POSTER INTRODUCTION - Washington University in...

POSTER INTRODUCTION

CHEMICAL REACTION ENGINEERING LABORATORY

CREL Annual Meeting

October 28, 2004

Chemical Reaction Engineering Laboratory

Department of Chemical Engineering

St.Louis, MO 63130

(SLURRY) BUBBLE COLUMN AND GAS-LIQUID

STIRRED TANK REACTORS

A. Experimental Techniques and Measurements

Ashfaq Shaikh

Bubble Column Reactors

Hydrodynamics

Hydrodynamics of High Pressure Bubble Column Slurry Reactor

Combination of two single modal tomographic techniques for three

dynamic phase flow imaging

Flow Regime Transition

Evaluation of CT for regime identification

New technique and its ‘flow regime identifiers’ developed

Scale-up A new hypothesis proposed

Experimental evaluation of proposed hypothesis

Development of ANN correlations for hydrodynamic parameters

Hydrodynamics Flow Regime Transition Scale-up

Characterization of Hydrodynamic Flow regime in

Bubble Column via Computed Tomography

Homogeneous/Bubbly

Flow

Heterogeneous/Churn-

turbulent Flow

Different hydrodynamic characteristics

Explored the potential of CT for flow regime delineation in

bubble column

Evaluated the developed approach with traditional

methods such as Drift Flux method

Investigated the effect of operating pressure on flow regime

transition

Slurry Bubble Column Reactors • Vertical cylindrical vessels, three-phase gas-liquid-solid systems with

solid particle sizes in the range 5-150µm and solids loading up to 50% by volume

• Simple to construct and do not involve any mechanically moving parts

• Exhibit excellent heat and mass transfer characteristics

Applications:

– Fischer-Tropsch (FT) Synthesis

– oxidation and hydrogenation

– chlorination and alkylation

– polymerization, methanol synthesis

– waste water treatment

– bio and biochemical processes

G – Reactant L – Reactant and/or Product S – Catalyst

The goal of this work is to measure the gas-liquid volumetric mass transfer

coefficient, kLa, in SBC with high gas velocity/pressure/solid loading, with

assistance of hydrodynamic information obtained using CARPT/CT

methodology.

Mass Transfer Measurement Techniques

for Slurry Bubble Column Reactors Lu Han, Muthanna Al-Dahhan. CREL, Oct. 2004

G

L+S

G

L+S

Optical Oxygen Probe

Probe Tip

Light from the blue LED

going to the probe tip Sol-Gel

Collected fluorescence

going to the spectrometer

Overcoat

O2

O2

O2

Sol-GelSol-Gel475 nm 475 nm

600 nm600 nm

O2

O2

Comparison of Data Fitting Using

CSTR and ADM models

B.C. DC8”, air-water, 0.1MPa,

SGV 12cm/s, z/L=0.8

0

0.2

0.4

0.6

0.8

1

1.2

0 20 40 60 80

t, s

C/C* Exp.

ADM Fitting

CSTR Fitting

Gas Tracer Technique

Gas Tracer Response Fitting Using

ADM Model (RTD)

B.C. DC8”, air-water, 0.1MPa, SGV

2cm/s

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40

t, s

Norm

aliz

ed C

T

He TracerResp.

ADM Fitting

Flows Dynamics in An Internal Loop Airlift Column

Bioreactor

Hu-Ping Luo, Muthanna H. Al-Dahhan

Chemical Reaction Engineering Laboratory (CREL)

Bioprocess & Bioreactor Engineering Laboratory (BBEL)

Chemical Engineering Department

Washington University in St.Louis

CREL Annual Meeting

October 2004

A Novel Modeling Approach for Predictions of the Dynamic

Growth of Microalgae in Multiphase Photo-bioreactors


Producing And Carbonylating of Dimethyl Carbonate: A

Process Development Study

A Novel Modeling Approach for Predictions of the Dynamic Growth of

Microalgae in Multiphase Photo-bioreactors

Substrates

Bubbles Bubbles

td

Cell 1

Product 1

tr1

td Bubbles

Cell 2

Product 1

tr2 Final Products

td

Complex interactions among

microorganisms (cells) metabolism,

kinetics, transportation, and

hydrodynamics in Bioreactors

Challenges in Reactor Design and Scale-up

How to see through the system

for LOCAL PHENOMENA of the

flow pattern in bioreactors

? A Case Study

Airlift Column Photobioreactor:

Integrating metabolism of autotrophic

microorganism with flow dynamics

Mass transfer in Bioreactors

0

10

20

30

40

50

60

70

80

0 100 200Time, hr

Cell'

s C

oncentr

ation,

*106

cell/

ml

SC_1cms SC_5cmsDC_5cms DC_1cmsBC_5cms

Photosynthesis Kinetics Bioreactor Performance

CARPT &

CT

Findings

Please stop by this

poster if interest

Flows Dynamics in An Internal Loop Airlift Column Bioreactor

RIS

ER

3 cm

9 cm

150

cm

13 cm

105

cm

RIS

ER

3 cm

9 cm

150

cm

13 cm

105

cm

CARPT CT

Study the macro- and micro-mixing and the liquid flow field in the

fully developed flow region as well as the Top and the Bottom

regions

Investigate the effects of superficial gas velocity and top and

bottom clearance on the hydrodynamics

Form the knowledge base for airlift reactors’ design and scale-up,

and provide a database for CFD modeling validations.

