Combinatorial Development of Solid Catalytic Materials : Design of High-Throughput Experiments, Data Analysis, Data Mining.
Title:
Combinatorial Development of Solid Catalytic Materials : Design of High-Throughput Experiments, Data Analysis, Data Mining.
Author:
Baerns, Manfred.
ISBN:
9781848163447
Personal Author:
Physical Description:
1 online resource (191 pages)
Series:
Catalytic Science Series ; v.7
Catalytic Science Series
Contents:
Contents -- Dedication -- Preface -- Chapter 1. Background of Combinatorial Catalyst Development (M. Baerns) -- Bibliography -- Chapter 2. Approaches in the Development of Heterogeneous Catalysts (M. Baerns) -- 2.1. Fundamental Aspects -- 2.2. High-throughput Technologies for Preparation and Testing in Combinatorial Development of Catalytic Materials -- 2.2.1. Selection of Potential Elements for Defining the Multi-parameter Compositional Space of Catalytic Materials -- 2.2.2. Experimental Tools for Preparing and Testing Large Numbers of Catalytic-material Specimens -- 2.2.2.1. Preparation of catalytic materials -- 2.2.2.2. Testing and screening of catalytic materials -- Bibliography -- Chapter 3. Mathematical Methods of Searching for Optimal Catalytic Materials (M. Holena) -- 3.1. Introduction -- 3.2. Statistical Design of Experiments -- 3.3. Optimisation Methods for Empirical Objective Functions -- 3.4. Evolutionary Optimisation: The Main Approach to Seek Optimal Catalysts -- 3.4.1. Dealing with Constraints in Genetic Optimisation -- 3.5. Other Stochastic Optimisation Methods -- 3.6. Deterministic Optimisation -- 3.6.1. Utilizability of Methods with Derivatives in Catalysis -- Bibliography -- Chapter 4. Generating Problem-Tailored Genetic Algorithms for Catalyst Search (M. Holena) -- 4.1. Using a Program Generator - Why and How -- 4.2. Description Language for Optimisation Tasks in Catalysis -- 4.3. Tackling Constrained Mixed Optimisation -- 4.4. A Prototype Implementation -- Bibliography -- Chapter 5. Analysis and Mining of Data Collected in Catalytic Experiments (M. Holena) -- 5.1. Similarity and Difference Between Data Analysis and Mining -- 5.2. Survey of Existing Methods -- 5.2.1. Statistical Methods -- 5.2.2. Extraction of Logical Rules from Data -- 5.3. Case Study with the Synthesis of HCN -- Bibliography.
Chapter 6. Artificial Neural Networks in the Development of Catalytic Materials (M. Holena) -- 6.1. What are Artificial Neural Networks? -- 6.1.1. Network Architecture -- 6.1.2. Important Kinds of Neural Networks -- 6.1.3. Activity of Neurons -- 6.1.4. What do Neural Networks Compute? -- 6.2. Approximation Capability of Neural Networks -- 6.3. Training Neural Networks -- 6.4. Knowledge Obtainable from a Trained Network -- Bibliography -- Chapter 7. Tuning Evolutionary Algorithms with Artificial Neural Networks (M. Holena) -- 7.1. Heuristic Parameters of Genetic Algorithms -- 7.2. Parameter Tuning Based on Virtual Experiments -- 7.3. Case Study with the Oxidative Dehydrogenation of Propane -- Bibliography -- Chapter 8. Improving Neural Network Approximations (M. Holena) -- 8.1. Importance of Choosing the Right Network Architecture -- 8.2. Influence of the Distribution of Training Data -- 8.3. Boosting Neural Networks -- 8.4. Case Study with HCN Synthesis Continued -- Bibliography -- Chapter 9. Applications of Combinatorial Catalyst Development and An Outlook on Future Work (M. Baerns) -- 9.1. Introduction -- 9.2. Experimental Applications of Combinatorial Catalyst Development -- 9.3. Methodology -- 9.4. Conclusions and Outlook -- 9.4.1. Applications of Combinatorial Methodologies in Practice -- 9.4.2. Computer-aided Methods for the Optimisation of Catalyst Composition and Data Mining -- Bibliography -- Index.
Abstract:
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. 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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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