Marine Corrosion of Steels

Chao Liu is a professor at the Institute of Advanced Materials and Technology, University of Science and Technology Beijing. His research focuses on localized corrosion mechanisms, corrosion big data, and the development of advanced corrosion-resistant steels. Bingqin Wang is an assistant researcher at the University of Science and Technology Beijing. His work centers on corrosion big data, including predictive modeling of atmospheric corrosion and corrosion image quantification, advancing methodologies for steel performance assessment. Xiaogang Li is a professor at the University of Science and Technology Beijing and a leader in corrosion science. His research includes corrosion theory, new steel development, and performance optimization of weathering steels. Shasha Zhang is an engineer at the School of Advanced Engineering, University of Science and Technology Beijing. Her research applies artificial intelligence and IoT technologies to corrosion detection and data-driven corrosion protection strategies. Zhong Li is an associate professor at the Institute of Advanced Materials and Technology, University of Science and Technology Beijing. Her expertise lies in microbiologically induced corrosion, stress corrosion cracking, and advanced materials for corrosion resistance.

• Table of ContentsPrefaceChapter 1. Stress corrosion behavior of high manganese steel in polluted marine atmospheric environments1.1 Introduction1.2 Early corrosion initiation behavior of composite inclusions in high manganese steel1.2.1 Materials and Methods1.2.1.1 Materials and Solutions1.2.1.2 Material Microstructure Characterization1.2.1.3 Electrochemical Testing1.2.1.4 Microcellular surface potential measurements1.2.1.5 In-situ immersion test for inclusions1.2.2 Physicochemical properties and corrosion localized corrosion1.2.2.1 Physicochemical properties of high manganese steel1.2.2.2 Typical inclusions morphology and micro-zone electrochemistry in high manganese steel1.2.2.3 Micro-corrosion of Inclusion Areas1.3 Corrosion Behavior and Mechanism of High-Manganese Steel in Environments Containing Sulfur and Chloride1.3.1 Materials and Methods1.3.1.1 Materials1.3.1.2 Cyclic immersion acceleration test1.3.1.3 Analysis of Corrosion Products1.3.1.4 Rust layer electrochemical testing1.3.2 Corrosion behaviour and electrochemical characteristics1.3.2.1 Corrosion weight loss and corrosion rate1.3.2.2 Analysis of corrosion products1.3.2.3 Analysis of Corrosion Morphology1.3.2.4 Electrochemical Analysis of Rust Layer1.3.3 Corrosion mechanism1.4 Research on the Stress Corrosion Cracking Behavior and Mechanism of High-Manganese Steel in Sulfur-and Chloride-Containing Environments1.4.1 Materials and Methods1.4.1.1 Materials1.4.1.2 Constant load U-bend circumferential dip test1.4.1.3 Slow Strain Rate Tensile Test1.4.2 Stress corrosion behaviour1.4.2.1 Behavioral analysis of high manganese steel U-bend SCC1.4.2.2 Analysis of Stress-Strain Curves for High-Manganese Steel1.4.2.3 Analysis of Fracture Morphology of High-Manganese Steel1.4.3 Stress Corrosion Cracking Mechanism1.5 Chapter SummaryChapter 2. Corrosion fatigue behavior of high manganese steel in atmospheric environment2.1 Introduction2.2 Early corrosion budding behavior of high manganese steel in simulated atmospheric environment2.2.1 Experimental Materials and Methods2.2.1.1 Materials and solutions2.2.1.2 Experimental Method2.2.2 Material Basis Properties and Localized Corrosion Emergence Behaviour2.2.2.1 Microstructure and mechanical properties2.2.2.2 Morphology and properties of typical inclusions2.2.2.3 Inclusions induce corrosion initiation2.3 Corrosion Laws and Mechanisms of High-Manganese