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

Abstract In the process of discovering new catalytic compositions by combinatorial methods in heterogeneous catalysis usually various potential catalytic compounds have to be prepared and tested. To decrease the number of necessary experiments an optimization algorithm based on a genetic algorithm for deriving subsequent generations from the performance of the members of the preceding generation is described. This procedure is supplemented by using an artificial neural network for establishing relationships between catalyst compositions—or more general speaking—materials properties and their catalytic performance. By combining a trained neural network with the genetic algorithm software virtually computer experiments were done aiming at adjusting the control parameters of the optimization algorithm to the special requirement of catalyst development. The approach is illustrated by the search for new catalytic compositions for the oxidative dehydrogenation of propane.

[1]  Wilhelm F. Maier,et al.  Kombinatorische Materialbibliothek im μg-Maßstab am Beispiel der Hydrothermalsynthese , 1998 .

[2]  Tadashi Hattori,et al.  Estimation of catalytic performance by neural network — product distribution in oxidative dehydrogenation of ethylbenzene , 1994 .

[3]  Ferdi Schüth,et al.  A Multipurpose Parallelized 49-Channel Reactor for the Screening of Catalysts: Methane Oxidation as the Example Reaction , 2001 .

[4]  Manfred Baerns,et al.  Experimental equipment for high-throughput synthesis and testing of catalytic materials , 2002 .

[5]  Huang Kai,et al.  Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling , 2001 .

[6]  Manfred Baerns,et al.  Fundamental insights into the oxidative dehydrogenation of ethane to ethylene over catalytic materials discovered by an evolutionary approach , 2003 .

[7]  Manfred Baerns,et al.  An evolutionary approach in the combinatorial selection and optimization of catalytic materials , 2000 .

[8]  Xiaoqun Wu,et al.  Artificial neural network aided design of catalyst for propane ammoxidation , 1997 .

[9]  W. F. Maier,et al.  Kombinatorische Entdeckung neuer Photokatalysatoren zur Wasseraufbereitung mit sichtbarem Licht , 2001 .

[10]  Selim Senkan,et al.  Application of combinatorial catalysis to the selective reduction of NO by C3H6 , 2000 .

[11]  Motoi Sasaki,et al.  Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite , 1995 .

[12]  Martin Holeňa,et al.  Feedforward neural networks in catalysis: A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction , 2003 .

[13]  Peter Claus,et al.  High-throughput synthesis and screening of catalytic materials: Case study on the search for a low-temperature catalyst for the oxidation of low-concentration propane , 2001 .

[14]  F. Schüth,et al.  A systematic study of the synthesis conditions for the preparation of highly active gold catalysts , 2002 .

[15]  Claude Mirodatos,et al.  Combinatorial Approaches to Heterogeneous Catalysis: Strategies and Perspectives for Academic Research , 2001 .

[16]  Manfred Baerns,et al.  Fundamental and combinatorial approaches in the search for and optimisation of catalytic materials for the oxidative dehydrogenation of propane to propene , 2001 .

[17]  W. H. Weinberg,et al.  High-throughput synthesis and screening of V–Al–Nb and Cr–Al–Nb oxide libraries for ethane oxidative dehydrogenation to ethylene , 2000 .

[18]  Howard Turner,et al.  Kombinatorische Parallelsynthese und Hochgeschwindigkeitsrasterung von Heterogenkatalysator‐Bibliotheken , 1999 .

[19]  Tadashi Hattori,et al.  Neural network as a tool for catalyst development , 1995 .

[20]  James Norman Cawse,et al.  Experimental Design for Combinatorial and High Throughput Materials Development , 2002 .

[21]  Selim Senkan.  Kombinatorische heterogene Katalyse – ein neuer Weg in einem alten Gebiet , 2001 .

[22]  Duncan Akporiaye,et al.  Hydrothermalsynthesen von Zeolithen mit Methoden der kombinatorischen Chemie , 1998 .

[23]  M. Richter,et al.  Combinatorial preparation and high-throughput catalytic tests of multi-component deNOx catalysts , 2002 .

[24]  Fischer–Tropsch synthesis with Co/SiO2–Al2O3 catalyst and steady-state modeling using artificial neural networks , 1998 .

[25]  Arne Karlsson,et al.  Combinatorial chemistry – The emperor's new clothes? , 2001 .

[26]  Selim Senkan,et al.  Entdeckung und Optimierung von Heterogenkatalysatoren durch kombinatorische Chemie , 1999 .

[27]  K. Schittkowski.  NLPQL: A fortran subroutine solving constrained nonlinear programming problems , 1986 .

[28]  Manfred Baerns,et al.  Ethylene and propene by oxidative dehydrogenation of ethane and propane: ‘Performance of rare-earth oxide-based catalysts and development of redox-type catalytic materials by combinatorial methods’ , 2000 .