Please stop

by this

poster for

details if

interest Bypassing and Stagnant may significant in

both the Top and the Bottom regions

RTD analysis Local Gas Holdups

Producing And Carbonylating of Dimethyl Carbonate:

A Process Development Study Hu-Ping Luo, Wen-De Xiao, Kai-Hong Zhu

East China University of Science and Technology, Shanghai, China

WHY Dimethyl Carbonate? •Environmentally benign chemicals

•Environmentally benign processes

•An excellent gesoline additives

•A building block: containing both the carbonyl

and the methyl group, an effective

carbonylation agent, a useful methylation agent

•An important organic solvent

Trans-esterification: producing

and carbonylating DMC

H3COCOC2H5

O

C2H5OH CH3OHH3COCOCH3

O

++cat

C2H5OCOC2H5

O

C2H5OH CH3OHC2H5OCOCH3

O

++

cat

Kinetic

Thermodynamic

Reactive Distillation

'

'

'

' ' ' ' ''' ' '

"

"""" " " "

""""

0 10 20 30 40 50 60 70 80 90

Time(min)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

DM

C c

on

ver

sio

n o

r D

EC

sel

ecti

vit

y

Conversion Selectivity" '

Add new reactants

Catalyst E+P

0.0 0.2 0.4 0.6 0.8 1.0

340

350

360

Methanol(1)+DMC(2)

Exp

UNIFAC prediction (old

parameters,Pattern 1)

UNIFAC prediction (old

parameters,Pattern 2)

UNIFAC prediction (this

work)

Tem

pera

ture

(K)

x1, y

1

0 20 80 10050

40

30

20

10 Methanol

Ethanol

DMC

MEC

DEC

Sta

ge n

um

ber

Liquid composition (mol%)Phase Equilibrium Catalytic System Reactive Distillation Simulation


Heat Transfer Coefficient

Measurement

Technique in High Pressure Slurry

Bubble Column Chengtian Wu, Muthanna Al-Dahhan

• The instantaneous heat transfer coefficient(hi) can be obtained from the

heat transfer flux(Q) and temperature difference between the probe

surface(Ts) and the bulk(Tb).

– The probe measures the instantaneous local heat flux(Q) and the

surface temperature(Ts).

– Three thermocouples are used to measure the bulk temperature(Tb).

)/( bsi TTQh

measurement unit & result

Heat transfer probe


1 2

4 5

3

Heat transfer measurement unit

1: thermocouples, 2: probe, 3: DC power,

4: amplifier, 5: DAQ system.

HTC measured in 6” air-water column under atmosphere

2

4

6

8

0 5 10 15 20 25

Ug(cm/s)

hw(Kw/(m2.C))

Center

Wall


dfiber=0.2 mm

Bubble Velocity, Chord Length and Specific Interfacial Area

Measurements in Bubble Columns Using Four-point Optical Probe

The performance of (slurry)

bubble columns is governed

by the hydrodynamics.

Validation of Computational

Fluid Dynamics (CFD) codes

requires also local

information on bubble

properties.

The measurement of bubble

properties is difficult,

especially in churn-turbulent

flow. A four-point optical

probe is employed in this

study to measure the bubble

properties.

Liquid/Slurry Outlet

Gas Outlet

Gas Inlet

Liquid/Slurry Inlet

Probe

A/D unit and computer

Liquid/Slurry Outlet

Gas Outlet

Gas Inlet

Liquid/Slurry Inlet

Probe

A/D unit and computer

Junli Xue, M. H. Al-Dahhan, M. P. Dudukovic, R. F. Mudde

Cofiguration of the Four-Point

Optical Probe


The operating conditions span from bubbly

flow to churn-turbulent flow.

Superficail gas velocity: 2~60 cm/s

Pressure: 1~10 bar

Four-Point Optical Probe Measurements in a

16.2 cm (6.4”) Bubble Column:

Measuring

position

Probe positioned

downwards

Probe positioned

upwards

The probe was positioned

both upwards and

downwards. So both

bubbles moving upwards

and downwards are

measured.

