Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

✍ Scribed by Chao Shang (auth.)

Publisher: Springer Singapore
Year: 2018
Tongue: English
Leaves: 154
Series: Springer Theses
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts.

The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.

✦ Table of Contents

Front Matter ....Pages i-xviii
Introduction (Chao Shang)....Pages 1-19
Monitoring of Operating Condition and Process Dynamics with Slow Feature Analysis (Chao Shang)....Pages 21-48
Control Performance Monitoring and Diagnosis Based on SFA and Contribution Plot (Chao Shang)....Pages 49-64
Recursive SFA Algorithm and Adaptive Monitoring System Design (Chao Shang)....Pages 65-81
Probabilistic Slow Feature Regression for Dynamic Soft Sensing (Chao Shang)....Pages 83-107
Enhanced Dynamic PLS with Temporal Smoothness for Soft Sensing (Chao Shang)....Pages 109-123
Nonlinear Dynamic Soft Sensing Based on Bayesian Inference (Chao Shang)....Pages 125-140
Conclusions and Recommendations (Chao Shang)....Pages 141-143

✦ Subjects

Quality Control, Reliability, Safety and Risk

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