in Appendix 8 were fitted to Eqn. 6.4

using a non-linear least square program to minimize the objective

function

where:

i =ethanol, water

j = , ,2,...,Nobs.
64

The parameters for Eqn. 6.4 are summarized in Tables 6.6

and 6.7 for the solvents TDOH/lsopar-M, T8P/lsopar-M, and

methyl ester, respectively. The variation of the parameter values

can be explained by proposing different extraction mechanisms

for ethanol and water, and the formation of different complexes.

Although the .model does not predict the plait point equilibrium

composition, it can be used with reasonable accuracy in the

practical range of extraction (low ethanol concentration).

6.3 Prediction of the Mutual Solubility Curve Using the UNIQUAC Model

Since all the liquid/liquid systems which are included in this

work contain water/ethanol mixtures in addition to different organic

SOlvents, there are very few models available which can predict

the equilibrium composition with an acceptable range of

accuracy. In general, a good model should describe the

.nonideality caused by the presence of the polar components (e.g.

ethanol and water) and their degree of association.

For any component i, the UNIQUAC liquid activity coefficient

is given by:

where:

9'i = (riXj)~rjX;

Bi = (qiXi)~qjXj

Bi' = (q/Xi)~qi'X;

l; =[Z/2 (rj - qj)] • (rj • 1)

(6.6)

(6.7)

(6.8)

(6.9)

Equation 6.5 requires only the pure component segment and

area fractions (9'it Bi) and the binary parameters, which are given

by:

(6.10)

The data sources for the interaction binary parameter Tij as

cited by Prausnitz (10) are:

1. Vapor-liquid Isotherms (P, y, x)

2. Vapor-liquid Isobars (T, y, x)

3. Total Pressure data (P, x or y)

4. Boiling or dew point data

5. Mutual solubility data

6. Azeotropic data

7. Activity coefficients at infinite dilution.
65

Table ~." The parameter values of Eqn. '4o.Uo for water

Solvent TDOH/ TBP/ Methyl Ester

Parameter Isopar-M Isopar-M CE-1218

·0 -1.280 -5.810 -5.131

a 1 0.000 0.000 0.000

8 2 3.530 3.880 2.780

a3 -0.495 1.230 1.390

a4 ·0.000 0.000 0.000

8 5 8.660 5.060 0.000

8 6 -3631.300 -652.300 -651.420

Standard 0.0029 0.0034 0.0006 Error

Table ".7 The parameter Values of Eqn.~lb for ethanol

Solvent TDOHI TBP/ Methyl Ester

Parameter Isopar-M Isopar-M CE-1218

aD 2.22 1.602 2.943

8 1 2.50 58.470 -34.137

&2 -1.03 -19.290 8.144

a3 1.20 0.985 -9.235

a4 0.00 0.000 37.170

as 0.40 3.780 0.000

a6 -3198.40 -1994.900 -2863.900

Standard 0.282 0.248 0.080 Error
66

In this work Iiquidlliquid equilibrium data and isobaric vapor/liquid

experimental data were used to estimate the binary interaction

parameters Tij.

The experimental weight fractions of the two liquid phases in

equilibrium are summarized in Tables B8 through B10 (Appendix

B) for the systems 2-ethyl-hexanol, methyl ester and Isopar-M,

respectively.

For a multicomponent liquid-liquid system:

(6.11 )

where:

Xi = mole fraction of component i in x-phase

PlIi =activity coefficient of component i in x-phase

Yi = mole fraction of component in y-phase

Pyi = activity coefficient of component in y-phase

Then

(6.12)

For a three-component system, there are always two

independent relations and the third dependent one is given by:

(6.13)

The non-linear least square algorithm (NONLS2) (9) was

used to fit the UNIQUAC binary interaction parameters Tij in Eqn.

6.5. The objective function was to minimize the sum of squares

of the two independent relations in Eqn. 6.12.

Min I:{ [(x,Px,/Py')j calc • (x,Px,lpy1)j Dba]2 + [(X2)'lC2 Ipy2)j calc - (X2)'lC2/Py2)j obs(};.) 4)

where j = 1,2,..•,Nobs.

The estimated binary interaction parameters obtained from

the liquid/liquid equilibrium data are summarized in Table 6.S.

The UNIQUAC interaction parameters hij) are strong

functions of temperature. The relation is given by:

Tij =expI (-CZi!T) - (t3iyT2)] (6.15)

However, the parameters CZij and ,t3ij are difficult to estimate

from the mutual solubility data since the temperature variation

was relatively small in the liquid/liquid experimental data. On the

other hand,' Table 6.8 exhibited different fitted values for the
67

Table ~.g UNIQUAC Parameters

System, Methyl Methyl ZEHOH ZEHOH Isopar-N

Parameter Ester Ester

ZO·C 6S·C ZO·C 70·C ZsoC

T1Z 1.66894 0.0730Z3 0.1876 0.15057 0.6916

T 13 Z.08981 0.76398 1.5677 0.70781 1.40359

TZ1 0.024711 0.027557 1.Z8686 2.48834·. 0.17337

T23 0.973E-1.1 1.49449 0.39089 0.91765 0.37363

T31 0.718E-3 0.002156S 0.318617 2.80112 0.393E-9

T32 0.023036 0.0014876 0.189968 0.06166 0.00291

Standard 0.001843 0.00587 0.00791 0.00776 0.00150

Error

Component:

1: ETOH

2: HZO

3: Solvent

same binary interaction parameters in Table 7.3. This variation

was due to using the ternary mutual solubility data to fit binary

parameters. The investigator recommends using the VLE binary

data to obtain the binary parameters.

This study suggested that UNIQUAC interaction parameters

are not unique, and their values are strong functions of the type

of fit (l.e. the regression variables) and the source of data (e.g.

VLE versus LLE).

The use of the UNIQUAC model for LLE design calculation

would require iterative calculations. The distribution coefficient

correlation was preferred for LLE prediction, because it can be

easily implemented in an integrated modular sequential design

program, such as RUNOPT.
68

6.4 References

1. J.W. Roddy and C.F. Coleman, "Distribution and Miscibility

Umits in the System Ethanol-Water-TBP", Oak Ridge

National Laboratory, (1981).

2. J.W. Roddy, "Distribution of Ethanol Water Mixtures to

Organic Uquids", IJld. Eng. Chem. Process Des. Dev.• vol.

20, (1981), pps. 104-108.

3. J.W. Roddy and C.F. Coleman, "Distribution of

Ethanol-Water Mixtures to Normal Alkanes from C6 to

C'6"' Ind. Eng. Chem. Fundam., vol. 20, (1981), pps.

250-254.

4. D.W. Tedder, et al., Fuel Grade Ethanol Recovery by

Solvent Extraction, Technical Progress Report, Georgia

Institute of Technology, (October 1982).

of Ethanol Extraction from

thesis, Georgia Institute of

W.Y. Tawfik, "Efficiency

Aqueous Mixtures", M.S.

Technology, (1982).

6. D.F. Othmer, R.F. White,· and E. Trueger, "Liquid-Liquid

Extraction Data", Industrial and Engineering Chemistry, vol.

33, no. 10, (1941), pg. 1240.

5.

7. J,L. Schweepe and J.R. Lorah, "Ternary System Ethyl

Alcohol-n-Heptane.Water at 30°", Ind. and Eng. Chemistry,

vol. 46, no. 11, (1941), pg. 2391.

8. C.M. Qualline and Van Winkle, "Ternary Liquid Systems",

Ind. and Eng. Chemistry, vol. 44, no. 2, (1952), pg. 1668.

9. D.W. Marquardt, "An Algorithm for Least Squares

Estimation of Non-Linear Parameters", J. Soc. Ind. App!.

Math.. vot, 11, no. 2, (1962), pg. 431.

10. J. Prausnitz et al., Computer CalcUlations for

Multicomponent Vapor Liquid and Liquid Liquid EqUilibria.

Prentice Hall, (1980).
69

CHAPTER 7

EXPERIMENTAL RESULTS AND DISCUSSION

(W. Y. Tawfik)

7.1 Vapor-Liquid Equilibrium

The need for a vapor-liquid equilibrium model existed for the

simulation of the extractive distillation and the solvent regeneration

columns. A literature review (1) indicated that liquid activity

coefficient models would be appropriate to predict vapor-liquid

equilibrium for the systems of interest to this study. That was

due to the presence of highly non-ideal components which have

strong hydrogen bonds, and therefore strong interaction effects

(solvation, association) were expected. Two liquid activity

coefficient models (UNIQUAC and UNIFAC) were highly

recommended by the literature for their capability to handle these

systems.

7.' ., Estimation of Antoine Vapor Pressure Parameters.

The Antoine equation was used to predict the vapor

pressures for pure components. A non-linear least square

program (NONLS2) (2) was used to fit the experimental data to

obtain Antoine vapor pressure parameters. which are given by:

In pv = -BvP/(T + Cvp} (7.1)

where T = temperature, oK.

The optimum Antoine parameters were found by minimizing the

objective function:

(7.2)

The parameters for Antoine's vapor pressure equation are

summarized in Table 7.1 for the solvents of interest. The

experimental data that were used to generate these estimates

appear in Appendix A.

For higher molecular weight solvents, the constant Cvp had

a negative value, causing a problem in the optimization.

Therefore, for optimization purposes. the constant Cvp was set to

zero. and Eqn. 7.1 was then reduced to:

In pv = Avp - (BvpIT)

where T = temperature, OK.

(7.3)

Isobaric binary VLE datam were then obtained experimentally

for selected solvent systems. The binary VLE determination

allowed a second screening for the solvents on the basis of

their stabilities at high temperatures. Solvents such as tributyt
70

Table 7.1 Vapor Pressure Correlations for VLE

Analysis. All pressures in mm Hg. All temperatures in oK.

MC'del

Species o\.p Bvp Cvp Standard

Deviation

Isopar-M 17.07 5262.1 0.0 1.06

hopar-M 17.33 5494.9 9.8 1.13

Tri-n-butyl 66.91 125254.0 1524.4 U2.82 phosphate

Tri-n-butyl 20.58 7759.7 0.0 :14.49 phosphate

Tridec:yl 22.37 8259.4 0.0 :!:1l.20 Alcohol

Tridec:yl 44.68 52473.3 845.3 U.31 acetate

Diisopropyl 8.05 152.5 -328.5 ~29.70 ketone

2-ethyl- 23.06 9179.6 102.8 :!: 2.10 hexanol

Methyl ester, 9.83 1173.5 -166.5 :!:17.20 CE-1218
71

phosphate and the methyl ester (CE·1218) were eliminated

because of the tendency to decompose, and form butanol and

methanol.

7.2 Thermodynamic Consistency Test

Binary vapor-liquid equilibrium data consist of pressure,

temperature, and composition of both phases. Only two of the

experimental values (e.g. P, y) are enough to completely

characterize the system. The additional experimental information

may be used to test the data for thermodynamic consistency.

The most reliable consistency test requires calculation of the

vapor phase composition from P-X or T-X data and then

comparison of the calculated Y's with the experimentally obtained

values (3). The smaller the difference between Vexp and Vcalc the

more thermodynamically consistent the data. The consistency test

used in this work was developed by. Abbott and Van Ness (4)

and programmed for detailed calculations by Fredenslund et al.

(3).

The starting point in the test uses the differential expression

of Gibbs free energy for an open homogeneous system. The

total Gibbs free energy depends on temperature, pressure, and

the number of moles of each component, n:

where:

dP =differential pressure

dT =differential temperature

G =molar Gibbs free energy

nj =number of moles of component i

v = molar volume

S = molar entropy

G'j =partial molar Gibbs free energy of component L.

By definition,

nT = ~ni and G'j = RT In Pi i=1,2,...,m

For a system with two phases in equilibrium, Eqn. 7.4 may be

expressed for non-isothermal, non-isobaric systems as follows:
72

where ve and HE are the excess volume and enthalpy,

respectively.

For isobaric data at low pressures, the Gibbs-Duham

equation (Eqn. 7.5) can be reduced to:

and

~Xi.d In Vi =0 i=1,2,...,m (7.6)

Applying Eqns. 7.6 and 7.7 to a binary system results in:

P =x,P,s(ltl,s/<l>,) exp{g + X2g' + [V,(P-P,S)/RT] J +

X2P2S(<l>2S/<l>2) exp{g - x,g' + [V2(P-P2S)/RT] J (7.8)

where:

In v, = 9 + X2g'

In V2 =9 - x,g'

g' = (dg/dx,)s 9=0 for x, and x, = 0

and where:

GE =excess Gibbs free energy

Vi =liquid activity coefficient of component i

P =total pressure

T = system temperature

The subscript 5 implies saturation conditions.

