Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm

Watanabe, Yuhsuke; Umegaki, Tetsuo; Hashimoto, Masahiko; Omata, Kohji; Yamada, Masahito

doi:10.1016/j.cattod.2004.02.001

Cited by 52 publications

(40 citation statements)

References 37 publications

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“…For example in the reactor available in our laboratory, the stirring rate is fixed to seven different values (200, 300, 400, 500, 700, 1000 and 1250 rpm, see Table 1) due to instrument constraint, and thus the stirring rate can only take a discrete set of values. When a subset or all of the process factors are discrete, more advanced methods are needed, such as genetic algorithms [3][4][5] and branch-and-bound method [49]. Note that these advanced methods are also applicable for optimizing continuous factors.…”

Section: Model-based Optimizationmentioning

confidence: 99%

“…In addition, the above three-stage procedure is typically operated in an iterative manner, where the information attained from previous iterations is utilized to guide the search for better response variables. This iterative exploration of experimental space has been adopted and applied in various model-based process optimization methods, such as those using genetic algorithms [3][4][5], and "active sampling" [6] that was originally developed in the machine learning society [7]. RSM is particularly applicable to problems where the understanding of the process mechanism is limited and/or is difficult to be represented by a first-principles mathematical model.…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, artificial neural network (ANN) was proposed to provide a more accurate approximation of the response surface, and it was demonstrated to give improved optimization results in various applications [11][12][13][14][15][16]. More recently, ANN has been combined with other methods, such as genetic algorithm, principal component analysis and clustering analysis, for modelling, analysis and optimization of various catalysis systems [3][4][5][17][18][19]. An alternative approach is support vector machine (SVM) that belongs to the family of kernel modelling methods [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts

Tang

Lau

et al. 2010

Chemical Engineering Journal

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Section: Model-based Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts

Tang

Lau

et al. 2010

Chemical Engineering Journal

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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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“…In 2000, for the first time, the ES was applied to library design for a catalyst optimization for the selective oxidation of propane [4,5]. Since then, a few successful applications have been published by various groups in catalysis [6][7][8][9][10][11][12][13][14][15][16][17][18][19] and material science [20]. However, detailed pragmatic study on the application of ES for this particular domain is lacking.…”

Section: Introductionmentioning

confidence: 99%

Application of Evolutionary Strategies in the Experimental Optimization of Catalytic Materials

et al. 2010

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The issues of heterogeneous catalyst optimization are presented in the framework of high throughput iterative screening. To be efficient, the optimization procedures should consider the limitations of the facilities in terms of screening capacities, experimentation costs, and experimental noise. The issues of algorithm reliability are also addressed. Based on the simulation results, this work highlights the most important features of the evolutionary strategies (ES) that lead to successful optimizations. We show that the monitoring of the population diversity during the optimization is a key parameter. Finally, we provide some best practice recommendations for experimentalists who are not experts in metaheuristic methods and who are willing to apply ES for material library designs.

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Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm

Cited by 52 publications

References 37 publications

Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts

Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts

Selective Hydrogen Oxidation Catalysts via Genetic Algorithms

Application of Evolutionary Strategies in the Experimental Optimization of Catalytic Materials

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