MEC E 668: Design of ExperimentInstructor: Professor Kajsa Duke

Final Projectby

Monisha AlamSupervisor: Dr. Zaher Hashisho

Design of Experiments to Optimize the Regeneration Process of Spent Activated Carbon Cloth by Resistive

Heating Method

Introduction

• Volatile organic compounds (VOCs) emitted from car painting solvents in automobile industries cause indoor air pollution

• Activated carbon cloth (ACC)• highly porous material, used as

adsorbent• adsorbs (VOCs) on surface &

inside pores2

http://northharfordcollision.net/wp-content/uploads/2013/06/car-painting.jpg

Introduction

• ACC used for 1 adsorption cycle: spent ACC

• Spent ACC reused for economic purpose

• Regeneration (VOCs are removed from pores of ACC) for economical reuse

• Resistive heating: higher heating rate, fast desorption

• To find optimized regeneration conditions:• “Factorial Design”: deals with several factors at a time• “Best-guess” : inefficient due to inadequate previous study• “One-factor-at-a-time”: lengthy, costly, no interaction

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Introduction

Successful regeneration indicates:• Minimum Heel (residual amount of strongly adsorbed VOCs

on ACC)

• Maximum Pore Volume available for adsorption

Objective:To identify optimum conditions to obtain regenerated ACC that contains:

• heel (< 5 wt% of the virgin ACC)

• pore volume (≥ 0.8 cm3/g)

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• Spent ACC samples wrapped in hollow cylinder shape (1.65 cm inner diameter, 10 cm length)

• Two stainless steel electrode tubes, with heating elements

Materials and Methods

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Regeneration CartridgeSpent ACC

Experimental Setup

6

Thermocouple

Quartz Reactor

3 Layered ACC

Electrode Tube

Application of Heat

Flow of N2

Measurement of Heel & Pore Volume

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Measuring Heel

Measuring Pore Volume

% of Heel = Mreg = Mass of regenerated ACCMV = Mass of virgin ACC

(MReg – MV ) / MV x 100%

Stage 1: Full Factorial DesignScreening Test with experimental & made-up data

• Responding Variables1. Amount of heel: < 5%2. Pore volume: ≥ 0.8 cm3/g

• Controlled Variables1. Heating rate: 10 °C/min2. Electrode Tubes: 1.65 cm dia

• Nuisance Factors1. Batch of ACC2. Voltage generator

8

0 1 2 3 4 5 66.78

6.82

6.86

6.9

Percentage of Heel vs. batches

Batch of ACC

Perc

enta

ge o

f Hee

l0 1 2 3 4 5 6

0.720.7220.7240.7260.728

0.73

Pore Volume vs. batches

Batch of ACC

Pore

Vol

ume

Effects of Nuisance Variables

Stage 1: Full Factorial Design

Low (-) High (+)1. Kinetic diameter (nm) 0.3 0.82. Molecular weight (g/mole) 80 1403. Heating temperature (°C) 60 2604. Heating duration (h) 1 35. Nitrogen flow rate (L/min) 1 3

No. of runs = 25 = 329

Manipulated Variables

Stage 1: Full Factorial Design-Results

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Pareto Chart of Standardized Effects: % of Heel - H

p=.05

(5)N2 Flow Rate-E

3by4

2by5

2by4

3by5

2by3

(1)Kinetic Diameter-A

(2)Molecular Weight-B(3) Heating Temperature-C

(4) Heating Duration-D

Stage 1: Full Factorial Design-Results

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Pareto Chart of Standardized Effects: Pore Volume - V

p=.05

4by5

3by5

1by5

2by4

1by2

1by4

(3)Heating Temp.-C

(2)Molecular Weight-B(1) Kinetic Diameter- A

1 by 3

Stage 2: Central Composite Design

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Responding Variables Objective Acceptable Rangei Percentage of Heel (%) To minimize < 5ii Pore Volume Recovered (cm3/g) To maximize > 0.8

Manipulated Variables -1.682 -1 0 +1 +1.682A Adsorbates Kinetic Diameter (nm) 0.22 0.25 0.30 0.35 0.38B Adsorbates Molecular Weight

(g/mole) 46.4 60 80 100 113.6C Heating Temperature (°C) 226.4 240 260 280 293.6

Controlled Variables Set Conditions1 Heating Duration (h) 12 Nitrogen Flow Rate (L/min) 13 Heating Rate (C/min) 104 Electrode Tube 1.65 cm dia

Curvature effects, surface response, 5 levels, No. of runs: 16

Central Composite Design: Results

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Pareto Chart of Standardized Effects: % of Heel-H

p=.05

1Lby3L

1Lby2L

2Lby3L

Molecular Weight-B(Q)

Heating Temp.-C(Q)

Kinetic Diameter-A(Q)

(3)Heating Temp.-C(L)

(1)Kinetic Diameter-A(L)

(2)Molecular Weight-B(L)

Central Composite Design: Results

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Pareto Chart of Standardized Effects: Pore Volume-V

p=.05

2Lby3L

1Lby2L

Kinetic Diameter-A(Q)

1Lby3L

Molecular Weight-B(Q)

