Energy & Fuels 2009, 23, 1931–1935
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Artificial Neural Network Aided Screening and Optimization of Additives to Co/SrCO3 Catalyst for Dry Reforming of Methane under Pressure Kohji Omata,* Yasukazu Kobayashi, and Muneyoshi Yamada Department of Applied Chemistry, Graduate School of Engineering, Tohoku UniVersity, Aoba 6-6-07, Aramaki, Aoba-ku, Sendai 980-8579, Japan ReceiVed NoVember 17, 2008. ReVised Manuscript ReceiVed January 16, 2009
An artificial neural network aided methodology succeeded to improve the catalytic performance of Co/ SrCO3 for dry reforming of methane at 1 MPa, 1023 K, SV ) 100 000 mL/h/g, feed composition ) CH4/ CO2/N2 ) 45/45/10. CH4 conversion after 100 h time on stream was enhanced from 18% with Co/SrCO3 to 33% with the optimum Co/SrCO3, and CH4 conversion was constant from 10 to 100 h with the optimum catalyst, whereas it decreased gradually with Co/SrCO3. Reduction of carbon deposition after 100 h run (from 18.6% with Co/SrCO3 to 5.8% with the optimum) contributes to the stability of the optimum catalyst. In the first step of the optimization, new additives to the Co/SrCO3 catalyst were screened by using an artificial neural network (ANN). Catalytic activities of 10 mol % CO + 1 mol % X/Sr3 (X ) B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re, or Tl) and 16 physicochemical properties of these 10 elements were used as training data of the ANN, and the effect of each additive among 53 elements was predicted by the trained ANN. From the prediction and experimental verification, no better additive than the training data was found. Hence, high CO yield was obtained by Re addition, and CO yield gradually increased by Nd addition. Then, the loading of Co, Re, and Nd was optimized by means of design of experiment (DOE) and ANN for higher performance in the second stage of the optimization. The optimum catalyst was decided as 4.3 mol % Co, 2.2 mol % Nd, and 2.3 mol % Re supported on SrCO3, and it showed the remarkable performance.
Introduction From an environmental concern over the increasing greenhouse gas concentration, there has been great interest in CO2 conversion in recent years, particularly for the production of syngas by dry reforming of methane CH4 + CO2 a 2H2 + 2CO
(1)
This process offers syngas with a low H2/CO ratio which is suitable as a blend gas with H2-rich syngas from steam reforming of methane for the methanol synthesis and FischerTropsch synthesis processes. In this way the dry reforming of methane has environmental benefits and economic advantages. However, being strongly endothermic, the dry reforming requires high temperatures (typically, 1073-1173 K). Operation at high pressure is also required because the successive process, i.e., methanol synthesis or Fischer-Tropsch synthesis, is performed under high pressures (typically, 1-5 MPa). These reaction conditions stimulate carbon deposition and/or metal sintering, resulting in catalyst deactivation and plugging of the reforming reactor, and thus, there are only a few commercial processes based on the CO2 reforming reaction.1 Hence, great efforts have been made on the development of catalysts which are highly active and highly tolerant with coking during dry reforming of methane. It is well known that Rh, Ru, * To whom correspondence should be addressed. E-mail: omata@ erec.che.tohoku.ac.jp. (1) Udengaard, N. R.; Hansen, J. H. B.; Hanson, D. C.; Stal, J. A. Oil Gas J. 1992, 90, 62–67.
