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Simultaneous Optimization of Preparation Conditions and Composition of the Methanol Synthesis Catalyst by an All-Encompassing Calculation on an Artificial Neural Network Kohji Omata, Yuhsuke Watanabe, Masahiko Hashimoto, Tetsuo Umegaki, and Muneyoshi Yamada* Department of Applied Chemistry, Graduate School of Engineering, Tohoku University, Aoba 07, Aramaki, Aoba, Sendai 980-8579, Japan
A combinatorial approach comprised of a high-pressure high-throughput screening (HTS) reactor, an artificial neural network (ANN), and an all-encompassing calculation was applied to the development of catalyst for methanol synthesis at 1 MPa. Because the optimum catalyst composition is usually dependent on both preparation parameters and reaction conditions, the composition of the catalyst (Cu-Zn-Al-Sc-B-Zr), calcination temperature (300-360 °C), and amount of precipitant (1.0-2.5 times that equivalent to total cations) were optimized simultaneously. In the HTS reactor using 96-well microplates, activities of 190 catalysts with random composition, prepared using the random amount of precipitant and calcined at random temperature, were measured under pressure (1 MPa). The results with an additional 44 datasets were used for the training of ANN. After training, the ANN can map the catalyst activities as a function of the catalyst composition, amount of precipitant, and calcination temperature. An all-encompassing calculation of the 2.6 million activities of all possible combinations of the parameters was conducted to find that the space-time yield of Cu0.43Zn0.17Al0.23Sc0.11B0.00Zr0.06O1.22 precipitated by 2.2 equiv of oxalic acid and calcined at 334 °C was the global optimum activity. Introduction Methanol and dimethyl ether are excellent candidates for clean transportation fuel. A compact and economic process has been proposed to produce them from dispersed unused carbon resouces.1 In the present study, we applied homemade combinatorial tools to develop an active catalyst for low-pressure (1 MPa) methanol synthesis. The tools consist of an automated catalyst preparation tool, a high-throughput screening (HTS) reactor,2,3 and informatics tools.4-7 In our first study, a genetic algorithm (GA) alone was used as the informatics tool for optimization of a Cu-based oxide catalyst.4 The GA is based on a biological metaphor of natural evolution, where a gene, fitness, and an environment were catalyst, activity, and reaction conditions, respectively. In our catalyst development, only a simple GA was used and so the crossover occurs at one point. Because the affinity between HTS and GA is high, the combination of HTS and GA with and without other evolutional methods is often used for optimization of a heterogeneous catalyst.8 In our next stage, an artificial neural network (ANN) was introduced5,9 in order to reduce laborious experimental steps to evaluate the fitness of genes such as catalyst preparation and activity test. After ANN is trained by the initial experimental results, it can evaluate the fitness of a gene in a GA program instead of experiments at every generation. In the previous study,6 catalyst compositions and preparation parameters, such as the amount of precipitant and calcination temperature, were optimized simultaneously using GA and ANN. A HTS reactor * To whom correspondence should be addressed. Tel.: +8122-217-7214. Fax: +81-22-217-7293. E-mail: yamada@erec. che.tohoku.ac.jp.
