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Statistics for Chemical and Process Engineers: A Modern Approach

Autor Yuri A.W. Shardt
en Limba Engleză Hardback – 27 oct 2015
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 Engineersprovides 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.
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Specificații

ISBN-13: 9783319215082
ISBN-10: 3319215086
Pagini: 414
Ilustrații: 85 schwarz-weiße und 48 farbige Abbildungen, Bibliographie
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.94 kg
Ediția:1st ed. 2015
Editura: Springer
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Professional/practitioner

Cuprins

1. Introduction to Statistics and Data Visualisation.- 2. Theoretical Foundation for Statistical Analysis.- 3. Regression.- 4. Design of Experiments.- 5. Modelling Stochastic Processes with Time Series Analysis.- 6. Modelling Dynamic Processes Using System Identification Methods.- 7.- Using MATLAB® for Statistical Analysis.- 8 : Using Excel® to do Statistical Analysis.

Notă biografică

Prof. Dr.Yuri A. W. Shardtis 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.

Textul de pe ultima copertă

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 Engineersprovides 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.
 

Caracteristici

Covers all concepts required by the American Fundamentals of Engineering Examination
Helps the reader perform correct data analysis by providing detailed guidance frameworks in addition to the conceptual presentation
Emphasizes examples relevant to chemical and process engineers especially those new to statistical analysis
Microsoft Excel Templates facilitate the use of the methods presented without requiring the practitioner to have access to specialized software
Generalized exposition of results means they can be put to use in the widest range of applications possible
Integrative approach to system identification, linear regression and statistical theory helps the reader to understand the similarities and differences in the methods used
Includes supplementary material: sn.pub/extras