25, Ch de la Tavallaz, CH-1816 Chailly s/Montreux, Switzerland - Tel.: +41 21 989 2121 - Telefax: +41 21 989 2120 - www.biazzi.com

CONFIDENTIAL

3D VIEW OF BIAZZI HYDROGENATION REACTOR

Projects realized:

• 41 plants built

• 16 of which cGMP

• Maximum 110 bar and 300°C

Customers:

• Fine Chemicals

• Pharmaceuticals

• Resins and Intermediaries

• Speciality sugars

• Edible oils

Operation modes:

• Continuous

• Dedicated cGMP and regular

• Multipurpose cGMP and regular

Countries:

• Europe: Italy, Belgium, Austria,

Switzerland, Netherlands, Germany,

England, Spain, France,

• Americas: Brasil, USA,

• Asia: South Korea, India, Japan,

Taiwan R.O.C., China, Russia

References:

Testing of Phase Transition and Bubble Dynamics Using A Four-Point Optical Probe Adam Wehrmeister, Junli Xue, M. H. Al-Dahhan, M. P. Dudukovic

Chemical Engineering Department, Washington University in St. Louis Center for Environmentally Beneficial Catalysis


The four-point optical probe

installed in a 2D bubble column.

Sketch of the ideal probe

response to a bubble piercing the

four tips of the optical probe.

Provides data on bubble size,

bubble velocity, local gas hold-up,

and specific interfacial area.

T0

T1

T2

T3

Tip0

Tip1

Tip2

Tip3

t1

t2

t3

Time

Vo

lta

ge

Bubble

Liquid

0

0.2

0.4

0.6

0.8

1

0 1 2 3Time (seconds)

Vol

tag

e

Tip 1

Tip 2

Tip 3

Tip 40

0.2

0.4

0.6

0.8

1

1 1.05 1.1 1.15 1.2

Figure 7. Probe response for

decane/CO2 at 32 oC and ~1050 psi

Figure 6. Probe response for

decane/CO2 at 43 oC and ~1000 psi

0

0.2

0.4

0.6

0.8

1

0 1 2 3

Time (seconds)

Vol

tage

Tip 1

Tip 2

Tip 3

Tip 40

0.2

0.4

0.6

0.8

1

0.18 0.19 0.2

Side view

L2 mm

r

Probe

Tip0

Tip3

Tip1

Tip2

Tip1

Tip3 Tip2

Tip0

r r

r0.6 mm

Bottom view

(Field of view 5x5 mm)

B. Modeling and Computational Fluid Dynamics (CFD)

(SLURRY) BUBBLE COLUMN AND GAS-LIQUID

STIRRED TANK REACTORS

Peng Chen and M. P. Dudukovic

CREL Meeting, 2004

Predicting Gas Holdup, Liquid Velocity Profiles

and Mixing in Bubble Column Flows

Accounting for Coalescence-Breakup



The CT Setup at CREL (Kumar, 1994)The CT Setup at CREL (Kumar, 1994)

Dzz

Drr

uz(r)

1-eL(r)

0 -R R

CT CT SCAN

CARPT

FLOW PATTERN

CFD + CARPT + CT

0 100 200 300 400

AFDU

0 100 200 300 400

1 0.8 0.6 0.4 0.2 0

Detector Level 1

1 0.8

0.6

0.4

0.2

0

Detector Level 6

Run 14.6

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Time (sec)

No

rma

lize

d R

es

po

ns

e

Sim_L1

Exp_L1

Sim_L4

Exp_L4

Sim_L7

Exp_L7

Pressure = 50 atm

Temperature =250 Deg. C

Ug = 25 cm/s

0 20 40 60 80 100

1 0.8

0.6 0.4

0.2

0

7

6

5

4

3

2

1

Liquid

Tracer

Gas Tracer Gas

Gas

DET.

Data

Model

Prediction

time (s)

time (s)

time (s)


Bridge the Gap — CFD Modeling of

Bubble Column Flows

Phenomenological

Model

Reactor Performance

Mixing and

Transport

Characteristics

Assessment

Needed information:

Gas holdup profile

Eddy diffusivity correlation

Liquid mixing length correlation

• CFD

• Experiments

• Correlation


16 – 29.10.55 -38.130.655 – 0.84925-300FT wax

170.880.86625Therminol LT

Surface

Tension

(dyne/ cm)

Viscosity

(cP)

Density

(g/ cm3)

Temperature

(ºC)

16 – 29.10.55 -38.130.655 – 0.84925-300FT wax

170.880.86625Therminol LT

Surface

Tension

(dyne/ cm)

Viscosity

(cP)

Density

(g/ cm3)

Temperature

(ºC)

Outlines

•Hydrodynamics of bubble columns

•Eulerian-Eulerian Two-Fluid model

•Algebraic Slip Mixture Model (ASMM)

•Hydrodynamics of (passive) tracers (gas/liquid) in bubble column flows

Computational Modeling of Gas-Liquid Flow in Bubble Columns P. Chen, M. Rafique and M. P. Dudukovic

CFD-based Compartmental

Modeling of Single Phase Stirred

Tank Reactors

Debangshu Guha, M.P.Dudukovic & P.A.Ramachandran

CREL Annual Meeting, 2004

CHEMICAL REACTION ENGINEERING LABORATORYCHEMICAL REACTION ENGINEERING LABORATORY

Motivation

Reactor Performance = f (kinetics, flow pattern and mixing)


