Statistics for Chemical and Process Engineers

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.