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Combinatorial Development Of Solid Catalytic Materials

Design Of High-throughput Experiments, Data Analysis, Data Mining

By: Manfred Baerns, Martin Holena

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Hardcover | 1 December 2009

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Hardcover
192 Pages

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22.86 x 15.24 x 1.27

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Engineering & Technology Mechanical Engineering & Materials Materials Science Science Chemistry Physical Chemistry Catalysis

Booktopia Publisher Services World Scientific Publishing

Preface	p. vii
Background of Combinatorial Catalyst Development	p. 1
Bibliography	p. 4
Approaches in the Development of Heterogeneous Catalysts	p. 7
Fundamental Aspects	p. 7
High-throughput Technologies for Preparation and Testing in Combinatorial Development of Catalytic Materials	p. 9
Selection of Potential Elements for Defining the Multi-parameter Compositional Space of Catalytic Materials	p. 10
Experimental Tools for Preparing and Testing Large Numbers of Catalytic-material Specimens	p. 13
Preparation of catalytic materials	p. 14
Testing and screening of catalytic materials	p. 16
Bibliography	p. 19
Mathematical Methods of Searching for Optimal Catalytic Materials	p. 21
Introduction	p. 21
Statistical Design of Experiments	p. 22
Optimisation Methods for Empirical Objective Functions	p. 24
Evolutionary Optimisation: The Main Approach to Seek Optimal Catalysts	p. 26
Dealing with Constraints in Genetic Optimisation	p. 31
Other Stochastic Optimisation Methods	p. 33
Deterministic Optimisation	p. 34
Utilizability of Methods with Derivatives in Catalysis	p. 36
Bibliography	p. 37
Generating Problem-Tailored Genetic Algorithms for Catalyst Search	p. 43
Using a Program Generator - Why and How	p. 43
Description Language for Optimisation Tasks in Catalysis	p. 47
Tackling Constrained Mixed Optimisation	p. 53
A Prototype Implementation	p. 57
Bibliography	p. 59
Analysis and Mining of Data Collected in Catalytic Experiments	p. 61
Similarity and Difference Between Data Analysis and Mining	p. 61
Survey of Existing Methods	p. 62
Statistical Methods	p. 63
Extraction of Logical Rules from Data	p. 67
Case Study with the Synthesis of HCN	p. 70
Bibliography	p. 75
Artificial Neural Networks in the Development of Catalytic Materials	p. 79
What are Artificial Neural Networks?	p. 79
Network Architecture	p. 80
Important Kinds of Neural Networks	p. 83
Activity of Neurons	p. 87
What do Neural Networks Compute?	p. 88
Approximation Capability of Neural Networks	p. 90
Training Neural Networks	p. 95
Knowledge Obtainable from a Trained Network	p. 103
Bibliography	p. 110
Tuning Evolutionary Algorithms with Artificial Neural Networks	p. 115
Heuristic Parameters of Genetic Algorithms	p. 115
Parameter Tuning Based on Virtual Experiments	p. 116
Case Study with the Oxidative Dehydrogenation of Propane	p. 119
Bibliography	p. 133
Improving Neural Network Approximations	p. 135
Importance of Choosing the Right Network Architecture	p. 135
Influence of the Distribution of Training Data	p. 136
Boosting Neural Networks	p. 138
Case Study with HCN Synthesis Continued	p. 141
Bibliography	p. 152
Applications of Combinatorial Catalyst Development and An Outlook on Future Work	p. 155
Introduction	p. 155
Experimental Applications of Combinatorial Catalyst Development	p. 155
Methodology	p. 163
Conclusions and Outlook	p. 169
Applications of Combinatorial Methodologies in Practice	p. 169
Computer-aided Methods for the Optimisation of Catalyst Composition and Data Mining	p. 171
Bibliography	p. 172
Index	p. 175
Table of Contents provided by Ingram. All Rights Reserved.

Combinatorial Development Of Solid Catalytic Materials

Design Of High-throughput Experiments, Data Analysis, Data Mining

At a Glance

Hardcover

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