advances in scale-up of multiphase systemscrelonweb.eec.wustl.edu/process development.pdf · 1...

1

Advances in Scale-Up of Multiphase Systems

AIChE Process Development Symposium (PDS)Palm Springs , California, June 11-14, 2006

Session: Understanding Your Process

Outline• Importance of multiphase reactors, selection and scale-

up• Contribution of micro systems to scale-up in parallel,

process intensification and distributed production• Other means of process intensification• Vertical scale-up: novel tools for multiphase flow

visualization, CFD validation and reactor model development

• Summary and conclusions

M.P. DudukovicChemical Reaction Engineering Laboratory (CREL)

Washington University in St. Louis (WUSTL), Missouri, USAhttp://crelonweb.che.wustl.edu

S1

LECTURE SLIDES AND ASSOCIATED TEXT

S1 It is a pleasure to speak at the process development symposium and especially at the session on Understanding Your Process. As an academician I firmly believe that only improved understanding and development of rational basis for scale-up and design will ultimately bring us to improved atom, mass and energy process efficiencies. My talk will focus on a broad issue of Advances in Scale-up of Multiphase Systems. The outline of my talk is as follows: First, a reminder of what we as chemical

engineers are supposed to do, and of the importance of multiphase chemical reactors in all of these endeavors. Then, I will discuss the approach to multiphase reactors selection and scale up for commercial applications. Here; I will mention the important role that micro-systems play. Then, I will spend the rest of my talk outlining some other ideas for process intensification. Finally, when vertical scale-up cannot be avoided, I will introduce new tools for visualization of multiphase flows, validation of CFD models and development of appropriate models for scale up and design.

2

Raw Materials

ProductsNon Renewable:• Petroleum• Coal• Ores• Minerals

Renewable:• Plants• Animals

FuelsMaterialsPlasticsPharmaceuticalsFoodFeedetc.

Chemical andPhysical

Transformations

Pollution

The domain of chemical engineering consists of chemical and physical transformation of starting materials to products

S2

Challenges:• Cleaner, sustainable processes• Increased atom and energy efficiency• Improved safety

Tunca, Ramachandran, Dudukovic, “Role of CRE in Sustainable Development”, Sustainable Engineering Principles, Elsevier (2006)

S2 You all know that the domain of chemical engineering is extremely broad as it involves physical and chemical transformations of raw materials (renewable and nonrenewable) and of the intermediates derived there-of into a myriad of products for the market. These products range from fuels, commodity and specialty chemicals, pharmaceuticals, materials, food, feed and a variety of consumer products. How we implement these transformations determines the extent of the damage of such activity to the environment. Clearly, there is a great incentive to minimize the creation of undesired substances at the source, that is in the reactors where the chemical transformation occurs! To ensure some degree of sustainability, an increased use of naturally occurring resources and of complete recycle of aged manufactured products is of increasing importance. All of this necessitates improved understanding of reactors where key chemical transformations take place.The key to a successful process, in addition to the judicious selection of process chemistry and catalysts, lies in the proper choice and operation of the chemical reactor which dictates the number of separation units and pollution abatement requirements. Increasingly, sustainability concerns should also be addressed.

3

Petroleum Refining

PolymerManufacture

EnvironmentalRemediation

Synthesis & Natural Gas Conversion

BulkChemicals

Fine Chemicals &Pharmaceuticals

HDS, HDN, HDM,Dewaxing, Fuels,Aromatics, Olefins, ...

MeOH, DME, MTBE,Paraffins, Olefins,Higher alcohols, ….

Aldehydes, Alcohols,Amines, Acids, Esters,LAB’s, Inorg Acids, ...

Ag Chem, Dyes,Fragrances, Flavors,Nutraceuticals,...

Polycarbonates,PPO, Polyolefins,Specialty plastics

De-NOx, De-SOx,HCFC’s, DPA,“Green” Processes ..

Value of Shipments:

$US 637,877 Million

Uses of Multiphase Reactor Technology

BiomassConversion

Syngas, Methanol, Ethanol, Oils, High

Value Added Products

EnergyCoal, oil, gas, nuclear power plants

S3. It is important to recognize that at the heart of chemical transformations in all process and energy industries is multiphase reactor technology (over 99% of systems require the presence of more than one phase).This multiphase technology generates a large contribution to the Gross domestic product (GDP).One needs however to be able to predict how will the molecules get together in large installations and how will the rates and selectivity differ from lab scale systems.That is where our CREL comes into play.

4

),()( bbb TCRCL η=

∑ η∆−=j

bbjjRbh TCRHTLj

),()()(

( )transport;kineticsf=η00 P,C,T

P,C,T

product, QREACTOR PERFORMANCE = f ( input & operating variables ; rates ; mixing pattern )

Reactor choice determines plant costs; Need improved reactor selection and scale-up

REACTOR MOLECULAR SCALEEDDY/PARTICLEfeed, Q

Chemical Reaction Engineering (CRE) Methodology

MOLECULAR SCALE (RATE FORMS)

Strictly Empirical Mechanism Based Elementary Steps

REACTOR SCALE

Axial Dispersion CFDPhenomenological Models

EDDY OR PARTICLE SCALE TRANSPORT

DNS / CFDEmpirical Micromixing Models

PROCESS SCALE

Steady State Balances Dynamic Models forControl & Optimization

10-10 m

102 m

10-16 (s)

104 (s)

PFR/CSTR

S4 Dudukovic, Larachi, Mills, Catalysis Reviews (2002), 44(1), 123-246

S4 The choice of the proper reactor type and operating conditions for a given process chemistry is the key factor in determining volumetric productivity and selectivity. So although the reactor typically represents between 5 – 15% of capital and operating costs of the plant, its choice determines the number and load on pre-reactor and post-reactor separation units and dictates the cost of the whole process.That is why the choice of the proper reactor type is essential and it should be based on a rational approach based on a reactor model. Such model must capture the events on a multitude of scales at the right level, and provide the ability to scale test tube discoveries to commercial processes. The complexity arises from the fact that the interactions of events on various scales are dependent on the scale of the equipment.It is increasingly necessary for use of novel more active catalysts to understand the change of the flow pattern with reactor scale and the interaction of it with meso-scale transport.Hence moving our level of understanding of all scales affecting reactor performance from the left to the right, that is being more quantitative and predictive, is needed for safer scale-up and design of the next generation of reactors. Unless, of course, one avoids vertical scale-up completely, and here micro-reactors come into play.

5

Multiphase Reactor Selection Methodology

S5

I. Particle (eddy) scale• Particle size (dp) for gas-solid systems• β for gas-liquid systems• Dp and β for gas-liquid solid systems

II. Contacting and flow pattern

• Overall flow pattern of gas and solid phases• RTD of each phase• Co-, counter-, or cross- current• Split addition, product removal in situ etc.

III. Flow regime• Homogeneous/churn turbulent

gas-liquid flow• Dense phase/dilute phase

gas-solid riser

Krishna, 1994 (Advances in Chem. Eng.)

