Optimization of Cu oxide catalyst for methanol synthesis under high CO2 partial pressure using combinatorial tools

Omata, Kohji; Hashimoto, Masahiko; Watanabe, Yuhsuke; Umegaki, Tetsuo; Wagatsuma, S.; Ishiguro, Gunji; Yamada, Masahito

doi:10.1016/j.apcata.2003.11.028

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…An irreversible deactivation of Cu catalysts was explained by recrystallization and an enhanced sintering tendency of the Cu particles by water addition [17,19], whereas no sintering and no Cu oxidation was observed by Omata et al [17] during their study on the deactivation due to the presence of water.…”

Section: Introductionmentioning

confidence: 99%

“…Additional reasons for the deactivation of Cu-based catalysts by water mentioned in the literature [17] are blocking of hydrogen adsorption sites, morphology changes of Cu [17,18], and the oxidation of the active Cu-phase [17]. An irreversible deactivation of Cu catalysts was explained by recrystallization and an enhanced sintering tendency of the Cu particles by water addition [17,19], whereas no sintering and no Cu oxidation was observed by Omata et al [17] during their study on the deactivation due to the presence of water.…”

Section: Introductionmentioning

confidence: 99%

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Methanol Synthesis from Industrial CO2 Sources: A Contribution to Chemical Energy Conversion

et al. 2017

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catalyst can operate in a stable way without the presence of carbon monoxide in the feed, which means that increased water contents in the product gas cannot destroy the catalyst's performance completely. The presence of benzene in the feed does not lead to a deactivation of the catalyst. With these findings methanol production starting from exhaust gases from steel mills seems to become an interesting alternative for sustainable methanol production.Abstract CO 2 hydrogenation as a route for the chemical energy storage over a commercial Cu/ZnO/Al 2 O 3 catalyst has been studied. To check the optimal conditions for an efficient methanol production the influence of temperature and space velocity on the catalytic performance has been demonstrated. Time-on-stream measurements in the absence and the presence of benzene in the gas feed mixture were performed to investigate the possibility to use alternative carbon sources, which contain traces of aromatics. The

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methanol Synthesis from Industrial CO2 Sources: A Contribution to Chemical Energy Conversion

et al. 2017

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“…Cu _ Zn _ X oxide catalysts were prepared as described previously 7) by the oxalate-ethanol co-precipitation method. Ethanol solutions of nitrates of Cu, Zn and so on were mixed (Cu/Zn/X molar ratio 6/3/1), and then an ethanol solution of oxalic acid was added to precipitate the mixed oxalic salts.…”

Section: Catalyst Preparation and Testingmentioning

confidence: 99%

“…In the present study, ANN was applied to identify the most effective additive X from both the physicochemical properties of element X and the activity of Cu _ Zn _ X catalyst for methanol synthesis as experimentally determined 7) . A radial basis function network (RBFN), a type of ANN, was employed to correlate the physicochemical properties of the additive element to the activity.…”

Section: Introductionmentioning

confidence: 99%

Screening Using Artificial Neural Network of Additives for Cu-Zn Oxide Catalyst for Methanol Synthesis from Syngas

Omata

Hashimoto

Sutarto

et al. 2005

J. Jpn. Petrol. Inst.

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The activity of Cu _ Zn oxide catalysts for methanol synthesis from syngas varies depending on the additives to the oxide, and optimum composition is sensitive to the reaction conditions. An artificial neural network (ANN) was applied to identify the most effective additives based on the experimental results already reported. The physicochemical characters of element X, such as ionic radii and ionization energy, and the activity of Cu _ Zn _ X oxide catalyst were correlated using the ANN. Twenty-two types of X were supplied for the training of the ANN, and 29 activities of Cu _ Zn _ X, the X of which was not included in the training data, were predicted. Beryllium was predicted as the most effective additive, which was verified experimentally.

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“…Baerns et al have studied the oxidative dehydrogenation of propane, using gaseous oxygen, [22][23][24] total propane oxidation, [25] and the production of hydrocyanic acid. [26] Yamada et al have investigated methanol synthesis, [27][28][29] and others have performed studies on (selective) oxidation, [30][31][32][33] reduction, [34] methane reforming, [35] and isomerisation. [36] Most of the catalysts used in these studies are mixed oxides containing four to five different metals.…”

Section: Qcementioning

confidence: 98%

Selective Hydrogen Oxidation Catalysts via Genetic Algorithms

et al. 2008

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Solid "oxygen reservoirs," such as doped ceria, can be successfully applied in a novel process for the oxidative dehydrogenation of propane. The ceria lattice oxygen selectively burns hydrogen from the dehydrogenation mixture at 550 8C. This gives three key advantages: it shifts the dehydrogenation equilibrium to the desired product side, generates heat in situ, which aids the endothermic dehydrogenation, and simplifies product separation. We have applied a genetic algorithm to screen doped cerias for their performance in the selective hydrogen oxidation. Three generations of doped ceria catalysts (61 catalysts in total), were synthesised. Dopants were chosen from a set of 26 elements, and with a maximum of two dopants per catalyst, at five different concentrations. The catalyst performance (activity and selectivity), is expressed by a fitness value. Each generation shows a higher average fitness value. The dopant type has a large effect on the catalyst fitness. We identified six dopant atoms which lead to selective hydrogen combustion catalysts, namely bismuth (Bi), chromium (Cr), copper (Cu), potassium (K), manganese (Mn), lead (Pb) and tin (Sn) ("good" dopants). Analysis of the effect of electronegativity, ionic radius and dopant concentration shows that most elements yielding a high fitness have electronegativities ranging from 1.5-1.9. Generally, the properties of catalysts containing two dopants can be predicted from the behaviour of singly doped ones. Synergy does occur for certain copper-, iron-and platinum-containing catalysts. The addition of calcium (Ca) or magnesium (Mg) to copper-doped catalysts doubles the activity, and the selectivity of iron doped catalysts can be improved by adding chromium (Cr), manganese (Mn) or zirconium (Zr). Importantly, the doped cerias show a high stability in the redox cycling, much higher than that of supported oxides. A Cr-and Zr-doped catalyst (Ce 0.90 Cr 0.05 Zr 0.05 O 2 ) was highly selective and active over 250 redox cycles (a total of 148 h on stream), with no phase segregation or change in particle size.

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Optimization of Cu oxide catalyst for methanol synthesis under high CO2 partial pressure using combinatorial tools

Cited by 18 publications

References 21 publications

Methanol Synthesis from Industrial CO2 Sources: A Contribution to Chemical Energy Conversion

Methanol Synthesis from Industrial CO2 Sources: A Contribution to Chemical Energy Conversion

Screening Using Artificial Neural Network of Additives for Cu-Zn Oxide Catalyst for Methanol Synthesis from Syngas

Selective Hydrogen Oxidation Catalysts via Genetic Algorithms

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