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Artificial Neural Network Aided Design of a Stable Co-MgO Catalyst of High-Pressure Dry Reforming of Methane Kohji Omata,* Noritoshi Nukui, and Muneyoshi Yamada Department of Applied Chemistry, Graduate School of Engineering, Tohoku University, Aoba 6-6-07, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Dry reforming of methane attracts much attention in order to convert the two greenhouse gases simultaneously to syngas. Preparation parameters of the citric acid method were surveyed to prepare a Co-MgO catalyst with a long life using the design of experiment (DOE), an artificial neural network (ANN), and a grid search (GS). The preparation parameters such as Co loading, amount of citric acid, calcination temperature, and pelletization pressure were determined according to an L9 orthogonal array. After the catalytic activity was measured in a conventional fixed-bed reactor under pressure, a good fitting of the simple power-law equation (SPLE) to the activity change was obtained. The preparation parameters and the resultant SPLE parameters were used for the training of the ANN. The optimum was determined by a GS and verified experimentally to be stable. The combination of SPLE parameters by DOE, ANN, and GS was found to be a useful tool for the development of the catalyst. Introduction High-pressure dry reforming of methane converts greenhouse gases (methane and carbon dioxide) simultaneously to useful syngas with a low hydrogen/carbon monoxide ratio, which is suitable for Fischer-Tropsch synthesis and dimethyl ether (DME) synthesis. We are developing a compact and simple process to produce DME from dispersed unused carbon resources,1 and the process would demand operation at 1 MPa. Thus, an active and stable reforming catalyst under pressure is requested to attain the compact and simple process. However, only a few catalysts among nonnoble metal catalysts were reported to be effective catalysts under pressure. Rapid catalyst development for high-pressure dry reforming is required. The stages of catalyst development from the first screening for the “hits” catalyst to the final commercial plant were summarized as first screening f second screening f standard labo reactor f pilot plant f commercial plant.2 Combinatorial catalysis using a high throughput screening (HTS) is a promising methodology to accelerate the first and second screening steps. The present authors are developing new methodology to replace the HTS step, which usually demands an expensive parallel reactor system. Screening for effective additives in the periodic table was reported.3 On the basis of the limited number of experiments, an artificial neural network (ANN) was trained to predict the effectiveness of catalyst additives, and then the effectiveness of all of the elements was calculated using the ANN. Tin was discovered to be effective for promoting the selectivity of methanol carbonylation with a nickel/active carbon catalyst. A similar methodology to correlate the character of the element and its effectiveness was reported using partial least squares instead of ANN.4 These methods might be alternatives to the first screening using HTS reactor system hardware. We also optimized the preparation conditions of catalysts and the catalyst compositions using the design * To whom correspondence should be addressed. Tel.: +81-22-217-7215. Fax: 81-22-217-7293. E-mail: omata@ erec.che.tohoku.ac.jp.
of experiment (DOE) and ANN. The ANN was successfully trained using the limited number of experiments designed by the orthogonal array of the Taguchi method.5,6 The methodology is developed as a second screening tool. In the present study, the life test was partially replaced by ANN, which was trained using DOE. Because we found that the Co-MgO catalyst prepared by an oxalate coprecipitation method showed a high activity for high-pressure dry reforming of methane,7 the methodology was applied to develop a stable CoMgO catalyst. The preparation conditions by the citric acid method were determined by DOE, and the activity change of the catalyst was parametrized by the simple power-law equation (SPLE) of which parameters were given to ANN as training data. A grid search (GS) successfully found the optimum parameters for a stable catalyst. To our knowledge, this is the first report on the life test in the field of combinatorial catalysis. Experimental Section Catalyst Preparation by the Citric Acid Method. The catalyst was prepared by the citric acid method. Cobalt nitrate and magnesium nitrate were dissolved in melting citric acid monohydrate in a beaker heated on a hot plate. The resultant mixture was treated in a vacuum oven at 70 °C for 3 h and then calcined in air at 170 °C for 1 h and at 400-700 °C for 4 h. If the precursor was calcined over 700 °C, the activity was quite low. 7 The oxide was pelletized at 0-60 MPa and sieved (20/36 mesh). The target preparation conditions were the Co content (mol %), calcination temperature (°C), amount of citric acid (equivalent), and pelletization pressure (MPa). Activity Tests. Dry reforming of methane was conducted in a fixed-bed flow reactor. A reactor tube made of quartz (i.d. ) 3 mm) was inserted in a stainless steel tube. The catalyst was packed inside the quartz tube to prevent the direct contact of the reaction gas with the wall inside the stainless steel tube. Between the two tubes, nitrogen was fed to compensate for the pressure inside and outside of the quartz tube. After catalysts
10.1021/ie049302q CCC: $30.25 © 2005 American Chemical Society Published on Web 12/13/2004
Ind. Eng. Chem. Res., Vol. 44, No. 2, 2005 297 Table 1. Results of Experiments Designed by an L9 Orthogonal Array preparation parameters
catalytic performance
citric Co calcin. T acid pellet. P CO yielda no. (mol %) (°C) (equiv) (MPa) (%)
C depos.b (mg/g of catalyst)
1 2 3 4 5 6 7 8 9
7 7 7 10 10 10 12 12 12
400 550 700 400 550 700 400 550 700
L9 Orthogonal Array 1 0 1.5 30 2 60 1.5 60 2 0 1 30 2 30 1 60 1.5 0
24.2 16.5 0 22.8 32.2 0 23.9 28.6 0
63 19 95 47 244 106 70 2332 55
10
9
400
Optimum A 1.7 0
33.8
52
a
Figure 1. GS on RBFN.