Pd, Pt, and Ir are catalytically active toward this reaction.2 Besides these noble metals, the preferred base metal in literature is nickel from the viewpoint of its high activity and low cost. The rate of carbon deposition is, however, higher for the Nibased catalysts, and it was pointed out that the rate of carbon deposition is dramatically increased at elevated pressure with most types of Ni-based catalysts.3 On the contrary, the rate of carbon deposition is relatively slower for the Co-based catalysts, and the catalysts have also attracted interest as an active metal for dry reforming of methane. In our former work,4-6 it was revealed that the Co-MgO catalyst showed the highest activity (31% CO yield at SV ) 400 000 mL/h/g) under pressurized conditions among the Co-based catalysts. Aika et al.7,8 also reported that a Co/TiO2 catalyst is highly tolerant against coking during dry reforming of methane, even at the total pressure of 2 MPa where carbon formation is highly possible. Instead, the Co/TiO2 catalyst loses its activity due to the oxidation of the metallic cobalt under the reaction conditions. On the basis of the above result and other reports,9,10 the oxidation of cobalt (2) Rostrup-Nielsen, J. R.; Hansen, J. H. B. J. Catal. 1993, 144, 38–49. (3) Armor, J. N.; Martenak, D. J. Appl. Catal. A: Gen. 2001, 206, 231– 236. (4) Omata, K.; Nukui, N.; Hottai, T.; Yamada, M. Catal. Commun. 2004, 5, 771–775. (5) Omata, K.; Nukui, N.; Hottai, T.; Showa, Y.; Yamada, M. J. Jpn. Petrol. Inst. 2004, 47, 387–393. (6) Omata, K.; Nukui, N.; Yamada, M. Ind. Eng. Chem. Res. 2005, 44, 296–301. (7) Nagaoka, K.; Takanabe, K.; Aika, K. Appl. Catal. A: Gen. 2003, 255, 13–21. (8) Nagaoka, K.; Takanabe, K.; Aika, K. Appl. Catal. A: Gen. 2004, 268, 151–158. (9) Ruckenstein, E.; Wang, H. Y. J. Catal. 2002, 205, 289–293.
10.1021/ef8010004 CCC: $40.75 2009 American Chemical Society Published on Web 03/13/2009
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metal species was pointed out as another reason for the deactivation of Co-based catalysts. Therefore, in addition to coking, the oxidation of cobalt metal would influence the stability of a cobalt catalyst. With regard to the two factors, we reported that the Co/SrCO3 catalyst shows high activity with high tolerance to oxidative atmosphere under the reaction conditions.11 As it was concluded that the active cobalt metal species of the Co/SrCO3 catalyst is not oxidized during the dry reforming reaction of methane at 1023 K under 1 MPa, a Co/ SrCO3 catalyst could be a suitable base metal catalyst if its resistance to coking is promoted. In the present study, new additives to a Co/SrCO3 catalyst with the aim of promoting its resistance to coking was investigated by means of data mining techniques, such as an artificial neural network (ANN), design of experiment (DOE), and a grid search. An ANN is one of the multifunctional approximators representing catalytic performance (product yield, product selectivity, and so on) as a function of factors controlling the performance (catalyst composition, preparation condition, reaction condition, and so on). Hattori et al. are the pioneers in applying an ANN to search for new catalysts or additives.12 They applied an ANN for estimating the acidity of mixed metal oxides.13 An ANN represented the catalytic performance as a function of physicochemical properties. Then an ANN was applied for constructing response surface from a finite number of data.14 Such a usage is useful for catalyst development or optimization of various conditions related to catalyst preparation and catalysis, especially when an ANN is combined with the high-throughput screening technique and evolutionary methodology such as a genetic algorithm.15 As reviewed by Klanner et al.,16 there are two possible situations for catalyst development: (i) screening based on prior information and catalytic systems and (ii) discovery of a new catalyst without a preceding catalyst. Works of Hattori et al. can be categorized as the latter (ii), and such methodology would take full advantage when no criteria of effective additives are available for such a new type catalyst. We already succeeded in predicting an effective additive to a Co/SrCO3 catalyst for the preferential oxidation of CO in H2 from the 10 catalytic activities of Co + X/SrCO3 catalysts (X ) B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re, or Tl) and 16 physicochemical properties of those 10 elements.17,18 We also succeeded in optimizing the catalyst preparation parameters, such as Co loading, catalyst calcination temperature, and so on, by using an ANN according to a L9 orthogonal array.5 DOE combined with an ANN and a grid search was useful for catalyst (10) Shao, H.; Kugler, E. L.; Ma, W.; Dadyburjor, D. B. Ind. Eng. Chem. Res. 2005, 44, 4914–4921. (11) Omata, K.; Nukui, N.; Hottai, T.; Showa, Y.; Yamada, M. Catal. Commun. 2004, 5, 755–758. (12) Hattori, T.; Kito, S. Catal. Today 1995, 23, 347–355. (13) Kito, S.; Hattori, T.; Murakami, Y. Anal. Sci. 1991, 7, 761–764. (14) Sasaki, M.; Hamada, H.; Kintaichi, Y.; Ito, T. Appl. Catal. A: Gen. 1995, 132, 261–270. (15) Rodemerck, U.; Baerns, M.; Holena, M.; Wolf, D. Appl. Sur. Sci. 2004, 223, 168–174. (16) Klanner, C.; Farrusseng, D.; Baumes, L.; Mirodatos, C.; Schueth, F. QSAR Comb. Sci. 2003, 22, 729–736. (17) Omata, K.; Kobayashi, Y.; Yamada, M. Catal. Today 2006, 117, 311–315. (18) Omata, K.; Kobayashi, Y.; Yamada, M. Catal. Commun. 2007, 8, 1–5.