S5 For multiphase systems, Professor Krishna, among others, advocated that one should strive to understand the key process features on a multitude of scales. In other words, based on the established reaction scheme and reasonable operating conditions, one needs to understand what magnitude of interfacial areas between phases is needed and, if dealing with catalyst particles, bubbles or drops, how their diameter affects selectivity and rates. One needs then to determine the contacting pattern that is best for the process at hand, and implement it in practice. The flow regime issue arises due to necessity to remove heat efficiently, prevent bypassing etc. Most importantly one must be aware to predict how these quantities change with the change in equipment scale.

6

Process R & D ApproachesIdeal: Select the best chemistrySelect the most suitable reactor typeConsider separation-reaction multi-functionalityScale –up bench scale resultsActual:Find chemistry that does the job by trial and error Use reactors familiar with and needed separationsDetermine ‘best conditions’ via statisticsBuild the plant, continue experimenting on the

plant

S6. Let us then briefly consider how should Process R&D be conducted and compare this to what happens in everyday practice. Ideally, we should seek the best chemistry that maximizes atom, mass and energy efficiency. Based on the understanding of the reaction pathways involved, we should seek the reactor with the best flow pattern and phase contacting pattern. We should examine opportunities for effective coupling of reaction and separation. Then bench scale experiments should be scaled-up. How I will address in the next slide. Here, I want to describe how most of the R&D seems to be conducted . The chemistry that will do the job is found by trial and error ( combinatorial analysis etc). Usually reactors that the company is familiar with are tried, and best operating conditions are sought by statistical approaches with limited understanding of the underlying phenomena. Plant are build based on construction companies procedures that essentially are 1950s correlations dressed up in Excel or Power Point. Plants, especially reactors invariably have problems and experimentation with full plants continues.

7

Bench scale achieved desired conversion, yield, selectivity, productivity

S2 CHEMICAL REACTION ENGINEERING LABORATORYS7

Commercial productionScale-up

Alternatives:1. Scale-up in parallel (Scale-out, scale-up by multiplication, etc).2. Scale-up vertically – account for effect of change in equipment scale on interaction of transport and kinetic phenomena

S7 Once the reaction system was successfully run to produce the desired conversion, yield and selectivity, the issue is how to reproduce the results at a commercial scale. Now horizontal scale-up (scale-up in parallel or scale-up by multiplication) offers one alternative while vertical scale up offers another. Only the latter must account for the effect of equipment scale on the interplay of transport and kinetics. The former keeps the geometry, flow and contacting pattern and flow regime the same but has to deal with the logistics of system integration and flow distribution. Hence, without proper understanding of the system, relying solely on statistical approaches has a high likelihood of failure.

8

Direct Scale-up of Tubular and Packed Bed Wall-Cooled Reactors: Scale-up by Multiplication

LPF

Single tube of diameter dt and length L at given feed conditions (Po, To, Co) and given feed rate Q (l/h), produces the desired product at the rate of (mol P/h) and the desired selectivity.

S Identical tubes of diameter dt and length L produce then the commercial production rate FpC (S = FpC ( ), using identical feed conditions and flow rate, at the desired selectivity.

SAME PRINCIPLE USED IN MICROREACTORSPossible Problems: - External heat transfer coefficient

- Flow manifold for flow distributionS8

LPF

o

o

CT

CT

o

o

CT

CT

S8 Scale up by multiplication is practiced routinely for wall cooled tubular and packed bed reactors. Once the satisfactory performance of one tube is accomplished, the number of tubes needed for commercial production is determined.Key to success: reliable single tube data, good knowledge base for manifold

and flow distributor design, avoiding external heat transfer limitations upon scale-up.For many decades’ industrial chemists were experimenting with a catalytic

tube of 1” to 2” in diameter of desired lengths ( - 2 m or longer). Once the feed temperature and composition and flow rate used (i.e., men residence time) produce the desired result, scale-up is simple in principle. It requires using N tubes identical to the one used in the laboratory packed with the same catalyst particle and receiving the same feed at the same flow rate as the tube in the lab. The number of tubes N needed is given by the ratio of the desired commercial production rate and that achieved in the laboratory that is by the scale- up ratio. Lurgi and others build reactors with up to 50,000 tubes! This concept of the ability to scale up in parallel is a great advantage that micro reactors offer also. Let us look now at some of the other advantages.

9

●High surface-to-volume area; enhanced mass and heat transfer;

●high volumetric productivity;

●Laminar flow conditions; low pressure drop

Advantages of Micro reactors


• Residence time distribution and extent of back mixing controlled

• Low manufacturing, operating, and maintenance costs, and low power consumption

• Minimal environmental hazards and increased safety due to small volume

• “Scaling-out” or “numbering-up” instead of scaling-up

S9 Clearly micro reactors offer a whole series of advantages such as: 1) high surface to volume ratios and, due to small dimensions enhanced mass and heat transfer coefficients by one to two orders of magnitude, 2) laminar flow conditions and low pressure drop but ability to make RTD narrow by introduction of another phase, 3) controllable RTD and back mixing, 4) high volumetric productivity, 5) low manufacturing and operating costs, 6) increased safety due to small amount of material, 7) scale up in parallel (scale out).

10

Multiphase Flows in mFluidic Systems• Multiphase flows are important

– Reactions – oxidation, hydrogenation, fluorination, …– Materials synthesis – crystallization, nanoparticles, colloids, …– Separation – extraction, gas-liquid separation, ….

• Performance = f(understanding and ability to manipulate)

immiscible liquid-liquid

gas-liquid

liquid-solid

gas-liquid-solidKlavs Jensen’s group at MIT

S10

S10 The MIT group of Klavs Jensen, among others, has recognized the importance of being able to manipulate multiphase systems in micro reactors and have shown that one can get competitive performance for various reactions, separations and in material synthesis. The achieved performance of the micro reactor depends on the level of understanding of the chemical system and the ability to manipulate micro reactor design so as to meet the reaction contacting requirements best. If the desired figures of merit are met, then scale up in parallel ensues.

11

Scaling Out Micro reactors

• Uniform flow distribution and nature of contacting pattern• Methods for design of multi-phase reactors• Integrated sensors for gas-liquid flows

Single channelFlow regimes, • Interfacial area• Mass transfer

• Scale-out µg ⇒ g ⇒ ton

Multi-channel design

churnslug

annular

bubbly

0.001

0.01

0.1

1

10

j L(m

/s)

0.1 1 10 100j G (m/s)

wavy annularµ Heat-

exchangers

µReactors

SlugSlug

Annular

N. de Mas, et al., Ind. Eng. Chem Research, 42(4); 698-710 (2003)S11

S11 Jensen and his co-workers have also shown that, via multi-channel integrated design , in principle, scale up to large production rates is possible even for highly exothermic reactions such as direct fluorination of aromatics.

12

RTD in µFluidic Channels

L1

L2

t

t

Piezoelectric disk

Tracer reservoirGlass substrate

PDMS microchannels

F. Trachsel, et al., Chem. Eng. Sci., 60 (2005), 5729S12

S12 Jensen and his students also showed that introduction of gas bubbles into liquid in micro-channels makes the liquid RTD much narrower and this could lead to mono-dispersity in systems involving nucleation and particle growth.

