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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 artifici...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
World Scientific
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000M 4500 | ||
|---|---|---|---|
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| 245 | 0 | 0 | |a Combinatorial Development Of Solid Catalytic Materials : |b Design Of High-Throughput Experiments, Data Analysis, Data Mining. |
| 260 | |b World Scientific |c 2009. | ||
| 300 | |a 1 online resource (192) | ||
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| 505 | 0 | |a Cover13; -- 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 -- 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 8212; 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. | |
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Catalysis |x Computer simulation. | |
| 650 | 0 | |a Catalysis |x Mathematical models. | |
| 650 | 6 | |a Catalyse |x Simulation par ordinateur. | |
| 650 | 6 | |a Catalyse |x Modèles mathématiques. | |
| 650 | 7 | |a Catalysis |x Computer simulation |2 fast | |
| 650 | 7 | |a Catalysis |x Mathematical models |2 fast | |
| 655 | 4 | |a Electronic resource. | |
| 720 | |a Baerns Manfred Et Al. | ||
| 758 | |i has work: |a Combinatorial development of solid catalytic materials (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGX4X9dgM93r4FXFY6HX3P |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1679344 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL1679344 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n 275985 | ||
| 994 | |a 92 |b IZTAP | ||