b
CO yield after a 4 h run. Carbon deposition after a 4 h run.
were reduced with H2 at 850 °C for 10 min under 0.1 MPa, reactant gas (CH4/CO2/N2 ) 45/45/10) was introduced into the catalyst bed at 750 °C under 1 MPa and the space velocity was set to 400 000 mL/(g/h). Analysis of the reaction products was performed with an online gas chromatograph (micro-GC, M-200, Agilent Technologies, Inc.). The catalytic activity was indicated as the yields of CO (CO production rate/[CH4 + CO2] feed rate). The amounts of carbon deposition on the catalysts were quantified by carbon dioxide produced during temperature-programmed oxidation in an air flow after the 4 h reforming reaction. After the catalytic activity was measured, the activity change with time on stream was fitted to a SPLE.8 The activity was expressed as c
activity ) a/(b + t)
(1)
where the activity is the CO yield and t is time on stream. Each catalyst was featured by the three parameters in addition to the conventional parameters such as the CO yield and carbon deposition. DOE. An L9 orthogonal array of the Taguchi method9 was used for DOE. Only nine experiments as shown in Table 1 (nos. 1-9) were necessary for four factors with three levels instead of 81 experiments by full factorial design. One of the advantages of the Taguchi method is such a small requirement for the number of experiments. ANN. ANNs were applied to correlate four preparation parameters, such as the Co content, amount of citric acid, calcination temperature, and pelletization pressure, with the catalytic parameters. Among many types of ANN, the radial basis function network (RBFN) was used. It was constructed using STATISTICA Neural Network 6.0 (StatSoft). The number of nodes in a hidden layer was set as identical with that of the training data. RBFN is denoted as RBFN4-9-3 for example, where 4 is the number of nodes in the input layer, 9 is that in the hidden layer, and 3 is that in the output layer. GS. A GS, formerly reported as an “all-encompassing calculation”,10-12 was conducted to find the global maximum of the ANN. The program was coded using macrocommands of STATISTICA. At first, all combinations of parameters were generated. The search ranges and calculation intervals (as shown in parentheses) for the Co content, calcination temperature, amount of citric acid, and pelletization pressure were 7-12 (0.5) mol %, 400-700 (10) °C, 1-2 (0.1) equiv, and 0-60 (10) MPa,
respectively. The number of possible combinations was 26 257. Then the trained ANN predicted all of the catalyst activities using each input parameter set. Response surfaces can be obtained as functions of the Co content and calcination temperature using the predictions by ANN. When other parameters such as the amount of citric acid and pelletization pressure are different, the response surfaces differ from each other. If all of the response surfaces are overlapped, the top view represents the so-called envelope of the response surfaces and we can find the global maximum on the envelope. Hereafter, such an overlapped response surface is used to find the global maximum. Result and Discussion Active Catalyst with Low Carbon Deposition. The results of the activity test using a conventional fixed-bed reactor are summarized in Table 1, nos. 1-9. In the first step, the four preparation parameters and two catalytic performances were used for the training of RBFN4-9-2 in order to find the preparation conditions for high activity and low carbon deposition.5 The CO yield in Table 1 is that after a 4 h run. After rapid training of RBFN4-9-2, CO yields and carbon depositions on the catalysts were calculated by GS on the RBFN, and all CO yields are plotted in Figure 1 as functions of the Co content and calcination temperature. Catalysts with carbon deposition of less than 5 mg/g are plotted as triangles, and others are plotted as bars. To find the optimum preparation conditions, parameters for catalysts with a CO yield of more than 30% were searched for among the triangles. From the candidates, the “optimum A” (listed in Table 1, no. 10) was selected and experimentally verified. Usually an active catalyst with high CO yield tends to give a large amount of carbon deposition, as shown by closed triangles in Figure 2. The optimum A, shown as the open square in Figure 2, achieved both high activity and low carbon deposition at the same time. Choice of RBFN Adequate for the Prediction of the Catalyst Life. The activity change of optimum A after 4 h was tested. The CO yield is shown in Figure 3. The activity was reached at steady state after 4 h and then decreased again after 15 h to reach 25% yield at 24 h. Because the amount of carbon deposition on this catalyst should be small, other causes for deactivation
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Figure 2. Catalytic performance by DOE (closed triangles) and of optimum A (open square).
