带钢热连轧生产过程质量安全监测与稳定控制（英文版）
定 价：86 元
- 作者：董洁 著
- 出版时间：2018/4/1
- ISBN：9787502477707
- 出 版 社：冶金工业出版社
- 中图法分类：TG335.13
- 页码：294
- 纸张：胶版纸
- 版次：1
- 开本：16开
《带钢热连轧生产过程质量安全监测与稳定控制（英文版）》讲述了There are 9 chapters in this book. Chapter 1 introduces some basic concepts of hotstrip mill process and the motivation of the book. Chapter 2 is process description ofmodern hot strip mill. The key dynamic mathematical model of hot strip rolling is de-scribed in chapter 3. Some control strategies oriented to product performance such aswidth control， gauge control and shape control are presented in chapters 4， 5 and 6，respectively. In order to improve the quality of products， hot strip quality monitoringmodel， quality-related fault detection， quality-related fault diagnosis， root cause diag-nosis and propagation path identification are discussed from chapter 7 to chapter 9.This book is suitable for the process control researchers， the technicians engaged inthe process control of hot strip rolling， and the field maintenance.
1 Introduction
1．1 Basic concepts
1．1．1 Rolling
1．1．2 Hot rolling
1．1．3 Cold rolling
1．1．4 Rolling mill
1．1．5 Tandem mill
1．1．6 Totally integrated automation
1．1．7 Distributed control system
1．2 Motivation
1．3 Outline of the contents
2 Process Description of Modern Hot Strip Mill
2．1 General description
2．2 Reheat furnace
2．3 Roughing mill
2．4 Finishing mill
2．4．1 Control requirement
2．4．2 Finishing mill disturbances
2．5 Laminar cooling
2．6 Downcoiler
3 Modelling of Finishing Mill
3．1 Simplified plant
3．2 Looper-strip system
3．3 Main drive
3．4 Roll actuators
3．5 Coiler
3．6 Transport delay
3．7 Deformation
3．8 Heat transfer
3．9 Slip
3．10 Model completeness analysis
3．11 Linear state-space form
3．11．1 State-space representation of a single stand
3．11．2 State-space representation of the entire mill
4 Automatic Width Control
4．1 Basic control strategy
4．2 SS control
4．3 RF-AWC
4．4 FF-AWC
4．4．1 Control calculation in Level 1
4．4．2 Calculation in Level 2
4．5 The edger FB-AWC
4．5．1 Calculation in Level 1
4．5．2 Calculation in Level 2
4．6 Tension FB-AWC
4．6．1 Dynamic control calculation
4．6．2 Influence coefficient calculation
4．7 Direct generalized predictive control based on RBF in finishing strip width control
4．7．1 Finishing mill automatic width control
4．7．2 Generalized predictive control method
4．7．3 Simulation
5 Automatic Gauge Control
5．1 Gap compensations
5．2 Backup roll bearing speed and force effect （oil film compensation）
5．3 Roll thermal and wear
5．4 Work roll bending force compensation （roll bending compensation）
5．5 Tension loss compensation
5．6 Gaugemeter
5．6．1 Modes of operation
5．6．2 Implementation
5．7 Eccentricity compensation
5．8 X ray monitor regulator detail
5．8．1 X ray deviation
5．8．2 Regulator
5．8．3 Gain changes by product thickness
5．8．4 Extra gain
5．8．5 Effects of transport delay
5．8．6 Auto fan
5．8．7 Inhibit overshoot
5．8．8 Load balance
5．8．9 Tension effects
5．8．1 0Draft compensation
5．8．1 1Maximum correction clamp
5．8．1 2Gaugemeter dependency
5．9 Feedforward detail
5．9．1 Description
5．9．2 Additional features
5．9．3 Tuning up
5．10 Mass flow compensation
5．10．1 Mass flow concepts
5．10．2 Mass flow errors
5．10．3 Mass flow calculation errors
6 Automatic Profile and Shape Control
6．1 Description of HSMP in Ansteel
6．1．1 Hot strip mill process
6．1．2 Mill configuration
6．2 Gap crown model of roll system
6．2．1 Roll thermal crown model
6．2．2 Roll wear crown model
6．2．3 Compositive gap crown of roll system
6．3 Prediction model of strip shape
6．3．1 Relationship between strip crown and flatness
6．3．2 Influence of lateral flow
6．3．3 Prediction of strip crown and differential strain
6．3．4 Prediction of strip flatness
6．4 The strategy of shape setup model
6．4．1 Range of profi]e contro]
6．4．2 Strategy of crown a]]ocation
6．4．3 Reference values for mechanical actuators
6．4．4 Shape evaluation
6．5 Application in Ansteel 1700 mm HSMP
7 Theory of Quality Monitoring for Hot Strip Mill
7．1 The theory and method of multivariate statistical process monitoring
7．1．1 Quality-relevant fault monitoring based on efficient projection to latent structures with application to hot strip mill process
7．1．2 A scheme to key performance indicator based prediction and diagnosis in complex industrial process and its application to hot strip mill
7．2 Quality monitoring method for nonlinear process
7．2．1 Contribution rate p]ot for non]inear quality-related fault diagnosis with app]ication to hot strip mi]] process
7．2．2 Quality-related process monitoring based on total kernel PLS model and its industrial application
7．3 Quality monitoring method for non-gauss process
7．3．1 A new kernel independent and principal components analysis based process monitoring approach with application to hot strip mill process
7．3．2 A new data-driven process monitoring scheme for key performance
7．4 Dynamic non-gaussian approach for quality-related fault diagnosis
7．4．1 Methodology
7．4．2 Quality-related process monitoring based on DPLS-ICA model
7．4．3 Industrial application
7．5 Quality monitoring method for batch and multimode process
7．5．1 A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes
7．5．2 Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to hot strip mill
7．5．3 Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and muhi-batch measurements
8 Data-Driven Quality Monitoring Techniques for Distributed Parameter Systems with Application to Hot-Rolled Strip Laminar Cooling Process
8．1 Process description
8．1．1 Modeling of distributed parameter systems description
8．1．2 Finite dimensional approximation for distributed parameter systems in laminar cooling process
8．2 Model-based design of residual generator for DPS
8．2．1 Design of residual generator
8．2．2 Residual evaluation and threshold setting
8．3 Benchmarky
8．3．1 Data-driven realization of system identification for state space representation
8．3．2 Data-driven realization of the obtained kernel representation in HSLC
9 Root Cause Diagnosis and Propagation Path Identification for Quality-Related Fault
9．1 Joint data-driven fault diagnosis integrating causality graph with statistical process monitoring for complex industrial processes
9．1．1 Correlation analysis of process variables
9．1．2 Monitoring of CI by using PPCA for fault detection
9．1．3 Integrated fault diagnosis based on casuality graph and statistical process monitoring
9．1．4 Application in hot strip mill
9．2 A novel data-based quality-related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process
9．2．1 MCVA-based quality-related fault detection for dynamic processes
9．2．2 Root cause diagnosis of quality-related faults
9．2．3 Case study on hot strip mill process
References
书单推荐
