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RESEARCH NOTES Prediction of Effective Additives to a Ni/Active Carbon Catalyst for Vapor-Phase Carbonylation of Methanol by an Artificial Neural Network Kohji Omata and Muneyoshi Yamada* Department of Applied Chemistry, Graduate School of Engineering, Tohoku University, Aoba 6-6-07, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Effective additives were investigated in order to suppress the formation of methane, a major byproduct of vapor-phase carbonylation of methanol with a Ni/active carbon (AC) catalyst. An artificial neural network was applied to relate the physicochemical properties of an element (X) with experimentally determined methane selectivity of the catalyst containing the element (NiX/AC). The trained artificial neural network succeeded in finding Sn as effective based on the training data where information about methane selectivity of Ni-Sn/AC was not included. Introduction Acetic acid (AcOH) is one of the most important chemicals used as a raw material and as a solvent. Methanol carbonylation (CH3OH + CO f CH3COOH) is the major reaction to produce AcOH, and more than 60% of industrial processes employ the reaction in the liquid phase.1 A rhodium-catalyzed process developed by Monsanto is widely used because of its high activity and selectivity. Despite the high efficiency of the process, it has some disadvantages such as rhodium cost and complicated separation operations. For an alternative process, therefore, heterogeneous, low-cost catalysts have been explored by several groups. Nickel supported on active carbon (Ni/AC) with a methyl iodide promoter was reported as one of the most active catalysts in the vapor phase.2-4 The catalyst shows a high tolerance to CO2, water, and hydrogen sulfide.5 Small amounts of hydrogen even accelerate the carbonylation activity by 2-3 times without degradation of the selectivity for carbonylation.6 The characteristics may result in a simple process that can utilize low-fuel-grade methanol and carbon monoxide without sophisticated purification processes. Methanol is first converted to methyl acetate (AcOMe) and dimethyl ether (DME) in parallel reactions on the Ni/AC catalyst with assistance of the iodide promoter. DME is carbonylated to AcOMe, and AcOMe is finally converted to AcOH directly or via acetic anhydride. Thus, all of the byproducts except methane (CH4) are converted to AcOH.7 The origin of CH4 is not clear yet, and its formation is most undesirable. The aim of the present paper is to find effective additives for Ni/AC to suppress methane formation based on the restricted number of high-pressure experiments. An artificial neural network (ANN) was em* To whom correspondence should be addressed. Tel.: +81-22-217-7214. Fax: +81-22-217-7293. E-mail: yamada@ erec.che.tohoku.ac.jp.
ployed to relate the physicochemical properties of an element used as an additive to the methane selectivity (MS). In general, ANNs are used to find the relationship between the experimental conditions and the results, probably because ANN is not good at extrapolating speculation. Only a few successful cases have been reported in which catalytic properties were predicted from physicochemical properties of the catalyst elements.8,9 In the present study, optimum additives were surveyed in a grid-search manner over the periodic table using a trained ANN. Experimental Section Catalysts were prepared by the impregnation method. AC (Kureha, BAC-MO) was impregnated in an aqueous solution of nickel acetate and salts of the additives followed by drying. The catalysts were used without reduction by hydrogen. The nickel content was 2.5 wt %, and the molar ratio of Ni/additives was 1/1. The carbonylation reaction was conducted using a conventional fixed-bed reactor apparatus. Reaction conditions were as follows: 250 °C, 1 MPa, CO/CH3OH/CH3I ) 5/0.95/0.05 (molar ratio), W/F ) 10 g‚h/mol. Organic products were analyzed by an online gas chromatograph equipped with a flame ionization detector: AcOH, AcOMe, DME, and CH4. MS was calculated by MS ) YCH4/(YAcOH + YAcOMe × 0.5) × 100 (%), where Y is the product yield calculated by a methanol base. A radial basis function network (RBFN) was built as a kind of ANN by STATISTICA Neural Networks, version 6 (StatSoft). Physicochemical properties of additives (X), collected from database software (Periodic Table X, version 3.5; Synergy Creations), and MS (%) of the catalyst (Ni-X/AC) was used as training data. As a representative of the ligand effect of additives, first ionization energy (IE, eV) was used. Heat of vaporization (HV, kJ/mol) and atomic radius (AR, pm) were used as factors for ensemble effects. A total of four properties including melting point (MP, K) were given to the input
10.1021/ie049609p CCC: $27.50 © 2004 American Chemical Society Published on Web 09/01/2004
Ind. Eng. Chem. Res., Vol. 43, No. 20, 2004 6623 Table 1. Data Set for Training and Validation of RBFNa element Cu Zr V Ag Pb Sb Bi Zn Tl K Mg
HV 307 567 460 258 178 166 179 114 166 79.1 128
IE
AR
Validation 7.73 128 Training 6.63 6.75 7.58 7.42 8.61 7.29 9.39 6.11 4.34 7.65
160 134 144 146 140 150 134 170 227 160
MP
MS
1358
3.1
2125 2175 1234 601 904 545 693 577 336 922
3.3 3.4 3.7 3.9 4.6 4.7 5.0 5.4 6.3 6.7
a HV: heat of vaporization (kJ/mol). IE: first ionization energy (eV). AR: atomic radius (pm). MP: melting point (K).
