Data-Driven Prediction for Industrial Processes and Their Applications
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...
| Main Authors: | , , |
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| Corporate Author: | |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2018.
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| Edition: | 1st ed. 2018. |
| Series: | Information Fusion and Data Science,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-94051-9 |
Table of 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.