Mixing = f (flow pattern and turbulence characteristics)

The performance prediction can be improved if flows and

turbulence characteristics can be used from CFD

Most available phenomenological models for mixing do not

account for the flow pattern and the turbulence

inhomogeneities in the reactor

CFD-based Approach


i,j,k

i,j,k+1

i,j,k-1

i-1,j,k i+1,j,k

i,j-1,k

i,j+1,k

u

u

ur ur

uz

uz

Solve macroscopic

equations for all

compartments

simultaneously

Solve flow equations

using CFD to

generate the flow

field in the tank

Calculate flows in and out

of the compartments;

Estimate the exchanges

due to fluctuations

Macroscopic equation consists of convection due to main flow, dispersion due toturbulence and the reaction terms

i,j,k

i,j,k+1

i,j,k-1

i-1,j,k i+1,j,k

i,j-1,k

i,j+1,k

u

u

ur ur

uz

uz

Solve macroscopic

equations for all

compartments

simultaneously


using CFD to

generate the flow

field in the tank




due to fluctuations


i,j,k

i,j,k+1

i,j,k-1

i-1,j,k i+1,j,k

i,j-1,k

i,j+1,k

u

u

ur ur

uz

uz

i,j,k

i,j,k+1

i,j,k-1

i-1,j,k i+1,j,k

i,j-1,k

i,j+1,k

u

u

ur ur

uz

uz

i,j,k

i,j,k+1

i,j,k-1

i-1,j,k i+1,j,k

i,j-1,k

i,j+1,k

u

u

ur ur

uz

uz

Solve macroscopic

equations for all

compartments

simultaneously


using CFD to

generate the flow

field in the tank




due to fluctuations


Gas-Liquid Flow Generated by a Rushton Turbine in

Stirred Vessel: CARPT/CT Measurements and CFD Simulations

Grid Details :

r z : 58 95 64

Impeller blade: 14 3 18

Inner region : 12 k 53

j 42

• CARPT/CT measurements were obtained in

STR for gas-liquid flows

•Can liquid phase velocity profiles be predicted

apriori with no experimental inputs?

•Can the gas holdup profiles in the STR be

predicted via modeling?

•Role of Lagrangian measures from CARPT in

validating CFD approaches ?

•Extension of Computational Snapshot to

predicting two phase flows in STR ?

PACKED BED, STRUCTURED BED,

CRICULATING FLUIDIZED BED


Satish Bhusarapu,

M. H. Al-Dahhan and M. P. Duduković

CREL Annual Meeting

October 28, 2004



St.Louis, MO 63130

Poster 1

Solids Flow Mapping in a Fast Fluid Bed


Air

inlet

Riser

section

7.9 m (26’)

tall

15.2 cm

(6”) I.D.

Downcom

er

5.5 m

(18’) tall

5.1 cm

(2”) I.D.

Mechanical

valve

To air

filter

Cyclone

1 m (3.3’) tall

0.1m (4”) I.D.

4.4 ft3 Feed

hopper

Disengage

ment

section

1.5 m (5’)

tall

0.6 m (24”)

I.D.

Splash

plate

Mechanical

valve

Air

inlet

Riser

section

7.9 m (26’)

tall

15.2 cm

(6”) I.D.

Downcom

er

5.5 m

(18’) tall

5.1 cm

(2”) I.D.

Mechanical

valve

To air

filter

Cyclone

1 m (3.3’) tall

0.1m (4”) I.D.

4.4 ft3 Feed

hopper

Disengage

ment

section

1.5 m (5’)

tall

0.6 m (24”)

I.D.

Splash

plate

Mechanical

valve

z = 5.85 m

L/D = 38.5

z = 4.6 m

L/D = 30.5

Challenge : Obtain solids flow mapping in the riser

CARPT in a Pilot-plant set-up

46Sc particle coated with a polymer (Parylene®

density 1.1 g.cm-3 ) to adjust the density and

prevent attrition of the radioactive tracer

Soft glass beads

r= 2.5 g.cm-3 ; dp (sauter mean) = 150 mm

Radioactive tracer particle

46Sc particle

(136 mm)

ParyleneN coating

(7 mm thickness)


Satish Bhusarapu,


CREL Annual Meeting

October 28, 2004



St.Louis, MO 63130

Poster 2

Solids RTD in a Gas-Solid Riser at Low and High Fluxes:

Single Radioactive Particle Tracking


Challenge : To obtain RTD in an “open” system like riser

Impulse responses in “open-open” systems are

not representative of the RTD. Naumann &

Buffham, 1983.

In recirculating systems like CFBs, first passage

times in the riser cannot be determined uniquely

from impulse responses. Shinnar et al., 1971.