Figure 3. Catalyst life of optimum A.
should be considered. Oxidation of cobalt metal was also reported as a significant factor for deactivation of the cobalt-based catalyst for dry reforming of methane reported.13 The reaction atmosphere can be oxidative and reductive depending on the reaction conditions and conversions. If it is oxidative and cobalt metal is oxidized, the conversion should decrease. In the case of Co-MgO, addition of hydrogen in the reaction feed prevented deactivation.7 This result supports the deactivation mechanism. If this is the case, it is impossible for RBFN4-9-2 to predict the deactivation after 4 h because the RBFN was constructed based on the preparation parameters, CO yield at 4 h, and carbon deposition during the 4 h reaction. A new methodology for the prediction of the catalyst life after 4 h was discussed and explored. Hattori and Kito reported that ANN can be applied for the prediction of deactivation in methanol conversion by a modified zeolite catalyst.14 Two methods were reported: (i) zeolite parameters such as the Si/Al ratio were related to the rate constant of the empirical deactivation equation; (ii) a catalyst decay curve was predicted from the initial decay data. It was pointed out based on the results in both cases that ANN can be a powerful tool to estimate the deactivation behavior of catalysts. In the present study, a variety of ANN, especially RBFN, was also constructed, as shown in Figure 4, to predict the catalyst life. The input factor, input layer, hidden layer, output layer, and output factor are located from left to right in each model. Connections between nodes are partially displayed. RBFN4-9-2 in model 0 was the same model as that already used to predict the CO yield and carbon deposition in Figure 1. In this model, the time factor is not
Figure 4. RBFN model for catalyst life prediction.
included. The number of datasets ()node number in the hidden layer) for training was nine because they were designed by the L9 orthogonal array. The input factor and training data for RBFN4-9-4 in model 1 are the same as those for RBFN4-9-2. The output factor is the CO yield at 1, 2, 3, and 4 h. Therefore, the CO yield at 4 h from RBFN4-9-4 should be the same as that from RBFN4-9-2. RBFN5-36-1 in model 2 can predict the CO yield at any time because the time was included in the input factor. Four parameters of catalyst preparation, four kinds of reaction time (1, 2, 3, and 4 h), and CO yields at each time were used as training data. Thus, the number of training datasets was 36 ()9 × 4). The deactivation profile of catalysts prepared based on the parameters from the L9 orthogonal array was well fitted by SPLE. The typical samples of the fitting are shown in Figure 5, and the fitted parameters are summarized in Table 2 with coefficient of determination r2. The output factors from RBFN4-9-3 in model 3 are parameters of each SPLE, namely, a, b, and c in eq 1. c
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Figure 6. Prediction of the CO yield by RBFNs: model 1 (closed circles); model 2 (broken line); model 3 (solid line); experimental result (open circles).
Figure 5. Fitting to SPLE: (a) no. 1, (b) no. 3, and (c) no. 8 of an L9 orthogonal array. Table 2. SPLE Parameters Fitted to the Result of Experiments Designed by an L9 Orthogonal Array preparation parameters citric Co calcin. T acid pellet. P no. (mol %) (°C) (equiv) (MPa) 1 2 3 4 5 6 7 8 9
7 7 7 10 10 10 12 12 12
400 550 700 400 550 700 400 550 700
10
9
400
a
SPLE parametersa 3+ a b log(c) r2 b
L9 Orthogonal Array 1 0 81.6 1.5 30 22.4 2 60 9.56 1.5 60 24.2 2 0 48.2 1 30 0.14 2 30 25.8 1 60 30.3 1.5 0 0.06 Optimum A 1.7 0
CO yield ) a/(time +
b)c. b
35.8
2.20 0.55 1.63 0.14 0.93 0.37 0.00 0.11 0.19
2.823 2.293 3.661 1.760 2.394 3.277 1.776 1.799 3.279
0.998 0.997 0.982 0.979 0.981 0.988 0.996 0.874 0.984
0.00 1.699 0.987
Coefficient of determination.