Figure 1. Variation of the CH4 conversion (b), CO yield (2), and carbon deposition (9) with the reaction time during dry reforming of methane over the 10 mol % Co/SrCO3 catalyst.
development. The same methodology was applied for a dry reforming catalyst in the present study. Experimental Section Catalyst Preparation. Strontium carbonate was prepared by a precipitation method, namely, reaction of Sr(NO3)2 with NH4CO3. Cobalt catalysts were prepared by an impregnation method using SrCO3. Strontium carbonate was impregnated in ethanol solution of cobalt nitrate (0.05 mol/L). After impregnation, the Co/SrCO3 catalyst was rinsed with distilled water to remove nitrate salts in the catalyst and dried at about 353 K. The Co/(Co + Sr) molar ratio of Co/SrCO3 catalysts rinsed with distilled water was determined by ICP-MS (Agilent 4500). The series of Co + X/SrCO3catalysts was prepared by impregnation of the Co/SrCO3 catalyst with ethanol solution of a nitrate, an acetate, or an alkoxide of X element. For some catalysts used for training data of ANN, diluted HNO3 was added in the catalyst preparation step to the solution of X element salt in order to average the effect of nitrate salt on those catalysts. Finally, all catalysts were dried at about 353 K and calcined at 673 K for 4 h. Catalytic Activity Tests. The catalytic activity tests for dry reforming of methane were carried out in a fixed-bed reactor. A reactor tube made of quartz (i.d. ) 3 mm) was inserted in a stainless steel tube. In each test, 10 mg of catalyst diluted with 40 mg of quartz sand was used. It was fixed with quartz wool inside the quartz tube to prevent direct contact of the reaction gas with the inside wall of the stainless steel tube. The premixed reactant gas consisted of 45 vol % CH4, 45 vol % CO2, and 10 vol % N2, and the space velocity (SV), defined as the volume of gaseous feed (measured at 293 K and 0.1 MPa) per gram of catalyst per minute, was set to 100 000 mL/h/g. The catalysts were pretreated in a hydrogen flow at 1123 K for 10 min under 0.1 MPa, and then the reactant gas was introduced into the reactor at 1023 K under 1 MPa. The product gas was analyzed by an online micro-GC (Agilent, 3000A). Methane conversion and CO yield were defined as
(
CO yield (%) )
(
) )
CH4,flowout × 100 CH4,flowin
(2)
COflowout × 100 CH4,flowin + CO2,flowin
(3)
CH4 conversion(%) ) 1 -
The amounts of carbon depositions on the catalysts were quantified by methane produced during TPH (temperature-programmed hydrogenation) of the catalyst in a hydrogen flow after the dry reforming reaction of methane. Screening Methodology. Among many types of ANN, a radial basis function network (RBFN) was used. Two RBFNs were
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Figure 2. (a) Experimental catalytic activity of 10 mol % Co/SrCO3 (9) and 10 mol % Co + 1 mol % X/SrCO3 (b) for ANN training, and (b) predicted catalytic activity of 10 mol % Co + 1 mol % X/SrCO3 (]) and verified catalytic activity of Ba-, Li-, or Cs-added catalysts ([).
