Design issues for membrane-based, gas phase ... - CiteSeerX

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Chemical Engineering Science 55 (2000) 3065}3075

Design issues for membrane-based, gas phase microchemical systems David J. Quiram , I-Ming Hsing, Aleksander J. Franz , Klavs F. Jensen *, Martin A. Schmidt Department of Chemical Engineering, Massachusetts Institute of Technology, 24 Ames Street, Room 66-566, Cambridge, MA 02139-4307, USA Department of Chemical Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 24 Ames Street, Room 66-566, Cambridge, MA 02139-4307, USA Received 16 April 1999; accepted 12 November 1999

Abstract The use of "nite element simulations to characterize the operating behavior of a microchemical reactor and enhance its design is described. Important design issues for a microreactor, speci"cally heat and mass transfer, are explored with the design of a micro-#ow sensor and the redesign of the reactor heater. The impact of the design choices for a #ow sensor is quantitatively evaluated and an optimal design is proposed. This design was fabricated and testing con"rmed the simulation work. The reaction channel heater was redesigned to improve thermal uniformity in the reaction zone. Simulations showed that before ignition signi"cant non-uniformity still existed, but this was dramatically reduced after ignition. The use of previously reported kinetic models for ammonia oxidation in microreactor simulations is also discussed. It was found that kinetic models evaluated in conventional reactor experiments are inadequate for use in microreactor simulations because of the lower operating temperature and broader range of temperatures of the microreactor. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Microreactor; Microchemical; Microfabrication; Anemometer; Modeling; Oxidation; MEMS

1. Introduction During the past century, the chemical industry has #ourished on the guiding principal of economies of scale. This attitude has resulted in huge chemical plants that have come to represent the industry in the public mind. Although these production facilities make economic sense, serious safety and environmental problems arise from the need to transport and store large quantities of hazardous chemicals. A recently proposed alternative has been the on-site/on-demand production of chemicals by mini-chemical plants (Benson & Ponton, 1993). These facilities would be mass-produced and sold as complete, highly automated systems. Unfortunately, each site will likely have unique production requirements, so a further breakdown in the production unit is advantageous. Micromachining provides a manufacturable solution to this problem, since very small individual units can be pro-

* Corresponding author. Tel.: #1-617-253-4561; fax: #1-617-2539695. E-mail address: [email protected] (K.F. Jensen)

duced in mass quantities. Large quantities of products can then be produced by connecting the components in parallel}a scale-out approach instead of scale-up (Lerou et al., 1995). The considerable research e!ort already in place in the "eld of Microelectromechanical systems (MEMS) has demonstrated many of the components necessary for chemical production: valves (Barth, Beatty, Field, Baker & Gordon, 1994; Esashi, Shoji & Nakano, 1989), pumps (Esashi et al., 1989; van Lintel, van de Pol & Bouwstra, 1988; Gerlach, 1997), #ow sensors (Lammerink, Tas, Elwenspoek & Fluitman, 1993), static mixers (MoK bius, Ehrfelt, Hessel & Richter, 1995; Krog, Branebjerg, Nielsen & Gravesen, 1996), and separation devices (Terry, Jerman & Angell, 1979). Recently, many government/educational/industrial groups have started active research projects in the development of microchemical systems (Brenchley & Wegeng, 1998; Baselt, FoK rster, Herrman & Tiebes, 1998). Currently, the result of these e!orts has been the development of microchannel structures to serve as chemical reactors. One operating con"guration has reactants #owing through all the channels, and thus, the system operates like a catalytic

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D.J. Quiram et al. / Chemical Engineering Science 55 (2000) 3065}3075

Nomenclature h q , ¹ ¹ M d

adjustable parameter dependent on reactor geometry heat #ux in the direction transverse to #ow temperature ambient temperature half-width of the heater

monolith (LoK we et al., 1998; Wei{meier & HoK nicke, 1998). The micromachining aids in increasing the surface-to-volume ratio inside the chemical reactor. For reactions that do not run favorably under adiabatic conditions, a microchannel device based on a layering structure can be used (JaK ckel, 1995; HoK nicke & Wei{meier, 1995). In this scheme, one layer serves as a reactor and the following layer serves as a heat exchanger. Because of the extremely small channel dimensions (typically on the order of 0.1 mm), excellent heat transfer from the reaction channel to the cooling channel is obtained. This style of reactor favors highly exothermic, fast reactions so that modest chemical production rates can be achieved. Microchannel designs o!er two main advantages over traditional reactors: extremely high heat transfer rates and a small reactor volume. In particular, it has been suggested that microreactors may o!er considerable advantages over traditional reactors for certain classes of reactions that favor periodic operation (Baselt et al., 1998). This is achievable in microchemical systems because of the small system volumes and corresponding