Heating Temp.-C(Q)

(3)Heating Temp.-C(L)

(1)Kinetic Diameter-A(L)

(2)Molecular Weight-B(L)

Central Composite Design: Surface Plots – % of Heel

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Central Composite Design: Surface Plots–Pore Volume

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Central Composite Design: Regression Model

For Engineering Values• Percentage of Heel, H = 68.135 – 71.840 Ae + 169.60 Ae

2 + 0.091 Be – 0.456 Ce + 0.0008 Ce

2

• Pore Volume, V = – 4.3724 – 44.7958 Ae + 92.9736 Ae2 + 0.0311 Be –

0.0009 Be2 + 0.0818 Ce – 0.000155 Ce

2 – 0.020039 Ae Be – 0.0241919 Ae Ce

17A = kinetic diameter, B = molecular weight, C = heating temperature.Subscript “c” : coded value, “e” : engineering value.

Factors Relationships (coded to engineering)

Kinetic Diameter Ae = 0.0482 Ac + 0.3

Molecular Weight Be = 19.982Bc + 80

Heating Temperature Ce = 19.982Cc + 260

Fitted Surface: Pore Volume-V

> 1.1 < 1.1 < 1 < 0.9 < 0.8 < 0.7 < 0.6 < 0.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Kinetic Diameter-A

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Mol

ecul

ar W

eigh

t-B

Central Composite Design: Optimum Results

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Fitted Surface: % of Heel-H

> 8 < 8 < 7 < 6 < 5 < 4 < 3

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Kinetic Diameter-A

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Mol

ecul

ar W

eigh

t-B

Heel < 5%

Favorable Regions

MATLAB optimum results (coded values):Kinetic Diameter = -0.44Molecular Weight = -0.03Heel = 4.07%, Pore Volume = 0.82 cm3/g

Pore Volume ≥ 0.8 cm3/g

Central Composite Design: Optimum Results

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Favorable Regions

MATLAB optimum results (coded values):Molecular Weight = -0.03,Heating Temperature= -0.07Heel = 4.07%, Pore Volume = 0.82 cm3/g

Fitted Surface: Pore Volume-V

> 1 < 1 < 0.9 < 0.8 < 0.7 < 0.6 < 0.5 < 0.4

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Molecular Weight-B

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Heati

ng Te

mp.

-CFitted Surface: % of Heel-H

> 8 < 8 < 7 < 6 < 5 < 4 < 3

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Molecular Weight-B

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Heati

ng Te

mp.

-C

Pore Volume ≥ 0.8 cm3/g

Heel < 5%

Central Composite Design: Optimum Results

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Favorable Regions

MATLAB optimum results (coded values):Kinetic Diameter = -0.44Heating Temperature= -0.07Heel = 4.07%, Pore Volume = 0.82 cm3/g

Fitted Surface: % of Heel-H

> 8 < 8 < 7 < 6 < 5 < 4

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Kinetic Diameter-A

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Heati

ng Te

mp.

-C

Fitted Surface: Pore Volume-V

> 1.1 < 1.1 < 1 < 0.9 < 0.8 < 0.7 < 0.6

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Kinetic Diameter-A

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Heati

ng Te

mp.

-C

s

Heel < 5%Pore Volume ≥ 0.8 cm3/g

Discussions

• VOCs properties (molecular weight & kinetic diameter) more significant than process parameters (heating duration etc.)

• Obtained results in well agreement with literature• Models were verified (normal, residual, half normal plots

checked)• Effects of nuisance factors were checked & found negligible• Limited time & resources: physical experiments not done in 2nd

stage• Anticipated results comply best with real results• Future Works : perform real experiments & verify the results

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Conclusions• Heel minimized & Pores maximized for moderately high regeneration

temperature, lower molecular weight & smaller kinetic diameter VOCs

• Optimum results (heel = 4.1%, pore volume = 0.82 cm3/g) identified for:• VOCs molecular weight : 79.2 g/mol , kinetic diameter: 0.28 nm• Heating temperature: 259°C

• Recommendation• To reduce no. of runs: Fractional factorial in 1st stage• Taguchi method : better results in least no. of runs

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• Professor Kajsa Duke

• Professor Zaher Hashisho & all my colleagues from Air Quality Control group

• Ford Motor Company for financial support

Acknowledgement

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1. Kim, B. R., 2011, “VOC emissions from automotive painting and their control: A review”, Environ. Eng. Res., 16 (1), pp.1 – 9).

2. D.C. Montgomery, Design and analysis of experiments. 8th edition, John Wiley & Sons, New York, 2014.

3. Hou, P., Byrne, T., Cannon, F. S., Chaplin, B. P., Hong, S., and Nieto-Delgado, C., 2014, “Electrochemical regeneration of polypyrrole-tailored activatedcarbons that have removed sulfate”, Carbon 7 9, pp 4 6 –5 7

4. Dong, L., Liu, W., Jiang, R., Wanga, Z., 2014, “Physicochemical and porosity characteristics of thermally regenerated activated carbon polluted with biological activated carbon process”, Bioresource Technol, 171, pp. 260 - 264

References

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