13

t (min)

d m(n

m)

σ (%)

Silica Synthesis: Laminar Flow Reactor

• Wide particle size distribution (PSD) at low residence times– Particle growth is fastest, and hence most sensitive to residence

time variations• PSD at high residence times approaches batch synthesis results (8%

vs. 5%)

1 µm

S13Khan, et al., Langmuir (2004), 20, 8604

S13 They proved that concept in colloid silica synthesis from TEOS, ammonium hydroxide, ethyl alcohol and de-ionized water. In laminar flow in micro channels a wide particle size distribution (PSD) occurs and the standard deviation of the PSD approaches that of the batch system (8% vs 5%) only at large sizes and large residence times (due to Taylor diffusion effect).

14

Silica Synthesis: Segmented Flow Reactor

• SFR enables continuous synthesis with results that mirror those obtained from batch synthesis

1 µmGas Gas

σ (%)

BatchSFR

LFR

S14Khan, et al., Langmuir (2004), 20, 8604

S14 In contrast, in the segmented flow aided by gas bubbles, a more uniform size distribution is obtained with considerable lower standard deviation of the PSD.

15

Review of gas-liquid, gas-liquid-solid contacting patterns and transport properties in micro-reactors

- falling film- falling film on catalytic wall- overlapping channel and mesh

micro reactor- micro bubble columns- foam micro-reactors- packed bed micro-reactor- wall cooled micro-reactor

Improved mass and heat transfer coefficients, much larger interfacial area, controllable RTD, increased volumetric productivity, ease of scale-out

Applications demonstrated in lab scale- direct fluorinations- oxidations with fluorine- chlorinations- sulphonations- hydrogenations

S15

Hessel et al, I&EC Research, 44, 9750-9769 (2005)also presented at CAMURE-52 ISMR-4

S15 In their comprehensive review paper at CAMURE-5 and ISMR-4 Hessel et al, summarize well the contacting principles in gas-liquid and gas-liquid-solid micro reactors. They review the characteristics of a variety of contacting patterns attempted and report a vastly improved mass and heat transfer coefficients, much larger interfacial areas, controllable RTDs, increased volumetric productivity, ease of scale out. They offer demonstrations of successful bench scale use in direct fluorinations, oxidations with fluorine, chlorinations, sulphonations and hydrogenations.

16

Specific Interfacial Areas of Conventional and Micro Reactors

90-2050Impinging jets

27,000Falling film microreactor

(300 µm x 100 µm)

100-2000Mechanically stirred bubble columns

14,800Micro bubble column

(50 µm x 50 µm)

10-100Spray columns


(300 µm x 100 µm)

50-600Bubble columns


(1100µm x 170 µm)

10-35010-1700

Packed columnCountercurrent flowCo-current flow

Specific interfacearea

[m2/m3]

Type of microreactor

Specific interface area

[m2/m3]

Type of conventional

reactor

Oroskar et al. (2002), Proceedings of IMRET-5, Strasbourg, p. 153S16

S16 This slide for example illustrates the reported advantages of micro reactors in creating gas-liquid interfacial areas. They are clearly substantial.

17

Companies Currently Moving Micro-channelTechnology from R&D to Commercialization:

• Degussa: running a demonstration project for the evaluation of microreaction technology or DEMiSTM for propylene epoxidation with hydrogen peroxide

• Clariant: opened its Competence Centre for Microreactor Technology (C3MRT) to increase efficiency, improve safety and reduce the costs of pharmaceutical synthesis

- Continuous pilot plan for the synthesis of azopigments

• Axiva developed a process for continuous polymerization of acrylates (8 kg/h) using micro mixers

• Velocys using micro channels formed by a patented process in steel proposes to replace the conventional packed catalytic tubes in methane reformers.

S17

S17 Enthused by potential for significant process intensification a number of companies are involved in R&D for potential commercialization. Degussa, Clariant,,Axiva, Velocys and others are in hot pursuit of implementation of micro reactor technology.

18

S18

Perceived Disadvantages of Micro reactors:

• Short residence times require fast reactions• Fast reactions require very active catalysts that are stable (The two most

often do not go together)• Catalyst deactivation and frequent reactor repacking or reactivation• Fouling and clogging of channels• Leaks between channels• Malfunctioning of distributors• Reliability for long time on stream

Challenge of overcoming inertia of the industry to embrace new technology for old processes

Most likely implementation of micro-reactors in the near term:

• Consumer products• Distributed small power systems• Healthcare• In situ preparation of hazardous and explosive chemicals

S18 The following question then arises. With all their perceived advantages, and the technologies available to manufacture micro reactors in silicon, in glass and in steel, or other metals, why aren’t they more widely used? The answer is that they require very fast reactions and active stable catalyst (usually these two do not go together). Most importantly micro reactors are, due to small dimensions, more prone to fouling and clogging, leaks between channels, and their reliability and life on stream is an unknown. All of these are potentially solvable problems on a case by case basis. However, the perceived risk factor is too large for them to replace existing installations. Most likely rapid acceptance of micro-devices will occur in the consumer products, distributed power systems, highly energetic fast reactions, in-situ production of hazardous chemicals. Other applications will be slower.

19

Reservoir

P

P

Air Supply

Distributor

RotameterMonolith

Pump

UG (c

m/s

)

UL (cm/s) 30 40 50

50

40

30 Less uniform σ=0.075

More uniform σ=0.04


Uniform σ=0.046

Less uniform σ=0.058





Gamma Ray Tomography of Gas-Liquid Holdup Distribution in Monoliths

Al-Dahhan, et al. 2003

S19. Scale-up in parallel was suggested for monoliths also were the argument that monolith performance can be predicted based on single channel performance is often made. Iin the last four decades monoliths made inroads only in few applications – automobile exhaust (multiple distributed units) and power plant gas cleanup (SCR of NOx) for which they are the largest known reactors (up to 1000 m3 in size).

Applications in other areas, especially gas-liquid-solid reactions are slow to come The customer resistance factor is too large. Studies in our laboratory by gamma ray tomography reveal that in gas liquid flows uniform distribution in monolithic channels cannot be taken fro granted as there is only a narrow window of flow conditions that allows it.

20

Schematic of Bubble Column Type of Photo Reactors (Commercially Used)

A train of bubble columns (sparged reactors) through which liquid toluene and chlorinated products flow in series while chlorine is added into each column and hydrogen is removed from the column.

Typical selectivity to benzyl chloride: 90% But Toluene conversion is less than 30%.Can one do better?

Process Intensification via Multi-functionality.

S20Xue and Dudukovic, Chem. Eng. Sci., 54(10), 1397-1403 (1999)

S20 This brings us to the need for other means of process intensification that can be implemented on micro or larger scale. Thus, we will look at broader than the multi-scale approach introduced earlier. This involves coupling of reaction and separation. In our laboratory we studied photochemical chlorination of toluene to benzyl chloride as desired product. Since with respect to toluene we have consecutive reactions and with respect to chlorine competitive reactions, reaction engineering 101 teaches us that we need plug flow of toluene and the liquid phase and cross flow that is backmixed flow for chlorine. Commercially, to achieve the favorable flow pattern for the formation of the intermediate, the liquid phase flows through a series of bubble columns, with light wells, while chlorine is fed in parallel to keep its concentration low. So the liquid experiences plug flow and gas is close to well mixed at its exit composition. Typical selectivity of 90% can be obtained at toluene conversion 30% or lower.

21

Process Intensification via Multifunctionality by Reaction – Separation Coupling : Proposed Technology

Configured into a Semi-Batch Mode

S21

Schematic of Photo Reactive Distillation System

Allows in situ product removal and toluene recycle.

