CARPT Calibration Issues Poster 1 Bad Reconstruction Calibration Curve Counts Distance (cms) Photo Peak Compton 0.89 Mev 1.12 Mev Full Spectrum Proposed Solution to HPBCR c2 Approach Good Reconstruction Acquire Photopeak Good Calibration Curve

Gas Liquid Studies in Stirred Tank Reactor Poster 2 New techniques in CT implementation result in better reconstruction!!! How ??? Cross sectional gas holdup distributions (at different axial planes) in a Stirred Tank Reactor (for the first time!!) Detailed local fluid dynamic information in gas-liquid flows in stirred tank (for first time!!) N=100 rpm, QG=7.5 lit/min, Z=10.0 cms 2.3mm 1.0mm 0.8mm 250 mm 150 mm

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CHEMICAL REACTION ENGINEERING LABORATORY Peter Spicka research associate at CREL since 2001 bubble columns, trickle beds CT & CARPT experimental studies and CFD simulations Poster I: Review of CARPT/CT Experimental Database for Bubble Columns Operating in the Churn-Turbulent Regime Contributors: Jinwen Chen, Abdenour Kemoun, Sailesh Kumar, Shadi Saberi, Sujatha Degaleesan, Puneet Gupta, Booncheng Ong, Shantanu Roy, Qingi H. Wang Poster II:Effect of Nozzle Orientation of the Cross Sparger and Pressure on Bubble Column Hydrodynamics CHEMICAL REACTION ENGINEERING LABORATORY

Review of CARPT/CT Experimental Database for Bubble Columns Operating in the Churn-Turbulent Regime Different effects studied: gas superficial velocity, liquid properties, pressure column diameter, internals, gas distributor and solids concentration DOE project (1995-2002) Participants: Air Products, Ohio State University, Sandia National Laboratories and Washington University Objectives: effect of specific variables on observed flow patterns in bubble columns engineering type models for flow and mixing in bubble columns data for gas hold-up, velocity and turbulence profiles for validation of CFD codes Scale up of bubble columns: correlations for liquid centerline velocity, gas holdup, holdup and liquid velocity radial profiles and eddy diffusivities

Effect of Nozzle Orientation of the Cross Sparger and Pressure on Bubble Column Hydrodynamics Nozzle orientation of the sparger important design issue since it may affect the length of the flow development region (Schollenberger et al., 2000) sparse information in the available literature Goals to quantitatively determine the variation of gas hold-up, liquid recirculation and mixing due to variations in nozzle orientation and pressure to compare the exp. data with 1-D recirculation model due to Kumar (1992) Gas holdup Liquid velocity profile Experiment 6” I.D. stainless steel column cross-sparger, two nozzle orientations: facing upward and downward Air-water system Pressure: 1 bar and 4 bars UG= 5 cm/s (only CT) and 20 cm/s

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Solid tracer: Catalyst particles doped with an oxide of Mn56 Development of Improved Engineering Models for Flow, Mixing and Transport in Bubble Columns (Review of Phenomenological Model accomplished in CREL during 1995-2001) Tracer experiments during Methanol, Fischer-Tropsch and Dimethyl Ether Synthesis Gas tracer: Ar41 Liquid tracer: Powdered oxide of Mn56 (Mn2O3) suspended in the heat transfer oil Solid tracer: Catalyst particles doped with an oxide of Mn56

Contents Suitability of the Axial Dispersion Model One Dimensional Recirculation Model One Dimensional Recycle with Cross Flow and Dispersion Model Two-Dimensional Convection-Diffusion Model Gas Phase Recirculation and Dispersion Model Scale-up Strategy Comparison of experimental and simulated tracer responses What is the next step?

Radioactive Tracer Studies in the AFDU Reactor during Dimethyl Ether (DME) Synthesis cross-section along with scintillation detectors and their lead shielding

Schematic of the reactor compartmentalization for the gas-liquid mixing model with interphase mass transfer (Gupta, 2001)

Implementation of Breakup and Coalescence Models into CFD of Bubble Column Flows G L The engineering models needs input Only Eulerian model seems feasible for churn turbulent flow regime In churn turbulent regime, bubble size is widely distributed; the mean bubble diameter seems to be the simplest assumption However, it is difficult to choose the “right” bubble diameter without going through tedious trial-and-error procedure If one try to change to another column or operation condition, the “right” bubble diameter normally may not work any more We need to predict rather than input bubble diameter, we had better predict it locally! Good afternoon! Ladies & gentlemen, today I would like to discuss some of our efforts in Implementation of Breakup and Coalescence Models into CFD of Bubble Column Flows. ???