Steel in Simulated Atmospheric Environments2.3.1 Experimental Materials and Methods2.3.1.1 Materials2.3.1.2 Cyclic wetting and drying experiment2.3.1.3 Electrochemical Testing2.3.1.4 Analysis of Corrosion Products2.3.2 Corrosion behaviour and characteristics2.3.2.1 Corrosion Weight Loss and Corrosion Rate2.3.2.2 Analysis of Rust Layer Cross-Section2.3.2.3 Rust Layer Products Characteristics2.3.2.4 Electrochemical Analysis of Rust Layers2.3.2.5 Corrosion Morphology2.3.3 Corrosion Electrochemical Processes of High-Manganese Steel2.4 Corrosion Fatigue Laws and Mechanisms of High-Manganese Steel in Simulated Atmospheric Environments2.4.1 Experimental Materials and Methods2.4.1.1 Materials2.4.1.2 Axial stress corrosion fatigue experiment2.4.1.3 Characterization of Corrosion Fatigue Cracks2.4.2 Electrochemical properties and corrosion fatigue behaviour2.4.2.1 Electrochemical Testing2.4.2.2 Corrosion Fatigue Behavior2.4.2.3 Morphology of Corrosion Fatigue Fracture Surface2.4.2.4 Analysis of Secondary Cracks Due to Corrosion Fatigue2.4.3 Corrosion Fatigue Mechanism of High-Manganese Steel in Simulated Atmospheric Environments2.5 Chapter SummaryChapter 3. Effect of microalloying elements on the corrosion resistance of low density steel3.1 Introduction3.2 Effect of Cr and Ni on corrosion resistance of Fe-Mn-Al-C low density high strength steel3.2.1 Materials and Methods3.2.1.1 Materials3.2.1.2 Characterization of Experimental Materials' Microstructure3.2.1.3 Accelerated Indoor Simulation of Marine Atmospheric Environment Experiments3.2.1.4 Analysis of Corrosion Products and Morphology of Specimens after Rust Removal3.2.1.5 Macroelectrochemical Testing at the Initial Stage of Corrosion3.2.1.6 Macroelectrochemical Experiments for Short-Term Immersion3.2.1.7 Real-Time Corrosion Monitoring Experiment for Short-Term Immersion3.2.1.8 Random Forest Modeling Analysis3.2.2 Basic material properties and corrosion behaviour3.2.2.1 Microstructure Analysis3.2.2.2 Density and Mechanical Properties Analysis3.2.2.3 Corrosion Morphology3.2.2.4 Corrosion Kinetics Analysis3.2.2.5 Macroelectrochemical properties3.2.3 Study on dynamic corrosion process of Fe-Mn-Al-C Low-Density Steel by alloying elements3.2.3.1 Real-Time Monitoring and Analysis of Short-Term Immersion Corrosion3.2.3.2 Random Forest Modeling Analysis3.3 Effect of Cr-Ni Microalloying on the Corrosion Resistance of Fe- Mn-Al-C Low-Density Steel with heat-treatment3.3.1 Materials and Methods3.3.1.1 Materials3.3.1.2 Microstructure characterization3.3.1.3 Mechanical Performance Testing3.3.1.4 Immersion test3.3.1.5 Periodic Immersion Experiment3.3.1.6 Corrosion Morphology and Corrosion Product Analysis3.3.1.7 Macroelectrochemical Experiment3.3.1.8 Thermodynamic Calculations3.3.1.9 Immersion Corrosion Real-Time Experiment3.3.1.10 Random Forest Modeling Analysis3.3.2 Characterisation of basic properties and corrosion behaviour3.3.2.1 Microstructure3.3.2.2 Mechanical Properties3.3.2.3 Corrosion Morphology3.3.2.4 Corrosion Rate3.3.2.5 Corrosion Product3.3.2.6 Electrochemical properties3.3.2.7 Thermodynamic Calculation Analysis3.3.3 Corrosion mechanism of heat-treated Low-Density Steel with addiction of Alloying Elements3.3.4 Analysis of Corrosion Model for Fe-Mn-Al-C Type Low- Density Steel Based on Corrosion Big Data3.3.4.1 Dynamic corrosion current3.3.4.2 Random Forest Modeling Analysis3.3.4.3 Validation of Random Forest Prediction Data3.3.4.4 Analysis of Feature Variable Correlation3.4 Chapter SummaryChapter 4. Interaction of Multiple Corrosion Modes4.1 Introduction4.2 Corrosion Mechanism of TA2/Q345B Composite Plate4.2.1 Materials and Methods4.2.1.1 Material Preparation4.2.1.2 Crystallographic Information and