0 1 4 50

0.05

0.1

0.15

t

0

0.5

1

Solids FPTD in the Riser with "closed-closed" Boundaries

F -

curv

e

0

0.01

0.03

0.04

0 1 4 50

0.5

1Solids RTD in the Riser with "open -open" Boundaries

t

F -

curv

e

0 1 2 3 4 50

0.01

0.02

0 1 2 3 4 50

0.5

1Transient Response Function from a Conventional Tracer Experiment

t

F -

curv

e

Mean of FPTD = 13.52 sec

Stdev of FPTD = 33.6 sec

Dz = 2.1 m2/s

sec

Mean of RTD = 39.7 sec

Stdev of RTD = 59.94 sec

Dz = 0.8 m2/s

sec

Mean of TConv. = 65.13 sec

Stdev of TConv. = 81.9 sec

Dz = 0.5 m2/s

sec

Single Radioactive Particle Tracking

Transient response function as would be

obtained from conventional tracer injection

Overestimates:

Mean residence time by 64%

Underestimates:

Dimensionless variance by 31%

Dispersion coefficient by 38%

Poster 3


Satish Bhusarapu,


CREL Annual Meeting

October 28, 2004



St. Louis, MO 63130

An Alternating Minimization Algorithm for Image

Reconstruction in Computed Tomography


Challenge: To improve image quality of the CT data

Phase holdup profiles at

various axial positions

Estimation - Maximization -

Beer Lambert’s Law -

l

ijijeff

o

lI

IA ,ln mr

K

ijKijKijeff ,,, emrmr

An approximation is made in

the solution which is true only

for low attenuation values

Implement an Alternating Minimization (AM) algorithm (O’Sullivan

and Benac, 2001), where each step of minimization is exact.

Trickle Bed Reactors

Maxime Capitaine

M.P. Dudukovic, M.H. Al-Dahhan


Washington University in St. Louis

St. Louis, MO

J. Bousquet, D. Védrine, P. Tanguy

Centre Européen de Recherche et Technique, TOTAL

Harfleur, FRANCE

Hydrodynamics Parameters

• Liquid Distribution

• Pressure Drop

• Liquid Hold Up

Measurement Methods

• Collector Tray

• Computed Tomography

Results

• Effects of liquid and gas superficial velocities and packed bed height

Cell Network Modeling For

Catalytic Trickle-Bed Reactors


J. Guo, Y. Jiang, P. A. Ramachandran,

M. Al-Dahhan, M. P. Dudukovic

Washington University

St. Louis, Missouri

CREL Annual Meeting

10.28.2004

Gas Liquid

Gas Liquid

Inlet

Outlet

Cell operated

as C STR

Completely dry

Completely w etted

Half

wettedLiquid path

Gas Liquid

Gas Liquid

Inlet

Outlet

Cell operated

as C STR

Completely dry

Completely w etted

Half

wettedLiquid path

Single Cell


Cell (i, j)

Cell (i+1, j)

Layer i

Layer i+1

Cell (i+2, j) Layer i+2

Layer i+3 Cell (i+3, j)

1D Cell-Stack 2D Cell-Network

i,j

Mixing

Splitting

i, j i, j+1 i, j-1

i-1, j

i+1, j

Dynamics of Coupling Exothermic &

Endothermic Reactions in Directly Coupled

Adiabatic Reactors

R C Ramaswamy

Advisors

P A Ramachandran, M P Duduković

CREL Annual Meeting

Fall, 2004


A B

C D

-ΔH

+ΔH

Directly Coupled Adiabatic Reactor

(De Groote et. al. 1996, De Smet et. al. 2001, Hohn

and Schmidt 2001)

Endothermic

Exothermic

Counter Current Reactor

(Frauhammer et. al. 1999, Veser et. al. 2001, Kolios et. al.

2001, Kolios et. al. 2002)

Exothermic Endothermic

Reverse Flow Reactor

( Kulkarni and Dudukovic 1996, Kolios et. al. 2000)

Endothermic

Exothermic

Co-Current Reactor (Ismagilov et. al. 2001, Kolios et. al. 2002, Zanfir et. al.

2003)

Regenerative

Coupling

Recuperative

Coupling

Exothermic

Reaction

Endothermic

Reaction

Heat

Coupling

Exothermic

Reaction

- Combustion

Endothermic

Reaction

- Synthesis Gas

Generation

Heat

Regenerative Coupling

Directly Coupled Adiabatic Reactors

Mixed Catalyst Bed

(Exothermic &

Endothermic Catalysts)

Simultaneous DCAR Sequential DCAR

Products

Reactants

Exothermic

Catalyst Bed

Endothermic

Catalyst Bed

Modeling of Catalytic Partial Oxidation of

Methane to Synthesis Gas in a Short

Contact Time Packed Bed Reactor

CH4 & O2

(2:1)

Tin ~773 K

H2/CO ~ 2

CO2 & H2O

Texit ~ 1300 K

Partial Oxidation (Exo)

&

Steam Reforming (Endo)

Synthesis Gas (mixture of H2 and CO) (Pena et. al. 1996)