The liquid phase activity coefficient is calculated from

Vi = (YjPltlj) I (XifjO)

Fredenslund et at. (3) expressed g(x,) through the use of

Legendre polynomials by:

(7.9)

where

4(X,) =[(2k-1)(2x,-1)4.,(X,)· (k-1)4.2(X,)] I k (7.10)

l.o(x,) = 1 , L1(x, ) = 2x, - 1
(7.' 1)

73

The procedure is based on fitting Legendre polynomials for g(X1)

to the experimental P-T-Xi data. The results are sets of values

for Yi(calc) corresponding to the experimental T-Xi values. The

isobaric data, are considered consistent if the average difference

between Yi(calc) and Yi(exp) is less than 0.015. The choice of

0.015, however, is considered to be arbitrary. In most cases this

number corresponds to a reasonable value tor the sum of errors

in the measured liquid and vapor phase mole fractions for high

boiling point components. Fredenslund recommended Legendre

polynomials of third order for isobaric data. The results of the

consistency tests for the experimental isobaric binary data for

ethanol-water-Isopar-M-tridecyl alcohol systems are summarized in

Figures 7., through 7.4. The Legendre coefficients are

summarized in Table 7.2.

According to the previous criteria, Figures 7.1 through 7.4

suggested that the isobaric VLE data are thermodynamically

consistent. However, for mixtures with large boiling point

difference (e.g. ethanol - TDOH) the experimental error was

biased. In general, isobaric experimental data for such mixtures

are relatively harder to obtain accurately because of the difficulty

of controlling the lower system pressures.

7.3 UNIQUAC Liquid ActiVity Coefficient Model for VLE

The UNIQUAC model proposed the following forms of the

molar excess Gibbs energy:

gE =gE (combinatorial) + ge (residual)

For the binary mixtures:

gE(combinatorial) I RT = [x1In(<l>,Jx,)] + [x2In(<l>2Jx2)] + (zJ2) [q,x,ln(8,1a>,)

+ Q2x2In(82/<3)2)] (7.12)

gE(residual)/RT = -Q1'X, [In (8,' + 82'T21)] • q:z'x2[ln (82' + 8,'T'2)](7.13)

where the coordination number z is set equal to , 0 and

segment fraction, <3), area fraction, 8 and 8', tor any mixture with

m components, are given by:

(7.14)

(7.15)

(7.16)


76

Table 7.2 Legendre Coefficients -

ak Ethanol/'Water Ethanol/Isopar-H Ethanol/TDOH Isopar-M/TDOH

a1 1.253 1.891 1.144 3.14

a2 0.412 0.904 0.020 1.49

8 3 0.013 0.380 2.243 1.225

Table '7.3

Binary System

UNIQUAC Interaction Parameters Predicted

From Binary VLE Systems

l-Water

2·ETOH

l-Water·

2-Isopar-M

l-Water·

2-TDOH

l-ETOH

2-Isopar-M

I-ETOH

2-TDOH

I-hopar-M

2-TDOH

• values obtained fran (SO)

az • 0.005

a., . 0.005

aT • 0.5C1~

"p • 2 l1li B&

329.49

348 .66

326.10

-117.10

332.80

887.14

-29.43

762.37

342.40

1839.30

35.20

-348.89
77

For each binary combination in a multicomponent mixture,

there are two adjustable parameters, '1'12 and T21. These in turn

are given in terms of characteristic energies .dU12 and .dU21 by:

'1'12 = exp(-.du12/RT) = exp(-a12/T)

'1'21 = e?<p(-.du2,/RT) = exp(-a21rr)

(7.17)

(7.18)

The interaction parameters a12 and a21 are strong functions of

temperature in the following form:

a12 =(t:12 + (,81217)

a21 = (t:21 + (,B21/T)

(7.19)

(7.20)

The experimental isobaric data for the ethanol, water and

selective binaries were used to fit the UNIQUAC interaction

parameters a12 and a21. A maximum likelihood algorithm by

Prausnitz (1) was used to estimate the binary interaction

parameters aij with the non-linear regression on (P, x, y, T) as

recommended by the authors. Details of the algorithm are given

in reference 1.

The ethanol-water-VLE system has been widely studied by

different investigators since it has a special importance for the

ethanol dehydranon. Furthermore, there is a good agreement

among those who studied that system about the efficiency of the

UNIQUAC model for predicting the equilibrium compositions for

ethanol and water. Table 7.3 summarizes 'the best fitted

UNIQUAC interaction parameters for water-ethanol-lsopar-M-tridecyl

alcohol system.

7.4 References

1. J. Prausnitz et al., Computer Calculations for Multicomponent

Vapor Liquid and Liquid Liquid Equilibria, Prentice Hall,

(1980).

2. D.W. Marquardt, "An Algorithm for Least Squares Estimation

of Non-Linear Parameters", J. Soc. Ind. Appl. Math., vol,

11, no. 2, (1962), pg. 431.

3. A. Fredenslund, at al., Vapor Liquid Equilibria Using

UNIFAC, Elsvier Scientific Publishing Company, (1977).

4. M. M. Abbott and H. C. Van Ness, "An Extension of

Barker's Method for the Reduction of VLE: Data,n Fluid

Phase Equilibria.vol. 1 (1977) pg 3.
jo
CHAPTER 8

THE USE OF PERVAPORATION IN ETHANOL

RECOVERY FROM DILUTE AQUEOUS MIXTURES

(L.M. SROKA)

8.1 Suntnary

Experimental pervaporation data for ethanol/water/solvent systems are

presented using commercially available membranes. Economic

comparisons with equivalent distillation data are presented. Membrane

performance characteristics are described.

8.2 Introducti on

Pervaporation is a membrane process which employs a polymer film

as a barrier to the transport of one or more components from the

liquid feed solution to the vapor permeate. The preferential passing

of these components from the feed solution results in the formation of

two exit streams of different compositions, thereby causing a

separa ti on.

Here the stream which initially enters the membrane unit is

referred to as the feed and ; t may be obta i ned from many poi nts of an

ethanol recovery process. For example, the fi 1tra te from the

fermentor, the beer still distillate, or the extact from a solvent

extraction system may represent pervaporation feeds.

The stream which leaves the membrane unit after passing through

the membrane ; s referred to as the permea te and the rema i ni ng stream

as the concentrate. Depending on the characteristics of the membrane,

these latter streams may be products or wastes.

In the early 60IS Choo (1) and Binning (2) reported experimenta1

data on pervaporation. They provided some separation data and

theories pertaining to the mechanisms involved in the transport of.

components through the membrane. Solubility and diffusivity are

mentioned as the controlling factors and many correlations of permeate

size, shape, and polarity were presented. These cor-rel a tf cns , on the

most part were for pure components.

Sweeny and Rose (5) also did early pervaporation experiments with

ethanol and n-hexane. Their results suggested that ethanol could be

separated from n-hexane with cellulose acetate or polypropylene films.

P. 5ch; ssel (6) tested a number of reverse osmost s membranes wi th

ethanol and water sol utf ons that ranged in concentration from 2 to 97

wt% ethanol. Most membranes used in these pervaporation experiments
50

did not separate the ethanol and water solution appreciably. Two were

found that showed a good separation and were tested at many

concentrations. The Fi1mtec IT-30 membrane had a vapor-liquid curve

simu1ar in shape to a vacuum distillation of the solution exhibiting

sma11 separa ti on or' an azeotrope at hi gh concentra ti ons , The UOP RC100 had a vapor-liquid curve which had a definite azeotrope near 50

wt%.

8.3.PERVAPORATION THEORY

For the process of pervaporation, a solution diffusion mechanism

(1-4) is commonly used to explain the phenomena and predi ct the degree

of separation that is possible and the area requirements. This model

is based on a pore1ess membrane which separates the components by

their di fferences in sol ubi1 i ty in the membrane and their subsequent

diffusi on through the membrane. The i ndi vi dua 1 compounds are be1i eved

to di ssol ve into the 1i qui d surface of the membrane, di ffuse through

the membrane, and then evaporate from the membrane surface which has

the vacuum drawn on it. The fl ux of a compound across the membrane

(see Fig. 8.1) is then represented by:

C. 1 C. 2 J. = +0. 1m - 1m

1 1 t:. Z (8.1)

Where Ji is the fl ux of component i across the membrane, Di is the

diffusion coefficient of i in the membrane, t:. Z is the membrane

thickness and Ci ml and Ci m2 are the concentration of component i in

the high and low pressure sides of the membrane. This form of the

equation assumes a linear concentration profile in the membrane,

isothermal conditions and a diffusion coefficient which is independent

of concentration.

High Pressure

L1qu1d

MelIlDrane LllW Pressure

Vapor

Fig. 8.1 Schematic representation of

transport through a nonporous membrane
b ...

If the concentration in the membrane is proportional to the bulk

concentration of the solutions, then the membrane concentrations in

the equation can be replaced by the bulk concentrations and the

proportionality factor (Solubility) is given by:

c· 1 = C· 1m 1 1 1K· (8.2)

Substituting eqn. 8.2 into eqn. 8.1 gives

Ci l-C i 2 J; = K; Di' M. (8'03)

J. = p. (8'04) 1 1

Let kiDi = Pi' the permeability coefficient, then one obtains:

Ci l- C; 2

/).z

The driving force for separation is, therefore, the concentration

gradi ent across the membrane. Vacuum appl i ed to the permea te si de of

the membrane reduces the concentration of the component in the gas

phase so a fl ux of tha t component wi 11 occur even though, after the

vapor is condensed, the concentration of the permeate is greater than

the liquid feed concentration.

8.4 EXPERIMENTAL AI)PARATUS AND MATERIALS

Bench scale pervaporation experiments were performed to determine

the selectivity and flux of membranes. An Amicon (401 S) pressure

cell with an effective membrane surface area of 6.08 square inches

(39.2 cm) was used for these experiments.

To control th~rmal effects, a HAAKE D1 heating element provided a

constant operating temperature. A liquid trap was used to collect

permeate along with a condenser which was immersed in liquid nitrogen

(-19S.8oC) or acetone and dry ice (-7SoC). Figure 8.2 is a schematic

of the apparatus configuration.

Membrane permeation data were generated by analyses of the

initial feed, the permeate and the remaimnq concentrate

compositions. In all cases, these measurements were made by gas

chroma tography. The GC used was a Hewl ett Packard type S7l0A wi th a 6

foot, poropak Q 80/100 mesh packed column. Instrument grade helium

was used as the carrier gas. The GC was operated at an oven

tempera ture of 16SoC wi th the i njecti on port and detector at 2S00C.

The peaks were integrated using a Hewlett packard 3390A peak

integrator. Quantitative analysis was based on the method of internal

standards.
l:S2

Feed solutions were mixed from the following solvents which were

used as received from their respective vender. Isopar L is a heavy

narrow cut, isoparaffinic solvent mainly composed of a mixture of C12

branched alkanes. It has a specific gravity of 0.767 at 1S.SoC, a

viscosity of 1.99 cp at 2SoC and a boiling range from 188 to 206°C.

Isopar M is the next heavier isoparaffinic cut available. Both

solvents were obtained from Exxon Refining. Reagent grade ethanol and

hexane were used along with distilled water.

A• MelIIbrane ""'aratus

B • Constant T~rature Bath

C • Magnetfc Stfrrer

D- Lfqufd Trap

Il2 • Condenser

E • Tetllllerature Controller and Indfcatar

f2 • VacuUII PuIlII

G2 • Pressure Regulator and Indicator

Ffgure 8.2 Schematic Representatfon for the Pervaporation and Ultraffltration

Experf.ental Apparatus.
83

8.5 .~MBRAHES TESTED

The commercial membranes and the polymer films which were studies

are listed in Table 8.1. In most cases, the polymer films were not

manufactured for use as a separa ti on medi urn, but as a barri er to

transport.

8.6 DATA ANALYSES

8.6.1 SELECTIVITY

The membrane was assumed to be se 1ecti ve ; f the concentra ti on

differences between the feed and permeate were statistically

significant. To determine statistical significance for this small

sample group, the "students t-fe st" methodology was used. The "null

hypothesis" was accepted (no separataion) whenever the concentrations

of the feed and permeate were statistically equal. The "alternative

hypothesis" was accepted (a separation did indeed occur) whenever a

statistically significant difference between the feed and permeate was

obtained. The confidence level of this test was 95%.

Whether or not the membrane fai led the "null hypothesi s" test,

dimesionless indicators of selectivity were calculated for the

membranes. Pervaporation selectivity data are generally expressed in

terms of a separa ti on factor, • These parameters were useful for

comparisons to data available in the literature. The separation

factor, 0(, is defined as the concentration ratio of

TABLE 8.1

Membranes Studied

Manufacturer, Trade Name

General Electric

HEM-IOO

HEM-IOI

~'EM-I02

MEM-ZIJ

Product Description

Unbacked Dimethyl Silicone

Single backed Dimethyl Silicone

Double backed Dimethyl Silicone

Single backed Silicone-Polycarbonate

Rohm &Haas Cellulose Acetate 0.8 mil

Polypropylene Z.O mi 1

Dow

na = not aval1able

Polyethylene

Polyethylene

Cellophane

Z.o mil

1.0 mi 1

na
84

ethano1 to water in the permeate di vi ded by the ra ti a of ethanol to

water in the feed. The ethanol separation factor was calculated by:

xp

(l-xp) x

p l-xf

0( = = (8.5) xf l-x x p f

( l-X f )

As defined. values differing from unity indicate that a separation was

obtained by the membrane. :

8.6.2 FLUX

The value for the observed average fl ux across the membrane was

ca1cula ted by:

J - Q ave- At (8.6)

W~ere J is the average flux. Q is the amount of permeated collected in

t hours. and A is the membrane area.