small #ow and thermal time constants associated with the reactor. Considerable e!orts are also in progress to develop microchemical reactors for use as catalyst test-beds (Lerou et al., 1995; JaK ckel, 1995; Ehrfeld, Hessel, MoK bius, Richter & Russow, 1995). The small size of the reactor simpli"es the testing of potentially explosive reactions or reactions that involve hazardous chemicals. Furthermore, their size and ease of production has also sparked interest in their use as test beds for combinatorial chemistry techniques. As part of the e!ort to develop microreactors for catalyst testing and chemical production, we have developed the &T-shaped' microchemical reactor shown in Fig. 1 (Srinivasan et al., 1997). This reactor di!ers signi"cantly from the other designs since the reaction channel incorporates integrated heaters and temperature sensors, which o!er a higher level of heat management than is possible in a microchannel device. On the other hand, microchannel devices o!er higher levels of chemical throughput on a per volume basis. To explore the trade-o!'s associated with each microreactor design and for comparison with conventional reactor performance, it is necessary to have an in-depth understanding of their operating characteristics. For assistance in the interpretation of experimental data and to "ll in the voids of our measurement capabilities, we have developed a simulation methodology for the study of the T microreactor (Hsing et al., 2000). Their relatively simple design readily admits detailed modeling, which makes them an ideal system for reaction engineering analysis. Speci"cally, the small length scales associated with these devices results in #ow that is typically in the laminar region. Moreover, di!usive processes dominate

Fig. 1. Schematic drawings of the T microreactor: (A) Top view; (B) Cross-section perpendicular to #ow; (C) Cross-section parallel to #ow.

Ces=3175=Durai=VVC=BG D.J. Quiram et al. / Chemical Engineering Science 55 (2000) 3065}3075

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Fig. 2. Graph showing the interaction between the T microreactor design parameters and its operating characteristics.

convective processes, which keeps the equations elliptic. The modeling is also simpli"ed by the use of a thin-"lm catalyst in the T microreactor, which avoids the approximations for heat and mass transfer that are necessary in modeling packed bed reactors. In these cases, the inclusion of simpli"ed lumped parameter approximations make it di$cult to predict the performance of a new design when reactor data on a similar design is not available. Unfortunately, the characteristics of microreactors highlight new problems that are infrequently encountered in traditional reactor modeling. For example, in modeling the T microreactor the length scales of important features vary widely from &0.5 mm (channel width/height) to 0.001 mm (membrane thickness). Another problem encountered in modeling reacting #ow for the T reactor is the extremely broad temperature range inside the microreactor. This requires a robust kinetic model of the system of interest, which is di$cult to "nd since most kinetic data is taken over a relatively small range of temperatures. The traditional problems encountered in modeling full size reactors are also found in these systems. Speci"cally, the species, momentum, and energy balances are strongly coupled for systems with highly exothermic reactions. For example, we have previously shown data and simulations of the ignition/extinction behavior in the T microreactor for ammonia oxidation (Jensen et al., 1998). To better understand the operation of the T reactor we have performed an analysis of the heat and mass transfer characteristics of this design. Fig. 2 shows the generalized relationship between the design characteristics of the T microreactor to the performance of the reactor. We have explored the e!ect of these parameters on the operating behavior of this microreactor through experiments and simulations (Srinivasan et al., 1997). In this paper, we

focus on the use of simulations as a design and data interpretation tool. To this end we further examine aspects of heat and mass transfer in the microreactor to design a new #ow sensor, redesign the heater segments, and compare published kinetic models with experimental data. These problems serve to illustrate the capabilities of detailed simulations as well as the di$culties encountered in modeling these systems.

