Multi-objective optimization in combinatorial chemistry applied to the selective catalytic reduction of NO with C3H6

Gobin, Oliver C.; Joaristi, Alberto Martinez; Schüth, Ferdi

doi:10.1016/j.jcat.2007.09.025

Cited by 20 publications

(28 citation statements)

References 30 publications

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“…Genetic Algorithms (GA) have been done by various groups, such as Pereira et al 31 They reported a study of the effect of Genetic Algorithm (GA) configurations on the performance of heterogeneous catalyst optimization. Also, Gobin et al 32,33 used multi-objective experimental design of experiments based on a genetic algorithm to optimize the combinations and concentrations of solid catalyst systems. Moreover, the genetic algorithm has been merged with a knowledge-based system 34 , and boosted on a GPU hardware to solve a zeolite structure 35,36 .…”

Section: Literature Reviewmentioning

confidence: 99%

Simulating and Optimizing Hydrogen Production by Low-pressure Autothermal Reforming of Natural Gas using Non-dominated Sorting Genetic Algorithm-II

Azarhoosh

Ebrahim

Pourtarah

2016

Chem. Biochem. Eng. Q.

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Conventional hydrogen production plants consist of natural gas steam reforming to CO+3H 2 on Ni catalysts in a furnace, water-gas shift reaction for converting CO into CO 2 and CO 2 absorption. A new alternative method for highly endothermic steam reforming is autothermal reforming (steam reforming with air input to the reactor) without the need for external heating. In this study, hydrogen production by autothermal reforming for fuel cells (base case) was simulated based on a heterogeneous and one-dimensional model. In addition, the effect of operating variables on the system behavior was studied. Finally, Pareto-optimal solutions for the maximum molar flow rate of the produced hydrogen and methane conversion were determined by NSGA-II. There was a huge increase in the produced hydrogen molar flow to the base case, which showed the importance of optimizing autothermal reformers for hydrogen production.

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Section: Literature Reviewmentioning

confidence: 99%

Simulating and Optimizing Hydrogen Production by Low-pressure Autothermal Reforming of Natural Gas using Non-dominated Sorting Genetic Algorithm-II

Azarhoosh

Ebrahim

Pourtarah

2016

Chem. Biochem. Eng. Q.

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“…In such situations, another correlation measure is used, for which those values correspond to a more specific dependence, namely to linear dependence. This is the linear correlation coefficient of X and Y 46,47 , also called Pearson's correlation coefficient and commonly denoted corr(X,Y), which is defined (8) and has the following properties:…”

Section: Correlation Measure Based On Bounding Probability Distributions Besides Thementioning

confidence: 99%

Computing the correlation between catalyst composition and its performance in the catalysed process

Holeňa¹,

Steinfeldt²,

Baerns³

et al. 2012

Computers & Chemical Engineering

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The methodology for computing correlations between continuous descriptors of catalytic materials and their performance in the catalysed process is addressed. Continuous descriptors are typically molar fractions of individual components of the catalyst, whereas the performance is represented most frequently by yield or selectivity of reaction products or conversion of key feed components. Measures of various kinds of correlation are recalled, and their descriptor-wise application to catalytic data for computing correlations between the composition and performance of catalysts is presented. The paper also compares the application of correlation measures to catalytic data on the one hand with the analysis of variance, on the other hand with the application of regression trees. As a case study, the presented approaches are applied to data from high-temperature synthesis of hydrocyanic acid

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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: 99%

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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Multi-objective optimization in combinatorial chemistry applied to the selective catalytic reduction of NO with C3H6

Cited by 20 publications

References 30 publications

Simulating and Optimizing Hydrogen Production by Low-pressure Autothermal Reforming of Natural Gas using Non-dominated Sorting Genetic Algorithm-II

Simulating and Optimizing Hydrogen Production by Low-pressure Autothermal Reforming of Natural Gas using Non-dominated Sorting Genetic Algorithm-II

Computing the correlation between catalyst composition and its performance in the catalysed process

Selective Hydrogen Oxidation Catalysts via Genetic Algorithms

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