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CFD Modeling of Bubble Column Flows (Review of CFD activities in CREL 1995-2001) Gas Outlet Gas Inlet Bubbly flow regime Gas Outlet Contributors: Dr. M. P. Dudukovic Dr. M. H. Al-Dahhan Dr. S. Kumar Dr. Y. Pan Dr. M. Rafique Mr. P. Chen Compiled by: M. Rafique Research Associate Ph.D. (Fluid Mechanics), INPL, France Acknowledgement: DOE Contract: DE FC 22 95 PC 95051 Outline: Hydrodynamics of bubble columns Eulerian-Eulerian Two-Fluid Model Algebraic Slip Mixture Model (ASMM) Hydrodynamics of (passive) tracers (gas/liquid) in bubble column flows Implementation of the Bubble Population Balance in CFD Gas Inlet Churn turbulent regime

2D & 3D dynamic simulations D=15.2 cm 0.2 cm L=10D =152 cm Ug =1cm/s width 10 cm 2D 3D Mesh system & Gas holdup contours

Numerical particle tracking (calculation of turbulent diffusivities) Numerical (liquid) Tracer Study Numerical particle tracking (calculation of turbulent diffusivities) 0 sec 2 sec 4 sec 6 sec 9 sec 19 sec

Meso-scale modeling of bubble column flows M. Rafique, M. H. Al-Dahhan, M. P. Dudukovic Objective: To simulate the bubble column hydrodynamics by resolving meso-scale flow structures through grid refinement. To study the effect of attraction and repulsion forces on the hydrodynamics D=15.2 cm D=15.2 cm width L =110 cm Fine grid (0.2x0.25 cm) Coarse grid (0.5x0.5 cm) 10 cm 10 cm Ug Ug =1cm/s =1cm/s 0.2 cm

Instantaneous contours of gas volume fraction Coarse grid Cd+Cv Fine grid Cd+Cv Fine grid Cd+Cv+Catt

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Measurement of Bubble Dynamics in Bubble CHEMICAL REACTION ENGINEERING LABORATORY Measurement of Bubble Dynamics in Bubble Columns Using Four-point Optical Probe J. L. Xue, M. H. Al-Dahhan & M. P. Dudukovic In cooperation with Robert F. Mudde Delft University of Technology, The Netherlands Chemical Reaction Engineering Laboratory (CREL) Oct. 24, 2002 Optical Fiber Probes for Bubble Dynamics Measurement in Bubble Columns J. L. Xue, M. H. Al-Dahhan & M. P. Dudukovic In cooperation with Robert F. Mudde Delft University of Technology, The Netherlands Chemical Reaction Engineering Laboratory (CREL) November, 2001

CHEMICAL REACTION ENGINEERING LABORATORY Motivation bubble dynamics, i.e. bubble size distribution, bubble velocity distribution, specific interfacial area and gas holdup are among the key parameters that affect the hydrodynamics in bubble columns. Measurement of bubble dynamics is difficult, especially in churn-turbulent flows. Non-invasive techniques, e.g. video imaging techniques, are limited to 2-D transparent columns. and can not be used in real 3-D systems which are opaque due to high volume fraction of the dispersed phase. Optical probes can be applied in practical 3-D systems. The measurements of bubble dynamics by two-point probes are not reliable. Four-point optical probe was adopted in this research to measure the bubble dynamics in bubble columns.