8.7 EVALUATION CRITERIA

Qualitative criteria were used to determine if a tested membrane

and process were practical. The membranes were judged on their

selectivity. flux. compe tabt l i ty, and handling properties. A

practical process would receive "qcod" ratings in most catagories. A

process would be considered impractical if it received "poor" ratings

in either compatibility or selectivity. Other combinations of "qond ,

fair. and poor" would suggest further study. For example, a membrane

which had a fair rating in the flux catagory could compensate for the

drawback wi th a good ra ti ng in se 1ecti vi ty and compa ti bil i ty

ca ta tori es.

8.7.1 SELECTIVITY

Good. The cri teri a for a IIgoodll ra ti ng was tha t the membrane

pass two "student t-tests". The first test determined if a

statistically significant change in concentration occurred between the

feed and permeate. The second test determined if a statistically

significant difference was observed between the permeate concentration

and the equilibium vapor concentration.

Fair. The criteria for a "faf r" rating was that the mebrane passed a

"student t-te st" which determined if a statistically significant

change in concentration o~curred between the feed and permeate.

Poor. The criteria for a "poor" rating was that the membrane failed

tne'"""'lstudent t-tests".
8j

8.7.2 FLUX

Good. The criteria for a "qcod" rating was that the membrane

exFiibited observable fluxes which produced measurable permeate within

an hour.

Fair. The criteria for a "fa lr" rating was that the mebrane exhibited

observable fluxes which eventually produced a measurable permeate.

Poor. The criteria for a "poor" rating was that the membrane

exFiibited no observable flux within two hours.

8.7.3 COMPATIBILITY

Good. The cri teri a for a "good II ra ti ng wa s that the membrane exh i bi ts

no observable changes during and after the compatibility tests. Also,

the membrane withstood the conditions employed in the

se 1ecti vi ty and fl ux experi ments and coul d be retested with

reproducable results.

Fair. The criteria for a "fair" rating was that the membrane exhibits

any of the following properties.

(a) Changes such as discoloration or separation from the backing

in the compa ti bi 1i ty tests.

(b) Changes in form after the selectivity and flux experiments

such as brittleness, discoloration or separation from the

backing.

(c) If retested, the results were not reproducable.

Poor. The criteria for a "poor" rating was that the mebrane either

swelled to a point where it became gelatinous, or disolved totally in

the compatibility tests.

8.7.4 HANDLING

Good. The criteria for a "good rating was that the membrane requires

ri'OSpeci a1 care or pretrea tment procedures. Also, the physi ca 1 form

of the membrane was self-supporting or was provided with a backing.

Fair. The criteria for a "faf r" rating was that the membrane requires

precautions to be taken against drying or prewashing with an easily

available solvent The membrane was also self-supporting or backed.

Poor. The cri teri a for a "poor" ra ti ng was tha t the membrane requi res

compl i cated pretreatment procedures before use. An unbacked, nonself-supporting membrane also received a poor rating unless it was

available in a preassembled unit.

8.8 PERYAPORATION EXPERIMENTAL RESULTS AND DISCUSSION

Pervaporation experiments were completed with ternary mixtures of

ethanol, water and Isopar L or binary mixtures of ethanol and solvent

as feeds. The results of the pervaporation experiments are summarized

in Table 8.2. The separation factors obtained for the cellulose

acetate were within the range reported (17-130) by Sweeny and Rose
(5), but the polypropylene did not perform as well as expected. The

lower fl uxes and separa ti ons are presumed to be due to the di fference

in the quality of the polymer films.

The preferential passing of ethanol and retention of the non

polar hexane made the cellulose acetate a prime candidate for removing

ethanol from a solvent extract by pervaporation. A "f'atr-" separation

was obtained, but it is not as good as the separation obtainable by a

simple flash. A flash of the 3% ethanol, 97% Isopar M feed, when

operated at 360 mm:Hg and 700e produces a 90 wt% ethanol product.

A binary feed of ethanol and water was tried to avoid the

compatibility problem of the GE membranes with the organic solvents.

The pervapora ti on tests then performed had improved se 1ecti vi ty, a

change from "poor" to Ilfairll and IIgoodll for the GE MEM-2l3 and ~1EM-lOl

respectively. A series of tests employing the GE MEM-lOl were

performed at di fferent feed composi ti ons and temperatures to further

characterize the membrane. The results are summarized in Table 8.3.

A run was performed for 26 hours to examine composition variation with

time. Four permeate samples were collected, 96%, 93%, 85% and 92%

ethanol respectively from a 54.5 wt% feed solution. The samsles were

composited to get an average permeate of 92 wt% ethanol. The shut

down and start up of the appartus is a possible source of the water

contamination of the low ethanol sample.

T.bl, 1.2 "s,lts .f tilt GelIer.l Pl!rftpor.tf_ StlIdf,s

Fftd 1Je....tI MDunt Tf.. ,,"So TellO J

Etoll HrJ Etoll HrJ (.1) (hrl (-/ltJ1 °t • .1 Mettilrane ;Z;

telloplllne 79.0 2.0 1 100 .- PoIye thy Iene 79.0 2.0 1 100

Po1yetll)'Iene 85.1 1.6 8&.' 1.7 7 1 180 75 - 0.9& 0.16

GE MEII-213 70.8 0.8 &&.8 0.' 5 1 260 I.e 0.94 0.12

GE '€M-213 SO.O SO.O SO.O SO.O 100 1.5 250 60 1.0 1.7

GE-MEM-101 70.8 0.' 260 60

GE-MEII-I01 53.0 47.0 72.0 28.0 10 6 160 60 2.28 0.04

Cellulo. Atetaw 14.5 55.5" 94.6 4.4" 0.5 2.5 220 60 26.8 0.005

Po1yprop)'lene 42.2 57.8" 15.2 84.8" 0.5 1 220 60 0.2' 0.012

Po1yprop)'1ene 8&.0 1.2 1l.3 14.6 0.3 6 220 60 0.68 0.001·

talluloH Atetatl SO.O SO.O" 9&.6 3.4" 0.5 3.5 220 60 28. 0.003

callulo. Atetatlt 3.4 9&.6" 85.0 15.0" 0.8 8 220 60 161. 0.002

callulo. Atetltlt 3.4 9&.6- 4 220 60

"HIll.nl "lsoper "

Ilotl: Tllfrd ea.,onent 1n Etlllnol/watltrlsohent s)'sten lsopar L
Table 8.3 Results of the Pervlporatton Studies with the GE 0-101 Mellbrlne

feed Permeate Amount Time Pres. Temp J

Membrane Etoh "20 Etoh "20 (ml) ( hr) (mmhg) °c ml II

cm2m

GE MEM-I01 53.0 47.0 72.0 28.0 5 3.5 160 60 2.28 0.036

GE HEM-I01 83.6 16.4 86.7 13.3 10 4 160 60 1.27 0.063

GE HEM-101 54.0 46.0 16.0 24.0 10 6 160 60 2.7~ 0.042

GE HEM-101 97.4 2.6 96.3 3.7 30 4.5 160 60 0.69 0.168

GE HEM-lOI 52.9 41.1 71.1 28.9 20 160 60 2.19

GE-HEM-Wl 94.9 5.1 95.2 4.8 1 1.5 160 50 1.07 0.017

G£-M£M-I01 95.1 4.3 96.4 3.6 16 3 160 60 1.20 0.134

GE HEM-lOl 95.1 4.3 96..4 3.6 5 1 160 60 1.20 0.126 (X)

.....

GE MEM-101 94.9 5.1 3 160 40

GE HEM-101 8.0 92.0 4 220 60

GE HEM-lOl 35.0 65.0 96.4 3.6 0.8 2 220 60 50 0.009

GE HEM-lOl 35.0 65.0 96.4 3.6 1.0 2.5 220 60 50 0.01

GE MEM-I01 8.0 92.0 20.0 80.0 220 15 2.8

GE MEM-lOl 29.0 11.0 83.0 17.0 4.3 6 220 60 12 0.018

GE -~1EH-I0 1 32.0 68.0 93.0 1.0 3.5 6.5 220 60 29 0.014

G£-HEM-101 30.5 69.5 62.5 37.5 6 6 220 60 3.8 0.025

GE HEM-I0l 54.5 45.5 96.0 4.0 2 2.5 220 60 20 0.02

GE HEM-I0l 54.5 45.5 92.0 8.0 15 26 220 60 9.6 0.015
50

The efficiency of the separation of the GE Mn1-101 membrane is

compared to a vacuum distillation occuring at a similar temperature

and pressures in Figure 8.3. The vapor liquid equilibrium data was

plotted for the pressure range of 92 to 220 mm Hg at a temperat.ure of

SOoC (7). The poi nts for the membrane separa ti on fall above and below

this curve. At both low and high ethanol concentrations, 'it was

possible to obtain a 96 wt% ethanol product~

It is possible to rationalize the high percent water in some of

the permeate data: as bein.g the result of leaks and, therefore, to

discount them. After discounting these few points, the composition of

the permeate appears to be independent of feed concentration for the

range of 30 to 54.5 wt: ethanol at 600C and a vacuum side pressure of

220 I1ITI Hg.

One theory (8) states that the individual fluxes across the

membrane are proportional to the concentration gradient between the

vapor and liquid phases. The result of a constant composition

permeate tends to contradict this theory unless one considers that the

proportionality factor is the product of the solubility of the

component in the membrane and the diffusion coefficient through it.

When the solubility of the ethanol and water in the membrane is

independent of the concentrate composition (for example, when the

membrane is saturated) and remains saturated, then the separation

obtained is constant over a wide concentration range. The saturation

concentration in the membrane then determines the diffusivity of the

components through the membrane and the separation obtainable. If the

rate of solution of the components into the membrane is slower than

the rate at which they diffuse to the vapor side, then the rates that

the components dissolve into the membrane matrix (and their steady

sta te concentra ti on) are functi ons of feed concentra ti on. In thi s

latter case, the relative diffusivity rates through the membrane

should vary with the concentra ti on of the components in the feed and

the membrane.

9J

The data collected at the two different pressures on the vacuum

side of the membrane indicate a flux dependence on absolute pressure

as expected. The lower the absolute pressure, then the higher the

flux. In addition, the absolute pressure may also affect the

controlling mechanism of the separation. At an absolute pressure of

160 11111 Hg the fl ux and the concentrate appeared to depend on the

concentration of the feed. At an absolute pressure of 220 mm Hlg, the

ethanol flux and the permeate concentration did not vary within the

error associated with this work. The permeate flux was also sensitive

to temperature as noted by the lack of flux at 40oC, a low rate at

SOoC and a ten fold increase at 60oC.

8.9 DESIGN AND ECONOMICS

In order to determine the economi c advantages or di sadvantages of

pervaporation in ethanol dehydration, the energy and capt ta l

requirements of this process was compared to an azeotropic

distillation process. The combination of membrane, feed and operating

conditions which had the best separation was choosen for this

evaluation. From the available experimental data. the GE r~EM-101 was

choosen to be evaluated in. a hypothetical and optimistic actual case.

8.9.1 PROCESS DESCRIPTION

The first process. a hypothetical GE pervaporation unit. will

accept a low concentration feed from a beer still and produce a 98.5

wt% ethanol product and a waste stream with only trace ethanol

losses. A conceptual flow diagram of this process is in Figure 8.4

The second opti on was based on the most optimi sti c da ta from the

GE MEM-I01 experiments. This conceptuarpi="ocess consi sts of a beer

sti 11 to remove di sso1ved soli ds from the fermenta ti on beer and a

distillation column to partially concentrate the feed before it is fed

to the pervaporation unit. The concentrate from the pervaporation

unit is recycled back to the concentrator column. The permeate. 96.4

wt% ethanol. then may be dri ed further by an adsorpti on proce S5 (9).

to make it of comparable value. A conceptual flow diagram is in

Fi gure 8.5

The azeotropic distillation consists of a beer still. a binary

distillation column and a azeotropic column which uses penetane as the

water entrainer (10). The process flow diagram is shown in Figure 8.6


93

8.9.2 DESIGN

Each of the three processes was desi gned to produce 1040 pounds

per hour of ethanol product from a citrus molasses fermentation

beer. The material and energy balances were performed by SimSci's

Process Model (11) computerized simulator. The distillation columns

used in any of the three processes were simulated by Process's

Rigorous Disti 11ati on opera ti on. Thi s program performs tray and tray

equilibrium calculations to obtain the material and energy balances.

