Combinatorial Development Of Solid Catalytic Materials Design Of High Throughput Experiments Data Analysis Data Mining
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Combinatorial Development of Solid Catalytic Materials
The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade OCo evolutionary optimization and artificial neural networks OCo are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental OCo their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies. Sample Chapter(s). Chapter 1: Background of Combinatorial Catalyst Development (63 KB). Contents: Background of Combinatorial Catalyst Development (M Baerns); Approaches in the Development of Heterogeneous Catalysts (M Baerns); Mathematical Methods of Searching for Optimal Catalytic Materials (M Holena); Generating Problem-Tailored Genetic Algorithms for Catalyst Search (M Holena); Analysis and Mining of Data Collected in Catalytic Experiments (M Holena); Artificial Neural Networks in the Development of Catalytic Materials (M Holena); Tunning Evolutionary Algorithms with Artificial Neural Networks (M Holena); Improving Neural Network Approximations (M Holena); Applications of Combinatorial Catalyst Development and An Outlook on Future Work (M Baerns). Readership: Chemists and chemical engineers from academia and industry working in catalysis; materials scientists; graduate students dealing with catalytic chemistry interested in computer-aided methods.
Combinatorial Development Of Solid Catalytic Materials: Design Of High-throughput Experiments, Data Analysis, Data Mining
The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade — evolutionary optimization and artificial neural networks — are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental — their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies./a
Modern Applications of High Throughput R&D in Heterogeneous Catalysis
Author: Alfred Hagemeyer
language: en
Publisher: Bentham Science Publishers
Release Date: 2014-04-04
This eBook covers the application of high-throughput R&D to both fundamental and applied catalysis including catalyst synthesis, characterization, and testing in various reactor types. Chapters include topics such as applications ranging from optimizations of established industrial catalysts to the discovery of innovative new materials, examples of the development of innovative parallel characterization methods, and cases of real catalyst testing in small scale reactor systems. Readers will also find chapters that cover commodity chemicals produced using continuous gas phase processes as well as fine chemicals produced in liquid phase batch reactors. The potential of industrial chemicals production from biorenewable feedstocks is also presented. The steadily improving high throughput workflows are today being applied to relevant reactions and targets such as hydrotreating, Deacon oxidation, Fischer-Tropsch, propane dehydrogenation, C4 oxidation, methane coupling, exhaust gas catalysis, bio-based Nylon, fuel cells and vitamins. The topics presented in this eBook have been contributed by researchers from academia as well as industry, making this eBook a well-balanced reference, which could be of particular interest to professional, industrial or service R&D labs.