system using a variety of 96-well microplates was also used for both preparations and activity tests to handle 96 catalyst samples at the same time. Although the resulting catalyst showed high activity, it was not proved to be the global maximum. To find the global maximum, the “holographic research strategy”, for example, is reported as a robust tool.10 We suggest in the present study an “all-encompassing calculation”7 as an alternative to the strategy. Method Activity Test. An ethanol-oxalate method was employed for catalyst preparation. To prepare catalysts for conventional activity tests, an ethanol solution of nitrates of Cu, Zn, Al, Sc, boric acid, and zirconium oxynitrate was mixed with a given composition and then an ethanol solution of oxalic acid was added to precipitate the mixed oxalic salts. The resulting mixed oxalates were washed with ethanol and dried at 80 °C under vacuum. Oxalate precipitates were calcined at 320 °C. The oxide precursor was reduced in situ using synthesis gas. The surface area was measured by nitrogen adsorption at -196 °C (MS-19, Quantachrome Instruments). The Cu metal surface area of used catalyst was in situ measured by N2O titration. The oxide structure was analyzed by X-ray diffraction (XRD; Miniflex, Rigaku Co.). Catalysts used in the HTS reactor for the activity test to obtain NN training data were prepared according to the similar method using a 96-well microplate. All solutions were dispensed into a 96-deep-well microplate (well volume: 2 mL) using a liquid handler (222XL, Gilson Inc.). To the each well then was added an ethanol solution of oxalic acid. The amount of oxalic acid was randomly selected from 1.0, 1.5, 2.0, and 2.5 times the equivalent to total cations. The oxalate
10.1021/ie034173j CCC: $27.50 © 2004 American Chemical Society Published on Web 05/20/2004
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precursors were calcined at a temperature also randomly selected from 300, 320, 340, and 360 °C. After weight measurement (15 mg), these oxide precursors were transferred into Durham tubes (8 mm o.d. and 33 mm length) and reduced using synthesis gas. Activity tests were conducted both in a conventional fixed-bed reactor and in a HTS reactor.2 The reaction gas consisted of 60% H2, 30% CO, 5% CO2, and 5% Ar (as the internal standard). The reaction was performed at 225 °C, 1 MPa, and W/F below 1 g‚h/mol. Under these conditions, CO conversion is lower than the equilibrium limit of the methanol synthesis. Activity is shown as the space-time yield (STY; g of MeOH/kg of catalyst/ h). In the conventional apparatus, CO conversion and methanol were analyzed by a gas chromatograph equipped with thermal conductivity and flame ionization detectors. For the HTS reaction, a parallel product assay by a color reaction6 was used to analyze the produced methanol trapped for 3 h in water in a 96deep-well microplate placed outside of the HTS reactor. ANN. A radial basis function network (RBFN), a kind of an ANN, was constructed using STATISTICA Neural Network 6.0 (StatSoft Inc.). Normalized catalyst compositions of Cu-Zn-Al-Sc-B-Zr, the calcination temperature, and the amount of oxalic acid were given to an input layer, and only STY (g of MeOH/kg of catalyst/ h) came out from an output layer. The number of nodes in an association layer was the same as that of training data. First, 190 data were measured in a HTS reactor. Because the number is, nevertheless, insufficient to cover all of the combinations of parameters, retraining using an additional 44 semioptimized data was performed to improve the accuracy of RBFN prediction. After the RBFN was successfully trained, the activities could be mapped as a function of the catalyst composition, the amount of precipitant, and the calcination temperature. All-Encompassing Calculation. To find the global maximum of the RBFN, an all-encompassing calculation was conducted using macrocommands of STATISTICA. At first, all of the combinations of parameters were generated (resolution of catalyst composition, 5%; amount of oxalic acid, 0.25 times the equivalent to total cations; calcined at temperature, 10 °C) by the macrocommands. When the total of the catalyst composition of a parameter set was 100%, the parameter set was substituted in the spreadsheet of STATISTICA. The number of the parameter sets was 2 603 370. Then the trained RBFN predicted STYs from each parameter set. Thus, activities for all parameter sets were predicted in an allencompassing manner. A similar method was reported as “computational scanning” for optimization of a system for the kinetic spectrometric determination of Hg(II).11 In the second stage, the predictions were divided into small groups using parameters I and II in the parameter set, for example. In each divided group, the parameter set has the same parameter I and parameter II. Then only one parameter set with the highest predicted activity in the group was selected as the representative of the group. The selected activities were plotted as functions of parameters I and II as in Figure 1. For example, parameters I and II are both 0.4 for point A in Figure 1. Only one point appears on the surface there, but underneath the point, many activities with different parameters other than I and II are “hidden”. The landscape of the activity in Figure 1 was thus il-
Figure 1. Sample of the activity envelope as functions of parameters I and II by an “all-encompassing calculation”. Numbers on the contour line in the upper part are STY values. The lower part is the side view of the activity envelope.