Selectivity to benzyl chloride: 96% + up to toluene conversion of 98%.

Xue and Dudukovic, Chem. Eng. Sci., 54(10), 1397-1403 (1999)

S21 We have shown that by conducting the process in photo reactive distillation mode we can achieve selectivity of better than 90% at toluene conversion well above 90%. A continuous system would operate even better. Clearly, this idea can be implemented, in principle, on the micro scale or macro scale, but this reaction may be too slow for the micro scale.

22

F. Cottrell (U.S. Patent, 1938)-Foul Gas Deodorizer

S22

S22 In running adiabatic packed beds for exothermic reactions it is well known that the temperature rise adversely affects the achievable exit conversion due to equilibrium limitations, since the equilibrium constant goes down with increased temperature. Hence, an idea patented by Cottrell in the 1930s, of swinging the bed feed from one side to the other to achieve a more favorable inverted U temperature profile, was embraced by many in the reverse flow concept which was commercially implemented in sulfuric acid manufacture and VOC abatement etc.

23

S23

Asymmetric Reverse Flow ProcessWrong-Way Coupling (Kulkarni and Dudukovic (1996-1998)

Exo: Methanol combustionEndo: Methane Steam Reforming

S23 We have carried that idea further in our CREL by coupling an exothermic reaction (like methane combustion) with an endothermic one (like methane reforming) in periodic operation with feed switching from end to end. Modeling shows that there is a region of operability where complete conversion for both reactions and high thermal efficiency can be achieved. This concept was carried further in Hans Kuipers and Gerhard Eigenberger Laboratory and we are again working on it. Again it can be implemented from the micro to the large scale.

24

Molecular scale: Particle scale:Reactor scale:

Flow pattern & phase distributionsKinetics (chemistry)Liquid - Solid:

Gas – Liquid:

Gas – Solid:

Contacting

&

Transport

Reactor

scale

Uniform distribution

Maldistribution, stagnancy and bypassing

Particle scale: Completely wetted Partially wettedG G

L

L

G

G

L

L G

GL

Reactions can be gas or liquid reactant limited

Suggested scale-up of trickle beds maintains equal LHSV ( = liquid superficial velocity/reactor height)

With scale-up height and liquid velocity increase and affect phenomena below.

S24 Let us turn our attention now to scale-up of trickle bed reactors used in a number of industries. The celebrated rule of thumb is to keep constant LHSV which guarantees the constant volumetric flow of liquid per volume of the catalyst. It is inferred then that the mean contact time is the same. The problem with this approach is that the mean contact time between solids and liquid may not be the same because the liquid-solid contacting on the particle scale increases with increasing liquid mass velocity with increase in reactor scale. Hence, in absence of reactor scale mal-distribution scale up is forgiving for liquid limited reactions prevalent in the petroleum industry such as HDS etc., since contacting increases with reactor scale at constant LHSV. Unfortunately, for gas limited reactions the effect is the opposite , as an additional resistance for the gas arrival to the solid is created at larger liquid velocities. This points to the pitfalls of not understanding what rules of thumb really imply in a given system. Moreover, it is not just tj he mean contact time but its c variance that should be preserved upon scale-up.

25

0.511.0Contacting efficiency0.90.4Conversion (xB)

3/16 ´´ x 1/8´´3/16´´ x1/8´´Catalyst tablets 0.4250.425Bed porosity 110110Temperature (C) 7070Pressure (bar)312312GHSV (hr-1)

0.0671000H2 FLOW (STD) (m3hr-1)0.2626UL (LSV) (mhr-1)1.31.3LHSV (hr-1)

0.03410.455Diameter (m)0.23519.4Height (m)LABPLANTDATA

Example of Improper TBR Scale-UpAldehyde Hydrogenation, Scale-up done based on equal LHSV

3100,

, ≈==LCE

PCE

L

P

L

P

LL

HH

ηη

Lappapp

PL

kkmkgLmkgL

,

22

44.0sec/6sec/06.0

≈==

For gas limited reaction, need both LHSV = constant and HR = constant

25. Unaware of this, and blindly following the constant LHSV rule, led to a major embarrassment in a major chemical company as shown here. This could have easily been prevented as the result can be anticipated based on the understandingof the system

26

Effect of Cycle Split and Total Cycle Period on Trickle Effect of Cycle Split and Total Cycle Period on Trickle Bed Performance EnhancementBed Performance Enhancement

Gas Limited Conditions (γ ~ 20)Operating Conditions : Pressure=30 psig

Cycle Split (σ)= Liquid ON Period/Total Cycle Period (τ)

CREL

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 50 100 150Total Cycle Period, s

X(U

S)/X

(SS)

P=30 psig, C(AMS) feed = 1627 mol/m3Cycle Split = 0.33, L (mean) 0.24 kg/m2s

• Reduction in cycle split improves gaseous reactant supply and performance enhancement (~50% over steady state)

• Region of feasibility for performance enhancement between gas starvation (low cycle period) and liquid starvation (high cycle period) zones

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.2 0.4 0.6 0.8 1 1.2Cycle Split (ON time/Total Cycle Time)

Con

vers

ion

(X)

Khadilkar (CREL), 1998

26. Enhancement in trickle bed reactor performance achievable by forced cycling of the feed can be predicted based on the proper model of the system and has benconfirmed experimentally. Unfortunatly, there is large inertia to use it in industry due to improper understanding of the systems involved.

27

Liquid Distribution in Study Trickle Bed Reactors - Experimental Setup

• Exit Liquid Distribution

• Computed TomographyDISTRIBUTOR

Single point10mm diameter

liquid inlet

PACKING6’’ (14 cm)

Column

1<L/D<4

3mm diameterglass beads

COLLECTINGTRAY

25 crosssectional areas

x/R

y/R

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.400.360.320.280.240.200.160.120.080.040.00

εL

x/D=0.34 Hbed= 2D

x (mm)

y(m

m)

-60 -40 -20 0 20 40 60

-60

-40

-20

0

20

40

60

6.56.05.55.04.54.03.53.02.52.01.51.00.50.0

Exit liquid distribution, % of total flux (FT)

L/D = 2, FT = 85 kg.m-2.s-1, Mf = 0.054UL = 4 mm.s-1, UG = 0 m.s-1

• Computed TomographyLiquid holdup

Cross-sectional liquid holdup and exit liquid distribution are compared in the region close to the reactor bottom. Figures show that results are in good qualitative agreement even though two different parameters (i.e. liquid holdup and exit liquid fluxes) are compared.

• Exit Liquid Distribution

27. To help enhanced understanding of the steady and dynamic behavior of these systems we run both CT scans and appropriate CFD models, hoping that this will result in enhanced phenomenological models for scale-up and design.