– Feed stock for synthesis of liquid fuels, methanol

– Source of hydrogen for fuel cells

– Feed stock for ammonia plant, hydrogenation plant etc

MolKJHHCOOHCO

MolKJHHCOOHCH

MolKJHHCOOHCH

MolKJHOHCOOCH

K

K

K

K

/37,)4(

/185,42)3(

/222,3)2(

/800,22)1(

773222

7732224

773224

7732224

Catalytic Partial Oxidation of Methane to Syngas (De Smet et. al., CES 56 , 2001)

High Active Catalysts (Rh)

Short Contact Time

Reactors

(4-15 milli seconds) Hohn & Schmidt, 2001

YSOA

XSD

SDSOO

SASOP

k

k

k

k

4

3

2

1

21

32

41

32

41

kk

kk

kk

XSDSOO

YSASOPkk

kOk

Rearranging,

•A is the desired product

•X & Y deactivate the catalyst

The configurations to consider are

•CSTR (both P & O in low conc)

•P in high conc (Plug flow)

and O in low conc – CSTR in

series with addition of O in each

CSTR

Reactor Modeling and Design for Solid Acid

Catalysis Test Bed

Iso-butane (P) + Butene (O) Alkylate (A - gasoline)

Performance studies of a solid-catalyzed gas-liquid monolith reactor: Effect of flow maldistribution

Shaibal Roy Muthanna Al-Dahhan


CREL Annual Meeting

28th October 2004

Introduction

• Multiphase reactors (for solid catalyzed gas-liquid reaction) used extensively in petroleum, petrochemical, biochemical, material, and environmental industries

•Catalytic monolith reactor have shown promise to overcome some of the drawbacks of conventional reactors as well as give higher productivity (Krautzer et al. 2003, Nijhaus et al., 2001)

Gas out

Gas in

Liquid out

Gas in Gas out

Liquid in

Liquid out

Gas in

Gas out

Gas

in

Gas

out

Liquid

in

Liquid

out

Background

Previous researches have assumed uniform flow distribution across a monolith cross-section in the Taylor flow regime.

However, this is not always the case as demonstrated by recent non-invasive flow measurement techniques (Mewes et al., 1999; Gladden et al., 2003)

Nijhaus et al., 2001 Krautzer et al., 2003 Liu, 2001

Experimental performance studies (small diameter reactor)

Monolith reactor performance modeling (single channel model)

Edvinsson et al. 1994 Cybulski et al. 1999 Nijhaus et al., 2003

Objectives •What is the effect of the following operating parameters on the flow distribution:

•Gas and liquid velocities

•Type of liquid distributor

•Cell density and void fraction of monolith

•How is the performance of monolith reactor (conducted in a large diameter reactor) affected by flow distribution

•How does monolith reactor performance compare with trickle bed reactor

•Does monolith scale reactor model (integrating flow distribution in the model) fare better than

single tube model

Gladden et al 2003 using MRI

Mewes et al. 1999 using Capa. Tomo.

Developing User Friendly

Modules for Modeling

Multiphase Reactors

By

Canan Tunca

Two friendly user simulation packages have been developed.

User specifies several parameters needed in reactor design

calculations.

Liquid-solid circulating bed

reactor for alkylation process

Trickle bed reactor for

phenol oxidation process

MICROREACTORS

Micro Reactors Evaluation for Environmentally Benign Processes

• Radmila Jevtic, Milorad Dudukovic, and Muthanna Al-Dahhan


(taken from http://www.mikroglas.com)

http://www.mikroglas.com/

Introduction

The potential advantages of using

microreactors instead of

conventional reactors are

(Jensen, 2001):

• Higher surface to volume ratio

• Higher mass and heat transfer

rates

• More aggressive reaction

conditions with higher yields

• Safer operation

• Higher throughputs

• Minimal environmental hazards


OH OO2 HNO

3

+Caprolactam

& Adipic acid

Nylon 6 and Nylon 66

KA-mixture

>120 C ~15 bars

cobalt catalysts

4% conversion of cyclohexane ;

80% selectivity is for cyclohexanol and cyclohexanone.

The reaction has been performed under atmospheric pressure,

both at room and the elevated temperatures (up to 90oC), with or

without catalyst (cobalt naphthenate), and with various oxidants

(air, oxygen, ozone, and hydrogen peroxide).