2. Physical system We have already demonstrated a prototype microchemical reactor, the T reactor, that consists of two inlet #ow channels with #ow sensors, and a reaction channel with local heating and temperature sensing devices. Fig. 1 shows a schematic diagram of the top and crosssectional views of the T reactor. The reaction channels are sealed on top by a 1 lm thick silicon nitride membrane, which has platinum heaters and temperature sensors on the outer side and a platinum catalyst on the channel side. The channel is sealed from the bottom by an aluminum plate. The metalization on the top of the membrane layer forms "ve heater segments over the reaction channel and two #ow sensors over the inlet channels. The heater segments are 3.55 mm in length and 0.3 mm wide, and they are centered in the channel. Each segment has one temperature sensor located on the upstream end and one located on the downstream end. The #ow sensors were designed to operate in a time-of-#ight mode and consist of a heater with a temperature sensor on either side. The microreactor is fabricated from bulk silicon using photolithography and etching techniques to de"ne the channel structures and metal layers. A more in-depth description of the physical characteristics of



the T reactor and its fabrication method can be found in the article by Srinivasan et al. (1997).

3. Simulation methodology The simulation models developed are based on the conservation equations present in all chemical reactor analysis: mass, momentum, energy, and species. A detailed description of these equations and the boundary conditions used in the problem formulation can be found in Hsing et al. (2000) The general issues in developing numerical solvers for reacting #ow models are the numerical scheme used for discretization; the solution technique used for the sets of non-linear algebraic equations obtained; and the techniques used to handle numerical issues such as multiple steady states. We have chosen to use the GFEM approach for the discretization of the conservation equations due to its broad generality and its excellent convergence properties in handling elliptic problems. As part of the discretization process, the physical domain of the system must be transformed into a computational domain via a mesh. A commercial mesh generator, ICEM-CFD (1993), is used to convert a CAD drawing of the microreactor into a discretized mesh. The boundary conditions for the mesh are then added using a preprocessor. This makes the simulation tools completely general in their applicability to microreactors with di!ering geometries. Newton's method is used to solve the large set of nonlinear algebraic equations generated by the discretization procedure. Each Newton iteration in turn produces a set of algebraic equations that are then solved using a frontal algorithm. Although, Newton's method has a quadratic convergence property, the initial guess must be su$ciently close to the real solution for convergence. This becomes di$cult for the highly nonlinear behavior exhibited by reaction systems that display ignition/ extinction. To address this problem, Keller's pseudo-arc length continuation scheme was used to trace the solution branches around the limit points of the ignition/ extinction S curve (Keller, 1982,1977). Other numerical problems arise when the Reynolds number becomes su$ciently large so that the problem is convection dominated. To retain the convergence properties of the GFEM approach, the Brooks-Hughes Streamline Upwind Petrov-Galerkin (SUPG) scheme was implemented (Brooks & Hughes, 1982). More details concerning the numerical issues involved in microreactor simulation can be found in the work by Hsing et al. (2000).

4. Results and discussion We have already demonstrated the value of our simulations tools in modeling the behavior of the T micro-

Fig. 3. Comparison between the predicted and experimental temperature vs. power behavior of the silicon membrane microreactor. (䢇) Experimental data; (=) Simulation data.

reactor under reacting and nonreacting #ow conditions. In particular, the model was able to capture observed ignition/extinction behavior (Hsing et al., 2000). The model also successfully duplicated the temperature of the membrane heater segments under varying #ow and power input conditions. This latter result is particularly signi"cant since there are no adjustable parameters in the pure heat transfer problem. Continuing with this e!ort, we have also used the simulation tools to predict the temperature vs. power behavior of a slightly modi"ed T reactor as shown in Fig. 3. In this case the 1 lm thick silicon nitride membrane was replaced with a 2.6 lm silicon membrane, which reduces the membrane thermal resistance by an order of magnitude. An important validation feature of this graph is that the simulation data was obtained before this device was even fabricated. With the success encountered in simulating the strict heat transfer problem we applied the simulation tools to the design of a better #ow sensor. 4.1. Design of a micro yow sensor For the next version of the T microreactor, the timeof-#ight #ow sensor previously used was replaced with a #ow anemometer. This type of sensor functions by detecting changes in the temperature "eld around a heater element caused by changes in gas #ow rate as depicted in Fig. 4. For this problem we were constrained with using 1 lm thick silicon nitride as the membrane material with a 0.5 mm channel width and a 0.55 mm channel depth. The adjustable parameters were the length of the heater segment, the power input into the heater, and the placement of the temperature sensor(s)


around the heater segment. We were also interested in knowing the e!ect of the gas properties on #ow sensor performance as well as the e!ect of the membrane properties should the fabrication process be changed in the future. Fig. 5a shows the temperature pro"le down the centerline of the channel for various gas #ow rates. To better show how the temperature pro"le changes with #ow rate Fig. 5b gives the delta temperature pro"le, which is the di!erence between the temperature pro"le of interest and the no #ow temperature pro"le. This "gure

