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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico eBook
Idioma:Inglés
Publicado: World Scientific 2009.
Temas:
Catalysis > Computer simulation.
Catalysis > Mathematical models.
Catalyse > Simulation par ordinateur.
Catalyse > Modèles mathématiques.
Catalysis > Computer simulation
Catalysis > Mathematical models
Electronic resource.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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