was used as 3 + log(c) in the training for the adjustment of the degree of nonlinearity of the parameter. Each RBFN was trained by experimental results or SPLE parameters in Table 2, nos. 1-9. After the rapid
training was finished, the validity of each RBFN was checked by the result of the life test (in Figure 3) of optimum A that was prepared with the optimum parameters of RBFN4-9-2. The parameters listed as no. 10 in Table 1 were input to each RBFN to predict the CO yield. The results are illustrated in Figure 6. The experimental results are shown by open circles, and the CO yields by model 1 (closed circles), model 2 (broken line), and model 3 (solid line) are compared. The results were not satisfactory. In model 1, only the prediction of the CO yield at 4 h was identical with the experimental result. Using this model, a stable catalyst would be selected from the tentative candidates of which activities at 2 and 4 h were almost at the same level. Thus, the prediction on the CO yield at 2 h is important. The variance at 2 h in Figure 6 prevents us from using this model. The result of model 2 shows the constraints of RBFN. Clearly, the RBFN is not good at extrapolation, and the prediction outside of the experimental range is not reliable. The prediction by model 3 is also far from the experimental results. However, the apparent variance comes from the overestimation of parameter a in eq 1, and the most important parameter for the catalyst life, c in eq 1, seems almost correct. Thus, the most promising model among the three is model 3. Stable Catalyst. For better and precise prediction by model 3, retraining of the RBFN was performed using additional datasets, nos. 1-3 in Table 3. The resultant RBFN is RBFN4-13-3. By the GS on the RBFN4-13-3, all combinations of parameters a, b, and c were predicted and then the CO yield at 8000 h was calculated based on the parameters and eq 1. The top view of overlapped response surfaces as functions of the Co content and calcination temperature is illustrated in Figure 7 with the contour line of the CO yield at 8000 h. The maximum CO yield was attained by Co-MgO,
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Table 3. SPLE Parameters for Additional Training of RBFN preparation parameters citric Co calcin. T acid pellet. P no. (mol %) (°C) (equiv) (MPa) 1 2 3
9 10 12 a
550 400 400
1.25 1 1
40 60 60
SPLE parametersa 3+ a b log(c) r2 b 28.4 0.00 0.970 0.911 25.1 0.01 1.722 0.991 42.5 3.25 2.326 0.800
CO yield ) a/(time + b)c. b coefficient of determination.
Figure 7. Predicted CO yield at 8000 h. Figure 9. Simple hill-climbing strategy for catalyst development. Table 4. Optimization by Simple Hill-Climbing Steps max CO yield of citric step Co calcin. T acid pellet. P optimum each step (%) no. (mol %) (°C) (equiv) (MPa) of Xa 1 2 3 4 a
Figure 8. Prediction of the CO yield of an optimum catalyst: optimum A (a), optimum B (b); predicted activity by RBFN4-13-3 (broken line), experimental result (closed squares and solid line).
optimum B, prepared by 8.5 mol % Co, 1.0 equiv of citric acid, calcined at 430 °C, and pelletized at 20 MPa. The catalyst will give 35% CO yield after a 24 h run. The predicted and experimental time courses of the catalytic activity of optimum A and B are illustrated in Figure 8. Broken lines are activities predicted by RBFN4-13-3, and closed squares are the experimental results with solid lines. Parameters of SPLE for optimum A were included in the dataset for retraining of ANN, but the experimental
X 10.0 10.0 10.0
550 X 510 510
1.5 1.5 X 1.3
60 60 60 X
10.0 510 1.3 60
21.5 22.7 24.5 24.5
The target parameter of each step is shown as X.
result fitted to eq 1 was that of a 4 h run (shown in Table 2, no. 10). Thus, a small gap was observed during 4 h. After 15 h, the gap gets larger rapidly and the catalytic stability of optimum A after 15 h is not enough. On the contrary, the activity of optimum B is rather lower than predicted, whereas the stability after 15 h is better than that of optimum A. The catalyst life of optimum B is clearly longer than that of optimum A, and the prediction of a catalyst with a long life by RBFN4-13-3 was experimentally verified. A simple hill-climbing strategy for parameter optimization was simulated using RBFN4-14-3. In a conventional way, parameters are often optimized sequentially by a simple hill-climbing strategy. At first, the Co content was optimized. Other tentative catalyst preparation conditions for the first catalysts were reasonably determined: 1.5 equiv of citric acid (usually an excess amount of the chelating reagent is used), calcined at 550 °C (to burn off the organic chelating reagent), and pelletized at 60 MPa (for easy handling of the fluffy powder after calcination). As shown in Figure 9a, the highest CO yield was obtained by a 10 mol % Co-MgO catalyst. Then the calcination temperature, citric acid, and pelletization pressures were optimized sequentially as shown in Table 4 and parts b-d of Figure 9, respectively. Finally, calcination at 510 °C, 1.3 equiv of citric acid, and pelletization at 60 MPa resulted in the 24.5% CO yield. The yield is somewhat lower than that by optimum B, which was predicted based on 13 experimental runs.