The membrane' units . were simulated by Process's Component

Separator program. This program will separate a feed into two

products of specified composition by use of material balances. This

is adequate for the membrane process simulation when experimental or

hypothetical data are available as input for the permeate

compos; ti on. When thi s . data is not avai 1ab1e. predi cti on of the

permeate concentration and flux may be determined theoretically. The

energy requi rements for the pervaporati on uni ts were ca1cul a ted with

Process I s Heat Exchanger program-.

The area for the membrane uei t ; s determi ned by:

A -,- (8.7) - u

ave

Jave is the average fl ux through the mebrane and Q is the mass flow

rate.

The average fl ux which ; s needed to ca1cul a te the membrane uni t

area can be obtained from the experimental data or theoretically when

the permeabi 1; ty of the membrane and concentra t; on profi 1es in the

membrane unit are known. See Eq, 8.42 For these cases, the

experimental average value of O.022lb/hr ft was used.

For the purposes of comparison, the installed cost for the

membrane uni t was assumed to be S100/ft2• The cost for the membrane

uni twas deri ved from conversa ti ons with reverse osmosi s venders and

literature values (1,12,13,14) corrected to 1984' dollars. The costs

of commercial large scale pervaporation units was not available, but

it was assumed that the equipment costs would be similar to those of

large scale reverse osmosis equipment.

The other major pieces of equipment was sized and costed by usual

methods (IS).

8.9.3 RESULTS OF FEASIBILITY DESIGN CALCULATIONS

The installed costs for the three processes are summarized in

Tables 8.4-8.6. As can be seen from these tables, the capital

investment for the membrane processes is approximately four times

greater than the investment for the distillation process. The major

equipment cost appears in the purchase of the membrane unit which

accounts for almost 80: of the total capital investment. The

influence of membrane cost on the projects return on investment is

discussed later.
94

Table 8.4 Estilllated Installed Equipment Costs for the

Hypothetical Pervaporation unit.

ltEM

Membrane Unit (49,500 ft2

at $1OOO/ftZ

Beer Still

(2.5 ft 0 x 12 ft, cs)

Beer Still Internals

Heat EXChang~rS

HI (155 ft2

, es)

H2 (196 ftZ

' es)

H3 (70 ft, es)

rermentation System

Plant Storage

TOTAl EQUIPMENT INVESTMENT

Mis Index • 773.2

IRStALLED coStS

$4,950,000

21,000

4,000

17,000

18,000

14,000

768,000

162,000

$5,954,000

Table 8.6 Estillllted Installed Equipment Costs for the

Azeotropic Distillation Process

ITEM

Ethanol Concentrator

Azeotropic Purification

rermentation System

Plant Storage

TOTAL EQUIPMENT INVESTMENT

M&S Index • 773.2

INSTALLED COSiS

$ 396,000

138,000

768,000

16Z,OOO

$1,464,000
95

Table 8.5 Estimated Installed Equipment Costs "for the

Pervaporatfon Unit and Adsorption Syste••

ITEM

Membrane U2it (49,500 ft2)

at SIOO/ft

Beer Still

(2.5 ft d x 12 ft, CS)

Beet Still Internals

Concentrator Column

(3.0 ft Q x 40 ft, CS)

Concentrator Internals

Heat Exchangers

HI (626 !t2, CS)

H2 (7 ft cs)

H3 (70 ft2~ cs)

H4 (290 f~ ' cs)

H5 (30 ft , CS)

Adsorption Columns (4)

(2.0 ft 0 x 30 ft, CS)

Adsorption Column Internals

Fermentation System

Plant Storage

TOTAL EQUIPMENT INVESTMENT

MIs Index 173.2

INSTALLED cosTs

S4,950,OOO

21,000

4,000

42,000

8,000

30,000

10,000

14,000

18.000

11,000

100,000

12,000

768,000

162.000

$6,152.000
96

Tables 8.7-8.9 compare prcductf on costs, energy requirements, and

profitability of the three processes. From this analysis it appears

that the azeotropic distillation, even though its energy requirements

were relatively hi gher than the membrane processes, has the greatest

possibility of being profitable. The relatively low capital

investment and proven technology give it an economic advantage over

the membrane processes.

Table 8.7

Estimated Annual Gross Profits for 98.991 Ethanol Production

Option I Option II Disti 11ation

ANNUAL COSTS

Molasses $ 991.000 991.000 991.000

Solvent 4.000

Cooling Water 9,100 12,900 13,500

Steam 175,500 196.700 240,000

Electricity 3,000 3,000 3,000

Labor 100,000 100,000 100,000

Nutrients 10,000 10,000 10,000

Fi xed Costs* 1,985,000 2,051,000 146,000

TOTAL COSTS 1.507.500

ANNUAL SALES

Dry Ethanol S 1.525,000

By-Product 575,000

TOTAL SALES $ 2,100,000

GROSS PROFIT $-1,173.600

1.525,000

575,000

1.525,000

575.000

2.100.000

592,500

*33.3S for Option I & II. 10: for the Distillation
97

The two membrane processes show losses at this time due to their

higher fixed costs and similar operating costs. A reduction in either or

both of these costs would improve the membrane process 1 s prof; tabi 1i ty.

The di fferences in the estima ted opera ti ng costs between the membrane

processes and the distillation are in the energy requirements and

additional solvent for the azeotropic distillation. Since the membrane

processes do not require solvent, a savings is already realized in this

area. The present energy requirements for the membrane processes are

nearly as high as the azeotropic distillation. Therefore, the energy

consumption needs. evaluation for possible areas of reduction such as

economizers and less energy· intensive pretreatment steps.

The energy intensive step in option I, the hypothetical

pervaporation unit, is the beer still which removes the dissolved solids

from the fermentation beer. The beer still is equipped with an

economizer which preheats the feed with the exiting vapor. This saves

approximately 4000 BTU/gal and reduces cooling water consumption. If a

pretreatment process is available which could remove the dissolved solids

without a phase change, the hypothetical pervaporation unit would greatly

reduce the energy requirements for dehydrating ethanol. Reverse osmosis

could be the solution. From literature reports (16) reverse osmosis can

effectively reduce the dissolved solids concentration and not

appreciabily alter the ethanol water ratio of the feed.

The beer still is also the energy intensive step in option II. In

this process, as in the azeotropic distillation, the economizer preheats

the beer still feed with the hot bottoms stream. This saves

approximately 4000 BTU/gal and does not increase the reboiler duty of the

concentrat.ion column. The replacement of the beer still by a process

which removes the di sso1ved so 1ids waul d a1so lower the energy

requi rements of thi s process. The tota1 energy sa vi ngs would not be as

great as in option I unless the replacement process could effectively

concentrate the feed stream also.

The fixed costs for the membrane processes were estimated at 33.3%

of the capt tal investment.. which reflects a 3 year life expectancy

instead of a 10 year life. To reduce this cost, the membrane life

expectancy assumption could be increased to 10 years as for the

azeo trap; c di sti 11a ti on. But without da ta on the membrane 1i fe

expectancy, 3 years in an optimistic estimate.

The tnfl uence of membrane costs and a 3 year 1i fe expectancy on the

projects return on investment in shown in Figure 8.7. It can be seen

that if the costs per unit area is lowered, or if the flux per unit area

is increased. the pervaporation process cannot compete economically with

the distillation. The estimated short life of the equipment and its high

capital cost are economic disadvantages which can be overcome with

improvements in the membrane.

99

Figure! 8.8 shows the inf1 uence of membrane costs on the rate of

return when a 10 year life expectancy is assumed. As can be seen in the

figure, the longer life expectancy improves the economics for the

membrane processes, If the costs per unit area is lowered, or if the

fl ux per unit area is increased, then the pervapora ti on process can

compete economi cally with the disti 11ati on. For the same product; on

rates, the ROI for the distillation and extraction processes was about

21%. To obtain this return with membrane costs at $100 per ft2, option I

needs a flux of .25 pounds per square foot per hour or at an average flux

of .022 pounds per square foot per hour the costs of the membrane uni t

would be approximately $10 per square foot. If the flux can be increased

above 0.5 pounds per square foot per hour, then the cost of the membrane

does not c~use the ROI to fall below the 20% mark even at prices as high

as $200 ft4:·.

8.10 CONCLUSIONS

The inovative nature of these processes along with the low return on

investment wi 11 defer investment into such large ventures. The cost of

the membrane unit is the major equipment expenditure, but has a potential

to decrease as technology improves. The technol og; ca1 improvements may

appear as increased flux, greater selectivity or inexpensive membrane

manufacturiing techniques. Thus membrane processes have the possibility

of becoming practical unit operations in the future.

Of the membranes examined, pervaporation with the GE MEM-I01

membrane resulted in the best separation of ethanol, and it may be a

practi ca1 a1terna ti ve to disti 11ati on for recoveri ng ethano1 from a 50

wt% feed IOf ethanol and water in some cases. The economi cs of the

process suggests a need for improvements in the membrane flux and

selectivity before it can compete with conventional methods. Also,

technological improvements in the manUfacturing of the membrane unit

which decrease its costs would benefit the ecomonic outlook of the

process.

Pervaporation with cellulose acetate films also had good results for

recovering the ethanol from organic solvents. The flux and selectivity

need improvement before thi s process can become prac tt ca1•

8.11 References

1. Choo, C.Y. and Sanders, B.H., "Latest Advances in r'1embrane

Permeation," Petroleum Refiner, 39, 6:133-138 (1960).

2. Binning, R.C., Lee, R.J., Jennings, J.F., and Hartin, E.C.,

"Separation of Liquid ~1ixtures by Permeation, II Ind. and Eng. Chern.,

53:45-50 (1961). ------

3. Choo, C.Y., "Advances in Petroleum Chemistry," Vol. 16, Ch 2.

Interscience, NY (1962).
100

4. Ballweg, A.H., Bruschke, H.E.A., Schneider, W.H., Tusel, G.F.,

"Pervaporation Membranes An Economical Method to Replace

Conventional Dehydration and Rectification Columns in Ethanol

Distilleries", Proc. Fifth International Alcohol Fuel Technology

Symposium, GFT Engineering for Industrial Plants, 06650, Homburg,

Saar GFR. (1982).

5. Sweeny, R.F. and Rose, A., "Factors Determining Rates and Separation

in Barrier Membrane Permeation", I&EC Prod. Res. Dev., 4, 4:248-251

(1965). - ---

6. Schi ssel, P., "F'Y 1982 Annual Progress Report for the r~embrane

Research Subtask, Alcohol-Fuels Program'·, SERI/PR--255-l776, Golden,

CO, Solar Research Institute, (1983).

7. Larkin, J.A., and Pemberton, R.C., "Thermodynamic Properties of

Mi xtures of Water and Ethanol Between 298.15 and 383.15 K", NPL

Report Chem. 43, (Jan. 1976).

8. Lee, C.H., "Theory of Revserse Osmosis and Some Other Membrane

Permeati.on Operations," 1:.. Appl. Polymer Science, 19:83-95 (1975).

9. Bienkowsk, P.R., Voloch, r~., Landish, M.R. and Tsao, G.L,

"Noni sotherma1 Adsorpti on of Wa ter Vapor on Corn, I. 1983 Wi nter

Meeting, American Society of Agricultural Engineers.

10. Black, C. "Distillation Modeling of Ethanol Recovery and Dehydration

Processes For Ethanol and sasonct ," Chemical Engineering Progress,

9:78-85 (1980).

11. Process Simulation Program," Simulation Sciences, Inc. Houston, TX

12. "Summary of Progress on the Development of Ethanol - Selective

t4embranes at the Southern Research Instt tute", (Subcontract XB-1-

9189-3).

13. Beaton, N.C., '·Applications and Economics of Ultrafiltration", I.

Chem. E. Symposium Series No. 51 59-70. - -- 14. Sherwood, T.K., Brian, P.L.T., and Fisher, R.E., "Desa l tne t lon by

Reverse esmcst s," I & EC ~., 6, 1:2-12 (1967).

15. Peters, ~1.S. and Timmerhaus, K.D., IIPlant Design and Economics for

Chemical Engineers,lI 3rd. Edition, McGraw Hill NYC (1980).

16. Matsuura, T. and Sourirajan, S., "Char-acter-i ze tf on of Membrane

Material, Specification of Membranes, and Predictability of Membrane

Performance in Reverse Osmosis , I. Ind Eng. Chem Process Des Dev.,

17:419-428 (1978). - - - --
101

CHAPTER 9

RECIPROCATING PLATE COLUMN

CN. Y. Tawfik)

9.1 Empirical Correlations for the Reciprocating Plate Column

The proposed process in this work deals with the recovery

of ethanol from diluted aqueous solutions (1/2 - 7 wt % ethanol).

