Optimization problems occurring regularly in chemistry, vary from selecting the best wavelength design for optimal spectroscopic concentration predictions to geometry optimization of atomic clusters and protein folding. Numerous optimization tactics have been explored to solve these problems. While most optimizers maintain the ability to locate global optima for simple problems, few are robust against local optima convergence with regard to difficult or large scale optimization problems. Simulated annealing (SA) has shown a great tolerance to local optima convergence and is often called a global optimizer. The optimizaton algorithm has found wide use in numerous areas such as engineering, computer science, communication, image recognition, operation research, physics, and biology. Recently, SA and variations thereof have shown considerable success in solving numerous chemical optimization problems. The main thrust of this book is to demonstrate the use of SA in a wide range of chemical problems.The potentiality of SA, GSA and other modifications of SA to serve specific needs in a variety of chemical disciplines are covered. A detailed discussion on SA and GSA is given in Chapter 1, presenting the theoretical framework from which a computer program can be written by the reader. The remainder of the book describes applications of SA type algorithms to a diverse set of chemical problems. The final chapter contains an algorithm for GSA written in the MatLab programming environment. This program can be easily adapted to any optimization problem and with only slight modifications, can be altered to perform SA. A general flowchart is also given.

We never create anything, We discover and reproduce. The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems has a distinguished theme. It is concerned with bridging the gap between the academic and the industrial worlds of Artificial Intelligence (AI) and Expert Systems. The academic world is mainly concerned with discovering new algorithms, approaches, and methodologies; however, the industrial world is mainly driven by profits, and concerned with producing new products or solving customers’ problems. Ten years ago, the artificial intelligence research gap between academia and industry was very broad. Recently, this gap has been narrowed by the emergence of new fields and new joint research strategies in academia. Among the new fields which contributed to the academic-industrial convergence are knowledge representation, machine learning, searching, reasoning, distributed AI, neural networks, data mining, intelligent agents, robotics, pattern recognition, vision, applications of expert systems, and others. It is worth noting that the end results of research in these fields are usually products rather than empirical analyses and theoretical proofs. Applications of such technologies have found great success in many domains including fraud detection, internet service, banking, credit risk and assessment, telecommunication, etc. Progress in these areas has encouraged the leading corporations to institute research funding programs for academic institutes. Others have their own research laboratories, some of which produce state of the art research.

Wavelets seem to be the most efficient tool in signal denoising and compression. They can be used in an unlimited number of applications in all fields of chemistry where the instrumental signals are the source of information about the studied chemical systems or phenomena, and in all cases where these signals have to be archived. The quality of the instrumental signals determines the quality of answer to the basic analytical questions: how many components are in the studied systems, what are these components like and what are their concentrations? Efficient compression of the signal sets can drastically speed up further processing such as data visualization, modelling (calibration and pattern recognition) and library search. Exploration of the possible applications of wavelets in analytical chemistry has just started and this book will significantly speed up the process. The first part, concentrating on theoretical aspects, is written in a tutorial-like manner, with simple numerical examples. For the reader's convenience, all basic terms are explained in detail and all unique properties of wavelets are pinpointed and compared with the other types of basis function. The second part presents applications of wavelets from many branches of chemistry which will stimulate chemists to further exploration of this exciting subject.

The application of modern methods in numerical mathematics on problems in chemical engineering is essential for designing, analyzing and running chemical processes and even entire plants. Scientific Computing in Chemical Engineering II gives the state of the art from the point of view of numerical mathematicians as well as that of engineers. The present volume as part of a two-volume edition covers topics such as computer-aided process design, combustion and flame, image processing, optimization, control, and neural networks. The volume is aimed at scientists, practitioners and graduate students in chemical engineering, industrial engineering and numerical mathematics.

The rivers run into the sea, yet the sea is not full Ecclesiastes What is quantum chemistry? The straightforward answer is that it is what quan tum chemists do. But it must be admitted, that in contrast to physicists and chemists, "quantum chemists" seem to be a rather ill-defined category of scientists. Quantum chemists are more or less physicists (basically theoreticians), more or less chemists, and by large, computationists. But first and foremost, we, quantum chemists; are conscious beings. We may safely guess that quantum chemistry was one of the first areas in the natural sciences to lie on the boundaries of many disciplines. We may certainly claim that quantum chemists were the first to use computers for really large scale calculations. The scope of the problems which quantum chemistry wishes to answer and which, by its unique nature, only quantum chemistry can answer is growing daily. Retrospectively we may guess that many of those problems meet a daily need, or are say, technical in some sense. The rest are fundamental or conceptual. The daily life of most quantum chemists is usually filled with grasping the more or less technical problems. But it is at least as important to devote some time to the other kind of problems whose solution will open up new perspectives for both quantum chemistry itself and for the natural sciences in general.

The limited coverage of data analysis and statistics offered in most undergraduate and graduate analytical chemistry courses is usually focused on practical aspects of univariate methods. Drawing in real-world examples, Practical Guide to Chemometrics, Second Edition offers an accessible introduction to application-oriented multivariate meth

In analytical chemistry and pharmaceutical technology attention is increasingly focussed on improving the quality of methods and products. This book aims at fostering the awareness of the potential of existing mathematical and statistical methods to improve this quality. It provides procedures and ideas on how to make a product or a method less sensitive to small variations in influencing factors. Major issues covered are robustness and stability improvement and ruggedness testing. General strategies and a theoretical introduction to these methods are described, and thorough overviews of methods used in both application areas and descriptions of practical applications are given.Features of this book:• Gives a good overview of mathematical and statistical methods used in two application areas, i.e. pharmaceutical technology and analytical chemistry• Illustrates the different approaches available to attain robustness• Gives ideas on how to use methods in practical situations.The book is intended for those who develop and optimize, and are responsible for the overall quality of, analytical methods and pharmaceutical technological products and procedures.

The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses.* Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered

Revised, and updated Design and Optimization in Organic Synthesis presents strategies to explore experimental conditions and methodologies for systematic studies of entire reaction systems (substrates, reagent(s), catalyst(s), and solvents). Chemical phenomena are not usually the result of a single factor and this book describes how statistically designed methods can be used to analyse and evaluate synthetic procedures. The methodology is based on multivariate statistical techniques. The accompanying CD contains data tables and programmes. This book is essential reading for anyone working in process design and development in fine chemicals or the pharmaceutical industry, and is suitable for those with no experience in the field.* Contains recalculated models and redrawn figures, as well as new chapters on for example, the design of combinatorial libraries * Presents strategies to explore experimental conditions and methodologies* Enables the analysis and prediction of the best synthetic procedures

Computer simulation techniques are now having a major impact on almost all areas of the physical and biological sciences. This book concentrates on the application of these methods to inorganic materials, including topical and industrially relevant systems including zeolites and high Tc superconductors. The central theme of the book is the use of modern simulation techniques as a structural tool in solid state science. Computer Modelling in Inorganic Crystallography describes the current range of techniques used in modeling crystal structures, and strong emphasis is given to the use of modeling in predicting new crystal structures and refining partially known structures. It also reviews new opportunities being opened up by electronic structure calculation and explains the ways in which these techniques are illuminating our knowledge of bonding in solids. - Includes a thorough review of the technical basis of relevant contemporary methodologies including minimization, Monte-Carlo, molecular dynamics, simulated annealing methods, and electronic structure methods - Highlights applications to amorphous and crystalline solids - Surveys simulations of surface and defect properties of solids - Discusses applications to molecular and inorganic solids