constructed by STATISTICA Neural Networks (version 6; Stat Soft Inc.), and they were used to correlate physicochemical properties with CO yield after 15 min and that after 4 h reaction, respectively. The number of nodes in a hidden layer was set to be identical with that of the training data. The RBFN was denoted like RBFN8-101, where the numbers show the number of nodes in the input layer, hidden layer, and output layer, respectively. Physicochemical properties of elements were collected in database software (Periodic Table X, version 3.5; Synergy Creation) and the literature.19 After excluding rare gases, radioactive atoms, and so on, 63 elements were selected as candidates of additives to a Co/SrCO3 catalyst. Because all of the data were available for the selected 63 elements, 16 physicochemical properties of these elements for input of an ANN were selected as follows: atomic number (AN), atomic weight (AW, g/mol), melting point (MP, K), boiling point (BP, K), heat of vaporization (HV, kJ/mol), heat of fusion (HF, kJ/mol), specific heat capacity (HC, J/g/K), covalent radius (CR, pm), ionic radius (IR, pm), electronegativity (EN), first ionization energy (1I, eV), second ionization energy (2I, eV), electric dipole polarizability (ED, cm3), density (DS, g/cm3), thermal conductivity (TC, W/m/K), and formation enthalpy of metal oxide (FE, kJ/mol). The range and dispersion of physicochemical properties included in the training data were important factors for the quality of the predictions. The minimum number of training elements were selected, of which properties are dispersing over the wide range of the properties; the physicochemical property of at least one of the training elements is near the maximum or the center or the minimum of these physicochemical properties of the 63 elements. High solubility of nitrates, oxides, or alkoxides in water or ethanol, low decomposition temperature, low toxicity, and so on, are the other selection criteria. Thus, the elements for the training of an ANN were selected as the training elements: B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re, and Tl.
Results and Discussion Activity Change of Co/SrCO3 Catalyst. Long run (100 h) over the 10 mol % Co/SrCO3 catalyst was conducted at 1023 K, space velocity ) 100 000 mL/h/g under 1 MPa. As shown in Figure 1, CH4 conversion and CO yield decreased gradually while carbon deposition increased with the reaction time. In particular, both the decrease of the catalytic activity and the increase of carbon deposition were much more serious during the initial 4 h than the successive 100 h. A similar trend was observed in a Co-MgO catalyst,4 and it can be presumed that it is important to suppress the initial carbon deposition with the aim of stabilizing the catalytic activity for a long time. (19) Barin, I.; Sauert, F.; Schultze-Rhonhof, S.; Sheng, W. Thermochemical data of pure substances; 2nd ed.; VCH: Weinheim, 1993.
Figure 3. Variation of the CO yield with the reaction time during dry reforming of methane over the 10 mol % Co/SrCO3 (b), 10 mol % Co + 1 mol % Nd/SrCO3 (∆), 10 mol % Co + 1 mol % Re/SrCO3 (9), and 10 mol % Co + 1 mol % Nd + 1 mol % Re/SrCO3 (]) catalysts. Table 1. Parameters for DOE and Grid Search Co content [mol %]
Nd content [mol %]
Re content [mol %]
DOE level 1 level 2 level 3
15 20 25
range interval steps
10-30 1 21
0.5 2.5 4.5
0.5 2.5 4.5
0-5 0.1 51
0-5 0.1 51
grid search
Training of ANN and Experimental Verification of Its Prediction. To suppress the initial decrease of CH4 conversion and CO yield, the effect of additive was investigated by means of an ANN, because no criteria of effective additives are available for such a new catalyst. Catalytic activities of 10 mol % Co/SrCO3 and 10 mol % Co + 1 mol % X/SrCO3 are shown in Figure 2a. On the catalysts above the broken line, the activity after 4 h reaction was higher than that at 15 min. These 10 catalytic activities of 10 mol % Co + 1 mol % X/SrCO3 and 16 physicochemical properties of the 10 elements were used as training data of an ANN. The number of nodes in the output layer of the ANN was one, and the output was CO yield after 15 min reaction or that after 4 h reaction because the predictions by ANNs with two nodes in the output layer were bad. The number and kind of nodes in the input layer should be different for each output. CO yields after the 15 min reaction were
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Table 2. Activity and Composition of Catalysts Prepared According to DOE nominal loading [mol %]
loading [mol %]
no.