Fig. 4. Contour plot of the gas temperature at the channel center around the heater: (top) 0 sccm oxygen #ow rate; (bottom) 30 sccm oxygen #ow rate (Contours range from 310 to 460 K with 15 K intervals).

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clearly indicates that the most sensitive con"guration for this type of anemometer is to place the temperature sensor slightly upstream of the heater. The explanation for this result is given in Fig. 4, which shows a contour plot of the temperature "eld around the heater segment for the no #ow and the high #ow conditions. Increasing the #ow rate has the e!ect of #attening/broadening the contour lines after the heater segment. For the region before the heater segment, the spacing between contour lines narrows indicating that the temperature gradient becomes steeper in front of the heater segment. Thus, changing the #ow rate has a large e!ect on the temperature pro"le immediately in front of the heater segment. Next, the e!ect of the heater segment length was explored, and this result is illustrated in Fig. 6a. The simulations gave the expected result that increasing the heater segment length resulted in the temperature di!erence peaks being taller and broader. It also had the e!ect of moving the peak locations slightly farther (&6 lm) from the heater. The e!ect of increasing the heater length was not large considering there is a factor of four di!erence between the shortest and longest segment. Nonetheless, we used a 200 lm heater in our design and in the remaining sensitivity studies shown here because the temperature sensor length of 90 lm requires broad peaks. These could also be obtained by increasing the heater power, as shown in Fig. 6b; however, there were concerns for the membrane stability (the membrane buckles at high temperatures, which can lead to breakage) and the electromigration limits of the platinum heater lines. Thus, for these simulations the maximum temperature in the heater segment under no #ow conditions was kept

Fig. 5. (A) Temperature pro"le along the center of the membrane around the #ow sensor heater for various gas #ow rates: ( == ) No #ow; (} } } 䉬 } } }) 10 sccm; (2;2) 30 sccm; (B) Delta temperature pro"le (==) 1 sccm; (} } } 䉬 } } }) 5 sccm; (2;2) 20 sccm.



Fig. 6. E!ect of design parameters on the delta temperature pro"le for a 20 sccm oxygen #ow rate (A) Heater length (***) 50 lm; (*䉬*) 100 lm; (2;2) 200 lm; (B) Heater power (***) 12.0 mW; (*䉬*) 17.2 mW; (2;2) 24.0 mW; (C) Membrane thermal conductivity (***) 1.2 W/(m K); (} } } 䉬 } } }) 4.5 W/(m K); (2;2) 10 W/(m K) (D) Gas properties (***) Hydrogen; (} } } 䉬 } } }) Oxygen; (2;2) Ammonia.

constant at 2003C by adjusting the total power in the heater segment. That is for each design variation the power was "rst adjusted so that the maximum temperature under the no #ow condition was 2003C. The e!ect of the membrane thermal conductivity/ thickness on the #ow sensor performance was also explored because of the possibility of future changes in the fabrication process. Moreover, the thermal conductivity of the silicon nitride membrane is uncertain since its value depends strongly on the deposition process. To address this problem a series of simulations were performed exploring the range of reasonable thermal conductivity values of thin "lm silicon nitride. Fig. 6c shows these results, which demonstrates that changing the thermal conductivity of the membrane has no e!ect on the delta temperature peak location. This is extremely important, since it means that the optimum temperature sensor location is insensitive to changes in the membrane thermal conductivity. Of course, changing the thermal conductivity does e!ect the delta temperature peak

heights and breadths so decreasing the resistance to heat transfer in the solid phase decreases the #ow sensor's performance. In this case, changing the thermal conductivity from 1.2 to 10.0 W/(m K) results in a 35% loss in sensitivity. This result also applies to changes in membrane thickness since it has the same e!ect on the heat transfer behavior. That is, doubling the membrane thermal conductivity is equivalent to doubling the membrane thickness. This result will hold as long as there is not a signi"cant temperature variation inside the membrane along its depth direction. The previous results were all generated using oxygen as the #owing gas. However, the #ow sensor will be used for a variety of gases in future testing, so the e!ect of the gas properties on the #ow sensor performance is of considerable importance. Fig. 6d shows a comparison of the delta temperature pro"les for hydrogen, oxygen, and ammonia. The thermal di!usivity for these gases ranges from 1.4;10\ m/s for ammonia to 1.3;10\ m/s for hydrogen. This range encompasses most common gases,