Figure 2. Product yield as a function of methanol conversion.
Figure 1. Effect of additives on the product yield at 1 MPa and 250 °C.
layer of the network. In the hidden layer, 10 nodes, the same as the number of training data, were used. From this network, only MS was issued from the output layer. Optimum additives with the lowest MS were explored by grid search (formerly reported as an all-encompassing calculation10) in two steps. In the first step, both search ranges and intervals were large in order to determine the optimum area for the next step. In the second step, intervals were narrowed to identify the element as shown in Table 1. Relative distance ri from the virtual optimum element was used to rank the element i: ri ) {sum of [(Pj,i - Pj,optimum)/(Pj,max Pj,min)]2}0.5, where P is a physicochemical property and j is its suffix. MS of 242 592 and 367 647 situations were predicted by means of the trained RBFN, respectively.
Figure 3. MS as functions of additive parameters: (a) HV; (b) IE; (c) AR; (d) MP; (e) prediction by RBFN.
Results and Discussion Product yield11 and MS are shown in Figure 1 sorted by MS. All additives gave a higher selectivity to CH4 compared with Ni/AC without additives indicated as “none”. As shown in Figure 2a, the AcOH yield strongly depends on methanol conversion. The result suggests that DME and AcOMe are finally converted to AcOH as explained above and that additives slightly influence carbonylation product selectivities. On the contrary, the CH4 yield is almost independent of methanol conversion, as shown in Figure 2b. Physicochemical properties of each additive are important factors and greatly influence the CH4 yield. The CH4 yield was normalized by their yield of carbonylated product to compensate for
the indistinct dependency on methanol conversion, and thus MS was used as an output of the RBFN. Each physicochemical property shows a low correlation coefficient to MS, as shown in Figure 3a-d. The experimental results and properties of additives except Cu were used as training data of the RBFN. Those of Cu were used as validation data, as shown in Table 1. After rapid training, the RBFN gives a good prediction, as shown in Figure 3e. Grid search was used as the optimization algorithm using the parameters shown in Table 2. Response surfaces of MS as functions of the atomic radius and first ionization energy were obtained by the grid search. The bottom view of the overlapped surfaces is illustrated
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Table 2. Ranges and Intervals for the Grid Searcha range HV IE AR MP
30-840 3.6-10.5 85-265 200-3800
HV IE AR MP
270-330 6.9-7.5 125-145 400-800
interval First 30 0.3 10 200 Second 1 0.1 1 10
steps
minimum at
28 24 19 19
300 7.2 135 600
21 7 21 41
300 7.3 131 640
a HV: heat of vaporization (kJ/mol). IE: first ionization energy (eV). AR: atomic radius (pm). MP: melting point (K).
Figure 4. MS as functions of AR and IE on overlapped response surfaces predicted by RBFN. MS is shown as contoured lines and numbers.
