Modeling, Optimization, and Control of Zinc Hydrometallurgical Purification Process

Front-Mat_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgica
Copyrig_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgical-
Content_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgical-
	Contents
About-the-au_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurg
	About the authors
Prefac_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgical-P
	Preface
Acknowledgm_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgi
	Acknowledgments
Chapter-1---Intr_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometal
	1 Introduction
		1.1 Overview
		1.2 Zinc hydrometallurgy technologies
			1.2.1 Roasting-leaching-electrowinning zinc hydrometallurgy technology
				1.2.1.1 Roasting process
				1.2.1.2 Leaching and purification processes
				1.2.1.3 Electrowinning process
			1.2.2 Atmospheric direct leaching zinc hydrometallurgy technology
		1.3 Solution purification process
		1.4 Organization and scope of text
		References
Chapter-2---Modeling-and-optimal-cont_2021_Modeling--Optimization--and-Contr
	2 Modeling and optimal control framework for the solution purification process
		2.1 Problem analysis
			2.1.1 Challenges in the modeling of the solution purification process
			2.1.2 Challenges in the optimal control of the solution purification process
		2.2 Modeling and optimal control framework
			2.2.1 Process modeling based on fusion of reaction kinetics and production data
				2.2.1.1 Definition of a comprehensive state space descriptive system
				2.2.1.2 Typical modeling approaches
					State space-based first-principle modeling
					Machine learning-based input/output modeling
					Comparison between SS-FPM and ML-IOM
				2.2.1.3 Hybrid first-principle/machine learning modeling frameworks
					Naive integration of a kinetic model and a data-driven compensation model
					Integration of a subkinetic model and a subdata-driven compensation model
					Weighted hybrid kinetic model and data-driven compensation model with time-varying weights
					Comprehensive hybrid modeling framework
			2.2.2 Cooperative optimization and control of cascaded metallurgical reactors
		References
Chapter-3---Kinetic-modeling-of-th_2021_Modeling--Optimization--and-Control-
	3 Kinetic modeling of the competitive-consecutive reaction system
		3.1 Process description and analysis
		3.2 Kinetics of copper removal reactions
			3.2.1 Influencing factor analysis
				3.2.1.1 Temperature
				3.2.1.2 Reaction time
				3.2.1.3 pH
				3.2.1.4 Composition of leaching solution
				3.2.1.5 Zinc powder dosage
				3.2.1.6 Solid content of underflow
			3.2.2 Copper cementation kinetics
			3.2.3 Cuprous oxide precipitation kinetics
		3.3 Modeling of the competing reactions system
			3.3.1 Model structure determination
			3.3.2 Model parameter identification
				3.3.2.1 Data sample labeling and classification
				3.3.2.2 Data sample balancing
				3.3.2.3 Parameter identification based on EA-PSO
				3.3.2.4 Results
		References
Chapter-4---Additive-requirement-ra_2021_Modeling--Optimization--and-Control
	4 Additive requirement ratio estimation using trend distribution features
		4.1 Definition of additive requirement ratio
		4.2 Case-based prediction with trend distribution features for ARR
			4.2.1 Variation trend extraction and classification
				4.2.1.1 Smoothing and normalization of process variables
				4.2.1.2 Differentiation of process variables and setting primitive thresholds
				4.2.1.3 Identifying trends
			4.2.2 Extracting trend distribution features
				4.2.2.1 Sorting the qualitative primitives
				4.2.2.2 Estimating the trend distribution probability
			4.2.3 Case-based prediction with a trend distribution feature
				4.2.3.1 Similarity measurements for the trend distributions and industrial variables
				4.2.3.2 Prediction of ARR
		4.3 Results
		References
Chapter-5---Real-time-adjustment-o_2021_Modeling--Optimization--and-Control-
	5 Real-time adjustment of zinc powder dosage based on fuzzy logic
		5.1 Copper removal performance evaluation based on ORP
			5.1.1 Relationship between copper ion concentration and ORP
			5.1.2 ORP-based process evaluation
		5.2 Controllable domain-based fuzzy rule extraction for copper removal
			5.2.1 Data preparation
			5.2.2 Controllable domain determination
		5.3 Results
		References
Chapter-6---Integrated-modelin_2021_Modeling--Optimization--and-Control-of-Z
	6 Integrated modeling of the cobalt removal process
		6.1 Process description and analysis
		6.2 Kinetics of cobalt removal reactions
			6.2.1 Influencing factor analysis
				6.2.1.1 Temperature
					Reaction rate
					Reaction product morphology
					Distribution of cathode current
				6.2.1.2 Dosage of arsenic trioxide
				6.2.1.3 Dosage of zinc powder
				6.2.1.4 Flow rate of spent acid
				6.2.1.5 Concentration of zinc ions and copper ions
				6.2.1.6 Other influencing factors
			6.2.2 Analysis of reaction type and steps
			6.2.3 Relation between ORP and reaction rate
			6.2.4 Kinetic model construction
		6.3 First-principle/machine learning integrated process modeling
			6.3.1 Integrated modeling framework
			6.3.2 Working condition classification
				6.3.2.1 Deep feature extraction
				6.3.2.2 Deep feature space partitioning
					Rough division using a KD-Tree
					Fine division based on LR
			6.3.3 Model performance evaluation
		References
Chapter-7---Intelligent-optimal-se_2021_Modeling--Optimization--and-Control-
	7 Intelligent optimal setting control of the cobalt removal process
		7.1 Problem analysis
		7.2 Normal-state economical optimization
			7.2.1 Problem formulation
				7.2.1.1 Zinc powder utilization efficiency factor
				7.2.1.2 Cobalt removal ratio
				7.2.1.3 Optimization problem formulation
					Gradient optimization of ACP
			7.2.2 Two-layer gradient optimization under normal-state conditions
				7.2.2.1 Online estimation of ZPUF
				7.2.2.2 Rolling gradient optimization of ACP
		7.3 Abnormal-state adjustment
			7.3.1 Data-driven online operating state monitoring
			7.3.2 CBR-based adjustment under abnormal-state conditions
		7.4 Results
		References
Chapter-8---Control-of-the-cobalt-r_2021_Modeling--Optimization--and-Control
	8 Control of the cobalt removal process under multiple working conditions
		8.1 Problem analysis
		8.2 Robust adaptive control under model–plant mismatch
			8.2.1 Nominal process model
			8.2.2 Model–plant mismatch analysis
			8.2.3 Design of a robust adaptive tracking controller
			8.2.4 Control performance analysis
		8.3 Adaptive dynamic programming for working conditions with unknown model parameters
			8.3.1 Problem formulation
			8.3.2 Model-free zinc powder dosage controller
			8.3.3 Control performance analysis
		References
Chapter-9---Intelligent-con_2021_Modeling--Optimization--and-Control-of-Zinc
	9 Intelligent control system development
		9.1 Framework of intelligent control systems
		9.2 Data acquisition and management
		9.3 Process monitoring and control
Chapter-10---Conclusions-_2021_Modeling--Optimization--and-Control-of-Zinc-H
	10 Conclusions and future research
		10.1 Summary
		10.2 Future research directions
			10.2.1 Autonomous control of reactors
			10.2.2 Plant-wide intelligent cooperation
			10.2.3 Epilogue
		References
Inde_2021_Modeling--Optimization--and-Control-of-Zinc-Hydrometallurgical-Pur
	Index