Fig. 7. Upstream and downstream temperature sensor measurements for varying #ow rates of oxygen; (䉬) Upstream temperature sensor measurement (experimental); (0) (simulation); (䢇) Downstream temperature sensor measurement (experimental); () ) )) (simulation).

so Fig. 6d serves as an indicator of the robustness of the design. Because of the high-thermal di!usivity of hydrogen it has a relatively short and narrow peak that is slightly farther from the heater (&24 lm) compared to ammonia and oxygen. Therefore, the #ow sensor loses two-thirds of its sensitivity switching from ammonia to hydrogen. In practice, the power to the heater can be increased to accommodate the loss in sensitivity, but physical limitations may not always allow this. On the positive side, this indicates that the device could also be used to measure the thermal di!usivity of a gas. The value for the di!usivity could provide a rough estimate of the product gas composition of the reactor. However, this requires knowledge of the #ow rate since the e!ect of #ow rate and gas thermal di!usivity have very similar e!ects on the membrane temperature pro"le as shown in Figs. 5b and 6d. Recently, we have fabricated new microreactors with the #ow sensor design detailed above and tested its performance. As shown in Fig. 7, the #ow sensor is extremely responsive to changes in #ow rate between 0 and 10 sccm. Sensitivity decreases as the #ow rate increases further due to the development of a thermal boundary layer. Fortunately, #ow rates typically used in this microreactor design are less than 20 sccm. As predicted by the simulation results, the experimental data clearly indicates that the upstream temperature sensor outperforms the downstream temperature sensor. Fig. 7 also shows an excellent agreement between the experimental #ow sensor performance and the performance predicted by the simulation. For these results, the experimental data was "rst used to determine the appropriate value of thermal conductivity for the silicon nitride mem-

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brane since this value is highly dependent on the deposition procedure. Once this was obtained, the #ow sensor performance was predicted by the simulation tools. The value of thermal conductivity found for the silicon nitride membrane was 13.1 W/(mK). This is in the range of thermal conductivity for bulk silicon nitride, which is reported from 5 to 30.0 W/(mK). However, silicon nitride thin "lms have lower thermal conductivities that are highly dependent on the deposition process and the "lm thickness. The literature values for these vary from 1.2 to 13 W/(m K)(Zhang & Grigoropoulos, 1995; Mastrangelo, Yu-Chong & Muller, 1990; Govorkov, Ruderman, Horn, Goodman & Rothschild, 1997; Eriksson, Andersson & Stemme, 1997). From this analysis, it is clear that both the gas and membrane properties play substantial roles in determining the heat transfer characteristics of the T microreactor. Since the gas properties and #ow rates are "xed by the reaction system of study, the designer must alter the membrane material/thickness to achieve the appropriate heat transfer characteristics. For example, we have replaced the silicon nitride membrane with a thicker silicon membrane that increased the heat transfer rate by an order of magnitude. This resulted in the quenching of the ignition/extinction behavior observed in ammonia oxidation. In addition to altering the membrane, the heater layout can also be modi"ed to increase heat removal (by decreasing the distance between the heater and the side walls) or to increase the thermal uniformity of the reaction zone. 4.2. Redesign of the heater segment The original T microreactor heater design consisted of a 50 lm platinum line that meandered across the 300 lm heater width down the length of the channel. This design is e!ective in providing uniform power for the heated

Fig. 8. Top and side view schematics of the two heater designs: (Top) Old heater design; (Bottom) New heater design.



Fig. 9. Temperature pro"le across the membrane for the heater designs. (A) Silicon nitride membrane reactor; (B) Silicon membrane reactor (***) Old heater design; (2䉬2) New heater design.