CHEMICAL REACTION ENGINEERING LABORATORY The Configuration of the Four-point Optical Probe With a new data processing algorithm, it can measure: Bubble velocity vector Bubble size Specific interfacial area Gas holdup Side view Bottom view

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CHEMICAL REACTION ENGINEERING LABORATORY Computed Tomography in Slurry Bubble Column Reactors Ashfaq Shaikh, M.H. Al-Dahhan Acknowledgements: DE-FG-26-99FT40594 CREL Annual Meeting October 24, 2002

CHEMICAL REACTION ENGINEERING LABORATORY Evaluate the CT/Overall gas holdup algorithm System: Therminol LT-air-glass beads (150 m) Mimic fluid Single source CT Two-phase systems Three-phase systems One equation, two unknowns Need one more equation CT/Overall gas holdup (Rados, 2002) Sensitivity analysis Assumptions in CT/Overall gas holdup procedure has been critically examined

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L.A. Tarca, B.P.A. Grandjean, F. Larachi Assessing and Reinforcing the Phenomenological Consistency of Multiphase Flow Artificial Neural Network Correlations L.A. Tarca, B.P.A. Grandjean, F. Larachi Classically built ANN models may be phenomenological consistent in vicinity of some data points but not in the whole input space  Large prediction errors mL Piché et al. (2001) [3] Counter-current packed bed

Pressure drop in counter-current packed beds Using phenomenological error (PCE) of the trained models we guide a Genetic Algorithm search for pertinent dimensionless numbers to predict a reactor characteristic Pressure drop in counter-current packed beds Phenomenological Consistency Error Phenomenological and prediction error decrease by combining multiple “good” ANNs … bm 1 S 2 3 b g Z P L r / D U G m a T e f C s , log ^10 N 10 13 14 18 23 26 27 9 21 24 17

THEORY OF TRICKLE BED MAGNETOHYDRODYNAMICS IN INHOMOGENEOUS MAGNETIC FIELDS – Potential route to process intensification by I. Iliuta and F. Larachi for positive magnetic gradients, gas magnetization force amplifies the effect of gravity (macro-gravity) and two-phase pressure drop is reduced When magnetic gradients are positive, liquid holdup increases with increasing |BdB/dz| because the driving force decreases G=1.2 kg/m3 G=47 kg/m3 - for negative magnetic gradients, the upward gas magnetization force reduces the effect of gravity (sub or micro-gravity) and two-phase pressure drop increases When magnetic gradients are negative, liquid holdup decreases with increasing |BdB/dz| because the driving force (two-phase pressure drop) increases

Elevated levels of magnetic field gradient improve the liquid holdup and thus the wetting efficiency of the catalyst particle Because phenol oxidation is liquid-reactant limited, as catalyst wetting improves the phenol conversion increases significantly

Prediction of HETP for randomly packed tower operation: Integration of aqueous and non-aqueous mass transfer characteristics into one consistent correlation Simon PICHÉ, Stéphane LÉVESQUE, Bernard GRANDJEAN, Faïçal LARACHI Department of chemical engineering & CERPIC Québec, CANADA G1K 7P4 L G S POLLUTION CONTROL: Particulate removal, SO2, NOX, VOC & TRS scrubbing WATER PURIFICATION: Ammonia stripping and recovery, VOC stripping DISTILLATION: Styrene purification (Ethylbenzene - Styrene separation) (Vacuum or Pressure) Demethanizers (ex: CH4 removal / heavy feedstock) OBJECTIVE: Build a new, efficient & consistent correlation using an artificial neural network, dimensionless analysis & data (HETP, KGaW, KLaW) reconciliation procedure that could predict either HETP for distillation or kGaW, kLaW, KGaW & KLaW for absorption and stripping DATABASE: 3770 absorption/stripping measurements 2357 distillation measurements

General procedure and Results (1) Testing of ANN correlations developed for aqueous solutions [1] on HETP (non-aqueous solutions) (2) Extraction of pseudo interfacial areas from HETP and mass transfer coefficients (with previously developed ANN-kg ) & development of new interfacial area correlation (ANN-aW) (3) Weights reconciliation on the 6 mass transfer parameters (HETP, aw, kLaw, kGaw, KLaw, KGaw) General procedure and Results ANN-kgI kg (cal) aw (pseudo) (5,802 data) aw (exp) (325 data) ANN-aw(3) ANN-aw(3) & ANN-kgI weights reconciliation HETP, KLaw, KGaw, kLaw, kGaw, aw (6,127 data) ANN-awII ANN-kgII HETP, Kgaw & kgaw (exp) ANN-awI & ANN-kgI testing on HETP m = 76%, s = 100% [1] Piché, S., Larachi, F. & Grandjean, B., Reconciliation procedure for gas-liquid interfacial area and mass transfer coefficient in randomly packed towers, Ind. Eng. Chem. Res., 41 (19) (2002) 4911-4920. ANN STRUCTURES: g = G (gas) and L (liquid) [1] aw/aT = f (ReL, FrL, EoL, wall factor, c) – ANN-awI kg /(aTDg) = f (Reg, Frg, Scg, c) – ANN-kgI This work aw/aT = f (ReL, FrL, EoL, wall factor, c, RSI) - ANN-awII kg /(aTDg) = f (Reg, Frg, Scg, c) – ANN-kgII RSI (Relative stability index) = (dsL/dxvol) / sL(mixture) RSI=0 (aqueous solutions); RSI<>0 (non-aqueous mixtures) STATISTICAL PERFORMANCE: HETP (N=2357, m=21%); aW/aT (N=325, m=24%); kLaW/kGaW (N=1461, m=23%); KLaW/KGaW (N=1984, m=29%)