lustrated. Therefore, the surface of the landscape is an envelope for activities. The optimum parameters I and II for the global maximum catalyst and shape around the maximum can be easily displayed in the figure. Around the maximum, the all-encompassing calculation can be performed again, if necessary, with higher resolution to find the precise maximum point. To find the optimum parameter quickly, the side view of the activity envelope is more convenient, as shown in the lower part of Figure 1. One can find the optimum parameter I for the global maximum. Such side views are used in Figure 4. Results and Discussion Parameters for Optimization.6 To pick up adequate and influential parameters for active catalyst, the effect of additives was investigated at first on CuZn-X catalysts where Cu/Zn/X ) 6/3/1 (X: additive, molar ratio). In Figure 2, the activity and Cu metal surface area by N2O titration are shown. Preparation conditions were identical. The clear correlation between the activity and the Cu metal surface area was observed. Thus, additives in the upper-right section, such as Al, B, and Sc, are effective for high activity and high surface area, while Cr shows no effect. Zr increased the Brunauer-Emmett-Teller (BET) surface area (not shown in the figure), but the high surface area was not effective for STY in this case. In an ethanol-oxalate method, precipitated mixed oxalate is converted to mixed metal oxide by calcination. The morphology of the resulting oxide is influenced by the calcination temperature and the amount of precipi-
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Figure 2. Influence of additives on STY and the Cu surface area at 1 MPa, 225 °C, and Cu/Zn/X ) 6/3/1 molar ratio.
Figure 3. Effect of the calcination temperature (closed circles) and the amount of oxalic acid (closed squares) on the activity at 1 MPa and 225 °C.
tate (oxalic acid). The effect of the calcination temperature is shown in Figure 3 (closed circle), where Cu/Zn/ Al ) 6/3/1 and an equivalent amount of oxalic acid was used for their precipitation. The catalyst obtained by 300 °C calcination shows the highest activity. XRD patterns of these oxides showed that Cu and Zn oxides were sintered at higher temperature while decomposition of oxalates was not enough at 250 °C. In Figure 3 are shown the effect of the amount of oxalic acid (closed square). Contrary to the catalytic activity, the BET surface area of oxide precursors shown in parentheses in the figure increases when the quantity of oxalic acid increases. The contrariety can be solved from a viewpoint of Cu sintering. If the initial temperature rise by rapid reaction start-up was avoided, a catalyst prepared using a larger amount of oxalic acid showed higher activity. Clearly, the use of excess oxalic acid results in an active but thermally sensitive catalyst. Totally, the balanced parameters for high activity and for high stability are important, and thus simultaneous optimization of catalyst components (Cu, Zn, Al, Sc, B, and Zr) and preparation conditions (amount of oxalic acid and calcination temperature) was attempted. Comparison of Optima Found by a GA and an All-Encompassing Calculation. In the previous study,6 190 data were measured in a HTS reactor. Because the accuracy of RBFN prediction based on the 190 data was not enough, retraining using additional 44 semi-optimized data was performed. After the RBFN was successfully retrained, the prediction of maximum activity
Figure 4. Activity envelope as functions of the catalyst composition, the calcination temperature, and the amount of precipitant.
