A coherent, concise and comprehensive course in the statistics needed for a modern career in chemical engineering; covers all of the concepts required for the American Fundamentals of Engineering examination
.
This book shows the reader how to develop and test models, design experiments and analyse data in ways easily applicable through readily available software tools like MS Excel® and MATLAB®. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory,
Statistics for Chemical and Process Engineers
provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
Edition
Softcover reprint of the original 1st ed. 2015
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
85 s/w Abbildungen, 48 farbige Abbildungen
XXVI, 414 p. 133 illus., 48 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 23 mm
Weight
ISBN-13
978-3-319-38749-9 (9783319387499)
DOI
10.1007/978-3-319-21509-9
Schweitzer Classification
Prof. Dr.
Yuri A. W. Shardt
is currently the chair of the Department of Automation Engineering (DE: Fachgebiet Automatisierungstechnik) in the Faculty of Computer Science and Automation (DE: Fakultät Informatik und Automatisierung) at the Technical University of Ilmenau (DE: Technische Universität Ilmenau), working in the fields of big data, including process identification and monitoring with an emphasis on the development and industrial implementation of soft sensors; holistic control, including the development of advanced control strategies for complex industrial process; and the smart world, including such implementations as smart factories, smart home, Industry 4.0, and smart grids. Previously, he worked at the University of Waterloo in the Department of Chemical Engineering and at the University of Duisburg-Essen in the Institute of Control and Complex Systems (DE: Fachgebiet Automatisierungstechnik und komplexe Systeme, AKS) as an Alexander von Humboldt Fellow. He has written 30 papers appearing in such journals as Automatica, Journal of Process Control, IEEE Transactions on Industrial Electronics, and Industrial and Engineering Chemistry Research on topics ranging from system identification, soft sensor development, to process control. He has presented his research at numerous conferences and taught various courses in the intersection between statistics, chemical engineering, process control, EXCEL®, and MATLAB®. Prof. Dr. Shardt completed his doctoral degree under the supervision of Prof. Dr. Biao Huang at the University of Alberta. His thesis examined the methods for extracting valuable data for system identification from data historians for application to soft sensor design. In addition to his academic work, he has spent considerable time in industry working on implementing various process control solutions. He also has interests in linguistics, as well as software internationalisation and localisation.
Introduction to Statistics and Data Visualisation.- Theoretical Foundation for Statistical Analysis.- Regression.- Design of Experiments.- System Identification.- Data Mining.- Appendices: A Brief Review of Set Theory and Notation; A Traditional Approach to Ordinary, Linear Least Squares Regression' A Traditional Approach to Weighted, Linear Least Squares Regression; A Traditional Approach to Factorial Design Analysis; Using Excel for Statistical Analysis; Using MATLAB® for Statistical Analysis.