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Modelling of Radioactive Tracer Distribution in Bubble Columns CHEMICAL REACTION ENGINEERING LABORATORY Modelling of Radioactive Tracer Distribution in Bubble Columns by Chengyu Mao Advisor: Prof. M.P. Dudukovic Prof. P.A. Ramachandran

CHEMICAL REACTION ENGINEERING LABORATORY Many engineering models are available for description of flow, mixing, and transport in bubble columns. However, their accuracy to simulate and predict experimental data needs to be verified. Liquid Recirculation Model Recycle with Cross Flow and Dispersion Model (RCFDM) Single Bubble Class Model (SBCM) Distributed Bubble Size Model (DBSM) Two Dimensional Convection with Eddy Diffusion Model Now there are several engineering models for flow, mixing and transport in bubble columns. And we have built the corresponding experiments to obtain the related parameters for these model. So, we wonder whether these models work well and whether they can simulate and predict the experiment data. Objective: From the experiments, get the experiment data and the pertinent parameters for the available models. Using the model, calculate to get the tracer concentration in the bubble column. Based on the model, simulate the detector responses and compare it with the experiment data, verify the available models.

CHEMICAL REACTION ENGINEERING LABORATORY Single Bubble Class Model (SBCM) Two Dimensional Convection with Eddy Diffusion Model Liquid tracer concentration distribution Method 1 Calculate Cross-Sectional concentration, normalize and compare with data Method 2 Calculate 2D Response accounting approximately for the attenuation of radiation, normalize and compare with data Method 3 Calculate 3D Response accounting fully for the attenuation of radiation, normalize and compare with data (point to the bottom and top of the column) Distributor zone and disengagement zone, they are considered as CSTR. (Point to the middle part of the picture), here are the fully developed part of the flow, which occupies most of the column. It consists of 4 zones. Gas down flow, gas up flow, liquid down flow and liquid up flow. At each zone, the concentration is uniform at a given elevation. According to this compartmentalization, we can get the SBCM equations. (Turn to next slide)

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M.P. Dudukovic, M.H. Al-Dahhan, P. Spicka Liquid Maldistribution in Trickle Bed Reactors Experimental and CFD Modeling Study Nicolas Dromard Master in Process Engineering, INPL, Nancy, FRANCE Chemical and Process Engineer, ENSIC, Nancy, FRANCE M.P. Dudukovic, M.H. Al-Dahhan, P. Spicka Chemical Reaction Engineering Laboratory, Washington University St Louis, USA D. Védrine, J. Bousquet Centre Européen de Recherche et Technique, TOTALFINAELF Harfleur, FRANCE

Liquid Maldistribution in TBRs An Experimental Study How to quantify maldistribution? Determination of the parameters responsible for maldistribution A step to CFD modeling Generate a pseudo random porosity profile ?

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Multiphase Packed-bed Reactor Modeling CHEMICAL REACTION ENGINEERING LABORATORY CREL Annual Meeting, 2002 Jing Guo Dr. M. H. Al-Dahhan Multiphase Packed-bed Reactor Modeling output input C0, usL Xa, C, usL, Motivation Scale up of packed bed: Simulation vs huge amount of experimental work ? How to choose multiple level model for reaction system of interest Reactor scale Completely inactively wetted Half wetted Pellet scale Understand and compare the hierarchy of model for catalytic multiphase packed-bed reactors Capture the time-dependent reaction features of catalytic wet oxidation in packed beds Objective Completely actively wetted