28

FOR VERTICAL SCALE-UP ADVANCES IN MULTIPHASE REACTORS REQUIRE:a) capturing the physics of flow by experimental means;

b) using CFD models and validating the results experimentally;c) physically based engineering reactor models for flow, mixing and reaction

G

G

S

S

G

G

LS

G

G

G

G L+S

G

G

L

L

Dudukovic, AICHE Symposium Ser., 321, 30-50 (1999)

Dudukovic, Larachi, Mills, Catalysis Reviews (2002), 44(1), 123-246

REACTOR SCALE MODELS FOR CONTACTING OF TWO MOVING PHASESIdeal Reactor Concepts:

A) Plug Flow (PFR)

B) Stirred Tank (CSTR)U1

U2

K1

2C) Axial Dispersion Model

D) Need More Accurate Flow & Mixing Description ViaPhenomenological models based on: 1) CFD Models (Euler-Euler Formulation)2) Experimental Validation: Holdup Distribution and Velocity Field

U1

U2K

1

2

S28

S28 When we have a reactor system with two moving phases it is important to be able to describe the flow pattern of each. In the past we relied on ideal reactor assumptions of treating each phase as being either in plug flow or perfectly mixed. When reality did not conform to these assumptions the axial dispersion model is often used to match experimental observations. It has been recognized, however, that ADM is not predictive and that one needs more accurate flow and mixing models based on the physical phenomena that occur in the system. Since multiphase systems like these are opaque, it was important to develop means to measure phase holdup and velocity distribution in them. Such data is essential for validation of CFD models that must be used on these reactor scales.

Thus, the modern approach to reactor modeling and scale-up requires:a) capturing the physics of flow by experimental meansb) Using appropriate CFD models and validating the results experimentallyc) completing physically based engineering models for flow and mixing..

29

COMPUTER TOMOGRAPHY (CT)

S29

Superficial Gas Velocity 18 cm/s at 7 atm.

0.300.35

0.400.45

0.500.55

0 2 4 6 8r (cm)

Gas

Hol

dup

CHEMICAL REACTION ENGINEERING LABORATORY

Single source CT is a technique for measurement of the cross-sectional density distribution of two phase flow by measuring the attenuation distribution in two phase systems ( e.g. G-L, …).

Diameter of scan region = 12 inchesTotal number of Projections 22,275Number of views = 99

∑=−=l

ijij,eff0

l)(IIlnA ρµ

∑=K

ij,Kij,Kij,eff )()( ερµρµ

Kumar (1994), Kumar et al., (1995)

S29. For this purpose we built a gamma ray CT scanner that can provide us with the density distribution at any desired cross-section of the reactor. Shown is the schematic of the equipment, its actual picture as it scans a bubble column at atmospheric pressure and the result at elevated pressure and at , very high gas velocity. Note that in these conditions of churn turbulent flow the average gas holdup is very high and is a parabolic function of the radius. So gamma ray CT can provide us with the accurate time averaged distribution of the phases.

30

Radioactive Particle Tracking

CARPT (RPT) Schematic Experimental Setup

Lin et al. (1985), Moslemian (1986), Devanathan (1990), Devanathan et al. (1991), Chaouki, Larachi and Dudukovic (1997)

CHEMICAL REACTION ENGINEERING LABORATORYS30

S30 To measure the motion of the solids or liquid phase we have introduced computer assisted single radioactive particle tracking (CARPT) to obtain the Lagrangian trajectories of a single tracer particle (labeled with Sc 46) made dynamically similar to the phase being traced. For monitoring solids motion a particle of the same density and size as the solids used is employed, for liquids a neutrally bouyant particle is used whenever possible.

31

Particle Tracking in Multiphase Systems


Dudukovic, Oil & Gas Sci. and Tech., Rev. IFP, 55(2), 135-158, (2000)

S31 We see the glass bubble column on the right operated at relatively low superficial gas velocity of a few centimeters per second and at atmospheric pressure.

On the left we are looking at the radioactive particle tracer moving in a slurry stainless steel column 16 cm in diameter at 20% wt solid loading at gas superficial velocity of 45 cm/s.

From the obtained Lagrangian trajectories, the instantaneous velocities are obtained, as well as time average ( ensemble average) velocity values, as well as various statistical properties of the fow (i.e. eddy difusivities).

Let us illustrate the power of CARPT/CT on a number of examples!

32

Radioactive Particle Tracking (CARPT) Provides Solids Velocity and Mixing Information

Computer Tomography (CT) Provides Solids Density Distribution

S32

HOPPER

R

I

S

E

R

EDUCTOR

High Pressure Side (80-100 psi)

Low Pressure Side ( <80 psi)

WATER TANK

PUMP

RECYCLE LINE

6′11"

6′′

PP

P

P

9′11"

Cold Flow Model

Tracer Studies Confirm Liquid In Plug Flow (N > 20)Solids Flow and Distribution??


S32. Liquid solid riser has been considered as a reactor for solid acid catalyzed alkylations in replacing HF and sulfuric acid. The key design issue, due to rapid catalyst deactivation, is the flow pattern of liquid and solids in the riser, and the extent of backmixing of the solid catalyst in the riser. Classical impulse response tracer studies confirm that liquid is in plug flow. How about solids? The working design assumption is plug flow of solids, or some leakage of solids backwards interpreted by empirical axial dispersion coefficient. Use of CARPT and CT provides the data base coupled with CFD model for an improved model of solids flow and solids backmixing.

33

Trace over 38 s (1900 positions)

CARPT Results

-505

t = 60 sTime Average(25 - 100 s)

Z = 100 cm

Z = 125 cm

0

50

100

150

-7.6 -2.6 2.4 7.4

x-Position, cm

z-Po

sitio

n, c

m

-8

-3

2

7

12

17

0 1 2 3 4 5 6 7

Radial Position, cm

Axial Solids Velocity, cm/s

0

0.05

0.1

0.150.2

0.25

0.30.35

0.4

0 1 2 3 4 5 6 7

Radial Position, cm

Solids Holdup

0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7Radial Position, cm

Granular Temperature, cm2/s2

Comparison of CFD with Data

Ready for plant design, optimization and model based control

Final2-D Convection

DiffusionReactor Model for

the Riser

CFD Results

UL

Us

Dz

Dr

Roy and Dudukovic, I&EC Res., 40, 5440 (2001); AICHEJ (2005) S33

S33 A single radioactive particle, monitored by CARPT, exhibits a tortuous path through the riser during a single visit . Multiplying the difference in subsequent positions with the sampling frequency yields instantanous velocities. Averaging 2000 or more of such pathways yields a symmetric solids ensemble averaged velocity profile, with particles rising in the middle and falling by the wall. CFD computations reveal a highly complex 3 dimensional instantaneous flow structure (which explains the single particle trajectory) but 75 seconds of averaging produces the symmetric flow pattern of rising solids in the middle and falling by the wall. The agreement between simulations and data is great for solids velocity, solids holdup distribution and granular temperature, i.e., solids kinetic energy.

This now provides us with the proper model for the riser. It consists of plug flow of liquid, developed solids velocity profile with superimposed axial and radial diffusivities. This model can now be coupled with appropriate reaction and deactivation kinetics. CFD can predict all the model parameters.