Microstructural Analysis4.2.1.3 Electrochemical Testing4.2.1.4 Immersion Testing4.2.1.5 Micro-Region Electrochemical Testing4.2.1.6 Thermodynamic Calculations4.2.2 Corrosion Behaviour of TA2/Q345B Composite Plates4.2.2.1 Microstructure of TA2/Q345B Composite Plate4.2.2.2 Corrosion Resistance of Titanium-Steel Composite Plates4.2.2.3 Surface Morphology of Titanium-steel composite Samples After Immersion Test4.2.2.4 The Localized Electrochemical Properties Associated with the Inclusion of Al2O3•MnS4.2.3 Corrosion Mechanism4.3 Degradation Process of TA2/Q345B Composite Sheet in Synthetic Contaminated Seawater Environment4.3.1 Materials and Methods4.3.1.2 Analysis of Corrosion Morphology and Corrosion Products4.3.1.3 Weight Loss Calculation4.3.1.4 Electrochemical Testing4.3.2 Corrosion Behaviour of TA2/Q345B Composite Plates in Polluted Marine Solutions4.3.2.1 Surface Morphology Observation After Immersion Experiments4.3.2.2 Corrosion Morphology of Point Defects in Titanium-Steel Composite Plates4.3.2.3 Influence of Point Defects on the Corrosion Rate of Titanium-Steel Composite Plates in Simulated Marine Solution4.3.3 Galvanic Current and Galvanic Potential in Simulated Polluted Marine Solution4.3.4 Effect of Linear Defects on the Corrosion Rate of Titanium-Steel Composite Plates4.3.4.1 Corrosion Product Analysis After Immersion Experiments4.3.4.2 The Corrosion Kinetics of Titanium-Steel Composite Plates in a Marine Environment4.3.4.3 Corrosion Resistance of Titanium-Steel Composite Plates in a Polluted Marine Environment4.3.5 Corrosion Mechanism4.4 Chapter SummaryChapter 5. Effects of Corrosion Inhibitors and Flow rate on the Corrosion Resistance of Ductile Iron Pipes5.1 Introduction5.2 Study on the Difference of Microstructure and Corrosion Resistance5.2.1 Experimental Materials and Methods5.2.2 Material Structure Characterization Analysis5.3 Corrosion Resistance Difference of Materials in Simulated Solution   5.3.1 Experimental Materials and Methods5.3.1.1 Materials and Solutions5.3.1.2 Electrochemical Test5.3.2 Study on the Difference of Corrosion Resistance of Materials in the Environment without Corrosion Inhibitor5.3.2.1 Open Circuit Potential Analysis5.3.2.2 Polarization Curve Analysis5.3.2.3 Electrochemical Impedance Spectroscopy Analysis5.3.3 Effect of Environmental Factors on Corrosion Kinetics of Ball- milled Cast Iron in Corrosion Inhibitor-free Solution5.3.4 Corrosion Resistance of Materials in Corrosion Inhibitor Environment5.3.4.1 Corrosion Resistance of Three Materials under the Environment of Ethanolamine5.3.4.2 Corrosion Resistance of Three Materials in the Environment of Sodium Hexametaphosphate Corrosion Inhibitor5.4 Analysis of Corrosion Kinetics Process of Materials5.4.1 Experimental Materials and Methods5.4.1.1 Materials and Solutions5.4.1.2 Immersion Test5.4.1.3 Rust Layer Structure Characterization5.4.2 Flow Rate Immersion Experiment5.4.2.1 Flow Rate Immersion Experiment in Corrosion Inhibitor-free Environment5.4.2.2 Flow Rate Immersion Experiment in Corrosion Inhibitor Environment5.4.2.3 Rust Layer Analysis5.5 Charter SummaryChapter.6. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Rebar Corrosion Resistance Modulation6.1 Introduction6.2 Mechanism of the effect of Cr/RE modulation on the corrosion resistance of rebars in Cl− containing environments6.2.1 Materials and methods6.2.1.1 Experimental materials and solutions6.2.1.2 Electrochemical testing6.2.1.3 Corrosion immersion test6.2.1.4 Surface corrosion morphology and oxide film analysis6.2.2 Electrochemical properties of rebar in chlorine-containing environments6.2.2.1 Potential polarization curves6.2.2.2 Electrochemical impedance spectra6.2.2.3 Mott-Schottky