Combination of Reactor and Pellet Scale Model CHEMICAL REACTION ENGINEERING LABORATORY Combination of Reactor and Pellet Scale Model Pellet scale Wet side Dry side Ci,j C i, N C i, N-1 C i, j C i, 2 C i, 1 C i, 0 Reactor Scale El-Hisnawi Model Beaudry Model Reaction wet oxidation of phenol over deactivating catalyst I:reactant component J:Cell sequence El-Hisnawi Model Reactor Axis Pellet scale Ul, Ug Predict axial concentration distribution Beaudry Model Active site distribution after 110 hours Refine local effectiveness factor Predict catalyst local performance

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Shaibal Roy Muthanna Al-Dahhan Poster 1 New CT Setup and studies on Gas-Liquid Hold-up in Structured Packing using CT Shaibal Roy Muthanna Al-Dahhan 4”   4" Guide for the source Plate 1 Plate 2 Main difference between the old and new CT Resolution Characteristics of new CT setup Gas Liquid Flow Characteristics (hold-up and pressure drop) in a 12 inch Structured packing

A Study of Structured Packing for Solid Catalyzed Gas-Liquid Reaction Poster 2 A Study of Structured Packing for Solid Catalyzed Gas-Liquid Reaction Shaibal Roy Muthanna Al-Dahhan Motivation Structured Packing for solid catalyzed gas-liquid reaction provides Large Volumetric productivity Lower pressure drop Excellent mass transfer properties Higher selectivity (low axial dispersion, short diffusion length scale) Ease of scale-up

Research Objective Research Goal The overall objective of the proposed study is to develop better understanding and fundamentally based model for comparison of structured packing (e.g. Monolith and other selected configuration) with conventional reactors for multiphase reactions Research Goal Hydrodynamic aspects in structured packing Apparent Kinetic model with extrudates And washcoated monolith Combined effects in overall performance of structured packing for multiphase reactions Compare with conventional packed bed reactor Develop model for performance prediction

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A Non-Invasive Method for Overall Solids Flux Measurements in a Circulating Fluidized Bed (CFB) Satish Bhusarapu, Pascal Fongarland, M. H. Al-Dahhan and M. P. Dudukovic’ CREL Annual Meeting October 24, 2002 Chemical Reaction Engineering Laboratory Department of Chemical Engineering St.Louis, MO 63130

Challenge : How to measure overall solids mass flux accurately in a CFB ? Obtain solids velocity and concentration in a standpipe where solids hold up is high Solids velocity – “time of flight” measurements – track a single radioactive tracer using a two detector set-up Solids concentration – g-ray line densitometry Overall solids flux at varying operating conditions

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Hu-Ping Luo, Muthanna Al-Dahhan CHEMICAL REACTION ENGINEERING LABORATORY Photobioreactors for Culturing High Value Microalgae and Cyanobacteria: Experimentation and Modeling Hu-Ping Luo, Muthanna Al-Dahhan Chemical Reaction Engineering Laboratory (CREL) Washington University Good morning, professors. My name is huping luo. The topic for my presentation is: advanced dia….. CREL ANNUAL MEETING October 24, 2002

Cells’ growth depends on their movement CHEMICAL REACTION ENGINEERING LABORATORY Cells’ growth depends on their movement Wall or internals Split Column In Nonlinear and Dynamic Systems, results are determined by the process Feeding to the cells is irregular in bioreactors, especially for light Spiral movements, time scale: 1 s Light distribution A cell’s movement in the reactor determines its accessibility to the light, nutrients, the shear stress need to endure, etc. We are more interesting in the movement of the cells in the reactor. CARPT technique is a very useful tool for us since it reproduce the movement of the radioactive particle in the reactor for very long time, good for statistic analysis, at high frequency, 50 Hz. Since we can analogize the particle’s movement to the cell’s movement, we can use CARPT data straightforward to calculate the growth rate. Here is a trajectory of the particle for 20 second. That’s not good for analysis. So we extract only one circulation of the particle for analysis. We called it single trajectory. So far, if we can calculate the light intensity distribution in the reactor, we can obtain the irradiance pattern for each trajectory. Let’s see the following figures y, cm radius, r, (cm) x, cm