Test Reaction and Methodology

CATALYSIS CHARACTERIZATION AND

DEVELOPMENT

Heterogeneous Kinetics and Particle Chemistry Laboratory


Washington University, St. Louis MO

Research Personnel:

Professor John Gleaves Mrs. Rebecca Fushimi Miss. Pam Buzzetta

Professor Gregory Yablonsky Mr. Mike Rude Mr. Sean Mueller

Dr. Anne Gaffney Mr. David French Mr. Joseph Swisher

Mr. Josh Searcy

Atomic Tailoring of Catalyst

Surfaces for High Selectivity:

Partial Oxidation of Propane

Funded by the National Science Foundation’s GOALI (Grant Opportunities for Academic Liaison

with Industry) Initiative

Monomers Research

727 Norristown Road, PO Box 904

Spring House, Pennsylvania

Changing the Surface Transition Metal Composition of Bulk

Catalysts

Creating Nanoscale Concentration Gradients of Transition Metal Species on Bulk

Metal Oxide Catalysts

Transition metal source

Catalyst particle

Atomic beam Laser beam

Sample holder in transfer arm

(Vacuum - 10-8 torr)

Vibrate bed

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

VPO - Te Deposition

VPO

VPO - Cu Deposition

Bu

ten

e

Con

ve

rsio

n

Pulse Number

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

VPO

VPO - Te

VPO - Cu

Pulse Number

Norm

aliz

ed

Fu

ran

Yie

ld

Changing the Surface Transition Metal Composition of Bulk

Catalysts

Preliminary Results

Pulsed Hydrocarbon

Reduction

ROx

+ O2

POx, TRx, tRx

VPO catalyst

Oxygen-enriched

nanolayer

Butene Furan

TAP Vacuum Pulse Response and Normal

Pressure Studies of Propane Oxidation over

MoVTeNb Oxide Catalyst

1Dept. of Chem. Eng., Washington University 2Rohm & Haas Company

Rebecca Fushimi1, Sergiy O. Shekhtman1, Michael Rude1, Anne

Gaffney2, Scott Han2, Gregory S. Yablonsky1, John T. Gleaves1

Experiment description

All studies were performed in

TAP-2 experimental system using

a three-zone reactor configuration

at normal and vacuum conditions.

TC

Pulse valve

Microreactor

Mass spectrometer

Catalyst

Vacuum (10-8 torr)

Reactant mixture

Results

Steady-State Normal Pressure

0

0.2

0.4

0.6

0.8

1

240 280 320 360 400

Cnv.PrCnv.O2AACOCO2AcA

High AA yield on

the “lower” branch

Complete Oxygen

Conversion on

the “upper” branch

High CO 2 yield on

the “upper” branch

Cooling

Regime

Re

act

an

t C

on

ve

rsio

n/P

rod

uct

Yie

ld

Temperature (˚C)

Oxygen

Conversion

Propane

Conversion

CO2

Acrylic Acid

CO

Acrolein

Figure 2. Conversion and yield versus temperature. Contact time = 3.5 s. Oxygen = 19.1%,

propane = 9.2%, balance argon passed through water bubbler at 65 C

Statistical Analysis of Complex Diffusion-

Reaction Process in a Temporal Analysis of

Product (TAP) System

Elizabeth Maroon, Zhengjun Zhang, Michael Rude, Gregory S. Yablonsky

Department of Mathematics, Washington University

Department of Chemical. Engineering, Washington University

Pure Diffusion – is it Normally Distributed?

BIO-REACTORS, ENVIRONMENTAL

A Bioenergy-Based Bench-Scale Experiment for

Undergraduate Engineering Students Using Fermiol Super HA®

Bia Henriques, Fan Mei, Kursheed Karim, Muthanna Al-Dahhan


Objectives: To create an experiment for undergraduate chemical engineering students that exposes

them to bioprocesses and biofuels

To give the students hands-on experience and knowledge about the dry grind corn to

ethanol process

To determine the effects of different sets of parameters on the fermentation process

To study the effect of initial substrate concentration on ethanol production and yeast

growth

To examine the following: 1) Effects of different yeast strains on fermentation

2) Optimization of parameters of a kinetic model and prediction of fermentation performance

3) Effects of various design and operation parameters on corn syrup fermentation and product inhibition

Accomplishments: Studied the effect of substrate concentration on corn syrup fermentation using a specific

strain of Saccharomyces cerevisiae

Collecting and analyzing data to validate existing kinetic models with and without the

product inhibition term

Optimized batch model parameters using experimental data

Simulation and Design of a Process Control System for a Pilot

Plant-Scale Distillation Unit Bia Henriques, Jonathan Lowe, Robert Heider, Terry Tolliver, Rachel Vazzi, Kwaku Opoku-Mensah


Objectives: To simulate the distillation unit of SIU-E corn to ethanol pilot plant in HYSYS

To study the design of the distillation unit by configuring its process control system in

DeltaV

To interface HYSYS and DeltaV to provide optimum process and process control design

to SIU-E

To study the behavior of the distillation unit’s control system and devise the best tuning

method for the system

To create an interactive model of the distillation unit in order to teach operators how the

system works for better use of controls

Accomplishments: Created an interactive learning model of SIU-Es pilot plant distillation unit

Studied the effect of different tuning methods on the distillation process control

Developed interface to use process simulation in HYSYS to control the system in DeltaV

Optimized process performance by studying the behavior of the process control system

Modeled all piping and instrumentation equipment found in SIU-E’s distillation unit