found by a GA was consistent with the experimental result. The retrained RBFN was examined by an allencompassing calculation with low resolution at first (resolution of composition, 5%; oxalic acid, 0.25 equiv; calcination temperature, 10 °C). The results of the calculation are illustrated in Figure 4 as a function of each component, the amount of oxalic acid, and the calcination temperature. The maximum STY was easily found to be 488 g of MeOH/kg of catalyst/h by Cu0.40Zn0.20Al0.25Sc0.05B0.00Zr0.10O1.25 precipitated by 1.5 equiv of oxalic acid and calcined at 330 °C. Activities around the maximum can be examined, and one can narrow the range for calculation with high resolution. Because the calculation is easily enlarged in an all-encompassing calculation with high resolution, reduction of the calculation range is preferable. An allencompassing calculation with high resolution (resolution of composition, 1%; oxalic acid, 0.1 equiv; calcination temperature, 2 °C) showed that the global maximum of STY is 495 g of MeOH/kg of catalyst/h and the optimum catalyst is Cu0.43Zn0.17Al0.23Sc0.11B0.00Zr0.06O1.22 precipitated by 2.2 equiv of oxalic acid and calcined at 334 °C. The optimum catalyst found by a GA in the previous study6 (Cu0.43Zn0.17Al0.23Sc0.11B0.00Zr0.06O1.22 precipitated by 2.2 equiv of oxalic acid and calcined at 332 °C; the experimental STY was 427 g of MeOH/kg of catalyst/h) was quite similar to the above catalyst.
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Figure 5. Effect of the amount of precipitant on the activity of the catalyst at maximums: Cu0.43Zn0.17Al0.23Sc0.11B0.00Zr0.06O1.22 calcined at 334 °C. Predicted (solid line) and experimental (closed circles) data are compared.
In this case, therefore, a GA succeeded in the discovery of the global optima. The effect of the amount of oxalic acid was predicted and experimentally checked, as shown in Figure 5. Although the predicted effect of oxalic acid was not confirmed and STY was almost constant if the amount of oxalic acid was increased in contrast to the prediction, the optimum catalyst showed actually high activity.6 The activities in Figure 5 are predicted based on only 234 datasets, whereas the number of possible combinations in an all-encompassing calculation with high resolution (resolution of composition, 1%; oxalic acid, 0.1 equiv; calcination temperature, 2 °C) is 79 billion. The dataset for the prediction is only 3 ppb and too small compared with the vast search space. In this case, therefore, one can conclude that the prediction agreed well with the experimental results. Comparison of a GA and an All-Encompassing Calculation. During the optimization process by a GA, genes with low activity disappear in the early stage of the evolution and almost no genes have high fitness to the environment. Calculation for low activity is not necessary in a GA program, resulting in the small requirement for computer resources. However, generation alternation should be repeated to find maximum genes, although thus the calculation for each generation
is compact. However, one can get no warranty if the maximum is global. On the other hand, in the allencompassing method, calculation for low activity also should be computed. It demands larger computer resources than a GA does. The importance is that the global maximum can be found definitely by this method even at such penalties. Other advantages and disadvantages of these methods are compared below. In the former study, the target of optimization is the same as the target in this study: Cu, Zn, Al, Sc, B, Zr, calcination temperature, and amount of oxalic acid. These parameters are coded to one binary code. In this case, the maximum STY appeared at the 80th generation during evolution by a GA.6 Afterward convergence of Cu and Zn compositions toward one singular area was observed, but other parameters, the amount of oxalic acid and the calcination temperatures, scattered randomly. The genes from the 80-90th generations are superimposed in Figure 6 a as functions of the preparation conditions to check the dispersion. Two peaks at 2.2 and 1.6 equiv with calcination at 332 °C appeared obscurely. It is curious, however, that almost no active genes are distributed above the 332 °C line. Because calcination below this temperature could not be extremely unfavorable as a pretreatment, gene coding probably causes the uneven distribution. The calcination temperature in Figure 6a was coded by binary coding as shown in Table 1. In this case, a gap between 330 and 332 °C is very large because five bits in the code should be changed at a time for the interconversion. Because such a change is not probable in our GA program, where only one-point crossover and one mutation occurs, transformation between 330 and 332 °C is very difficult. Unfortunately, the optimum calcination temperature (332 °C) is located at the gap. To avoid this trouble, Gray coding12 was adopted instead of the binary coding. Always only one bit is changed from the adjacent code, as shown in Table 1. The result of evolution during 80-90 generations is shown in Figure 6b. The irregular gap at 332 °C disappeared, and two peaks at 2.2 and 1.6 equiv certainly appear. In a GA program, the coding method is very important and the optimization is influenced much by coding.