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Thus, prediction of SPLE parameters by DOE, ANN, and GS was found to be a better strategy for the development of a catalyst with a long life. Conclusion Dry reforming of methane attracts much attention in order to convert the two greenhouse gases simultaneously to syngas. Preparation parameters of the citric acid method such as the Co loading, amount of citric acid, calcination temperature, and pelletization pressure were optimized to find a stable Co-MgO catalyst prepared by the citric acid method. The activity and stability were coded by a SPLE. The preparation parameters were determined according to an L9 orthogonal array. After catalytic activity was measured in a conventional pressurized fixed-bed reactor, a good fitting of SPLE to the experimental result was obtained. The preparation parameters and the resultant SPLE parameters were used for the training of an ANN. The optimum was determined by a GS and verified experimentally to be active with long life. Literature Cited (1) Yamada, M. High-Quality Transportation Fuels. Energy Fuels 2003, 17, 797. (2) Jandeleit, B.; Schaefer, D. J.; Powers, T. S.; Turner, H. W.; Weinberg, W. H. Combinatorial Materials Science and Catalysis. Angew. Chem., Int. Ed. 1999, 38, 2494. (3) Omata, K.; Yamada, M. Prediction of Effective Additives to Ni/AC Catalyst for Vapor Phase Carbonylation of Methanol by an Artificial Neural Network. Ind. Eng. Chem. Res. 2004, 43, 6622. (4) Suh, C.; Rajan, K. Combinatorial design of semiconductor chemsitry for bandgap engineering: “virtual” combinatorial experimentation. Appl. Surf. Sci. 2004, 223, 148. (5) Omata, K.; Nukui, N.; Hottai, T.; Showa, Y.; Yamada, M. Development of a Co-MgO catalyst for high-pressure dry reform-
ing of methane based on design of experiment, artificial neural network and grid search. J. Jpn. Pet. Inst. 2004, 47, 387. (6) Omata, K.; Watanabe, Y.; Hashimoto, M.; Umegaki, T.; Yamada, M. Optimization of Cu-based Oxide Catalyst for Methanol Synthesis Using a Neural Network Trained by Design of Experiment. J. Jpn. Pet. Inst. 2003, 46, 387. (7) Omata, K.; Nukui, N.; Hottai, T.; Yamada, M. Cobaltmagnesia catalyst by oxalate co-precipitation method for dry reforming of methane under pressure. Catal. Commun. 2004, accepted for publication. (8) Bartholomew, C. H. Mechanisms of catalyst deactivation. Appl. Catal. A 2001, 212, 17. (9) Hirose, K.; Ueda, T. Introduction to Taguchi Method Analysis; Doyukan: Tokyo, 2003. (10) 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. J. Jpn. Pet. Inst. 2003, 46, 383. (11) Omata, K.; Hashimoto, M.; Watanabe, Y.; Umegaki, T.; Wagatsuma, S.; Ishiguro, G.; Yamada, M. Optimization of Cu oxide catalyst for methanol synthesis under high CO2 partial pressure using combinatorial tools. Appl. Catal. A 2004, 262, 207. (12) Omata, K.; Watanabe, Y.; Hashimoto, M.; Umegaki, T.; Yamada, M. Simultaneous Optimization of Preparation Conditions and Composition of Methanol Synthesis Catalyst by All-Encompassing Calculation on an Artificial Neural Network. Ind. Eng. Chem. Res. 2004, 43, 3282. (13) Ruckenstein, E.; Wang, H. Y. Carbon Deposition and Catalytic Deactivation during CO2 Reforming of CH4 over CO/γAl2O3 Catalysts. J. Catal. 2002, 205, 289. (14) Kito, S.; Satsuma, A.; Ishikura, T.; Niwa, M.; Murakami, Y.; Hattori, T. Application of neural network to estimation of catalyst deactivation in methanol conversion. Catal. Today 2004, 97, 41.
Received for review August 3, 2004 Revised manuscript received October 25, 2004 Accepted November 1, 2004 IE049302Q
