Data-Driven Prediction for Industrial Processes and Their Applications

Description

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 within the machine learning and data analysis and mining communities. Front Matter ....Pages i-xvi Introduction (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 1-11 Data Preprocessing Techniques (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 13-52 Industrial Time Series Prediction (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 53-119 Factor-Based Industrial Process Prediction (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 121-157 Industrial Prediction Intervals with Data Uncertainty (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 159-222 Granular Computing-Based Long-Term Prediction Intervals (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 223-267 Parameter Estimation and Optimization (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 269-350 Parallel Computing Considerations (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 351-383 Data-Based Prediction for Energy Scheduling of Steel Industry (Jun Zhao, Wei Wang, Chunyang Sheng)....Pages 385-436 Back Matter ....Pages 437-443