Anaerobic Digestion of

Animal Waste

Rebecca Hoffmann, Khursheed Karim,

Muthanna Al-Dahhan, Lars Angenent


Bioprocessing and Bioreactor Engineering Laboratory (BELL)

2004 CREL Annual Meeting

October 28, 2004

Background Anaerobic Digestion

Breakdown of organic molecules by microorganisms to produce methane gas which can be used as an energy source

Waste management option that is a renewable energy source

Role of mixing Substrate and microorganism distribution throughout the reactor

Ensures uniform pH and temperature

Prevents stratification and scum accumulation in dilute waste slurry

Prevents accumulation of inert solids which decreases the active volume of a digester

Effect of mixing is not well understood. Past research shows contradictory findings

Effect of Shear on Performance and Microbial Community

in Anaerobic Digesters Treating Cow Manure

Objective:

Study the effect of mixing intensity, or applied shear, on digester performance, microbial ecology, and syntrophic relationships

Hypotheses:

Higher mixing intensities have a detrimental effect upon reactor stability.

Different mixing intensities selectively create different microbial communities within each reactor.

Higher mixing intensities break up and/or prevent the formation of larger flocs of syntrophic microorganisms

Evaluation of Upflow Anaerobic Solids Removal

(UASR) Digester for Animal Waste Digestion

Objective:

Evaluate the UASR as a new approach for animal waste slurry

digestion and bioenergy production, focusing on the effect of

increased solids concentration on digester performance


Development of a Predictive Model

for Distiller’s Dried Grains/Solubles

M.N. May and R.L. Heider

Introduction

• DDGS

– Co-product of dry grind ethanol process

– Used in animal feed

• Goal

– Develop predictive models for chemical and physical properties of DDGS

• Improve quality of DDGS product

• Neural Networks

– Derive meaning from complicated data and detect trends

– Applicable in any industry to gain insight and answers to process questions

Rajneesh Varma Muthanna Al-Dahhan


Bioprocess and Bioreactors Engineering Laboratory (BBEL)

CREL Annual Meeting October 28th 2004


Gas Holdup Studies With CT In Anaerobic Bioreactors

ANIMAL WASTE :Environmental Perspective and

motivation for Treatment

Unsafe and improperly disposed

Surface & groundwater contamination Ammonia leaching Methane emission Odors

Waste can be used to generate Methane Methane = Energy, 1 m3 biogas = 1.7 kWh of electricity

Biomass has applications of fertilizer and land fill Gas mixed anaerobic bioreactors are found to most the popular

choice.

Objective of The Present Work

Study the gas distribution with the aid of CT in the draft tube of a

gas mixed anaerobic bioreactor.

Compare the efficiency of ejectors ( single point gas injection system) versus a sparger is gas mixed bioreactors.


38 mm

334 mm

140 mm

153 mm

26mm

25 0 Angle

40 mm

Level 2

Level 1

50 m m

153 mm Diameter

100 mm

50 m m

Angle

22

0 m

m

15

0 m

m

38 mm

33

4 m

m

140 mm

153 mm

26mm 25

0

40 mm

Level 1

Level 1

50

m m

100 mm

mm

Gas

injection

port

Key Results

o X-ray diffraction and VSM

results of the powder

collected show the

presence of pure Fe2 O3

with high saturation

magnetization.

o Flame pyrolysis of Iron

pentacarbonyl gives

Maghemite, Ferrocene

gives Magnetite; whereas

Iron nitrate gives Hematite.

oPost heat treatment of

maghemite and magnetite

showed to gradually

transform to hematite at

500o C.

Prakash Kumar, M. P. Dudukovic, Da-Ren Chen, Richard Axelbaum, Ronald Indeck, Pratim Biswas

Structural and magnetic properties of flame aerosol

synthesized nanoparticles as a function of size

Biomedical Applications

o Biocompatible ferromagnetic particles for targeted drug delivery

o Selective deposition of magnetic particles for Tumor necrosis

o Magnetic particles guided by external magnetic field for Aneurysm

treatment

Modified DMA Flame Reactor

SEM –γ Fe2O3

Product Development in ChE:

• New Courses Added:

– New Product and Process Development (ChE 450)

– Product Development Methodologies (ChE 452)

• Unconventional Topics:

– Creativity and Innovation

– Intellectual Property

– The Theory of Inventive Problem Solving (TRIZ)

– Design of Experiments

– Impact of the Customer/Consumer

– Fermi Problems

– Product Focused Economics

• The Instructor:

– Nick Nissing, Adjunct Faculty

– Ex-P&G product development

– Patent agent

– Corporate IP Consultant

• How could we be useful to

industry?

– Brainstorming?

– Consumer testing?

– New Product Ideas?

– In class or outside of class?

Date post:	12-May-2018
Category:	Documents
Upload:	trinhkhanh
View:	214 times
Download:	1 times

POSTER INTRODUCTION - Washington University in...

Documents