Literature reviews (1-3), however, indicated that the present

correlations of extractors' performances (4, 5, 6-12, 13-23, 24-28)

do not apply to the case of very dilute solutes in the feed,

especially in the presence of surfactants.

In this study, the Karr reciprocating plate column was

chosen mainly due to its ability to handle solids and ~igh ratios

of continuous to dispersed phase. The availability of experimental

data by Karr (27) on a 3-ft diameter column was an additional

advantage for the development of a more generalized

reciprocating plate column model.

Previous HETS correlations for the reciprocating plate

column were developed by Eckles (5) and Bensalem (4). Eckles

measured the performance of the extraction column in terms of

the percentage extracted of the solute. He also measured the

high equivalent to theoretical stages for the Isopar-M-ETOH-Water

system. SUbsequently, Eckles correlated those performance

measures for the case of mass transfer from dispersed phase to

continuous phase.

Bensalem (4) correlated hydrodynamic parameters as well as

performance measures for acetic acid-toluene-water systems as

functions of operating variables such as the reciprocation,

frequency, amplitude, velocities of dispersed and continuous

phases, and the hold up of the dispersed phase in the column.

Those studies have been very helpful to this work in providing

the hydrodynamic correlations as well as the experimental data

that was used in the present models.

The mass transfer of a solute between two immiscible liquid

phases can be expressed in terms of flux by:

(9.1)

If the flow of both liquid phases is considered as a plug

flow, and the superficial velocities of both phases have only a

one-dimensional vector in the direction of flow of each phase

within a length of dz of the column, Eqn. 9.1 was given in

differential form as:

dNA =Kxa(x-x)dz =Kya(y·-Y)dz (9.2)
102

where:

NA = mass flux of solute, gm/cm2sec

x = weight of solute in X-phase

y = weight of solute in Y-phase

=mass transfer coefficient in phase l, gm/cm2sec

a =intertacial area per unit volume, 1Icm

When the flow pattern deviates from ideality and both phases

flow partially in the opposite directions, a back flow model is

then considered and the mass balance about an incremental

height of the column is given by:

(9.3)

or

where:

Uj =velocity of phase i, em/sec

aj =back mixing ratio of phase i

The previous mass transfer models lead to different

equations of the height of the transfer unit. When the plug flow

model is considered, the HTU, or y-phase, is given by:

(9.5)

When both operating and equilibrium lines can be approximated

to straight lines, Eqn. 9.5 becomes:

(9.6)

where:

He = height of the column

where (Y"-Y)lm is the logarithmic mean between y, and Yn+,'

For the back flow model, Misek and Rod (29) gave a

general solution to Eqns. 9.3 and 9.4, with the analogy to the

stagewise extractors:

(9.7)
103

When the analogy with stage-wise contactors is made, and

where the operating and equilibrium lines are approximated to be

straight lines, the number equivalent to a theoretical stage was

given by:

where

and

where:

NETS =log [(1/u) (1-1/E) + 1/E] IlogE

Dei =distribution coefficient of solute at position i

(9.8)

(9.9)

(9.10)

E' = extract mass flow rate, gm/sec

A' = raffinate mass flow rate, gm/sec

subscripts 0, n denote the top and bottom positions of the column

The height equivalent to theoretical stages can be given by:

HETS = He / NETS (9.11)

The heights equivalent to theoretical stage were chosen in

this work as a good measure for the performance of a Karr

reciprocating plate column. The dependence of HETS on the

operating variables and the physical properties of- the two phases

of the extraction system are summarized in Figures 9.1 through

9.4.

Previous studies (6-12, 13-23, 30-42) on similar columns

(pulsed column and rotating disk column) suggested that the

HETS is a function of the following variables:

(9.12)

where:

o = column diameter, cm

E =extraction factor in weight basis

UT =total throughput, cm3/cm2 sec

A = amplitude of reciprocation, cm

F =frequency of reciprocation, 1/sec
104

4p =density difference, gm/cm3

Pc = continuous phase density, gmlcm3

a = interfacial tension, dyne/cm

Pc-=continuous phase viscosity, gm/cm sec . .

g = 981 cmlsee2

Assuming an exponential form for the previous function,

HETS = eoD8EbUrC(AF)d4pg8Pchail-'ci (9.13)

Using the Buckingham Pi theorum (43), a dimensionless analysis

of Eqn. 9.13 leads to the following dimensionless relationships:

HETS/D = coEb(04pg/PcUT2}C (,ucIDpcUT)d (O'/DpcUl)h (AF/UT)i (9.14)

The experimental values available for the continuous phase

viscosity exhibited very JiWe variation (0.9 < fI.e < 1.1 cp),

Therefore, another function for HETS for low continuous phase

viscosity was proposed in the following form:

(9.15)

where H = the plate spacing, cm.

Similar dimensionless analysis led to the following

correlation:

HETS/D = eoEb (D4pg/pCUl )8 (AFD/HUr)d (0'/DPcUT

2}i (9.16)

Non-linear least square analysis indicated that the term Eb

is statistically insignificant It was found that the replacement of

D in the middle term of Eqn. 9.16 with a dummy variable

representing the length dimension would lead to a better

correlation. The mean plate thickness (1m) was chosen to replace

the column diameter (D) in the middle group. This choice,

however, was arbitrary and was made because 1m changes very

liWe in different columns.

The experimental data from this work were combined with

data by Karr et al. (44, 33) for 3-ft and 1-in diameter columns

respectively. A non-linear least square program (45) was used to

minimize the objective function:

S = ~[(HETS/D)obsi - (HETS/D)c:elcj]2 ,j =1,2,...,NPTS (9.17}

For low viscosity systems (I-'c <. 1.1 cp), the best correlation

was found to be:



108

with an average standard deviation of ±16.4%. The correlation is

restricted to:

10 < a < 40 dyne/em

and operating conditions below the flooding point.

Equation .9.18 can .be rearranged in the following form: . .

HETS = 1.03Do.3(~pg)-o.075Pc-o.55(AF)"1.30 UTo·20aO.625(WH)-1.3 (9.19)

The exponent of AF agrees reasonably well with the

experimental data from Karr (33), Karr and La (44), and

Bensalem (4) which have an average exponent of -1.28. The

experimental data of different investigators (4, 44, 46, 33)

suggests an average exponent for UT of 0.1986, which agrees

reasonably well with the value from Eqn. 9.19 of 0.2.

Henley and Seader (34) suggested a value of 0.645 for the

exponent of the interfacial tension and 0.333 for the column

diameter. These values agree with those obtained from Eqn. 9.19

of 0.625 and 0.3 for the interfacial tension and column diameter

exponents, respectively.

The experimental values of HETS/D is plotted against the

calculated values from Eqn. 9.18 in Figure 9.5. The deviation of

the experimental values from those calculated using Eqn. 9.18

appears randomly distributed along the 45° line.

9.2 References

. 1. Lo, Baird and Hansen, Handbook of Solvent Extraction,

Wiley, (1983).

2. P. A. Schwieitzer (ed.), Handbook of Separation

Techniques for Chemical Engineers, McGraw Hill, (1974).

3. L. Alder, Wguid Wguid Extraction, Elsevier, (1975).

4. A.K. Bensalem, "Hydrodynamics and Mass Transfer in a

Reciprocating Plate Extraction Column", OTC thesis, Swiss

Federal Institute of Technology, Zirich, (1985).

5. A.J. Eckles, "Modeling of Ethanol Extraction in the Karr

Reciprocating Plate Column", M.S. thesis, School of Chern.

Eng., Georgia Institute of Technology, (1984).

6. L. O. Smoot and A. L. Babb, "Mass Transfer Studies in

a Pulsed Extraction Column", I. & E. C. Fund., vol. 1,

no. 2, (1962), pps. 93-103.

7. L. O. Smoot, B. W. Mar, and A. L Babb, "Flooding

Characteristics and Separation Efficiencies of Pulsed
109

Sieve-Plate Extraction Columns"t Ind. Eng. Chern., vet, 51,

no. 9, (1959), pps. 1005-1009.

8. B. W. Mar and L. A. Babb, "Longitudinal Mixing in a

Pulsed Sieve-Plate Extraction Column", Ind. Eng. Chern.,

yolo 51t no. 9, (1959), pps. 1009-1018.

9. Richard' H. Bell .and Albert L Babb, "Holdup and Axial

Distribution of Holdup in a Pulsed Sieve-Plate Solvent

Extraction Column", I. & E. C. Proc. Des.· and Dev., vol.

8, no. 3, (1969), pps. 392-400.

10. Richard L Bell, The Effect of Interfacial Tension on the

Holdup of Pulsed Sieve-Plate Solvent Extraction Columns,

Ph.D. thesis, University of Washington, Seattle, WA.,

(1964).

11. D. H. Logsdail and J. D. Thornton, "Mass Transfer in a

Pulsed Plate Column", Trans. Inst". Chern. Eng., vol. 35,

(1957), pps. 331-335.

12. H. L Toor and J. M. Marchello, AIChE J., vol. 4, (1958),

pps. 97-101.

13. E. Y. Kung and R. B. Beckman, IIDispersed...Phase Holdup

in a Rotating Disk Extraction Column", AIChE J., vol. 7,

no. 2, (1961), pps. 319-324.

14. Hiromichi Fumoto, Eric Zimmer, Ryohei Kiyose and Erich

R. Merz, "A Study of Pulse. Columns for Thorium Fuel

Reprocessing", Nuclear Tech.. vol. 58, (1982), pps.

447-464.

15. V. Kemanghorn, G. Mu·rattet, and H. Angelino, "Study of

Dispersion in a Pulsed Perforated-Plate Column",

Proceedings of ISEC, The .Canadian lnst of Mining and

Metallurgy, vol. 1, (1979); pg. 429.

16. V. Kemanghorn, J. Molinier, and H. Angelino, "Influence of

Mass Transfer Direction on Efficiency of aPSE-Column",

Chem. Eng. Sci., vol. 33, (1978), pg. 501.

17. S. D. Kim and M. H. I. Baird, "Axial Dispersion in a

RPE...Columnlt, Can. J. Chern. Eng., vol. 54, (1976), pg. 81.

18. Ibid, "Effect of Hole Size on Hydrodyamics of

RPE-Columns", Can. J. Chern. Eng., vol. 54, (1976), p.

235.

19. A. N. Kolmogoroff, "Dissipation of Energy in the Locally

Isotropic Turbulence", Comptes Rend. Acad. Sci., URSS,

vol. 32, (1941), pg. 19.
110

20. I. Komasawa and H. Ingham, "Effect of System Properties

on the Performance of Extraction Columns

Oldshue-Rushton Column", Chem. Eng. Sci., vol. 33,

(1978), pg. 479.

21. W. J. Korchinski, R. AI-Husseini and C. H. Young,

"Extraction Column Mass Transfer Parameters from

Concentration Profiles", Proceedings of ISEC, Denver,

(1983), pg. 106.

22. A. Kumar, "Hydrodynamics and Mass Trasfer Studies in a

Khuni-Column", Ph.D. thesis in preparation at TCl,

ETH-Zurich, (1985).

23. A. Kumar, D. K. Vohra, and S. Hartland, "Sedimentation

of Droplet Dispersions in Counter-Current Spray Columns",

Can. J. Chern. Eng., vol. 58, (1980), pg. 154.

24. S. Malanowski, "Experimental Methods for Vapor-liquid

Equilibrium, Part I. Circulation Methods.", Fluid Phase

Eguilibria, vol. 8, (1982), pps. 197-219.

25. C. Berro, M. Rogalski, and A. Peneloux, H A New

Ebulliometric Technique", Fluid Phase Eguilibria, vol. 8,

(1981), pps. 55-73.

26. A.H.P.Skeliand, private communication to W. Y. Tawfik,

December, 1985.

27. 0.5. Abrams and J.M. Prausnitz, AIChE J., vol. 21, (1975),

pg. 116.

28. M.S. Peters and K.D.·. Timmerhaus, Plant Design and

Economics for Chemical Engineers, 3rd Edition, McGraw

Hill, (1980).

29. Process Simulation Program Version III by Simulation

Sciences, Inc., (1984). .

30. H. Groothius and F.J. Zuiderweg, "Coalescence Rates in a

Continuous-Flow Dispersed Phase System", Chern. Eng.

Sci., voL 19, (1964), pg. 63.

31. W. J. Howarth, "Coalescence of Drops in a Turbulent

Flow Field", Chern .Eng. Sci., vol. 19, (1964), pg. 33.

32. Robert E. Treybal, liguid Extraction, 3rd Edition, (1982).

33. Andrew E. Karr, "Performance of a Reciprocating-Plate

Column", AIChE J., vol. 15, no. 2, (1959), pps. 232-239.

