Statistics for Chemical and Process Engineers ― A Modern Approach

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 ExcelR and MATLABR. 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.

Dr. Yuri A.W. Shardt is currently an Alexander von Humboldt Fellow working at the University of Duisberg-Essen in fault detection and isolation for complex systems. He has written many papers appearing in journals such asAutomatica, Journal of Process Control, and Industrial and Engineering Chemistry Research on topics ranging from systems identification, soft sensor development, and process control, as well as attending and presenting his research at numerous conferences. He has taught various courses in the intersection between statistics, chemical engineering, process control, EXCEL, and MATLABR. Dr. Shardt completed his doctoral degree under the supervision of Professor 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.