Table Of ContentEngineering Applications of Computational Methods 3
Min Wu
Weihua Cao
Xin Chen
Jinhua She
Intelligent
Optimization and
Control of Complex
Metallurgical
Processes
Engineering Applications of Computational
Methods
Volume 3
Series Editors
Liang Gao, State Key Laboratory of Digital Manufacturing Equipment
and Technology, Huazhong University of Science and Technology,
Wuhan, Hubei, China
Akhil Garg, Department of Mechatronics Engineering, Shantou University,
Shantou, Guangdong, China
ThebookseriesEngineeringApplicationsofComputationalMethodsaddressesthe
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More information about this series at http://www.springer.com/series/16380
Min Wu Weihua Cao Xin Chen Jinhua She
(cid:129) (cid:129) (cid:129)
Intelligent Optimization
and Control of Complex
Metallurgical Processes
123
Min Wu WeihuaCao
ChinaUniversity of Geosciences ChinaUniversity of Geosciences
Wuhan, China Wuhan, China
HubeiKey Laboratory of Advanced HubeiKey Laboratory of Advanced
Control andIntelligent Automation Control andIntelligent Automation
for ComplexSystems for ComplexSystems
Wuhan, China Wuhan, China
XinChen Jinhua She
ChinaUniversity of Geosciences Schoolof Engineering
Wuhan, China Tokyo University of Technology
Tokyo,Japan
HubeiKey Laboratory of Advanced
Control andIntelligent Automation ChinaUniversity of Geosciences
for ComplexSystems Wuhan, China
Wuhan, China
ISSN 2662-3366 ISSN 2662-3374 (electronic)
Engineering Applications of Computational Methods
ISBN978-981-15-1144-8 ISBN978-981-15-1145-5 (eBook)
https://doi.org/10.1007/978-981-15-1145-5
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Preface
Iron and steel industry is the basis for the development of a country. Its level of
developmentdeterminestheeconomiclevelandcomprehensivenationalpowerofa
country. However, accelerated energy consumption and significant CO emissions
2
have limited the development of the iron and steel industry. Metallurgical
automation technologies have been developed to yield the green manufacturing
of the iron and steel industry.
The iron and steel industry involves complex processes, such as raw material
proportioning,sintering,blastfurnaceironmaking,steelmaking,continuouscasting,
and rolling. They have the common characteristics of strong nonlinearity and a
largenumberofparameters.Inordertoachievethegreenmanufacturingoftheiron
and steel industry, we have to consider the problems of energy saving, emission
reduction, environmental protection, etc. These problems involve the modeling,
optimization,andcontrolofcomplexprocesses.However,conventionalmethodsof
modeling, optimization, and control are not suitable for actual applications for
complex processes with uncertainties. Advanced intelligent optimization and con-
trol technologies have been applied to the iron and steel industry to improve pro-
duction efficiency and reduce CO emissions.
2
We summarized our work and experience in the control of complex metallur-
gicalprocessesoverthelasttwodecadesinthismonograph.Wehopethatitwould
be a useful reference for engineers in the field of metallurgical automation and
complex process control, and for graduate students interested in computational
intelligence and its applications of industrial process control.
Themonographconsistsofsixchapters.Chapter1describesthecurrentstatusof
metallurgyprocessesandexplainsconventionalintelligentalgorithmsintheaspects
ofmodeling,optimization,andcontrol.Chapter2describesthemethodsofhowto
optimize the proportioning of a coal blending process in an iron ore sintering
process. An expert control strategy is designed based on a combination of back-
propagation networks, mathematical models, and rule models to accurately deter-
mine and track the target percentages for a coal blending process. An intelligent
integratedoptimizationsystemwithahierarchicalconfigurationisdevisedtosolve
the problem of optimizing the proportioning for an iron ore sintering process.
v
vi Preface
Chapter 3 focuses on the problems of how to measure and control the coke-oven
temperature. An integrated soft-sensing model of the coke-oven temperature and a
hybrid hierarchical control system of a coke-oven combustion process are devel-
opedtosolvetheseproblems.Chapter4describescontrolmethodsofthermalstate
parametersinsinteringprocess.Anintelligentcontrolofasinteringignitionprocess
is designed to stabilize the ignition temperature. An intelligent control system for
the burn-through point is designed to stabilize the point in an actual sintering
process.Chapter5describesintelligentdecouplingcontrolmethodsforboththegas
collection process of multiple asymmetric coke-ovens and a gas mixing-and-
pressurization process in an iron and steel company. Chapter 6 focuses on the
problem of how to control the rolling temperature and reduce the energy con-
sumptionoftheprocessofareheatingfurnace.Anoptimizationandcontrolsystem
for the heating furnace is devised to solve this problem.
