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Dynamic modeling of complex industrial processes : data-driven methods and application research / Chao Shang.

Shang, Chao, author.
2018
TS183
Available Online
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Title
Dynamic modeling of complex industrial processes : data-driven methods and application research / Chao Shang.
Author
Shang, Chao, author.
ISBN
9789811066771 (electronic book)
9811066779 (electronic book)
9789811066764
9811066760
DOI
https://doi.org/10.1007/978-981-10-6677-1
Published
Singapore : Springer, 2018.
Language
English
Description
1 online resource.
Item Number
10.1007/978-981-10-6677-1 doi
Call Number
TS183
Dewey Decimal Classification
670.1/5118
Summary
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.
Note
"Doctoral thesis accepted by Tsinghua University, Beijing, China."
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 27, 2018).
Series
Springer theses.
Available in Other Form
Print version: 9789811066764
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Introduction
Concurrent monitoring of steady state and process dynamics with SFA
Online monitoring and diagnosis of control performance with SFA and contribution plots
Recursive SFA algorithm and adaptive monitoring system design
Probabilistic SFR model and its applications in dynamic quality prediction
Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction
Nonlinear and dynamic soft sensing model based on Bayesian framework
Summary and open problems.

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