34

0

50

100

150

200

-8 -4 0 4 8

Trajectories

0 10 20 30 40 50 600

0 .1

0 .2

0 .3

0 .4Tota l N um ber of Trajectories = 1473

Residence Time, s

Freq

uenc

y

Ul= 15 cm/s; S/L = 0.15

0 10 20 30 40 50 600

0 .1

0 .2

0 .3

0 .4Total N um ber of Tra jectories = 877

Residence Time, s

Freq

uenc

y

0 10 20 30 40 50 600

0 .1

0 .2

0 .3

0 .4Tota l N um ber of Trajectories = 1833

Residence Time, s

Freq

uenc

y

Ul= 20 cm/s; S/L = 0.10 Ul= 23 cm/s; S/L = 0.20

SOLIDS RESIDENCE TIME DISTRIBUTIONS

2302 .D =σ

4602 .D =σ2602 .D =σ

2 ≤ Nsolids < 6OVERALL 610180 2 .. D ≤σ≤

S34CHEMICAL REACTION ENGINEERING LABORATORY

S34. As a bonus, by monitoring the time of entry and exit of the tracer particle from the riser section by CARPT one obtains the residence time distribution of the solids in the riser. This confirms that solids flow deviates sometimes significantly from plug flow!

35


( ) ( )

( ) ( )∫

∫

επ

επ= R

z

R

z

flowdrrurrr

drrCurrrC

0

0

2

2

COMPUTED COMPUTED RTDRTD’’ss OF THE LIQUID AND SOLIDS AT OF THE LIQUID AND SOLIDS AT UULL=20 cm/s, S/L = 0.15=20 cm/s, S/L = 0.15

S35

S35 The designer gets the final assurance that the CFD computed RTDs of the two phases are in good agreement with the data!

36


S36

S36 Let us look now at the application of CARPT-CT in developing an improved model for many of the bubble column uses. We have proven the validity of this model for FT, methanol and DME synthesis. In process industry bubble columns are operated at very high superficial velocity to increase their productivity. These are buoyancy dominated churn turbulent flows.

The working assumption in design companies is that liquid is well mixed ( but it is not perfectly mixeed and that can cause problems with over design) and gas is assumed either well mixed or in plug flow, neither of which is realistic. CARPT-CT allows us to do better.

37

Bubble Column ExampleCARPT-CT and other measurements are used to develop an appropriate phenomenological reactor flow and mixing model. CFD generated data are used to assess model parameters at pilot plant or plant conditions. Reactor flow and mixing model are coupled with the kinetic information.Degaleesan et al., Chem. Eng. Sci., 51, 1967(1996); I&EC Research, 36,4670 (1997); Gupta et al., Chem. Eng. Sci., 56, 1117 (2001)

Dzz

Drr

uz(r)

1-εL(r)

0-R R

CT CT SCAN

CARPT

FLOWPATTERN

CFD + CARPT + CT

AFDU

0 100 200 300 400

10.8

0.6

0.4

0.20

Detector Level 1

0 100 200 300 400

10.8

0.6

0.4

0.20

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)

Norm

aliz

ed R

espo

nse

Sim_L1Exp_L1Sim_L4Exp_L4Sim_L7Exp_L7

Pressure = 50 atmTemperature =250 Deg. CUg = 25 cm/s

0 20 40 60 80 100

10.8

0.6

0.4

0.20

7

6

5

4

3

2

1

Liquid Tracer

Gas TracerGas

Gas

DET.

Data

ModelPrediction

time (s)

time (s)

time (s)

S37

S37 We have learned from tomography, as shown earlier, that gas holdup profile is almost parabolic in churn turbulent flow. This profile drives, by buoyancy force differences, a single liquid recirculation cell (in a time averaged sense) which is confirmed by CARPT studies. CARPT also provides the axial and radial eddy diffusivities from the Lagrangian tracer trajectories.

When the model is applied on a pilot plant column( of the same diameter as the cold flow model) for methanol synthesis both gas phase and liquid phase tracer responses are well predicted at seven different elevations ( not shown here). The same is true (not shown) for FT and DME synthesis.

Of course the CARPT, CT data were obtained on the equipment of the same diameter, and holdup profile was experimentally determined. To have a scale up tool we need to show that CFD can predict CT-CARPT data and then use CFD to generate the parameters of our engineering model.

38

Ensemble Averaged Equations for TwoEnsemble Averaged Equations for Two--Phase FlowPhase Flow

( ) 0=ε+∂ε∂

ccc .t

u∇ ( ) 0=ε+∂ε∂

ddd .t

u∇

( ) ( ) ( )bccccvmdccccc

ccc p

tσ∇.σ∇.∇.∇ ε+ε++−ε−ερ=⎟

⎠

⎞⎜⎝

⎛ +∂

∂ερ MMguuu ( )vmdddddd

ddd p

tMMguuu

++ε−ερ=⎟⎠

⎞⎜⎝

⎛ +∂

∂ερ ∇.∇

Liquid Phase Gas Phase

CLOSURESCLOSURES

⎟⎠

⎞⎜⎝

⎛ −εε=Dt

DDt

DC dc

vmdcvmuuM

21

InterInter--Phase Momentum ExchangePhase Momentum Exchange StressesStresses

)(O.C ddvm23231 ε+ε+=

;d

6d3

p

dcd FM

πεε

=

( )dcdcD2pcd Cd

81 uuuuF −−πρ=

( ) ⎥⎦⎤

⎢⎣⎡

++=

438150124 6870

EoEof,Re.

RemaxC .

D

2

23

79

671867171

⎭⎬⎫

⎩⎨⎧

εε+

=c

c

..f

( ) )(O; ddc

*c

c*cc ε+ε+=

µµ

+µ=251T

cuu ∇∇σ

'u'u cccbc ρ−=σ

( ) dcpdbtbc

tbc

bc dk; uuuu T

c −ε=ν+νρ= ∇∇σ

)ttanConsEmpirical(2.1k b =

τρ≡≡ 2pcdgNumberEotvosEo

cdcpcdNumberynoldsReBubbleRe µ−ρ≡≡ uu

Input Parameter : Bubble Size, dp

S52S2 CHEMICAL REACTION ENGINEERING LABORATORYS38

S38 To extend the findings to other diameters we need a CFD model which then is validated at the different scale. The usually used Euler-Euler two interpenetrating fluid equations are shown here. We have executed them in CFDLIB (of Los Alamos), FLUENT and CFX. Closures are needed for liquid turbulence (e.g., bubble induced turbulence is shown here but k-epsilon and other models were also used) and for the drag (e.g. classical expression modified for presence of other bubbles is used).

Originally computations were based on experimentally observed orassumed bubble diameter., now we incorporate the population balance with coalescence and breakup. This eliminates the need to assign bubble diameter and finally yields much improved prediction of experimentally observed holdup profiles!

39

COMPARISON OF COMPUTED (CFDLIB) AND MEASURED FLOW FIELDCOMPARISON OF COMPUTED (CFDLIB) AND MEASURED FLOW FIELD

Dzz

(cm

2 /s)

τ (sec)

Ug = 12 cm/sDc = 8”

Dzz

(cm

2 /s)

τ (sec)

Ug = 10 cm/sDc = 18”

S39

-60.0

-40.0

-20.0

0.0

20.0

40.0

60.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Dimensionless Radius

Tim

e-av

erag

ed L

iqui

d A

xial

Vel

ocity

, cm

/s

CARPTTwo-fluidASMM, DVASMM, SV

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Dimensionless Radius

Tim

e-av

erag

ed H

oldu

p

CTTwo-fluidASMM, DVASMM, SV

44 cm – IDUg = 10 cm/s

Chen and Dudukovic, CES, AICHEJ(2004, 2005))

S39 Now we get good agreement in predictions of both time averaged velocity with CARPT data and holdup profiles and CT data and , most importantly, of the computed eddy diffusivities and CARPT data, This completes the validation of not only the mean flow field and holdup distribution, but of dynamic features of our model for bubble columns .