curves6.2.3 Corrosion behavior of rebar in simulated concrete pore solution containing different concentrations of NaCl6.2.3.1 Corrosion rate analysis6.2.3.2 Corrosion morphology analysis6.2.3.3 Localized corrosion behavior and Characteristics6.2.3.4 Evolution of surface oxide film of Cr/RE modified rebar6.2.4 Mechanism of the effect of Cr/RE modulation on the corrosion resistance of rebars in Cl−containing environments6.3 Service performance characterization of low alloy rebar based on corrosion online monitoring technology6.3.1 Materials and methods6.3.1.1 Outdoor exposure test6.3.1.2 Phase analysis of the rust layer6.3.1.3 Corrosion big data online monitoring technology6.3.2 Corrosion behaviour in outdoor service environments6.3.2.1 Corrosion morphology6.3.2.2 Outdoor corrosion data monitoring and analysis based on online corrosion monitoring6.3.2.3 Corrosion mechanism of low alloy rebar6.3.2.4 Corrosion resistance evaluation and design of steel reinforcement driven by online corrosion monitoring6.4 Chapter summaryChapter 7. Application of Novel Big Data Intelligent Corrosion Assessment Approach in blast furnace gas pipe steel corrosion resistance Analysis7.1 Introduction7.2 Thermodynamic analysis of corrosion resistance of blast furnace gas in complex environment7.2.1 Materials and Methods7.2.2 Degrees of freedom for mixed gas systems7.2.3 Thermodynamic deduction and analysis of acid dew point temperature in blast furnace gas system7.2.4 Corrosion thermodynamic calculation of pipe network materials7.3 Electrochemical behaviour of Q235 carbon steel in Complex blast furnace gas Environments7.3.1 Materials and Methods7.3.1.1 Materials and Solution7.3.1.2 Methods7.3.2 Electrochemical analysis of Q235 in different media7.3.2.1 Different Concentrations of Neutral NaCl Solutions7.3.2.2 Different pH Values of Acidic 3.5% NaCl Solutions7.3.2.3 Solution with different NaNO3 concentrations and 3.5 wt% NaCl solution with pH=17.3.2.4 Solution with different NaPO4 concentrations and 3.5 wt% NaCl solution with pH=17.3.2.5 Solution with different Na2SO3 concentrations and 3.5 wt% NaCl solution with pH=17.3.2.6 Solution with different Na2SO4 concentrations and 3.5 wt% NaCl solution with pH=17.3.2.7 Solution with different pH and different NaCl concentrations7.3.2.8 Na2SO3 solutions at different pH+ concentrations7.3.3 Correlation analysis of environmental factors to Q2357.4 Comprehensive analysis of Q235 carbon steel corrosion failure of blast furnace gas pipeline7.4.1 Materials and Methods7.4.1.1 Materials7.4.1.2 Methods7.4.2 Material Corrosion failure Characteristics7.4.2.1 Morphology analysis of failed pipe7.4.2.2 Analysis of tissue components7.4.2.3 Thickness loss rate7.4.2.4 Analysis of corrosion products7.4.2.5 Corrosion morphology and corrosion pit distribution characteristics7.4.2.6 Physical and chemical analysis of field condensate7.4.3 Failure cause and mechanism analysis7.5 Application of corrosion big data techniques in determining failure factors of gas pipelines7.5.1 Materials and Methods7.5.1.1 Materials7.5.1.2 Corrosion sensor test principle7.5.1.3 In-pipe monitoring7.5.1.4 Corrosion clock diagram7.5.1.5 Cumulative charge quantity method7.5.1.6 F index method7.5.1.7 Machine learning method7.5.2 On-line monitoring of corrosion patterns7.5.2.1 Dynamic corrosion results7.5.2.2 Dose response of corrosion7.5.2.3. Accelerated corrosion by interaction7.5.2.4 Simulated condensate water corrosion testing7.5.2.5 Temperature driving effect7.5.2.6 Corrosion monitoring via Bigdata technology7.6 Chapter SummaryChapter 8. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion-Resistant Low Alloy Steel Development8.1 Introduction8.2 3Cr Steel corrosion behavior in Tropical Marine Atmosphere: Effects of Mo and Sn Microalloying8.2.1 Materials8.2.1.1 Experimental Samples8.2.1.2 Microstructure Characterization and Corrosion Test Methods8.2.1.3 Work Function Calculation8.2.2 Basic properties and corrosion behavior8.2.2.1 Microstructure8.2.2.2 Electrochemical Behavior8.2.2.3 Corrosion Behavior in Simulated Tropical Marine Atmosphere8.2.3 Mo and Sn on corrosion mechanisms8.2.3.1 Influence of the Work Function8.2.3.2 Role of Mo on Corrosion Resistance8.2.3.3 Sn Alone on Corrosion Behavior of Low-Alloy Steel8.2.3.4 Mo and Sn alloying on Corrosion Resistance of Low-Alloy Steel8.3 Influence of Microstructure Differences on Corrosion Resistance8.3.1 Materials8.3.1.1 Experimental Materials8.3.1.2 Wet/dry Cyclic Immersion Test Based on Corrosion Big Data8.3.1.3. AFM Analysis8.3.2 Basic performance and corrosion big data characteristics8.3.2.1 Microstructure8.3.2.2 Electrochemical Behavior8.3.2.3 Corrosion Rates Analysis8.3.2.4 AFM Analysis8.3.3 Influence of microstructure changes on corrosion mechanisms8.3.3.1 Role of Microstructure on Corrosion Resistance8.3.3.2 Effect of Microstructural Differences on Rust Layer Evolution8.4 Study on Corrosion Resistance Evaluation Model8.4.1 Experimental Methods8.4.1.1 Machine Learning Methods8.4.1.2 Validation Test Methods8.4.2 Corrosion big data modelling and corrosion resistant alloy development strategy8.4.2.1 Pearson Correlation Analysis8.4.2.2 Corrosion Rate Prediction Model and Importance Analysis8.4.2.3 Pitting Depth Prediction Model and Importance Analysis8.4.2.4 Analysis of Validation Test Results8.5 Chapter SummaryChapter 9. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion Prediction and Data Mining Modeling9.1 Introduction9.2 The Dose-response prediction function modeling method for corrosion, driven by big data technology9.2.1 Materials and methods9.2.2 Results9.3 Method for corrosion prediction and data mining modeling driven by big data technology and machine learning for corrosion9.3.1 Experiment9.3.1.1 Materials9.3.1.2 CCT Test9.3.1.3 Data collection9.3.1.4 Modeling9.3.1.5 Partial dependence analysis9.3.1.6 Evaluation methodology9.3.2 Results9.3.2.1 Test results9.3.2.2 Corrosion decision model9.3.2.3 Intricate relationships between environmental variables9.3.2.4 Generalization and applicability of the CDM9.3.2.5 Data-based examination of corrosion9.4 Big Data-Powered Picture Recognition Technique for Predicting Atmospheric Corrosion9.4.1 Experiment9.4.1.1 Corrosion Acceleration Experiment9.4.1.2 Data acquisition9.4.1.3 Experimental characterization9.4.2 Algorithm9.4.2.1 LBP9.4.3 Modeling9.4.4 Results9.4.4.1 Outcomes of picture segmentation9.4.4.2 Correlation analysis9.4.4.3 Corrosion Prediction by ICPM9.5 Chapter SummaryChapter 10.  Perspectives on the Application of Artificial Intelligence in Investigating Corrosion Mechanisms of Steel and Designing Corrosion Resistant Alloys10.1 Introduction10.1.1 Economic losses caused by steel corrosion10.1.2 Industrial Challenges10.1.3 Industrial Challenges: Limitations of Traditional Corrosion Resistant Alloy Steel Research and Development10.2 Key directions for AI driven corrosion-resistant steel alloy design10.2.1 Feature extraction of cross scale representation data10.2.2 Revealing the main controlling factors of corrosion at different levels10.2.3 Integration of Molecular Dynamics Simulation and Machine Learning10.2.4 Dynamic Monitoring and Intelligent Analysis of Corrosion Process10.3 Challenges and Countermeasures10.3.1 Multi source data fusion and compatibility10.3.2 Analysis of Key Factors10.3.3 Computing resources adapted to bottlenecks10.3.4 Data Quality and Model Reliability10.4 Conclusion