Co
Nd
Re
Co
Nd
Re
1 2 3 4 5 6 7 8 9 10 11
15 15 15 20 20 20 25 25 25 28 10
0.5 2.5 4.5 0.5 2.5 4.5 0.5 2.5 4.5 0.6 2.4
0.5 2.5 4.5 2.5 4.5 0.5 4.5 0.5 2.5 2.7 2.8
3.4 4.8 5.2 5.3 7.7 7.4 13.3 9.4 9.8 7.2 4.3
0.6 2.1 3.5 0.5 2.2 4.0 0.6 2.7 4.1 0.9 2.2
0.4 2.0 3.2 1.2 3.6 0.4 3.9 0.5 1.9 6.9 2.3
CO yield [%] 15 min 4 h ∆Re
a Predicted values are 27% and 40%, respectively. are 6% and 35%, respectively.
25 12 6 36 28 3 36 15 6 42a 7b b
26 28 16 32 19 2 35 27 19 38a 25b
1 16 10 -4 -9 -1 -1 12 13 -1 18
Predicted values
Figure 4. Predicted CO yield after 15 min, 4 h reaction for 54 621 catalysts.
predicted using the RBFNn-10-1 for 10 mol % Co + 1 mol % X/SrCO3 catalyst where X is one additive among the 53 elements. Another RBFNm-10-1 was used for the prediction of CO yield after 4 h reaction. Predicted catalytic activities are shown in Figure 2b. As a consequence, it was predicted that improvement of the activity after 4 h is most significant with 10 mol % Co + 1 mol % Li/SrCO3. From the viewpoint of suppression of the initial decrease of CO yield, it was predicted that some additives are effective, and Ba- or Cs-added catalyst shows the highest CO yield after 4 h reaction. Therefore, Li-, Ba-, or Cs-added catalyst was prepared and tested. The result is shown in Figure 2b as filled rhomboid-shaped symbols. The Li- or Cs-added catalyst exhibited a different catalytic activity from the prediction, but the Ba-added catalyst exhibited almost the same catalytic activity as the prediction. The selected physicochemical properties as input of RBFNn10-1 for CO yield after 15 min reaction were 1I, AW, and HF, and those of RBFNm-10-1 for CO yield after 4 h reaction were 1I, 2I, AW, DS, CR, IR, HV, TC, HC, MP, HF, and AN. The tructures of the trained RBFNs were RBFN3-10-1 and RBFN1210-1, respectively. This result should be related to the mechanism of the dry reforming of methane with a Co/SrCO3 catalyst. Synergistic Effect of Re and Nd Addition. As shown in the previous section, the trained ANN succeeded in the prediction that the Ba-added catalyst shows higher CO yield after 4 h reaction than the catalyst without an additive and that the decrease in its CO yield is not serious. However, the Readded catalyst showed higher CO yield after 4 h experimental run than those of the other predicted and experimental results. Furthermore, it was noteworthy that the Nd-added catalyst showed promotion of CO yield after 4 h run. In the screening of a new catalyst using an ANN and the physicochemical
Figure 5. Variation of the CH4 conversion (b), CO yield (2), and carbon deposition (9) with the reaction time during dry reforming of methane over the 28 mol % Co + 0.6 mol % Nd + 2.7 mol % Re/ SrCO3 catalyst (open symbols) and 10 mol % Co + 2.4 mol % Nd + 2.8 mol % Re/SrCO3 catalyst (closed symbols).
