Data-Driven Prediction for Industrial Processes and Their Applications (Paperback)

Jun Zhao

ISBN 10: 3030067858 / ISBN 13: 9783030067854

Published by Springer Nature Switzerland AG, Cham, 2018

About this Item

Paperback. This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities. This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783030067854

Bibliographic Details

• Title: Data-Driven Prediction for Industrial Processes and Their Applications (Paperback)
• Publisher: Springer Nature Switzerland AG, Cham
• Publication Date: 2018
• Language: English
• ISBN 10: 3030067858
• ISBN 13: 9783030067854
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.

About the Author

Jun Zhao is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.

Chunyang Sheng is currently a lecturer with the School of Electrical Engineering and Automation, Shandong University of Science and Technology, China. 

Wei Wang is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.