Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials

Rodemerck, Uwe; Baerns, M.; Holeňa, Martin; Wolf, Dorit

doi:10.1016/s0169-4332(03)00919-x

Cited by 114 publications

(85 citation statements)

References 29 publications

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“…Similarly, Umegaki et al combined the GA and ANN with a parallel activity test for optimizing a Cu−Zn−Al−Sc oxide catalyst for methanol synthesis [54]. Rodemerck et al generalized the GA-assisted ANN method and proposed a general framework for new solid catalytic materials screening, in good agreement with their experimental data [55]. Based on the previous developments of the GA-assisted ANN methods, Baumes et al further developed an "ANN fliter" for the high-throughput screening (HTS) of heterogeneous catalysis discovery [56].…”

Section: Optimization Of Catalysismentioning

confidence: 95%

Application of Artificial Neural Networks for Catalysis: A Review

2017

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Abstract:Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.

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Section: Optimization Of Catalysismentioning

confidence: 95%

Application of Artificial Neural Networks for Catalysis: A Review

2017

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“…The ability of ANN to recognize and reproduce cause-effect relationships through training, for complex multiple input-output systems, makes them efficient to represent complex systems [5]. Roedemerck et al [6] have employed ANN to determine optimal catalyst composition for the oxidative dehydrogenation of propane. Gunay & Yildrim [7] also developed and optimized alumina-supported Pt-Co-Ce catalyst for selective CO hydrogenation in an H 2 -rich stream using ANN modeling while Molga [8] has presented a generalised neural network approach for the modeling of catalytic reactors.…”

Section: Introductionmentioning

confidence: 99%

Solids Residence Time Distribution in a Three-Phase Bubble Column Reactor: An Artificial Neural Network Analysis

Pareek¹,

Sharma²,

Cooper³

et al. 2008

TOCENGJ

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Abstract:Residence time distribution (RTD) study of solids in a three-phase pilot-scale bubble column photoreactor has been carried out in order to provide data for the development of an artificial neural network model usable for process optimisation. The experimental data indicated that the RTD of solids was a complex nonlinear function of gas and liquid velocities as well as the contacting pattern (co-current and countercurrent flow of gas and liquid). In this study, the solid particle RTD data were modeled using feed forward artificial neural networks (ANN). The networks were trained with 250-sets of input-output patterns using back-propagation algorithm. The trained networks were tested using 50-sets of RTD data previously unknown to the networks. Out of several configurations, a 3-layered network with 6-neurons in its hidden layer yielded optimal results with respect to the validation data. The optimal model and empirical data exhibited good agreement with a correlation coefficient of 0.995.

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“…In this work we develop procedures for applying these algorithms for positron lifetime spectroscopy. Genetic algorithms have been recently applied to numerous scientific subjects, ranging from catalyst design [7], protein structures [8] to search for extra-solar planets [9]. As far as we know, no attempt has been to apply these algorithms for positron lifetime spectra.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithms for Positron Lifetime Data

et al. 2008

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Recently, genetic algorithms have been applied for ultrafast optical spectrometry in systems with several convoluted lifetimes. We apply these algorithms and compare the results with POSFIT (by Kirkegaard and Eldrup) and LT programme (by Kansy). The analysis was applied to three types of samples: molybdenum monocrystals, Czochralski-grown silicon with oxygen precipitates, Si with under-surface cavities obtained by He + H ion co-implantation. In all three tests, the genetic algorithm performs very well, in particular for short lifetimes. Further developments to model the resolution function in genetic algorithms are needed.

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Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials

Cited by 114 publications

References 29 publications

Application of Artificial Neural Networks for Catalysis: A Review

Application of Artificial Neural Networks for Catalysis: A Review

Solids Residence Time Distribution in a Three-Phase Bubble Column Reactor: An Artificial Neural Network Analysis

Genetic Algorithms for Positron Lifetime Data

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