Data-Driven Prediction for Industrial Processes and Their Applications

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.

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.