40

CIRCULATING FLUID BED (CFB) REACTORMaleic Anhydride

Inert Gas

Air

Off-gas (COx, H2O,..)

ButaneFeed GasReoxidized

Catalyst

ReducedCatalyst

O2 O2V+3 V+4 V+5

HC HC

RiserRiser

Regen Riser

Catalyst Catalyst RedoxRedox

O OO

O2

Main ReactionMain Reaction

SolidsFlow

Direction

V+5


S40. Consider now a CFB with a gas solid riser. Gas solid risers have been used in FCC for many decades but more recently they were advocated as a reactor of choice for catalysts that can undergo an oxidation-reduction cycle and be made attrition resistant. For example, in producing maleic anhydride from butane on a vanadium catalyst the hydrocarbon selective oxidation occurs in the riser and the catalyst regeneration by oxidation is done in the fluid bed prior to catalyst recirculation. Clearly, the solids flow pattern is a critical issue and its change with the scale of the equipment. Amazingly we still did not have conclusive answers for predicting the true extent of solids backmixing in the riser or even for prediction of its residence time distribution. Most importantly we did not know how flow pattern, flow regime and mixing vary with scale and this can lead to failure of scale-up. So a pilot plant may work well but the commercial plant will not if the mean contact time and its variance are not reproduced well.

41

GAS-SOLID RISER Gas-Solid Riser

Gas-Liquid or Liquid Fluidized Bed CARPT Detectors on Gas-Solid Riser


S41. So we assembled a cold flow model where we could measure the arrival and departure of the tracer particle from the riser to establish the residence time distribution of solids at desired operating conditions. Far above the entry to the riser we instrumented a section for CARPT and did CT measurements at the same elevations.

42


Evaluation of Residence Time and First Passage Time DistributionEvaluation of Residence Time and First Passage Time Distributionss

Time spent by the tracer between B-C should not be counted in the residence time

Solids from Solids from hopperhopper

Air inlet

Riser

Solids + air into Solids + air into disengagement sectiondisengagement section

D

O

W

N

C

O

M

ER

Lead shields

Solids from Solids from hopper

Air inlet

Riser

Solids + air into Solids + air into disengagement sectiondisengagement section

D

O

W

N

C

O

M

ER

Lead shields

Typical trajectory of the tracer

BCA

D

20 30 40 50 600

200

400

600

800

Time (sec)

Cou

nts

(#)

Part of raw data from 3 detectors

Riser bottomRiser topDow ncomer top

( 1 ) A

( 2 ) B

(3 ) C D

Residence time

20 30 40 50 600

200

400

600

800

Time (sec)

Cou

nts

(#)

Part of raw data from 3 detectors

Riser bottomRiser topDow ncomer top

( 1 ) A

( 2 ) B

(3 ) C D

Residence time

Conventional tracer injection

FPTD

Bhusarapu et al., Chem. Eng. Sci., 59/22-23, 5381-5387, 2004

S42

S42. This slide illustrates that our CARPT technique provides the only way to determine residence times of solids precisely as it distinguishes whether the solid particle is entering or leaving the system at the entry or exit plane. For example, it is easy to determine that the time between A and B the solid spends in the system this is part of its residence time (while the time between B and C does not belong to residence time as the solid particle was outside (re-exited through the inlet plane). If one wants first passage times then only time C to D counts. This information cannot be deduced from traditional impulse-response experiments and, as well established by Nauman and Shinnar, such experiments are incapable of providing RTD information. Only CARPT can!

43


Bhusarapu et al., Ind. & Eng. Chem. Res., 44, 9739-9749, 2005

What is the true Dz?

FPTD versus Actual RTDFPTD versus Actual RTD

0 1 4 50

0.05

0.1

0.15

θ (τ = 13.52 )

Ε (

θ)

0

0.5

1Solids FPTD in the Riser with "closed-closed" Boundaries

F - c

urve

0

0.01

0.03

0.04

Ε (

θ)

0 1 4 50

0.5

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

θ (τ = 39.7 )

F -c

urve

Mean of FPTD = 13.52 secStdev of FPTD = 33.6 sec σ2 = 6.2 Dz = 2.1 m2/s

sec

Mean of RTD = 39.7 secStdev of RTD = 59.94 sec

σ2 = 2.3 Dz = 0.8 m2/s

sec

12

Actual

FPTD and RTD in FF regime (shown)Differences in mean residence time of 66%:

Differences in

Dimensionless variance by 170%

Dispersion coefficient by 163%

FPTD and RTD in DPT

( not shown)Differences:Dimensionless variance by 195%Dispersion coefficient by 207%

Ugriser = 3.2 m.s-1 ; Gs = 30.1 kg.m-2.s-1

S43

S43. To illustrate the distinction between RTD and FPTD, and point out the confusion that may arise from impulse-response measurements when it is unclear as to what is measured, we present this slide. Clearly the mean residence time and the variance and, hence, the solids backmixing inferred from the variance is vastly different, indicating that great caution is advised when interpreting the results from the literature if people did not use CARPT!

44


0 2 4 60

0.5

1

1.5

2

2.5

3

E (t

/<t>

), 1/

sec

0 2 4 60

0.5

1

t/<t> (<t>= 1.19 sec)

F- c

urve

0 2 4 60

0.005

0.01

0.015

0.02

l/<l>, (<l> = 358.4 cm)

E (l

/<l>

), 1/

cm

Mean of RTD = 1.19 sec St.dev. of RTD = 2 sec Total trajectories = 1379 Dz = 0.62 Pe = 2.5

Mean of trajectory len. = 358.4 cm St.dev. of trajectory len. = 469.2 cmTotal number of trajectories = 1379

0 2 4 60

2

t/<t> (<t> = 10.1 sec)

E (t

/<t>

), 1/

sec

0 2 4 60

0.5

1

F-cu

rve

0 2 4 60

.002

.004

.006

.008

.01

.012

l/<l> (<l> = 1733 cm)

E (l

/<l>

), 1/

cm

0 2 4 6

l

Mean of RTD = 10.1 sec St.dev. of RTD = 13.9 sec Total trajectories = 708 Dz = 0.37 m2/sPe = 6.2

Mean of trajectory len. = 1733 cm St.dev. of trajectory len. = 2333 cmTotal number of trajectories = 708

Solids Solids BackmixingBackmixing –– RTD, RTD, TLDsTLDs

◙ Core-annulus flow structure in riser results in a RTD with extended tail in the DPT regime, while in the FF regime it results in a hint of a dual peak along with the extended tail.

◙ Axial dispersion increases with solids mass flux at fixed gas velocity (not shown)

◙ ‘Macromixing index’ decreases with flux at fixed gas velocity (not shown)

lengthsticCharacterilengthtrajectoryofMeanM

⋅⋅⋅⋅

=

FF - Ugriser = 3.2 m.s-1; Gs = 26.6 kg.m-2.s-1

DPT - Ugriser = 5.49 m.s-1; Gs = 102 kg.m-2.s-1

M = 5.4

M = 9.6

S44

Bhusarapu et al., Ind. & Eng. Chem. Res., 44, 9739-9749, 2005

S44 The core-anulus flow structure in the riser results in the RTD with extended tail in the DPT regime and in a hint of a dual peak in the FF regime along with the extended tail.