34. Ernest J. Henley and J. D. Seader, Equilibrium Stage

Separation Operations in Chemical Engineering, John Wiley
111

and Sons, Inc., New York, N.Y., (1981), pps. 516-522-

35. Edward S. Taylor, Dimensional Analysis for Engineers,

Clarendon Press, Oxford, (1974).

36. A. H. P. SkeJland and N. Chadha, "Selection of Dispersed

Phase in Spray or Plate Extractions", Ind. Eng. Chern.

Proc. Des. Dev.,. vol, 20, no. 2, (1981), pps. 232-239.

37. A. H. P. Skelland, Diffusional Mass Transfer, Wiley, Chpt.

7, (1980).

38. C. A. Plank, J. D. Olson, H. R. Null, O. L. Muthu, and B.

D. Smith, Fluid Phase Equilibria, vel, 6, (1981), pps. 39-59.

39. M. M. Abbott and H. C. Van Ness, "An Extension of

Barker's Method for Reduction of VLE Data", Fluid Phase

Equilibria, vot, 1, (1977), pg. 3.

40. T. Ohta, J. Koyabu, and I. Naguata, Fluid Phase

Equilibria, vot, 7, (1981), pps. 56-73.

41. Wilson,1964. VLE XI. n A New Expression for Excess

Energy of Mixing", J. Am. Chern. Soc., vol, 86, (1964),

pps. 127-130.

42. T. F. Anderson, and J. M. Prausnitz, Ind. Eng. Chern.

Process Des. Dev., vol, 17, (1975), pps. 552-561.

43. M. Shoaei and D.W. Tedder, "Design Calculations for

Multicomponent Distillation by and Improved Shortcut

Method", Chern. End. R&D, (in press).

44. A.E. Karr and T.e. LO;' "Performance and Scale Up of

Reciprocating Plate Extraction Column", Proc. of ISEC,

Society of Chern. lnd., London, vol, 218, (1971).

45. D.W. Marquardt, "An Algorithm for Least Squares

Estimation of Non-Linear Parameters", J. Soc. Ind. Appl.

Math., vot, 11, no. 2, (1962), pg. 431.

46. T.C. Lo and A. E. Karr, "Development of a Laboratory

Scale Reciprocating Plate Extraction Column", Ind. Eng.

Chem. Proc. Des. Dev., vet 11, no. 4, (1972).
112
113

CHAPTER 10

SEED PROCESS OPTIMIZATION STUDIES

(Wahid Y. Tawfik)

The Process Simulation Program (1) was developed by

Simulation sciences, Inc., in Fullerton, California, and is available

on the ChE VAX system at the Georgia Institute of Technology.

The Process Simulation Program is a comprehensive simulation

system, combining the data resources of a large chemical

component library and extensive thermodynamic properties

prediction methods with advanced and flexible unit operations

calculation techniques.

Preliminary studies using two solvent systems (Isopar-M and

tridecyl alcohol) and three different feed concentrations (0.57, 1.90

and 5.15 wt % ethanol on the feed) were used to predict the

energy requirement for the integrated process. Figure 2.2

summarizes the energy required to recover 990/0 of the ethanol

in the feed with quality of 196-198 proof.

The process simulation program could not be used easily

for optimization purposes. (For example, the theoretical stages

cannot be treated as optimization variables in PROCESS.)

However, it was valuable in testing the feasibility of the integrated

process. The extraction column was simulated using a series of

three-phase flash units, each representing a theoretical stage. The

extractive distillation and the solvent regeneration unit were

simulated as a series of two-phase flash units with the

appropriate selection of -the feed stages and the appropriate

reboilers and condensers. The thermodynamic models. used here

were UNIFAC which was in .the thermo library of the program

and UNIQUAC with the experimental interaction parameters

developed earlier by this study.

10.1 Optimization an~ Economic Analysis

The optimization program that was used in the study was

RUNOPT, which was developed by D.W. Tedder (2) for

multivariate optimization. This program is not as accurate as

PROCESS, but it is much faster and oriented toward process

synthesis and optimization of distillation processes. Further

modification by Tedder, Tawfik and Poehlein (3) allowed the

RUNOPT program to handle solvent extraction and extractive

distillation units. Moreover, two thermodynamic algorithms (UNIFAC

and UNIQUAC) were added for the VLE calculations of the

extractive distillation and the solvent regeneration columns. The

distribution coefficient models developed earlier by this study were

used for the LLE calculations of the solvent extraction column.

The HETS correlation developed by this work was used to size

the extraction column, using Eqn. 9.18.
114

Theoretical stages for the solvent extraction column were

estimated using the Kremser equation. The liquid/liquid equilibrium

for the solvent extraction column was determined using the

distribution coefficient correlation (Eqn. 6.4).

The calcutanon cif the minimum theoretical trays for the

extractive distillation and solvent regeneration columns used the

Fenske equation:

(10.1 )

where the subscripts band t refer to the composition nodes of

the bottom and top of a given section of the column. Theoretical

trays were estimated using an extended Gilliland correlation

developed by Shoaei and Tedder (4, 5). Actual trays were

estimated using an efficiency correlation cited by Peters and

Timmerhaus (6). .

The UNIQUAC liquid activity coefficient model was used to

calculate the K-values for the extractive distillation and the

solvent regeneration columns. The interaction parameters for the

UNIQUAC model are tabulated in Table 7.3.

RUNOPT as developed by D.W. Tedder is a modular

sequential program. The overall process under optimization is set

up using the node composition technique. Sequential types of

calculations take place by assigning the composition of nodes' in

strategic locations. The SEED process was set up in this work

in fourteen nodes as shown in Fig. 10.1 consisting of three main

unit operation parts: solvent extraction, extractive distillation and

vacuum distillation. The desig'n. equations used by RUNOPT are

described in detail elsewhere· (7).

Newton's Search Technique was used in this work to

optimize nonlinear objective functions with linear constraints. The

venture analysis could be justified in that maximizing the net

profit would result in a zero rate of return of investment (ROI) at

11 • When a minimum incremental ROI is specified at investment

level 12, a difference in capital investments (I, - 12) is then

available for alternative investments with greater-than-zero ROI.

When the venture profit is maximized the venture cost is then

minimized. The venture profit is given by:

VP = NP - imf(r,D (10.2)

The objective function in this work was taken as the

venture cost. which is given by:

(10.3)

116

where

im = Minimum rate of investment

f(r,D = Function of the risk and investment

~s =Start-up costs

10 = Depreciation costs

t =Land investment

I = total investment

g =Capital recovery, annuity or sinking fund based on the actual plant life

The following is a list of the linear constraints for the

optimization of the integrated process.

2 PSI 4Ct Presstoo CDC .. 14.7 PSI

-0.9 .. FVAPFS 4Ct 1.0

-0.1 4Ct FVAPF6 4Ct 1.0

0.05 .. F5/F4 Ratio .. 2.0

1.01 .. (V!Vminh .;; 2.0

0.01 '.. P4/P1 Ratio 4Ct 2.0

0.5 PSI 4Ct Pressto p SRC 4Ct 14.7 PSI

-0.1 4Ct FVAPF7 .;; 2.0

1.01 4Ct (VNminh .;; 2.0

0.0 4Ct rpm 4Ct 1.0

0.01 4Ct FEQ 4Ct 0.99

where:

Texternal = external temperature

Textraction =extraction column temperature

PresstopCDC =pressure at top of extractive distillation column

c •
117

FVAPR = fraction of vaporization of feed i

F5/F4 Ratio = feed 5 to feed 4 ratio

rvNmin)j =ratio of vapor rate to minimum vapor rate for the ith column

~4/P1 Rati~ = product 4 to product 1 ratio

Presstop SRC = pressure at top of solvent regeneration column

'm =volume fraction of the modifier

FEQ = fractional approach to equilibrium in LLE column

The upper and lower bounds of the previous constraints were

based on the physical restrictions suggested by the experimental

runs as well as the Process Simulation programs.

The blended solvent system (Isopar-M.trldecyt alcohol) was'

used here to demonstrate the optimization between two types of

solvents. Since tridecyl alcohol is considered a recovery solvent,

it has the advantage of higher distribution coefficients over the

Isopar-M system. On the other hand, the Isopar-M solvent system

has a much higher selectivity than the tridecyl alcohol system.

The optimization analysis indicated, however, that the

optimum solvent blend would be achieved at 100% tridecyl

alcohol. This. result suggests that the effect of solvent loading is

much more important than the effect of the relative volatility of

water to ethanol in the extractive distillation column. Higher

ethanol loading leads td smaller diameter extractive distillation

columns and solvent regeneration columns. Moreover. the use of

a recovery solvent leads to a ' smaller number of transfer units in

the extraction column and, therefore, reduced height.

The economic analyses of the integrated process (Fig. 2.1)

based on RUNOPT for five different ethanol concentrations in the

feed (0.5, 2.0, 3.5, 5.0, 10 wt % ethanol in the feed) are

summarized in Tables 10.1 through 10.10.

The resulting energy requirements to recover 990/0 of the

ethanol in the feed with quality of 197 proof per gallon of the

product were plotted against the percentage of ethanol in the

feed in Fig. 10.2.

The cost of ethanol recovery using the solvent extraction

process is plotted against the ethanol concentration in the feed

in Fig. 10.3.

The economic analysis in this work was based on 100

million liters of ethanol per year, 7920 hours per year, M&S

index of 781.7 and 990/0 recovery for comparison purposes.

Table 10.1

Its

119

Total Capit.' Investment fOr Ethanol Recoyery ~I Case

Module

Cost

($1000)

Battery Limits Equipment Offs1te Costs

Totals

Contingency &Fees - 18S

Total Installed Cost (TIC) Working Capital - 15: TIC

Total Capitll Investment (Tel)

J 629Z.3

1258.5

7550.8

1359.1

8909.9

1335.5

$10246.4

T.ble 10.2 Manufacturing Costs for Ethanol Recovery • ~S Case

Bas1s: 100 million L Ethanol/yr (S1000) ($1000)

fixed Costs

Tel Finance Charge • 5 years at 121

Taxes &Insurance • 51 of TIC

Miintenance • 51 of TIC

Subtotal

Utilities

Electricity (SO.06/kw.hr) Natural Gas (S6.47/million BTU)

Water ($2.00/1000 gil)

Subtota'

Labor

(2 workers/shift •• S12/worker.hr)

Overhead

(501 of Labor and Mainten,nce

Annual Manufacturing Cost:

.•.

SIL Ethanol: 0.12

SIGal. Ethlnol: 0.45

Ethanol Selling Price (301 ROI before t••es)

SIL Ethanol: 0.16

S/GI1. Ethanol 0.59

S 2842.5

445.5

445.5

3733.5

1106.9

630.7

450.4

111921.5
Tabl. 10.3

Ite.

Total Capital 'nvestleRt for (thanol Recovery 21 Case

Modul.

Cost

($l000)

Tabl. 10.5 Total Capital Inv.stment fOr (thanol Recov.ry 3.51 Cas.

Modul.

Cost

($1000)

Battery ll.lts Equipment

o1f5tte Costs

Totals

Contingency I fees - 181

Total Installed Cost (TIC)

Working Capital - 151 TIC

Total Capital Investment (TCI)

S3912.1

782.4

469U

845.0

""l)j§:t

830.9

nun

8attery ll.lts Equipment Offstte Costs

Totals

Contingency I f••s - 181

Total Install.d Cost (TIC)

Workln9 Capital - 151 llC

Total Capital Investment (lCI)

sm3.1

605.4

3m:I

714.3 -mD

702.4

lS3ir.I

Table JO.4 Mlnuflcturlng Costs for (thanol Recover, - 21 Cise

8151s: 100 .Illion l [thlnol/yr (SJOOO) ~$IOOO)

Table 10.6 Mlnufacturlng Costs for Ethillol R.covery - 3.51 Cu.

8151s; 100.Illion l (thlnol/yr (SI000) (51000)

fixed Costs

TCI flnlnce Charge - 5 yelrs • 121

11.es I Insurance - 51 of TIC

Maintenance - 51 of TIC

Subtotal

Uttlltles

(lectrlclty (SO.06/k~-hr)

Nltural Gas ($6.47/.llllon 8TU)

Wlter (S2.00/1000 gal) Subtotal

Labor

(2 workers/shift at $12/worker-hr) Overhead

(501 of llbor and "iintenance) Annull Manuflcturlng Cost:

$/l [thlnol: 0.069

S/Gll (thlnol: 0.261

(thlnol Selling Prlc. (301 ROI before t.x.s)

S/l [thlnol: 0.09

SIGal [thlnol: 0.34

-,

$1767.2

. 276.9

. 276.9

"2UU

3i16.l

630.7

450.1

16ii1B.1

fixed Costs

TCI finance Charge - 5 years at 121

la.es I Insuranc. - 51 of TIC

"iintenanc. - 51 of IIC

Subtotal

Utnltles

[lectrlclt, ($0.06/k~-hr)

Nltural GaS ($6.47/.llllon ITU)

Wlter ($2.00/1000 gal) Subtotal

Labor

(2 workers/shift .t SI2/work.r.hr)

Overhead

(501 of l.bor and Mllnten.nc.)

