The Resource Data-Driven Prediction for Industrial Processes and Their Applications, by Jun Zhao, Wei Wang, Chunyang Sheng, (electronic resource)

Data-Driven Prediction for Industrial Processes and Their Applications, by Jun Zhao, Wei Wang, Chunyang Sheng, (electronic resource)

Label
Data-Driven Prediction for Industrial Processes and Their Applications
Title
Data-Driven Prediction for Industrial Processes and Their Applications
Statement of responsibility
by Jun Zhao, Wei Wang, Chunyang Sheng
Creator
Contributor
Author
Provider
Subject
Language
eng
Summary
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
Member of
http://library.link/vocab/creatorName
Zhao, Jun
Image bit depth
0
LC call number
QA76.9.D343
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Wang, Wei.
  • Sheng, Chunyang.
  • SpringerLink
Series statement
Information Fusion and Data Science,
http://library.link/vocab/subjectName
  • Computer science
  • Data mining
  • Artificial intelligence
  • Manufacturing industries
  • Machinery
  • Tools
  • Quality control
  • Reliability
  • Industrial safety
  • Computer Science
  • Data Mining and Knowledge Discovery
  • Manufacturing, Machines, Tools
  • Artificial Intelligence (incl. Robotics)
  • Quality Control, Reliability, Safety and Risk
  • Operations Research/Decision Theory
Label
Data-Driven Prediction for Industrial Processes and Their Applications, by Jun Zhao, Wei Wang, Chunyang Sheng, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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 prediction intervals -- Prediction intervals with noisy outputs -- Prediction intervals with noisy inputs and outputs -- Time series prediction intervals with missing input -- Industrial cases of prediction intervals -- Discussion -- Granular computing-based long term prediction intervals -- Introduction -- Basic theory of granular computing -- Techniques of granularity partition -- Long-term prediction model -- Granular-based prediction intervals -- Multi-dimension granular-based long term prediction intervals -- Discussion -- Parameters estimation and optimization -- Introduction -- Gradient-based methods -- Evolutionary algorithms -- Nonlinear Kalman-filter estimation -- Probabilistic methods -- Gamma-test based noise estimation -- Industrial applications -- Discussion -- Parallel computing considerations -- Introduction -- CUDA-based parallel acceleration -- Hadoop-based distributed computation -- Other techniques -- Industrial applications to parallel computing -- Discussion -- Prediction-based scheduling of industrial system -- Introduction -- Scheduling of blast furnace gas system -- Scheduling of coke oven gas system -- Scheduling of converter gas system -- Scheduling of oxygen system -- Predictive scheduling for plant-wide energy system -- Discussion
Dimensions
unknown
Extent
XVI, 443 p. 167 illus., 128 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783319940519
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-94051-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-94051-9
Label
Data-Driven Prediction for Industrial Processes and Their Applications, by Jun Zhao, Wei Wang, Chunyang Sheng, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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 prediction intervals -- Prediction intervals with noisy outputs -- Prediction intervals with noisy inputs and outputs -- Time series prediction intervals with missing input -- Industrial cases of prediction intervals -- Discussion -- Granular computing-based long term prediction intervals -- Introduction -- Basic theory of granular computing -- Techniques of granularity partition -- Long-term prediction model -- Granular-based prediction intervals -- Multi-dimension granular-based long term prediction intervals -- Discussion -- Parameters estimation and optimization -- Introduction -- Gradient-based methods -- Evolutionary algorithms -- Nonlinear Kalman-filter estimation -- Probabilistic methods -- Gamma-test based noise estimation -- Industrial applications -- Discussion -- Parallel computing considerations -- Introduction -- CUDA-based parallel acceleration -- Hadoop-based distributed computation -- Other techniques -- Industrial applications to parallel computing -- Discussion -- Prediction-based scheduling of industrial system -- Introduction -- Scheduling of blast furnace gas system -- Scheduling of coke oven gas system -- Scheduling of converter gas system -- Scheduling of oxygen system -- Predictive scheduling for plant-wide energy system -- Discussion
Dimensions
unknown
Extent
XVI, 443 p. 167 illus., 128 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783319940519
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-94051-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-94051-9

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