Data-Driven Prediction for Industrial Processes and Their Applications [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.Preface.- Introduction.- Why the prediction is required for industrial process.- Introduction to industrial process prediction.- Category of industrial process prediction.- Common-used techniques for industrial process prediction.- Brief summary.- Data preprocessing techniques.- Anomaly detection of data.- Correction of abnormal data.- Methods of packing missing data.- Data de-noising techniques.- Data fusion methods.- Discussion.- Industrial time series prediction.- Introduction.- Methods of phase space reconstruction.- Prediction modeling.- Benchmark prediction problems.- Cases of industrial applications.- Discussion.- Factor-based industrial process prediction.- Introduction.- Methods of determining factors.- Factor-based single-output model.- Factor-based multi-output model.- Cases of industrial applications.- Discussion.- Industrial Prediction intervals with data uncertainty.- Introduction.- Common-used techniques for pl£7