Annull Manuflcturlng Cost:

S/l (thlnol: 0.057

S/GI1. (thlnol: 0.22

(thlnol Selling Prlc. (301 ROI before t,I,S)

S/l (thlnol: 0.07

S/GI1. (thlnol: 0.28

$1493.9

234.1

214.1

-mr.r

630.7

450.4

f51lT.T

....

N

o
Table 10.7 Tot., C.pital Investment for Ethanol Recovery 51 Cise

Module

C.se

(S1000)

Table 10.9 Tot.l Clptt., Investment for Ethlnol Recovery 101 CI5e

Module

Clse

(S100D)

Battery Limits Equipment

Oflsite COSls

Toltls

Contingency I Fees· 181

Tot.l Inst.l'ed Cost (TIC)

Working Capt tal • 151 TIC

Total Capila' Investment (TIC)

$2497.1

495.8

2992:l

538.7

-mr:T

529.1

1lO6T:l"

Battery Limits Equipment

Offsll. Costs

Totals

Contingency I Fees· 181

Total Installed Cost (TIC)

Working Clpttal - 151 TIC

Total Capital Investment (TCI)

$1423.3

284.66

1707.96

307.43

2ols.39

302.31

12317. 7

Tlble 10.8 Manufacturing Costs for Ethanol Recove~ • 51 C.se

8asls: 100 .tllion L Ethanol/yr (SlOOO) eS1000) Table 10.10 Manuflcturing Costs for Ethlnol Recovery. 101 else8asis: 100 million l Eth.nol/yr (Sl000) (SIOOO)

Fixed Costs

Tel Finance Charge - 5 years It 121 • TillS I Insurlnce - 51 of TIC

Ma1ntenance • 51 of TIC

Subtotal

Ut11tttes

Electricity (SO.06/kw-hr)

Hatural GIS ($6.47/million BTU)

Water (12.00/1000 gil)

Subtotal

Labor

(2 workers/shtft It $12/worker.hr)

Overhead

(501 of Llbor Ind ".intenance)

Annull Manuf.cturtng Cost:

S/L Ethlnol: 0.051

SIGal. Ethlnol: 0.192

(thlnol Selling Price (301 ROJ before tlxes)

SIL (thlnol: 0.066

SIGal. Ethanol: 0.249

S1126.7

176.6

176.6

-mr:t

630.7

450.4

nm:r

Fixed Costs

Tel Finlnc. Chlrge - 5 yelrs at 121

Taxes I Insurance • 51 of TIC

Maintenance • 51 of TIC

Subtotal

Utl1tties

Electricity (SO.06/kw-hr)

N.tural GIS (S6.47/mI1110n BTU)

Water ($Z.00/1000 gal)

Subtotal

labor

(Z workers/shift at SI2/worker·hr)

Overhead

(501 of llbor Ind Malntenlnce)

Annull Manuflcturlng Cost:

S/l Ethlnol: 0.038

SIGal. Ethlnol: 0.14

Ethanol Selling Price (301 ROI before tlxes)

SIL fthlnol: 0.05

SIGal Ethlnol; 0.19

S 642.95

100.77

100.77

844.49

1871.42

630.7

450.4

13797.01
6.

122

This optimization explicitly considered the economic effects

of solvent selectivity versus that of solvent loading, Since solvents

with higher loadings exhibit lower selectivities, these two factors

are in competition with each other. It was learned that selectivity

is less important for the SEED process than is solvent capacity

in reducing costs. That is, the cost of removing more water in

the EDC column is less than the cost of recirculating larger

solvent rates :throughout the system. The RUNOPT model went to

an optimal solvent blend consisting of 100 vol% tridecyl alcohol.

Therefore, future studies should focus on the use of modifiers

with even larger capacities and lower selectivities than tridecyl

alcohol (e.g. tridecyl acetate or diols).

10.2 References

1. Process Simulation Program Version III by Simulation

Sciences, Inc., (1984).

2. D.W. Tedder, "The Heuristic Synthesis and Topology of

Optimal Distillation Networks", Ph.D. Thesis, Chemical

Engineering, University of Wisconsin, Madison, Wisconsin,

(1975).

3. D.W. Tedder, Y. W. Tawfik, and S.A. Poehlein, "Ethanol

Recovery from Low Grade Fermentates by Solvent

Extraction and Extractive Distillation: The SEeD Process",

Fourth Symp. ofSeparation Science, (1985).

4. M. Shoaei and D.W. Tedder, "Design Calculations for

Multicomponent Distillation by and Improved Shortcut

Method", Chern. Eng. R&D, (in press).

5. M. Shoaei, "An Improved. Method for Design Calculations in

a Multicomponent. Distillation Column", M.S. Thesis, School

of Chemical Engineering, Georgia Institute 0 Technology,

Atlanta, Georgia, (1983).

M.S. Peters and K.D.

Economics for Chemical

Hill, (1980).

7. D.W. Tedder, Chemical Process Synthesis. Desion and

Optimization, A ChE Course Notes, Georgia Institute of

Technology, (1985).
123

APPENDIX A

VLE Data

p ..

.. Vapor pressure t mm Hg

T : Temperature , X

Y1: Hole fraction of component i in the vapor phase

Xi: mole fraction of component i in the liquid phase

Table Al Experimental Ethanol Vapor Pressures

TCC)

25.0

30.0

50.0

70.0

P (mm Hg)

59.20

78.62

221.30

542.30
Table A2

124

Experimental Tridecyl Alcohol

Vapor Pressures

TeC)

192.5

196.5

210.5

214.5

223.5

235.5

239.5

251.6

P (mm Hg)

92.5

'121.5

200.0

247.5

317.5

457.7

496.5

736.5

Table A3 Expe~imental Diisopropyl Ketone

Vapor Pre ssures

TeC)

104.0

122.8

132.0

138.5

155.5

167.5

P(mm Hg)

158.0

286.9

410.6

505.9

742.9

760.0
Table A4

125

Experimental Isopar-M Initial

Boiling Point Vapor Pressure

Tee)

121.0

158.6

175.4

187.2

200.7

P (mm Hg)

40.7

134.3

208.3

"280.3

391.4 '.

Table AS Experimental Tri-n-butyl Phosphate

Vapor Pressures

rrc)

182. O.

189.0

233.0

257.2

P (mm Hg)

44.5

61.6

165.4

391.4
Table A6

126

Experimental Vapor Pressure Data

for Methyl Ester, CE-1218

TeC)

132.8

144.8

170.3

212.8

224.8

255.8

P(mm Hg)

125.2

167.5

310.0

450.0

535.5

740.0

Table A7 Experimental 2-ethy1hexanol

Vapor Pressures

rCC)

150.2.

159.5

169.2

172.2

182.2

P Cmm Hg)

270.0

3i1.1

SOL 2

543.7

740.2

Table AS Experimental Tridecyl Acetate

Vapor Pressures

TCC)

210.7

223.8

242.8

259.3

P (nm Hg)

190.1

272.1

447.1

739.1
Table A9

rtc)

127

Isobaric Ethanol Water VLE

at 380 MM Hg

81.15 0.0037 0 .. 9963 0.0290 0 .. 9710

69.65 0.0959 0.9041 0.4440 0.5560

68.75 0.0976 0.9024 0.4655 0.6345

64.25 0.3086 0.6914 0.6211 0.3789

64.05 0.4145 0.5855 0.6674 0.3326

63.65 0.6041 0.3959 0.6889 0.3111

61.05 0.6550 0.3450 0.7200 0.2800

63.05 0.941-1 0.0589 0.9599 0.0401

.'
128

Table AlO Isobaric Ethanol TDOH VLE

at 380 mm Hg

Tee) Xl Xz Yl Yz

179.35 0.2528 0.7472 0.7211 0.2789

110.85 0.2500 0.7500 0.9184 0.0816

80.25 0.4570 0.6430 0.9351 0." 0649

60.65 0.6420 0.3580 0.9452 0.0548

59.85 0.8098 0.1902 0.9487 0.0513

59.65 0.8754 0.1246 0.9558 0.0442

59.55 0.5404 0.4596 0.9386 0.0614
129

Table All Isobaric Ethanol Isopar-M VLE

at 380 mm Hg

TeC) Xl X2 Y1 Y2

168.0 0.0081 0.9919 0.5810 0.4190

134.0 0.0227 0.9773 0.7999 0.2001

93.0 0.0429 0.9571 0.9431 0.0569

66.7 0.0643 0.9357 0.9680 0.0320

63.0 0.4539 0.5461 0.9928 0.0072

61.1 0.7217 0.2783 0.9855 0.0145

61.9 0.7805 0.2195 0.9885 0.0115

60.3 0.8265 0.1735 0.9850 0.0150

63.5 0.8313 0.-1687 0.9872 0.0128

60.0 0.86.87 .·'0.1313 0.9866 0.0134

62.0 0.9128 0.0872 0.9954 0.0046

62.1 0.9884 0.0116 0.9970 0.0030

61.7 0.9992 0.0008 0.9992 0.0008
130

Table A12 Isobaric Isopar-M TDOH VLE

at 188 mm Hg

Tee) Xl X2 Y1 Y2

177.1 0.1529 0.8471 0.5672 0.4328

168.2 0.4416 0.5584 0.7302 0.2698

164.7 0.3753 0.6247 0.7423 0'.2577

163.2 0.5076 0.4924 0.7663 0.2337

160.9 0.4702 0.5298 0.8012 0.1988

158.5 0.4722 0.5278 0.7829 0.2171

155.8 0.5839 0.4161 0.8012 0.1988

152.0 0.6689 0.3311 0.8103 0.1897

151.1 0.6879 0.3121 0.8338 0.1662

149.0 0.8275 . ·0.1725 0.8356 0.1644
131

APPENDIX B

LL! Data

Xei Initial wt. fraction of ethanol in aqueous solution

X

e Weight fraction of ethanol 1n equilibrated aqueous

phase

Xn Weight fraction of dextrose 1n equilibrated aqueous

phase

Distribution coefficient of component i in wt. basis

:weight fraction of component i in equilibrated organic

phase

"
132

Table Bt The Measured Ethanol and Vater Distribution

Coefficients us1n, 2-Ethyl Hexanol as a Solvent

x.1 •

X b

•

0.34 0.06 85 0.85 0.042

0.42 0.07 85 0.99 0.052

..

0.34 0.07 60 0.81 . 0.041

0.42 0.0"8 60 0.88 0.047

0.09 0.02 23 0.49 0.025

0.34 0.09 23 0.57 0.030

0.42 0.13 23 0.71 0.032

0.58 0.28 23 0.77 0.092

0.77 0.4S 23 1.02 0.500

a Xe1 is weight fractions

.'

b

c

Xe is weight fraction

T in °c
fable B2

x • e1

133

The Heasure~ Etbanol and Yater »iltr1butioD Coefficient

Os1na Iacpar-L a•• SolveDt

I •

•

0.69 0.67 20 0.01? 0.0008

0.78 0.77 20 0.019 0.001

0.S8 0.86 20 0.039 0.004

0.'6 0.435 25 0.019 D.OOOf

0.8S 0.86 25 C.035 0.001.

