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Process Data Analytics
Towards Process System Intelligence
- 1st Edition - September 1, 2027
- Authors: Biao Huang, Nabil Magbool Jan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 4 4 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 4 5 - 1
Process Data Analytics is an accessible undergraduate text on data science for engineers, crafted as a helpful study tool for the growing field of courses specially tailored to… Read more
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Request a sales quoteProcess Data Analytics is an accessible undergraduate text on data science for engineers, crafted as a helpful study tool for the growing field of courses specially tailored to these subjects. The text aims to serve as the core book for fundamental courses in data science or machine learning and illustrates the various machine learning methods with process engineering applications. The book also contains advanced content, such as probabilistic models, suitable for use by postgraduate students and researchers. Split into four main parts, the authors first provide the mathematical foundations of regression, discussing the different types of data that students and practitioners may encounter in the process industry, as well as different data preprocessing and visualization techniques. Then, the book discusses various conventional statistical methods and eventually moves on to introduce some of the common machine-learning methods for regression. The final section covers the design of experiments, leading students with case studies and lessons with real-world applications. Process Data Analytics seeks to support readers in the field across several related disciplines, from Engineering to Mathematics courses. From covering basic lessons on subjects such as linear algebra and optimization to delving deeper into model representation of simple, multiple, non-linear, and Bayesian regression, the text ensures that students have a solid understanding of concepts before moving to more adventurous and advanced topics. The book has a unique concentration on dealing with the process data, as well as delves into regression methods. This valuable first edition delivers engaging explanations and illustrative examples while discussing the role and importance of data science in modern studies of the field.
• Explores foundational regression elements in order to build fundamental knowledge when applying concepts in statistics courses
• Presents material in a clear, accessible manner appropriate for an introductory course
• Includes numerous figures and examples, which depict and explicate the important regression and statistics concepts conveyed in the text
• Offers online support, including data sets and codes for students and instructors
• Presents material in a clear, accessible manner appropriate for an introductory course
• Includes numerous figures and examples, which depict and explicate the important regression and statistics concepts conveyed in the text
• Offers online support, including data sets and codes for students and instructors
Students in upper-level undergraduate courses in mathematics and engineering, as well as readers at the graduate and professional levels
Chapter 1: Introduction to Process Data Analytics
Chapter 2: Process Data
Chapter 3: Mathematical Foundations
Chapter 4: Introduction to Regression Analysis
Chapter 5: Simple Linear Regression
Chapter 6: Multiple Linear Regression
Chapter 7: Non-linear Regression
Chapter 8: Bayesian Regression
Chapter 9: Robust Regression
Chapter 10: Time Series Regression
Chapter 11: Regularization Methods
Chapter 12: Support Vector Regression
Chapter 13: Gaussian Process Regression
Chapter 14: Decision Trees
Chapter 15: Neural Networks
Chapter 16: D-Optimal Design of Experiments
Chapter 17: Factorial Design of Experiments
Chapter 2: Process Data
Chapter 3: Mathematical Foundations
Chapter 4: Introduction to Regression Analysis
Chapter 5: Simple Linear Regression
Chapter 6: Multiple Linear Regression
Chapter 7: Non-linear Regression
Chapter 8: Bayesian Regression
Chapter 9: Robust Regression
Chapter 10: Time Series Regression
Chapter 11: Regularization Methods
Chapter 12: Support Vector Regression
Chapter 13: Gaussian Process Regression
Chapter 14: Decision Trees
Chapter 15: Neural Networks
Chapter 16: D-Optimal Design of Experiments
Chapter 17: Factorial Design of Experiments
- No. of pages: 350
- Language: English
- Edition: 1
- Published: September 1, 2027
- Imprint: Academic Press
- Paperback ISBN: 9780443337444
- eBook ISBN: 9780443337451
BH
Biao Huang
Biao Huang obtained his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He also holds an MSc degree (1986) and a BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. Dr. Huang joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering and is currently a full Professor and Senior University of Alberta Engineering Research Chair in Smart Automation. He held positions of NSERC Industrial Research Chair in Control of Oil Sands Processes and AITF Industry Chair in Process Control. He is a Fellow of IEEE, Fellow of the Canadian Academy of Engineering, Fellow of the Asia-Pacific Artificial Intelligence Association, Fellow of the International Artificial Intelligence Industry Alliance, and Fellow of the Chemical Institute of Canada. He is a World Ranking Top 2% Scientist (Stanford University Lists), and has 13 Thomson Reuters ESI Highly Cited Articles, as well as the recipient of Germany’s Alexander von Humboldt Research Fellowship, the Canadian Chemical Engineer Society’s R.S. Jane Memorial Award, Bantrel Award in Design and Industrial Practice, the Syncrude Canada Innovation Award and D.G. Fisher Award, APEGAs Summit Research Excellence award, AsTech Outstanding Achievement in Science & Engineering Award and a Best Paper Award from Journal of Process Control. His research interests include data analytics, process control, system identification, control performance assessment, Bayesian methods and state estimation. He has applied his expertise extensively in industrial practice, particularly related to process data analytics.
Affiliations and expertise
Professor and Senior University of Alberta Engineering Research Chair in Smart Automation, CanadaNJ
Nabil Magbool Jan
Dr. Nabil Magbool Jan is an Assistant Professor in the Department of Chemical Engineering, Indian Institute of Technology Tirupati. Prior to this, he was a post-doctoral researcher at University of Alberta, Canada, and at RWTH Aachen, Germany. He earned his PhD in 2014 from Indian Institute of Technology Madras, Chennai, India. During his doctoral studies, he was awarded the Yggdrasil Fellowship for research at the Norwegian University of Science and Technology, Norway. He obtained his BTech (2007) in Petrochemical Technology from Bharathidasan University, and MTech (2009) in Chemical Engineering from Anna University. His research interests are in the broad area of Process Systems Engineering. More specifically, he works on Sensor Placement, Soft Sensing, Data Reconciliation, State Estimation, Model Predictive Control, Process Optimization, and Machine Learning in Process Engineering.
Affiliations and expertise
Assistant Professor, Department of Chemical Engineering, Indian Institute of Technology, Tirupati, India