We are grateful for the support of Natural Science Foundation of China under
Grant61210011,the111projectunderGrantB17040,theHubeiProvincialNatural
Science Foundation of China under Grant 2015CFA010, and the National High
Technology Research and Development Program of China (863 Program) under
Grant 2012AA040307.
Wearealsogratefulforthesupportofscholarsbothindomesticandabroad.We
would like to thank Emeritus Professor Michio Nakano of Tokyo Institute of
Technology; Professors Deyao Shen, Zixing Cai, and Qi Lei of Central South
University; Professor Hongye Su of Zhejiang University; Professor Feng Qian of
East China University of Science and Technology; Dr. Yanguang Sun of
AutomationResearchandDesignInstituteofMetallurgicalIndustry;andProfessor
Yonghua Xiong, Associate Professor Jianqi An, Drs. Zhentao Liu, and Luefeng
Chen of China University of Geosciences. We would also like to express our
appreciation for the great efforts of Drs. Xiaoxia Chen, Fang Gao, and Pan Yu of
Central South University, Drs. Jie Hu, Sheng Du and Yali Jin, Ms. Ting Wan and
Mr. Kexin Zhang of China University of Geosciences.
Wuhan, China Min Wu
Wuhan, China Weihua Cao
Wuhan, China Xin Chen
Tokyo, Japan Jinhua She
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Complex Metallurgical Processes. . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Modeling, Control, and Optimization of Complex
Metallurgical Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Intelligent Control and Optimization Methods . . . . . . . . . . . . . . . 6
1.3.1 Neural Network Modeling . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Fuzzy Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3.3 Expert Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.4 Decoupling Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.3.5 Hierarchical Intelligent Control. . . . . . . . . . . . . . . . . . . . . 19
1.3.6 Intelligent Optimization Algorithms . . . . . . . . . . . . . . . . . 21
1.4 Outline of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2 Intelligent Optimization and Control of Raw Material
Proportioning Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1 Process Description and System Configuration. . . . . . . . . . . . . . . 36
2.1.1 Process Description and Characteristic Analysis . . . . . . . . 36
2.1.2 Control Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.2 Intelligent Optimization and Control of Coal Blending
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.2.1 Quality-Prediction Models for Coal Blend. . . . . . . . . . . . . 41
2.2.2 Quality-Prediction Models for Coke . . . . . . . . . . . . . . . . . 43
2.2.3 Rule Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2.4 Determination of Target Percentages Based
on Rule Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
vii
viii Contents
2.2.5 Determination of Target Percentages Based
on Simulated Annealing Algorithm. . . . . . . . . . . . . . . . . . 49
2.2.6 Tracking Control of Target Percentages . . . . . . . . . . . . . . 51
2.3 System Implementation for Coal Blending Process. . . . . . . . . . . . 52
2.3.1 System Configuration and Implementation . . . . . . . . . . . . 52
2.3.2 Results of Actual Runs of Coal Blending Process . . . . . . . 53
2.4 Intelligent Integrated Optimization System for Proportioning
of Iron Ore in Sintering Process . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.4.1 Cascade Integrated Quality-Prediction Model
for Sinter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.4.2 Verification of Quality-Prediction Model. . . . . . . . . . . . . . 63
2.4.3 Optimization Model of Proportioning . . . . . . . . . . . . . . . . 65
2.4.4 Optimization Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.4.5 Verification of Optimization Algorithms . . . . . . . . . . . . . . 73
2.5 System Implementation for Proportioning of Iron Ore
in Sintering Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.5.1 System Configuration and Implementation . . . . . . . . . . . . 77
2.5.2 Results of Actual Runs in Sintering Process . . . . . . . . . . . 79
