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Process Systems Engineering

 

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Word Power Books

Process Systems Engineering


Amparo Galindo (Editor)
Michael Georgiadis (Editor)
Efstratios N. Pistikopoulos (Editor)
Vivek Dua (Editor)

 

Hardback

ISBN: 9783527316953

 

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Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas.


Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge. The ultimate reference for years to come.


 

ISBN 3527316957
ISBN13 9783527316953
Publisher Wiley-VCH Verlag GmbH
Format Hardback
Publication date 24/03/2010
Pages 336
Weight (grammes) 764
Published in Germany
Height (mm) 249
Width (mm) 180

Preface
CRYSTALOPTIMIZER: AN EFFICIENT ALGORITHM FOR LATTICE ENERGY MINIMIZATION OF ORGANIC CRYSTALS USING ISOLATED-MOLECULE QUANTUM MECHANICAL CALCULATIONS
Introduction and Background
Lattice Energy Calculation
CrystalOptimizer: Minimization Using LAMs
Results and Discussion
Conclusions
AN INTRODUCTION TO COARSE-GRAINING APPROACHES: LINKING ATOMISTIC AND MESOSCALES
Introduction
Rigorous Coarse Graining: Partition Function Matching
Coarse Graining by Matching a Specific Property
Coarse Graining for Specific Mesoscale Simulation Techniques
Conclusions and Future Outlook
Appendix A: Dissipative Particle Dynamics
Appendix B: Dynamic Mean-Field Density Functional Theory
HIERARCHICAL MODELING OF POLYMERIC SYSTEMS AT MULTIPHLE TIME AND LENGTH SCALES
Introduction
Atomistic Molecular Dynamics and Monte Carlo Simulation of Polymers: Basic Concepts and Recent Developments
Atomistic Molecular Dynamics and Monte Carlo Simulation of Polymers: Applications
Techniques for the Simulation of the Solubility and Permeability Properties of Polymers
Current Trends
Conclusions and Outlook
GROUP CONTRIBUTION METHODOLOGIES FOR THE PREDICTION OF THERMODYNAMIC PROPERTIES AND PHASE BEHAVIOR IN MIXTURES
Introduction
Pure Component GC Methods
Activity Coefficient GC Methods
GC Methods in Equations of State
The Statistical Associating Fluid Theory (SAFT)
Other Predictive Methods
Concluding Remarks
OPTIMIZATION-BASED APPROACHES TO COMPUTATIONAL MOLECULAR DESIGN
Introduction and Motivation
Quantitative Structure-Property Relationships
Problem Formulations for CAMD
Mathematical Techniques for the Solution of CAMD Optimization Problems
The Tabu Search Algorithm
Case Study
Conclusions and Future Directions
MOLECULAR MODELING OF FORMULATED CONSUMER PRODUCTS
Introduction
Performance Properties of Complex Liquid Formulations
Stability Assessment of Multiphase Formulations
Process Factors: Metastable States of Multiphase Mixtures
Summary
RECENT ADVANCES IN DE NOVO PROTEIN DESIGN
Introduction
De Novo Approach with Fold Specificity
De Novo Approach with Approximate Binding Affinity
Applications and Representative Results
Summary
PRINCIPLES AND METHODOLOGIES FOR THE CONTROLLED FORMATION OF SELF-ASSEMBLED NANOSCALE STRUCTURES WITH DESIRED GEOMETRIES
Overview of the Controlled Nanostructure Formation Approach
Statistical Mechanics and Ergodicity
Methodological Procedures for the Controlled Formation of Desired Nanostructures
Summary
COMPUTER-AIDED METHODOLOGIES FOR THE DESIGN OF REACTION SOLVENTS
Introduction
Solvent Effects on Reactions and the Transition-State Theory
Capturing Solvent Effects with an Empirical Approach
Solvent Design for an Sn2 Reaction with an Empirical Model
Concluding Remarks