0.69 0.635 60 0.039 0.006

0.'6 0.41 66 0.042 0.0006

0.88 0.83 66 0.071 C.DeS

0.46 0.36 IS 0.062 0.0006

0.69 O.~! . 8S 0.061 0.0036

0.88 C.S! 85 0.099 0.0039

•. Xe! 1. we1&ht fractions

b Xe is we:! &ht fractions

e T 1ft DC
Table B3

134

The Measured Ithanol and Vater Distribution

Coefficients Vsina Dimethyl Heptanone a. a

Solvent

c

D

w

0.24 0.11 25 0.21 0.0081

0.29 0.12 25 0.24 0.009

0.34 0.14 25 0.25 0.009

0.42 0.15 25 0.29 0.01

0.50 0.31 30 0.35 0.03

0.24 0.06 70 0.46 0.011

0.29. 0.10 70 0.79 0.03

. 0.09 0.01 80 1.3 0.036

0.5 0.1 " . 80 1.6 0.0,"2

a X.i 1. we1aht fraet10n

X. 18 weiEht fraet10n

e T 1n °c
table B4

135

The Measured Ethanol and Vater Distribution

Coefficients ua1na 20: Tr1deeyl Alcohol 1D

Norpar-12

x • e1

x 1»

• T c

0.017 0.11 24 0.12 0.0071

0.31. 0.21 24 0.16 0.0096

0.38 0.26 24 0.25 0.0164

0.17 0.0 4 70 0.34 0.0119

0.34 0.11 70 0.38 0.0162

0.58 a. l ! 70 0.49 0.0211

•

Xe1 15 ve1aht fract10D

Ie 1. veight fract10D

C T in 0 C
136

Table BS

The Experimental values of ethanol and water

distribution coefficients for ~OOR/I.opar M

system 1n presence of dextrose

D a

e

D a

v

X b

e

X b D • c

'r 100/T

0.089 0.0016 0.072 0.205 0.1 0.342

0.061 0.0018 0.198 0.215 0.1 0.342

0.082 0.0034 0.354 0.212 0.1 0.342

0.781 0.0174 0.031 0.217 0.4 0.342

0.561 0.0183 0.098 0.234 0.4 0.342

0·.648 0.0312 0.211 0.231 0.4 0.342

0.270 0.006 0.029 0.201 0.2 0.332

0.180 0.0052 0.105 0.226 0.2 0.332

0.210 0.0091 0.218 0.261 0.2 0.332

0.208 0.0029 0.032 0.214 0.1 0.291

0.21 0.0039 0.105 0.245 0.1 0.291

0.32 0.0058 0.195 0.240 0.1 0.291

0.318 0.0019 0.0091 0.491 0.1 0.291

0.188 0.0056 0.053 0.424 0.1 0.291

0.321 0.0088 0.146 0.474 0.1 0.291

1.98 0.031 0.003 0.225 0.4 0.291

0.97 0.058 0.032 ·0.244 0.4 0.291

1.54 0.062 0.141 0.235 0.4 0.291

a Weight fraction ratio

b Weight fraction

e Volume fraction before lIIixing with diluent
Tabl. B6

The experimental values of ethanol and water

distribution coefficients for TBP/leoper M

system 1n presence of dextrose

.~ 100fT

0.0791 0.0018 0.051 0.195 0.05 0.332

0.0442 0.0016 0.194 0.182 0.05 0.332

0.0524 0.0039 0.330 0.21 O.OS 0.332

0.103 0.0023 0.0386 0.357 0.05 0.332

0.089 0.0031 0.1110 0.411 o.OS 0.332

0.0982 0.0044 0.2153· 0.396 0.05 0.332

0.1079 0.0048 0.206 0.421 0.05 . 0.332

0.175 0.0059 0.079 0.442 0.05 0.292

0.210 0.0022 0.158 0.474 0.05 0.292

0.314 0.0013 0.022 0.441 0.05 0.292

0.685 0.0202 0.0196 0.216 0.5 0.332

0.470 0.0224 0.085 0.225 0.5 0.332

0.505 0.0351 0.178 0.276 0.5 0.332

1.591 0.0236 0.0054 0.400 0.5 0.332

0.52 0.021 0.059 0.449 0.5 0.332

0.923 0.0409 0.1011 0.424 0.5 0.332

0.665 0.029 0.0932 0.503 0.5 0.332

0.264 0.0059 0.0329 0.203 0.2 0.332

0.153 0.00522 0.127 . 0.202 0.2 0.332

0.179 0.0102 0.236 ' 0.245 0.2 0.332

0.374 0.00644 0.024 0.366 0.2 0.332

0.290 0.0067 ," 0.0.766 0.4301 0.2 0.332

0.251 0.0096 0.205 0.232 0.2 0.3332

0.291 0.0103 0.128 0.473 0.2 0.332

3.23 0.0222 0.0021 0.478 0.5 0.292

1.45 0.028 0.0168 0.519 0.5 0.292

1.868 0.039 0.0289 0.604 0.5 0.292
138

Table B7

The experimental values ot ethanol and vater

distribution coe!!!centa tor Meth1l ester

s1stem 1ft presence ot dextrose

100/'1'

0.1055 0.0024 0.07 0.0 0.3413

0.1075 0.0025 0.117 0.0 0.3413

0.2223 0.0034 0.147 --0.0 0.3413

0.1663 0.0037 0.278 0.0 0.3413

0.1416 0.0049 0.30 0.0 0.3413

0.145 0.0023 0.062 0.0519 0.3413

0.164 0.0029 0.099 0.0536 0.3413

0, .2358 0.0041 0.1365 0.0593 0.3413

0.1944 0.0049 0.2119 0.063 0.3413

0.1345 0.0049 0.279 0.062 0.3413

0.1597 0.002 0.0828 0.2118 0.3413

0.175 0.0033 0.1401 0.2215 0.3413

0.169 0.0047 0.1997 0.233 0.3413

0.1001 0.0048 0.2681 0.233 0.3413

0.3118 0.00145 0.045 0.0 0.2915

0.5088 0.0023 0.087 0.0 0.2915

0.5529 0.0035 0.133 0.0 0.2915

0.1825 0.004 0.225 0.0 0.2915

0.339 0.0029 0.043 0.0534 0.2915

0.2128 0.0029 . 0.089 0.054 0.2915

0.213 0.0031 0.126 0.0667 0.2915

0.34 0.0038 0.154 0.0661 0.2915

0.248 0.0039 . 0.209 0.0669 0.2915

0.2154 0.0037 0.065 0.2164 0.2915

0.4045 0.0045 0.0919 0.2344 0.2915

0.265 0.0047 0.1629 0.2422 0.2915

0.617 0.0059 0.1035 0.216 0.2915
Table B8 Mutual Solubility Data for Ethanol/water/2-ethyl-hexanol System

* Xe Xw Xs Ye Yw Ys T

0.024 0.9755 0.005 0.0169 0.0261 0.9570 293

0.050 0.9490 0.001 0.0350 0.0280 0.9370 293

0.097 0.9000 .~ 0.003 0.0520 0.0295 0.9185 293

0.232 0.7600 0.008 0.1660 0.0516 0.7824 293

0.011 ~.0.283 0.7060 0.2170 0.0652 0.7178 293 VJ "".

0.408 0.4990 0.09.3 0.4140 0.2710 0.3150 293

0.053 0.9450 0.002 0.0480 0.0290 0.9230 343

0.060 0.9350 0.005 0.0510 0.0390 0.9100 343

0.067 0.9230 0.010 0.0610 0.0440 0.8950 343

0.072 0.9080 0.020 0.0710 0.0490 0.880 343

0.205 0.7350 0.060 0.1950 0.1410 0.6640 343

•

Xi ,i Yi are mass fractions.
140

Tab1. B9

Mutual solubility data for

Ethano1/Water/Iaopar M ayatem at %98 oK

0.320

0.435

0.564

0.775

0.787

0.830

0.6798

0.5645

0.331

0.204

0.1854

0.115

0.0002

0.0005

0.005

0.021

0.0276

0.055

0.0093

0.0113

0.0130

0.0%08

0.0%52

0.0341

0.0004

0.0009

0.0010

0.0011

0.0012

0.0013

0.9903

0.9878

0.9850

0.9781

0.9736

0.9646

*

xi' Yi are mass fr3.ctions
•

141

Tabl. BID

Mutual solubility data for

Ethanol/water/Methyl Ester System

0.070 0.9292 0.0008 0.0073 0.0011 0.991 294

0.1170 0.S811 0.0009 0.0120 0.0018 0.9862 294

0.147 0.8517 0.0013 0.0216 0.0023 0.9761 294

0.276 0.721 0.003 0.0297 0.0026 0.9677 294

0.310 0.6885 0.005 0.0425 0.0034 0.9541 294

0.045 0.9542 0.0008 0.0145 0.0012 0.9843 338

0.095 0.9045 0.0015 0.0189 0.0014 0.9797 338

0.136 0.8620 0.002 0.0417 0.0021 0.9562 338

0.220 0.7760 0.004 0.0437 0.0028 0.9535 338

0.233 0.762 0.005 . 0.0635 . 0.0031 0.9334 338

*

Xi' Y1 are mass fraction.
142

APPENDIX C

Reciprocating Plate Extraction Column Data

."
Table cl Data on I" Reciprocating Plate Extraction Column

Reciprocation Feed Ratea, cc/min. Cone. of Solute, wt%

Run Speed, in the Aq. Pha.e BETS,

era/lee No. Org. Aq. •in out c.

1 4.0 92.0 6.2 50.94 25.92 133.3

2 4.0 89.2 6.2 50.94 25.43 131.4

3 4.1 102.5 6.2 50.94 24.21 128.5

4 4.8 84.3 6.2 52.71 16.19 88.5

5 4.2 92.8 5.8 50.60 23.54 125.0

6 4.3 82.8 5.7 50.70 20.76 111.8

7 4.1 .10.3 5.7 48.90 23.21 127.6

8 4.0 74.7 5.7 48.90 24.41 133.3 .... 9 4.1 101.4 5.8 48.90 23.41 129.5 v,)

10 4.4 132.3 5.8 49.78 18.52 104.0

11 4.8 115.5 5.8 50.55 16.45 89.1

12 4.5 133.5 5.8 50.55 17.63 96.3

13 5.0 105.6 5.8 50.84 16.02 82.2

14 4.3 98.5 5.8 50.84 24.06 128.6

15 4.6 99.2 5.8 50.84 17.51 96.7

16 4.5 99.6 5.8 50.84 18.98 104.0

17 4.5 93.9 5.8 50.84 19.21 101.6

18 4.5 122.3 2.2 50.39 19.03 100.6

19 4.5 121.8 2.2 50.56 19.62 105.9

20 8.3 111.3 2.2 50.41 11.04 13.36

21 7.9 130.4 2.2 50.08 11.81 13.3]

22 7.5 130.8 1.9 50.66 10.71 29.5

I" .pacinl between plate

Temperature range: 45(T(74·C

Solute .

.. Ethanol

Solvent: Isopar-H
Table C2 Data on 3' Reeiprocatinl Plate Extraction Column

Alitator Feed Ratee, GPH Cone. of Solute, wtl HETS

Speed,

SPH oraanic Aqueoul Org. In• Org. out Aq. in Ag. out Inche.

191 47.5 3.58 0.04 1.54 26.81 10.19 26.5

. 186 47.6 J.58 0.309 1.529 25.91 13.11 20.45 ....

J:-

150 47.0 3.58. 0.257 1.823 29.49 12.281 22.86 J:-

121 41.0 3.58 0.195 1.122 28.5 14.424 28.44

182 41.0 3.58 0.329 1.810 29.43 13.835 21.43

Xyhne-ac i tic acid-Water ayate. (water diaper.ed-Xylene extraction)

leur , Lo, 1916
Table C3 Data on 1" Reciprocatina Plate Extraction Coluan

Alit.tor reed Ratea, CPU/ft 2 hr Cone. of Solute, vt% HITS

Speed Ora Aq. Orl· 1n• Drs. out Ag. in Aq. out in.

360 249.0 323.0 0 14.545 18.705 5.715 3.1

345 249.0 323.0 0 14.295 18.75 5.4J 3.3

320 249 323.0 0 14.32 18.735 5.80 3.5

401 456.5 '456.5· . 0 12.15 17.05 2.91 2.8 ....

J:'-

VI

393 446.0 479.0 0 12.53 17.04 4.01 3.1

312 446.0 482.0 0 12.46 17.5 5.58 4.4

322 446.0 482.0 0 11.67 15.78 4.64 3-3

311 674 761 0 11.335 15.59 5.95 5.2

MlBI-Acltlc acid-Vater .,.tea (.ater di.per.ed-HIBK extraction)

larr • to. 1972

'.
Document Control 11. ~~RI Report No. 12. NTIS Accession No.

Page ~ERI/STR-32l-3l26

4. Title and Subtitle

Fuel Grade Ethanol by Solvent Extraction, Final

Subcontract Report

7. Author(s)

D. W. Tedder

9. Performing Organization Name and Address

Georgia Institute of Technology

Chemical Process Design Institute

School of Chemical Engineering

Atlanta, GA 30332-0100

12. Sponsoring Organization Name and Address

Solar Energy Research Institute

A Division of Midwest Research Institute

1617 Cole Boulevard ~n1 ci~n co ROLdll l11 ~q 1

15. Supplementary Notes

Technical Monitor: John Wright

16. Abstract (Limit: 200 words)

3. Recipient's Accession No.

5. Publication Date

June 1987

6.

8. Performing Organization Rept. No.

10. Project/TaSk/Work Unit No.

5260.10 11. Contract (C) or Grant (G) No.

(C) XX-4-04076-01

(G)

13. Type of Report & Period Covered

Technical Report

14.

This report summarizes final results for ethanol recovery by solvent extraction

and extractive distillation (SEED), di~cusses the comparative economics of the

two processes, and summarizes the economic analyses for the combined process.

Although the use of the SEED process for the recovery of ethanol enhances the

economics of gasohol production using fermentation, the benefits are not yet

sufficient for entry into the current market. The' process was found to be

technically feasible.

17. Document Analysis

a. Descriptors Feasibility studies, ethanol, ethanol fuels, solvent extraction,

distillation, gasohol

b. IdentifierS/Open-Ended Terms

c. UC Categories

18. Availability Statement

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, Virginia 22161

Form No. 0069 (3-25-82)

19. No. of Pages

158

20. Price

AD8