Figure 6. Dispersion of the catalyst parameter on an ANN during “evolution” at the 80-90th generation. The genes were coded by (a) binary coding and (b) Gray coding.
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Figure 7. Activity envelope surface as functions of the calcination temperature and the amount of precipitant: (a) low resolution; (b) high resolution. Table 1. Coding of the Calcination Temperature calcination temp (°C)
binary code
gray code
calcination temp (°C)
binary code
gray code
300 302 304 306 308 310 312 314 316 318 320 322 324 326 328 330
00000 00001 00010 00011 00100 00101 00110 00111 01000 01001 01010 01011 01100 01101 01110 01111
00000 00001 00011 00010 00110 00111 00101 00100 01100 01101 01111 01110 01010 01011 01001 01000
332 334 336 338 340 342 344 346 348 350 352 354 356 358 360 362
10000 10001 10010 10011 10100 10101 10110 10111 11000 11001 11010 11011 11100 11101 11110 11111
11000 11001 11011 11010 11110 11111 11101 11100 10100 10101 10111 10110 10010 10011 10001 10000
In an all-encompassing calculation, the optimum is easily discovered at the side views of the activity envelope, as shown in Figure 4. On the other hand, the top view of the activity envelope is useful to find a feature of the catalyst around the local maximum peaks. It is illustrated in Figure 7 using the amount of oxalic acid and the calcination temperature. The resolutions of composition, 5%, oxalic acid, 0.25 equiv, and calcination temperature, 10 °C, were used in Figure 7a and 2%, 0.1 equiv, and 2 °C in Figure 7b, respectively. For the latter figure, the calculation range was reduced based on the result in Figure 5a for convenience in calculation. The two local maximum peaks, which are the same as those predicted by GA, are clearly shown. Such a good visualization of the calculation result is another advantage of this method. Apparently, STY is different from parts a and b of Figure 7 at the same oxalic acid and calcination temperature. As explained in the Experimental Section,
only the maximum STY for each point appears at the top view of the STY landscape. If the catalyst composition is tuned up minutely, STY can be increased. Thus, the top view of the STY landscape depends on the resolution of the calculation. Such a tradeoff between the precise prediction and the computing power is the key point of the all-encompassing calculation. Effect of B Addition. Genes with a lower fitness to the environment hardly survive like natural selection to the next generation in a GA program, and the optimum catalyst contains no B. In the evolution process, therefore, information on the B effect on the activity is not available. B would be one of the important additives,13 as shown in Figure 4e, where the catalyst containing 30% B shows almost the same activity as the optimum one. Because the shape of RBFN depends on the training data and the number of datasets used for training is not enough compared with the whole experimental space, it is possible that the order of the peak height in Figure 4e transposed and the catalyst containing B became the optimum in an alternative RBFN. Catalysts containing 25-35% B were selected from the 2.6 million data obtained by an all-encompassing calculation. Again the envelope surface of the selected activity is illustrated in Figure 8 using the amount of oxalic acid and the calcination temperature as sorting keys. The maximum point is Cu0.30Zn0.25Al0.10Sc0.00B0.30Zr0.05O1.25 precipitated by 1.0 equiv of oxalic acid and calcined at 320 °C. Then the catalyst composition on the 320 °C line in the figure was investigated. In the catalyst with higher activity, no Sr is contained, contrary to Figure 7. This result suggests that the two additives (B and Sr) have no interaction. The effect of the B content x, where the catalyst is expressed as Cu0.30Zn0.25Al0.10Sc0.30-xBxZr0.05O1.25 (precipitated by 1.75 equiv of oxalic acid and calcined at 330 °C), was examined, as shown in Figure 9.