properties of elements, such phenomena that the training data is better than the predicted results are often observed. It is probably because such elements used for the training of ANN were specially selected based on their characteristic physicochemical properties. Figure 3 showed the variation of the CO yield with the reaction time during dry reforming of methane over the Re- and/or Nd-added catalysts. The Re-added catalyst exhibited the high initial CO yield, but it decreased gradually and reached 36% after 4 h reaction. On the other hand, the Ndadded catalyst showed the low initial CO yield, but it increased gradually during the initial 30 min reaction and reached 32% after 4 h reaction. It was concluded based on the above result that the addition of Re is effective to promote the catalytic activity and the addition of Nd is effective to stabilize the catalytic activity of the Co/SrCO3. Therefore, we prepared and tested the catalyst containing both Re and Nd to investigate the synergy effect of Re and Nd. Consequently, the 10 mol % Co + 1 mol % Re + 1 mol % Nd/SrCO3 catalyst exhibited the low initial CO yield, but it increased gradually during the initial 1 h reaction and reached 36% after 4 h reaction. Hence, Re and Nd promote the catalytic activity of Co/SrCO3 mutually in the synergistic way. It was reported that the addition of Re to Pt/γ-Al2O3 catalysts resulted in lower carbon deposition for the dry reforming of methane. Activity promotion by Re was attributed to its ability to dissociate CO2, and the inhibition of carbon formation was primarily attributed to an ensemble effect.20 It was also reported that no carbon formation was observed on the CoNdOx catalyst during the CO2 reforming, and the high resistance toward carbon formation of the CoNdOx catalyst is attributed to the strong interaction of metallic cobalt with Nd2O3 support, leading to an increase in the basicity of the support and/or a change in the electronic properties of the metallic cobalt finely dispersed on the support.21 Like these reports, Re and Nd are effective potentially to promote the catalytic activity and carbon formation stability for the dry reforming of methane. Optimization of Co, Nd, and Re Loading on SrCO3. For the best performance of the catalyst which consists of both Re and Nd, the loading of Co, Re, and Nd was optimized by using an L9 orthogonal array5 of DOE. Only nine experiments as shown in Table 1 are necessary for three factors with three levels (20) Richardson, J. T.; Garrait, M.; Hung, J. K. Appl. Catal. A: Gen. 2003, 255, 69–82. (21) Choudhary, V. R.; Mondal, K. C.; Mamman, A. S.; Joshi, U. A. Catal. Lett. 2005, 100, 271–276.
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Table 3. Comparison of the Catalytic Performance for Dry Reforming of Methane under Pressure catalyst a
Co/SrCO3 10% Co/SrCO3 7% Co-MgO Co/TiO2b Ni0.1Mg0.9O CO6W6C β-Mo2C R-WC a
reaction time [h] 100 100 100 24 4 90 72 72
conversion [%] CH4
CO
33
39 31 31 26
8
78 90 75
76 87 69
76 54
33 18 20 ∼54 82 83 63
yield [%]
CO2
H2
coke amount [wt %]
reaction T [K]
reaction P [MPa]
space velocity [mL/h/g]
ref
5.8 18.6 62 0.15 22
1023 1023 1023 1023 1123 1123 1220 1220
1 1 1 2 2 0.5 0.83 0.83
100 000 100 000 400 000 12 000 18 667 11 200 2870 [h-1] 2870 [h-1]
this study this study 4 8 22 23 24 24
10% Co-2.4% Nd-2.8% Re/SrCO3. b 0.45 wt % Co-0.05 wt % Ni/TiO2.
instead of 27 ()33) experiments, and these three levels of each parameter are listed in Table 1. These nominal loadings were used as input data, and the output was CO yield of the catalyst after 15 min or 4 h. The number of the training data was 9, and thus, the structure of the two kinds of ANN was the same as RBFN3-9-1. For optimization of the catalyst, the input should be controllable because the predicted optimum catalysts can be prepared easily. To find the global maximum of the ANN, a grid search was conducted using the macro commands of STATISTICA. All combinations of parameters were generated. Calculation intervals for Co content, Re content, and Nd content were 1, 0.1, and 0.1 mol %, respectively, as shown in Table 1. The number of possible combinations was 54 621 () 21 × 51 × 51). Then the ANN, which had been trained from the experimental results listed in Table 2, predicted CO yield after 15 min and 4 h reaction using each parameter set. The global maximum could then be predicted. All the predicted 54 621 CO yield are plotted in Figure 4. The optimum Co, Nd, and Re compositions in the