Fig. 10. E!ect of gas #ow rate on the thermal uniformity of the new heater design (silicon nitride membrane reactor) (***) 0 sccm; (} } } 䉬 } } }) 50 sccm; (2;2) 100 sccm.

zone, but it results in a nonuniform temperature across the heater. To reduce this e!ect, the heater was split into two symmetrically placed segments along the length of the channel as illustrated in Fig. 8. The simulation tools were then used to evaluate the e!ectiveness of this design. Fig. 9 shows the results for both the silicon nitride and the silicon membrane reactors. Because silicon has a much higher thermal conductivity than silicon nitride (150}13.1 W/(m ) K), respectively), its thermal uniformity for the new design is excellent. The simulations also indicated that there is no signi"cant di!erence in the thermal uniformity between the two membrane materials for the old heater design.

Fig. 11. E!ect of reaction heat generation on the thermal uniformity of the new heater design for ammonia oxidation (***) 5% NH in air (19% conversion); (} } } 䉬 } } }) 7.5% NH in air (58% conversion); (} } } 䊐 } } }) 10% NH in air (63% conversion); (} } } 䢇 } } }) 12.5% NH in air (66% conversion); (2;2) 15% NH in air (68% conver sion).

This initial study was performed using ideal conditions: there was no gas #ow and no reaction was occurring. By increasing the gas #ow rate, the heat loss to the gas phase is increased, and this decreases the temperature across the heater as shown in Fig. 10. Fortunately, the thermal non-uniformity } the di!erence between the maximum temperature of the heater and the temperature in the center of the channel } increases by only 5.43C for a 100 sccm change in #ow rate. The e!ect of adding a highly exothermic reaction on the thermal uniformity was then studied using the ammonia oxidation system. Fig. 11 shows the temperature pro"le across the channel


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for various NH concentrations that vary from the igni tion to the extinction region. For the cases shown, only 5% NH is operating at the lower steady-state of the S curve. This is indicated by the 19% conversion found for this case, which is considerably less than the 58% to 68% conversions found for the upper steady-state cases. Because the reaction provides an additional heat source in the center of the channel, a reactor operating in the ignition regime has a much higher level of thermal uniformity. For extremely exothermic reactions at high conversions, the temperature pro"le observed will resemble the pro"le for the meandering heater design with a hot spot in the center of the channel (see Fig. 9). Clearly, it is possible to design reactors that have very uniform temperatures, but detailed knowledge of the reaction kinetics must be known. 4.3. Reaction analysis We have developed 2D and 3D simulations tools to model reacting #ow to increase our understanding of the role of heat and mass transfer under reaction conditions. Unfortunately, the 3D reacting #ow problem results in a system of equations with approximately 60,000 unknowns. Although, we have the ability to solve these problems, the time required for them makes it preferable to use a 2D model if possible. However, the length scale in the width direction is comparable to the length scales of di!usion and heat transfer, so it is not possible to completely ignore species di!usion and heat transfer in the transverse direction. To account for this mode of heat transfer in the 2D problem, the following heat loss term was added to the membrane boundary condition. h q " (¹!¹ ) , d M

(1)

Here h is an adjustable parameter that depends on the reactor geometry, q is the heat #ux in the trans, verse direction, d is the half-width of the heater, ¹ is the temperature, and ¹ is the ambient temperature. The M parameter h is determined by comparing the heater temperature pro"le between the 2D and 3D simulations. It was found that with the appropriate value of h, the temperature pro"les match quite closely over a broad range of heater powers. However, for the reacting #ow problem species di!usion in the transverse direction cannot be so easily factored into the model. Fortunately, as shown in Fig. 12 the species concentration pro"les at the center of the channel are quite similar between the 2D and 3D cases for the same average heater temperature. The di!erence between the two is in the magnitude of the species concentration. This results in a conversion of 20% in the 2D case compared with the 15% found for the 3D case. Considering, that the heater only covers 3/5 of

Fig. 12. Contour plots of the NO species concentration at the center of the channel. (Contours range from 0.001 to 0.013 with 0.001 intervals. Units are mol/m).