in Figure 4 with contour lines of MS. The global minimum point can be located, and the following additive gives 2.3% as the lowest predicted MS: HV ) 300, IE ) 7.3, AR ) 131, and MP ) 640. Elements in the periodic table were ranked based on the relative distance to the virtual optimum element. Sn was found to be the nearest additive, and the top five elements, such as Sn, Ge, Al, Ga, and Cu, are plotted in the figure. In the next survey, MS was predicted using real properties of elements. The predicted MS, in addition to the experimental MS, is illustrated in Figure 5. Again Sn was predicted to give the lowest MS (2.4%), including the training data, and MS is even lower than that of Ni/AC without additive (2.6%). The prediction suggests that the addition of Sn suppresses methane formation. The experimental result with the Ni-Sn/AC catalyst showed that MS was 2.1%, which is better than that of Ni/AC. The RBFN succeeded in the prediction of a good additive from the data of catalysts with worse selectivity. In the literature, Sn was already reported as an effective additive to promote the activity at atmospheric pressure,3 whereas it works as a selectivity enhancer in the present study. The formation of a Ni-Sn alloy and enhanced CO adsorption were reported as major reasons.3 As shown in Figure 3a-d, MS is most correlated to AE. Metals with smaller AE alloying with nickel tend to concentrate on the surface. These observations suggest that the optimum surface concentration
Figure 5. Predicted MS by RBFN. Solid bar: predicted MS. Gray bar: experimental MS.
or an ensemble of nickel is essential for selective carbonylation. Conclusions Effective additives were investigated in order to reduce MS of the vapor-phase carbonylation of methanol by Ni/AC catalysts. Eleven additives were examined, and the experimental results were used for training and validation of an ANN with the physicochemical properties of the additives. The trained ANN was used for screening of the periodic table to find Sn as better additives. Its low MS was verified experimentally. Further experiments will be required, however, to confirm that Sn is the best additive. It is also possible that methane formation is suppressed further by optimization of the amount of Sn and/or by a second additive. Anyway, an ANN succeeded in finding better additives based on the training data containing the information about worse additives. We concluded that ANN is a useful tool for catalyst development based on the limited number of experimental data.
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Acknowledgment Financial support from “General Sekiyu Research & Development Encouragement & Assistance Foundation” is gratefully acknowledged. Literature Cited (1) Yoneda, N.; Kusano, S.; Yasui, M.; Pujado, P.; Wilcher, S. Recent advances in processes and catalysts fro the production of acetic acid. Appl. Catal. A 2001, 221, 253. (2) Fujimoto, K.; Shikada, T.; Omata, K.; Tominaga, H. Vapor phase carbonylation of methanol with supported nickel metal catalysts. Ind. Eng. Chem. Prod. Res. Dev. 1982, 21, 429. (3) Liu, T.-C.; Chiu, S.-J. Promoting Effect of Tin on Ni/C Catalyst for Methanol Carbonylation. Ind. Eng. Chem. Res. 1994, 33, 488. (4) Merenov, A.; Abraham, M. A. Catalyzing the carbonylation of methanol using a heterogeneous vapor phase catalyst. Catal. Today 1998, 40, 397. (5) Omata, K.; Yagita, H.; Fujimoto, K. Features of CarbonSupported Metal Catalysts for Vapor Phase Carbonylation of Methanol and Related Compounds. J. Jpn. Pet. Inst. 1997, 40, 1. (6) Fujimoto, K.; Bischoff, S.; Omata, K.; Yagita, H. Hydrogen Effects on Nickel-Catalyzed Vapor-Phase Methanol Carbonylation. J. Catal. 1992, 133, 370.
(7) Omata, K.; Fujimoto, K.; Shikada, T.; Tominaga, H. Vapor phase carbonylation of organic compounds over supported transition metal catalyst. 3. Kinetic analysis of carbonylation with nickelactive carbon catalyst. Ind. Eng. Chem. Prod. Res. Dev. 1985, 24, 234. (8) Kito, S.; Hattori, T.; Murakami, Y. Estimation of the Acid Strength of Mixed oxides by a Neural Network. Ind. Eng. Chem. Res. 1992, 31, 979. (9) Kito, S.; Hattori, T.; Murakami, Y. Estimation of catalytic performance by neural networksproduct distribution in oxidative dehydrogenation of ethylbenzene. Appl. Catal. A 1994, 114, L173. (10) Omata, K.; Watanabe, Y.; Hashimoto, M.; Umegaki, T.; Yamada, M. Simultaneous Optimization of Preparation Conditions and Composition of Methanol Synthesis Catalyst by an AllEncompassing Calculation on an Artificial Neural Network. Ind. Eng. Chem. Res. 2004, 43, 3282. (11) Omata, K. Study on Vapor Phase Carbonylation. Doctor Thesis, Graduate Shcool of Engineeering, Tokyo University, Tokyo, Japan, 1986.
Received for review May 10, 2004 Revised manuscript received August 2, 2004 Accepted August 25, 2004 IE049609P