Dynamic approach for photobioreactor analysis CHEMICAL REACTION ENGINEERING LABORATORY Dynamic approach for photobioreactor analysis Challenge: How to combine the knowledge of: Physiology of photosynthesis process Reactor hydrodynamic Current approaches: use Static kinetic model for bio-reactions, take into account only average light intensity (effects of hydrodynamic are ignored) Our new approach: combines dynamic kinetic model for bio-reactions with hydrodynamic information via CARPT experimental technique. Let’s look at a real example. Here I’d like to introduce the three-state growth rate model of photosynthesis. This model use the conception of photosynthetic factory, which is associated with the microstructures in a cell who are responsible for the photosynthesis. A basic assumption is that: PSE has three states: resting state, activated state and inhibited state. At the resting state (x1), the cell can be stimulated and transferred to the activated state if it captures a photon. it’s light dependent. At the activated state (x2), the cell can be either passing the gained energy to acceptors to start the photosynthesis (respiration processes, also called dark reaction. It’s associated with enzymatic reaction and is light independent) and then return to the open state (x1), or receiving another photon to be inhibited (transferred to x3). At the inhibited state (x3), meanwhile, cells recovered by a complicated and costly repair mechanism (Barber & Andersson, 1992) and return to the open state (x1). According to this, we can derive the differential equations as following: X1, x2, x2 is the fraction of the cells in the resting state, and also x2, x3. This is the summation of the states. It should be unity. Eq(5) is the growth rate. X is the total number of cells. And please note, only the reaction from x2 to x1 is associated to the growth. Me is a constant. Since if light is too weak, the biomass will decrease. This constant is to take account of these effects. If the light intensity is constant, we can see, these differential equations is a linear system. It’s very easy to be solved. Here I didn’t solve it, instead, I did some steady state analysis, which can give us some valuable information. Please stop at my poster, I’ll show you all the details

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CHEMICAL REACTION ENGINEERING LABORATORY FLOW PATTERN IMAGING INSIDE A SIMULATED ANAEROBIC DIGESTER USING CARPT AND CT Washington University Group: Rebecca Hofmann, Mehul Vesvikar, Rajneesh Varma, Khursheed Karim, Muthanna Al-Dahhan Oak Ridge National Lab. Group: Thomas Klasson, Alan Wintenberg, Chuck Alexander, David Depaoli

ANIMAL WASTE :Environmental Perspective and motivation for Treatment CHEMICAL REACTION ENGINEERING LABORATORY ANIMAL WASTE :Environmental Perspective and motivation for Treatment More than one billion tons of animal waste generated every year in the USA. Unsafe and improperly disposed Surface & groundwater contamination Ammonia leaching Methane emission Odors Methane = Energy source, hence animal waste = renewable energy source Biomass has applications of fertilizer and land fill High failure rate observed in digesters that is attributed to mixing related problems Effects of hydrodynamics and mixing in anaerobic bioreactors needs investigation. Objective of The Present Work To demonstrate the ability of single particle CARPT technique to visualize 3D flow patterns inside a simulated digester. To determine the gas phase hold-up of the three phase anaerobic system using single source CT.

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CHEMICAL REACTION ENGINEERING LABORATORY Modeling of Catalytic Partial Oxidation Reactors R.C. Ramaswamy, P.A. Ramachandran & M.P. Dudukovic CREL Annual Meeting October 24, 2002

Catalytic Partial Oxidation of Methane to Syn-gas CHEMICAL REACTION ENGINEERING LABORATORY Synthesis Gas (mixture of CO and H2) Feed stock to chemical process industries Feed stock to Syn-fuels, H2 for fuel cells Catalytic Partial Oxidation of Methane to Syn-gas CH4 & O2 (2:1) Tin ~773 K H2/CO < 2; CO2 & H2O Texit ~ 1300 K Exo. rxn. & Endo. rxn. A schematic of syn-gas packed bed reactor Reactions : Exothermic Combustion Reaction Endothermic Steam Reforming Reaction Slightly Exothermic Water Gas Shift Reaction Mathematical models are required for design, control and optimization purposes

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Transport and Capture of Particles in Magnetic Fields Prakash Kumar Advisors: Prof. Pratim Biswas, Prof. M. P. Dudukovic 1. Model to predict the particle trajectories in magnetic fields. 2. Setup to study the particle transport Magnetic fields.

3. Comparison of theoretical and experimetal results. 4. Effect of magnetic fields on particle aggregation. (Magnetic) Brownian (Friedlander 1964)

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