the channel width, these conversions are closer than expected. This is due to species di!usion in the transverse direction since, for this example, the characteristic time for di!usion from the heater to the sidewalls was 1 ms. The contact time of the reactants with the catalyst was 4 ms. With the 2D reacting #ow model, an attempt was made to reproduce the observed selectivity/conversion behavior of the microreactor for the ammonia oxidation reaction over platinum. The kinetic models used were originally given by Pignet and Schmidt (1975) and Hickman and Schmidt (1991). It was found that both models were inaccurate at predicting the behavior of the microreactor over the temperature range of interest. The model by Pignet and Schmidt gave very low conversions for temperatures less than 8003C. It was also found that the selectivity at lower temperatures heavily favored NO production. Experimentally, we observed that the selectivity to NO increases with temperature by a factor of "ve from 350 to 5003C (Srinivasan et al., 1997). The model by Hickman and Schmidt gave exceedingly fast kinetics at low temperatures and thus could not be used for the microreactor simulations. The lower operating temperatures of the microreactor, where the discussed kinetic models are no longer valid, account for these discrepancies. The temperature range of interest in the T microreactor is from 350 to 5003C. The operating temperature for an industrial ammonia oxidation reactor for which these models were made is around 800 to 9003C. Another problem with using these kinetic models is the unknown value for the platinum surface area in the T microreactor. This alone is not responsible for their failure since this would only a!ect the scale of the



conversion and not the selectivity predictions. In the microreactor, platinum is deposited using a PVD process, so its surface area is expected to be quite low. However, it has been experimentally observed that the surface area of a platinum catalyst increases by more than an order of magnitude during reactor operation. This occurs due to a simultaneous etching/deposition mechanism that causes signi"cant surface restructuring during ammonia oxidation (Lyubovsky & Barelko, 1994; Flytzani-Stephanopoulos, Wong & Schmidt, 1977). This restructuring of the platinum surface was also observed in the T microreactor where we found it di$cult to ignite the reaction on a fresh catalyst. However, ignition was easy to obtain after its initial use. The scaling of the reaction rate equations can be increased to compensate for the surface area e!ect, but this cannot explain the discrepancy in the selectivity behavior. Using the data from the T microreactor, an attempt was made to modify the kinetic model given by Pignet and Schmidt. It was found that by adjusting the preexponential factors in the rate equation we could model the e!ect of catalyst contact time on conversion. This adjusted kinetic model was used to model ignition/extinction behavior for the new heater design. However, this model did not capture the selectivity behavior because further parameter adjustments were necessary. Unfortunately, this required more extensive kinetic data than was available. Furthermore, the ignition/extinction behavior exhibited in the reaction makes the reduction of kinetic data di$cult because mass transfer e!ects become embedded in the reactor data. In the future, we can use thicker membranes with higher thermal conductivity that do not exhibit ignition/extinction for the purposes of kinetic modeling. This system is also easier to analyze numerically because of the absence of bifurcation behavior.

5. Conclusion We have demonstrated the use of simulation tools for the optimization and design of microreactors. In particular, the design of a new micro #ow sensor has been developed and tested in the current generation microreactor. The heater design was also altered to improve thermal uniformity in the reaction zone. Comparison with experimental results indicates that the simulations can be used as a data analysis tool to validate and improve kinetic models. Microreactors appear to have a larger temperature window of operation than conventional systems because of their high heat transfer rates (Franz et al., 1998). With care to avoid mass transfer limitations, it should therefore be possible to use microreactors to develop kinetic models valid over a wider range of conditions than typically possible with standard

instrumentation. This potential application is currently being investigated. From the analysis shown here, it is clear there are many choices that must be made in microreactor design, and design methodologies are clearly needed. This is not only true for the T microreactor, but for microchannel devices. The microchannel devices also have the same design characteristics as the T microreactor, which makes them ideal for modeling. However, the duplication of the reaction channel by thousands of times with each channel interacting with the others through heat transfer adds new complications to modeling. In this case, the detailed model can be used to study an individual channel, but reduced order models of these channels will be necessary to model the system as a whole. For both microreactor designs, the interactions between the design variables in determining operating behavior make it neither cost nor time e!ective to experimentally go through the many design iterations it would take to optimize a con"guration. In this way, simulations will become a necessary part of the development of microreactors for chemical production and catalyst testing.

Acknowledgements The authors thank the DARPA Micro Flumes Program (F30602-97-2-0100) for "nancial support. DJQ thanks the National Science Foundation for a graduate fellowship. Initial funding for this project from DuPont is also acknowledged.

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