The macromixing index increases with flux indicating further departure from plug flow. Wrong conclusion would be reached by looking at the estimated effective axial diffusivities which is the commonly used approach. CARPT provides us with the means to establish the right measures of solids backmixing and with the data to test CFD models and build reactor models for scale-up.

45

Cells’ Movement

0 10 20 30 40 50 60 70 80 900100200300400500600700

0 10 20 30 40 50 60 70 80 900

200

400

600

800

Time Series of Light intensity experienced by the Cells in the reactor

Flow VisualizationVelocity Profile

-30

-20

-10

0

10

20

30

40

50

0 0.2 0.4 0.6 0.8 1

Dimensionless Radial Position

Tim

e, a

zim

utha

l and

dep

th a

vera

ged

axia

l Ve

loci

ty, c

m/s

DC 1cm/sDC 5 cm/sBC 2cm/sBC 5 cm/sBC 12 cm/s

Draft Tube Column (DC) vs. Bubble Column (BC)

Airlift Airlift BioreactorBioreactorApplication:

PhotobioreactorAlgal Growth

Exposure light

Light distribution

Physiologically Based Photosynthesis rate model Dynamic Simulation

RIS

ER

19 c

m

14.5 cm

200

cm

20 cm

165

cm

0

10

20

30

40

50

60

0 100 200 300Time, hr

Cel

l co

ncen

trat

ion

(*10

6 ce

ll/m

l)

EXP, Ug = 0.39 cm/sSimulation of WuUg=5 cm/s (this work)Ug=1 cm/s (this work)

S45 in this slide, I show the success of putting together molecular level modeling ( cell level), obtaining the illumination time distribution of algae experimentally, developing appropariate reactor modeland being able to amtch the results in the reactor on biomass growth.

46

Summary and Conclusions● Advances in micro-technology have resulted in phenomenal

advances in micro-reactors offering intriguing alternatives for highly energetic, fast, and hazardous processes and other distributed systems.

● Advances in non-invasive monitoring of multiphase opaque flows make validation of practical CFD codes possible and open the door for rational and successful vertical scale-up.

● Implementation of these advances brings us closer to cleaner and more efficient processes.

● Tax incentives for implementation of novel more efficient technologies are called for to provide a boost to modern processdevelopment techniques.

S46

46. I can briefly summarize as follows.

47

Past & Present… … Future

Environmentally Benign Catalytic Processing ...

Art Science


S47 by improving our quantitative understanding of all the scales is the only way to process development of multiphase systems from the state of art to science.

48

Center for Environmentally Center for Environmentally Beneficial CatalysisBeneficial Catalysis

Designing environmentally responsible molecules, products, and processes –from the molecular scale to the plant scale.

Lead Institution: University of Kansas (KU)

Core Partners: University of Iowa (UI); Washington University in St. Louis (WUStL); Prairie View A&M University (PVAMU)

Director: Bala Subramaniam (KU); Deputy Director: Daryle Busch (KU)

Associate Directors: John Rosazza (UI); Milorad Dudukovic (WUStL); Irvin Osborne-Lee (PVAMU)

S48

S48 I also need to bring to your attention our National Science Foundation (NSF) Engineering Research Center: Center for Environmentally Beneficial Catalysis (CEBC) in which CREL is a core partner. This center presents a great opportunity for leveraging of resources and its focus is: Designing environmentally responsible molecules, products, and processes – from the molecular scale to the plant scale

49

CHEMICAL REACTION ENGINEERING LABORATORYS49

Environmentally BeneficialCatalytic Engineered Systems

TG1: Catalyst Design and Preparation

TG2: Media and Catalyst Supports

TG3: Experimental Design and Advanced Measurements

TG4: Multi-scale Process Model- Quantum effects- Molecular dynamics- Rate theories- Solvent thermodynamics and

kinetic effect- Micromixing- Multi-component transport- Turbulence- Mixing- Computational fluid dynamics- Reactor simulation- Plant simulation- Control- Optimization

CEBC – U. of Kansas, U. of Iowa, CREL-WUStL,Prairie View A&M University (PVAMU)

S46. The center embraces the systems approach in development of environmentally beneficial catalytic engineered systems. We have joined the lead of the University of Kansas, and with them and U of Iowa, and Texas A&M Prarie View University entered into a partnership for formation of a Center for Environmentally Beneficial Catalysis. CEBC became a reality as one of the 4 new NSF ERCs on September 1, 2003.The idea is to implement a highly interactive multiscale approach consisting of experiments and modeling for development of new environmentally beneficial catalytic processes.

50

Chemical ReactionEngineering Laboratory (CREL)http://crelonweb.che.wustl.edu

Objectives

M. Al-DahhanCo-Director

M. DudukovicDirector

• Education and training of students• Advancement of reaction engineering methodology• Transfer of state-of-the-art reaction engineering to industrial practice

Sponsors

S50

IFPInnoveneIntevep

Johnson MattheyMonsanto –Env.

SasolShell

StatoilSyntroleum

TotalUOP

Air ProductsBayer

BP Chevron-TexacoConoco-Phillips

DOEDupont

EastmanEnitechnologieExxon - MobilSponsors

PartnersSponsorsPartnersSponsorsPartners

S48 If you have a multiphase reactor modeling or scale up problem I invite you to visit the home page for our Chemical Reaction Engineering Laboratory and consider working with us.

51

Acknowledgement of Financial Support and Effort in Advancing Multiphase Reaction Engineering and

Establishing Unique CARPT/CT Technologies

Department of Energy: DE-FC22 95 95051, DE-FG22 95 P 95512

CREL Industrial Sponsors: ABB Lummus, Air Products, Bayer, Chevron, Conoco, Dow Chemicals, DuPont, Exxon-Mobil, ENTechnologie, IFP, Intevep, MEMC, Mitsubishi, Monsanto, Sasol, Shell, Statoil, Synetix, Union Carbide, UOP

CREL Colleagues and M.H. Al-Dahhan, J. Chen, S. Degaleesan, Graduate Students: N. Devanathan, P. Gupta, A. Kemoun, B.C. Ong, Y.

Pan, N. Rados, S. Roy, A. Rammohan, Y. Jiang, M. Khadilkar, Y. Jiang, A. Kemoun, B.C. Ong, Y. Pan, N. Rados, Shantanu Roy. H. Luo, S. Bhusarapu, Shaibal Roy

Special Thanks to: B.A. Toseland, Air Products and Chemicals, M. Chang, ExxonMobil, J. Sanyal, FLUENT, USA,B. Kashiwa, Los Alamos, V. Ranade, NCL, India

S51

SPECIAL APPRECIATION TO Prof. Klavs JENSEN OF MIT FOR SLIDES ON MULTIPHASE MICRO-REACTORS

S51 I need to acknowledge the effort and financial support of many. Special thanks are due to professor Klavs Jensen of MIT for his slides on multiphase micro-reactors.

advances in scale-up of multiphase systemscrelonweb.eec.wustl.edu/process development.pdf · 1...

Documents