catalyst preparation for the highest CO yield after 4 h reaction was found as 28, 0.6, and 2.7 mol %, respectively. On the other hand, the optimum Co, Nd, and Re compositions in the catalyst preparation for the most CO yield increase during the initial 4 h reaction was found as 10, 2.4, and 2.8 mol %, respectively. The results of ICP measurements were determined as summarized in Table 2. Therefore, these predicted optimum catalysts were prepared and tested to verify the predictions. The results are listed in Table 2 (nos. 10 and 11), and 7.2 mol % Co + 0.9 mol % Nd + 6.9 mol % Re/SrCO3 catalyst exhibited experimentally the highest CO yield after 4 h reaction, while 4.3 mol % Co + 2.2 mol % Nd + 2.3 mol% Re/SrCO3 catalyst showed the highest increase of CO yield during the initial 4 h reaction of 11 experimental results. Long runs over these optimized catalysts were conducted to investigate the stabilities at 1023 K, space velocity ) 100 000 mL/h/g under 1 MPa. As shown in Figure 5, CH4 conversion and CO yield decreased gradually with the reaction time over 7.2 mol % Co + 0.9 mol % Nd + 6.9 mol % Re/SrCO3 catalyst, while those of 4.3 mol % Co + 2.2 mol % Nd + 2.3 mol% Re/SrCO3 catalyst increased gradually during the initial 12 h reaction and reached steady state after 12 h reaction. From 12 to 100 h reaction, the performance of the latter catalyst was stable and little carbon deposition increased. Table 3 lists the catalytic performances of the 4.3 mol % Co + 2.2 mol % Nd + 2.3 mol % Re/SrCO3 catalyst prepared in this work and of the other main base metal catalysts reported in the literature4,8,22-24 for the dry reforming of methane under (22) Tomishige, K.; Himeno, Y.; Matsuo, Y.; Yoshinaga, Y.; Fujimoto, K. Ind. Eng. Chem. Res. 2000, 39, 1891–1897.
pressure. Among these catalysts, the most promising catalyst is Co-MgO catalyst because it exhibits the high catalytic performance at a high space velocity of 400 000 mL/h/g; therefore, it produces the largest amount of synthesis gas among these catalysts. However, its carbon deposition is serious during 100 h reaction. On the other hand, it should be noted that deactivation did not occur, and little carbon deposition increased over the catalyst prepared in the present work during the reaction time from 12 to 100 h reaction. While a longer term durability test is essential for the practical application, the observed stability demonstrates the high potential of the base metal catalyst application to the practical dry reforming reactor of methane. Conclusion In the present study the effect of additives was investigated to promote its coking resistance of 10 mol % Co/SrCO3 catalyst for the dry reforming of methane by means of the data mining techniques, such as an ANN, DOE, and a grid search. In order to find a new additive to 10 mol % Co/SrCO3 using the ANN, 10 elements (B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re, or Tl) were selected as training elements from the viewpoint of their diverse physicochemical properties. The catalytic activities of 10 mol % Co + 1 mol % X/SrCO3 and 16 physicochemical properties of these 10 elements were used as training data of the ANN, and the effect of each additive among 53 elements was predicted by the trained ANN. While the prediction of ANN failed to find no better additive than the training data, noble additives such as Re and Nd were found in the framework of the screening. Then it was concluded that the addition of Re and Nd is effective to promote the catalytic activity and to stabilize catalytic activity of the Co/SrCO3, respectively. As the synergy effect of Re and Nd was observed, the loadings of Co, Re, and Nd were optimized to achieve high performance by means of DOE, an ANN, and a grid search. The optimum catalyst was 4.3 mol % Co + 2.2 mol % Nd + 2.3 mol % Re/SrCO3. CH4 conversion after 100 h time on stream was enhanced from 18% with Co/SrCO3 to 33% with the optimum, while carbon deposition was reduced from 18.6% to 5.8% at 1023 K with a feed composition of CH4/CO2/N2 ) 45/45/10 at a space velocity of 100 000 mL/h/g under 1 MPa. Acknowledgment. Support of this work by Japan Oil, Gas and Metals National Corp. (JOGMEC) is gratefully acknowledged. EF8010004 (23) Iyer, M. V.; Norcio, L. P.; Punnoose, A.; Kugler, E. L.; Seehra, M. S.; Dadyburjor, D. B. Top. Catal. 2004, 29, 197–200. (24) Claridge, J. B.; York, A. P. E.; Brungs, A. J.; Marquez-Alvarez, C.; Sloan, J.; Tsang, S. C.; Green, M. L. H. J. Catal. 1998, 180, 85–100.