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Conclusion A combinatorial approach using a HTS reactor, ANN, and an all-encompassing calculation was applied to the development of a low-pressure methanol synthesis catalyst. Activities of 234 catalysts measured in the HTS reactor were used for ANN training. After training, the ANN can map the catalyst activity as a function of the catalyst composition and preparation parameters such as the amount of precipitant and calcination temperature. An all-encompassing calculation found that STY of Cu0.43Zn0.17Al0.23Sc0.11B0.00Zr0.06O1.22 precipitated by 2.2 equiv of oxalic acid and calcined at 334 °C was the global optimum one. The advantage of this calculation is its ability to find out the global optimum. The effect of B, which was not included in the optimum catalyst, was also clarified through visualization of the calculation result. The good visualization is another merit of the method. Acknowledgment We acknowledge financial support from Research for the Future Program of JSPS under the Project “Synthesis of Ecological High Quality Transportation Fuels” (JSPS-RFTF98P01001) and from a Grant-in-Aid for the COE project “Giant Molecules and Complex Systems” by the Ministry of Education, Culture, Sports, Science and Technology. Literature Cited
Figure 8. Activity envelope surface of catalysts containing 2525% B as functions of the calcination temperature and the amount of precipitant.
Figure 9. Additive effect of B and Sc on the activity. Predicted (solid line) and experimental (closed triangeles) data are compared.
Both the predicted (solid line) and experimental (closed triangles) STY were almost the same. The result shows that B and Sc are dilutor for each other and no synergy is observed. It has been reported that B escapes from the catalyst surface to expose a new active site, and as a result, the activity increases.13 The different promotion mechanisms of Sc and B would be the cause of their lack of synergy.
(1) Yamada, M. High-Quality Transportation Fuels. Energy Fuels 2003, 17, 797. (2) Omata, K.; Ishiguro, G.; Yamada, M. High-Throughput Screening of Heterogeneous Catalyst under Pressurized Conditionss Optimization of Cu-Mn Oxide Catalyst for Methanol Synthesis. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2000, 43, 317. (3) Omata, K.; Watanabe, Y.; Umegaki, T.; Hashimoto, M.; Yamada, M. Catalyst Development for Methanol Synthesis Using Parallel Reactors for High-throughput Screening Based on a 96 Well Microplate System. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2003, 46, 328. (4) (a) Omata, K.; Umegaki, T.; Ishiguro, G.; Yamada, M. Optimization of Cu-Zn-Al Oxide Catalyst for Methanol Synthesis Using Genetic Algorithm. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2001, 44, 327. (b) Umegaki, T.; Omata, K.; Ishiguro, G.; Watanabe, Y.; Yamada, M. Application of Genetic Algorithm to Optimize the Composition of Cu-Zn-Al-Sc Oxide Catalyst for Methanol Synthesis. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2003, 46, 181. (5) (a) Omata, K.; Umegaki, T.; Watanabe, Y.; Yamada, M. Optimization of Cu-Zn-Al Oxide Catalyst for Methanol Synthesis Using Genetic Algorithm and Neural Network as Its Evaluation Function. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2002, 45, 192. (b) Omata, K.; Umegaki, T.; Watanabe, Y.; Nukui, N.; Yamada, M. Design of Cu-Zn-Al-Sc Oxide Catalyst for Methanol Synthesis Using Genetic Algorithm Based on Radial Basis Function Network as the Evaluation Function. Sekiyu Gakkaishi (J. Jpn. Petr. Inst.) 2003, 46, 189. (c) Umegaki, T.; Watanabe, Y.; Nukui, N.; Omata, K.; Yamada, M. Optimization of Catalyst for Methanol Synthesis by a Combinatorial Approach Using a Parallel Activity Test and Genetic Algorithm Assisted by a Neural Network. Energy Fuels 2003, 17, 850. (6) Watanabe, Y.; Umegaki, T.; Hashimoto, M.; Omata, K.; Yamada, M. Optimization of Cu Oxide Catalysts for Methanol Synthesis by Combinatorial Tools Using 96 Well Microplates, Artificial Neural Network and Genetic Algorithm. Catal. Today 2004, 89, 455. (7) Omata, K.; Hashimoto, M.; Watanabe, Y.; Umegaki, T.; Yamada, M. Optimization of Cu-Based Oxide Catalyst for Methanol Synthesis by the Activity Map Envelope Derived from a Neural Network. Sekiyu Gakkaishi (J. Jpn. Pet. Inst.) 2003, 46, 383. (8) (a) Wolf, D.; Buyevskaya, O. V.; Baerns, M. An Evolutionary Approach in the Combinatorial Selection and Optimization of Catalytic Materials. Appl. Catal. A 2000, 200, 63. (b) Buyevskaya,
3288 Ind. Eng. Chem. Res., Vol. 43, No. 13, 2004 O. V.; Wolf, D.; Baerns, M. Ethylene and Propene by Oxidative Dehydrogenation of Ethane and Propane. Performance of RareEarth Oxide-Based Catalysts and Development of Redox-Type Catalytic Materials by Combinatorial Methods. Catal. Today 2000, 62, 91. (c) Buyevskaya, O. V.; Bruckner, A. E.; Kondratenko, V.; Wolf, D.; Baerns, M. Fundamental and Combinatorial Approaches in the Search for and Optimization of Catalytic Materials for the Oxidative Dehydrogenation of Propane to Propene. Catal. Today 2001, 67, 369. (d) Yamada, Y.; Ueda, A.; Nakagawa, K.; Kobayashi, T. Optimization of Fe/SiO2 Based Metal Oxides as Selective Oxidation Catalyst of Propane with Combinatorial Approach. Res. Chem. Int. Med. 2002, 28, 397. (e) Serra, J. M.; Chica, A.; Corma, A. Development of a low-temperature light paraffin isomerization catalysts with improved resistance to water and sulphur by combinatorial methods. Appl. Catal. A 2003, 239, 35. (f) Serra, J. M.; Corma, A.; Farrusseng, D.; Baumes, L.; Mirodatos, C.; Flego, C.; Perego, C. Styrene from Toluene by Combinatorial Catalysis. Catal. Today 2003, 81, 425. (g) Corma, A.; Serra, J. M.; Chica, A. Discovery of New Paraffin Isomerization catalysts Based on SrO42-/ZrO2 and WOx/ZrO2 Applying Combinatorial Techniques. Catal. Today 2003, 81, 495. (9) (a) Cundari, T. R.; Deng, J.; Zhao, Y. Design of a Propane Ammoxidation Catalyst Using Artificial Neural Networks and Genetic Algorithms. Ind. Eng. Chem. Res. 2001, 40, 5475. (b) Corma, A.; Serra, J. M.; Argente, E.; Botti, V.; Valero, S. Application of Artificial Neural Networks to Combinatorial Catalysis:
Modeling and Predicting ODHE Catalysts. ChemPhysChem 2002, 3, 939. (c) Huang, K.; Zhan, X.-L.; Chen, F.-Q.; Lu, D.-W. Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm. Chem. Eng. Sci. 2003, 58, 81. (10) (a) Vegvari, L.; Tompos, A.; Gobolos, S.; Margitfalvi, J. L. Holographic research strategy for catalyst library design. Description of a new powerful optimization method. Catal. Today 2003, 81, 517. (b) Tompos, A.; Margitfalvi, J. L.; Tfirst, E.; Vegvari, L. Information Mining Using Artificial Neural Networks and “Holographic Research Strategy”. Appl. Catal. A 2003, 254, 161. (11) Kompany-Zareh, M.; Tavallali, H.; Sajjadi, M. Application of generalized artificial neural networks coupled with an orthogonal design to optimization of a system for the kinetic spectrophotometric determination of Hg(II). Anal. Chim. Acta 2002, 469, 303. (12) Michalewicz, Z. Genetic Algorithms + Data Structures ) Evolution Programs, 3rd ed.; Springer-Verlag: New York, 1996. (13) Wu, J. G.; Saito, M.; Mabuse, H. Activity and stability of Cu/ZnO/Al2O3 catalyst promoted with B2O3 for methanol synthesis. Catal. Lett. 2000, 68, 55.
Received for review October 9, 2003 Revised manuscript received April 7, 2004 Accepted April 16, 2004 IE034173J