2.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3 Intelligent Optimization and Control of Coking Process. . . . . . . . . . 83
3.1 Characteristic Analysis and System Configuration . . . . . . . . . . . . 85
3.1.1 Process Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.1.2 Analysis of Characteristics . . . . . . . . . . . . . . . . . . . . . . . . 88
3.1.3 Control Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.1.4 System Configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.2 Integrated Soft Sensing of Coke-Oven Temperature . . . . . . . . . . . 93
3.2.1 Choice of Auxiliary Variables and Measurement
Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.2.2 Structure of Soft-Sensing Model for Coke-Oven
Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.2.3 Integrated Linear Regression Model . . . . . . . . . . . . . . . . . 95
3.2.4 Supervised Distributed Neural Network Model . . . . . . . . . 97
3.2.5 Model Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.3 Intelligent Optimization and Control of Coke-Oven
Combustion Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.3.1 Configuration of Hybrid Hierarchical Control System . . . . 101
3.3.2 Determination of Operating State . . . . . . . . . . . . . . . . . . . 103
3.3.3 Design of Coke-Oven Temperature Controller. . . . . . . . . . 105
3.3.4 Design of Controller for Gas Flow Rate . . . . . . . . . . . . . . 110
3.3.5 Design of Air Suction Power Controller . . . . . . . . . . . . . . 111
Contents ix
3.4 Operation Planning and Optimal Scheduling of Coking . . . . . . . . 112
3.4.1 Analysis of Operations Planning and Optimal
Scheduling of Coking . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
3.4.2 Configuration of Optimal Scheduling . . . . . . . . . . . . . . . . 114
3.4.3 Optimal Scheduling of Operating States . . . . . . . . . . . . . . 115
3.5 System Implementation and Results of Actual Runs . . . . . . . . . . . 122
3.5.1 System Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.5.2 Results of Actual Runs for Integrated Soft Sensing
of Coke-Oven Temperature . . . . . . . . . . . . . . . . . . . . . . . 124
3.5.3 Results of Actual Runs for Intelligent Optimization
and Control of Coke-Oven Combustion Process . . . . . . . . 124
3.5.4 Results of Actual Runs for Coke-Oven Operation
Planning and Optimal Scheduling. . . . . . . . . . . . . . . . . . . 129
3.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
4 Intelligent Control of Thermal State Parameters in Sintering
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
4.1 Process Description and Characteristics Analysis . . . . . . . . . . . . . 135
4.1.1 Description of Sintering Process. . . . . . . . . . . . . . . . . . . . 135
4.1.2 Characteristic Analysis of Thermal State Parameters
in Sintering Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
4.1.3 Control Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
4.2 Intelligent Control of Sintering Ignition Process . . . . . . . . . . . . . . 140
4.2.1 Control System Architecture. . . . . . . . . . . . . . . . . . . . . . . 140
4.2.2 Intelligent Optimization and Control Algorithm. . . . . . . . . 141
4.2.3 Subspace Modeling of Sintering Ignition Process. . . . . . . . 142
4.2.4 Periodic Disturbance Rejection Using
Equivalent-Input-Disturbance Estimation. . . . . . . . . . . . . . 147
4.2.5 Experimental Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 151
4.3 Intelligent Control System for Burn-Through Point. . . . . . . . . . . . 155
4.3.1 Control System Architecture. . . . . . . . . . . . . . . . . . . . . . . 155
4.3.2 Soft Sensing and Prediction of Burn-Through Point. . . . . . 157
4.3.3 Hybrid Fuzzy-Predictive Controller. . . . . . . . . . . . . . . . . . 161
4.3.4 Bunker-Level Expert Controller . . . . . . . . . . . . . . . . . . . . 165
4.3.5 Coordinating Control Algorithm. . . . . . . . . . . . . . . . . . . . 165
4.4 Industrial Implementation and Results of Actual Runs . . . . . . . . . 168
4.4.1 Industrial Implementation. . . . . . . . . . . . . . . . . . . . . . . . . 168
4.4.2 Results of Actual Runs . . . . . . . . . . . . . . . . . . . . . . . . . . 169
4.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
