Corrosion Monitoring Research Articles

Corrosion resistance in LNG plant design: Engineering lessons for future energy projects

Corrosion resistance is a critical consideration in the design and operation of liquefied natural gas (LNG) plants, where harsh environmental conditions and aggressive chemicals can significantly impact asset integrity and safety. This paper explores engineering lessons learned from past LNG projects, emphasizing the importance of corrosion management strategies to enhance the longevity and reliability of energy infrastructure. The study highlights key factors contributing to corrosion in LNG facilities, including materials selection, environmental exposure, and operational practices. By employing advanced materials such as corrosion-resistant alloys and coatings, engineers can mitigate corrosion risks, leading to reduced maintenance costs and extended equipment lifespan. The paper examines successful case studies where proactive corrosion management has been implemented, showcasing innovative engineering solutions such as cathodic protection, corrosion monitoring systems, and design modifications that enhance resistance to corrosion-related failures. Furthermore, it addresses the role of regular inspections, predictive maintenance, and risk-based approaches in identifying potential corrosion issues before they escalate into significant problems. As LNG plants continue to proliferate globally, the integration of corrosion resistance into design and operational frameworks becomes increasingly vital. This paper also discusses emerging technologies and materials that hold promise for improving corrosion resistance, including nanotechnology and smart coatings that provide real-time monitoring capabilities. By learning from past projects and embracing a proactive approach to corrosion management, future energy projects can enhance safety, reduce operational risks, and achieve greater sustainability in their operations. Ultimately, the lessons derived from this research will contribute to developing best practices for designing and operating LNG plants and other energy facilities, ensuring that they meet the challenges of a rapidly evolving energy landscape.

Real-time in-situ coatings corrosion monitoring using machine learning-enhanced triboelectric nanogenerator

Current methods for monitoring coating corrosion are limited by their inability to provide real-time data and dependence on external power sources. This study presents a novel in-situ corrosion monitoring system using a solid-liquid triboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for self-powered sensing. TENG signals and electrochemical impedance spectra were measured on a dopamine-modified lignin-polydimethylsiloxane coating on steel in 1 M NaCl solution under no corrosion, indentation, pitting, and broken conditions, respectively. We extract time-frequency features from the TENG signals to predict the coating’s corrosion condition by applying a customised convolutional neural network (CNN). By extracting time-frequency features from the TENG signals and applying a custom CNN, a prediction accuracy of 99 % for corrosion classification was achieved. Furthermore, the CNN regression model predicted coating impedance values with a high coefficient of determination (R² = 0.98), demonstrating its effectiveness in tracking corrosion progression. The developed TENG also facilitates defect localisation via a matrix electrode beneath the coating. Our approach introduces a promising real-time technology for in-situ corrosion monitoring.

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 2: Implementation

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors’ responses. It aims to determine, along with the rebars’ corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar.

Identification of corrosion nature using acoustic emission in representative scale samples

Monitoring corrosion is critically important across numerous applications, given its occurrence in key industrial sectors such as energy production, transportation, and civil engineering to prevent leaks and failures. Especially, determining the exact nature of corrosion phenomena could help planning specific maintenance operations, reducing costs and risks. Machine Learning methods have demonstrated capacities to discriminate corrosion phenomenona. An important step to create precise Machine Learning models is the quality of training datasets. With this goal in mind, this paper presents experiments performed on different metallic samples to create specific corrosion forms, respectively pitting and uniform corrosion to train a supervised model. This model is evaluated on 316L steel samples prone to endure both pitting and uniform corrosion. The usage of supervised learning is then discussed compared to other methods.

Corrosion measurement of thermally sprayed carbide coatings on stainless steel pipes

The petrochemical industries face a formidable challenge from corrosion, which leads to high maintenance costs and lost production. Traditional corrosion monitoring methods frequently fail to provide accurate and reliable results. As a result, these components must be replaced or repaired promptly to avoid increasing costs and limiting component life, which will reduce operation efficiency. Novel techniques with demands for high surface qualities in the future will be applied to develop alloys with better performance and more diverse applications. This study examines the corrosion resistance of cermet coatings, such as tungsten carbide (WC) and Chromium carbide (Cr3C2), in acidic solution by electrochemical impedance spectroscopy and electrochemical potentiodynamic polarisation. Additionally, utilizing optical microscopy techniques, the microstructural characteristics of coatings were assessed. According to the results of the Tafel polarisation test, Cr3C2 coating exhibited a lower rate of corrosion than other coatings. The Cr3C2 coated, WC coated, and uncoated samples reported corrosion rates of 0.924, 1.122, and 2.599 mmpy respectively. The corrosion resistance of Cr3C2 and WC coatings was increased by 64% and 53%, respectively, in comparison to uncoated specimens. This suggests that the coating maximizes economy, minimizes maintenance costs, extends the structure's lifespan, and reduces down the rate of corrosion.

Monitoring of metal corrosion by electrochemical methods

Monitoring of metal corrosion by electrochemical methods

Assessment of the protective potential of reinforcing steel in low carbon footprint concrete using electrical resistance corrosion monitoring

Assessment of the protective potential of reinforcing steel in low carbon footprint concrete using electrical resistance corrosion monitoring

Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

Study on the initial corrosion characteristics of weathering steel bridges with corrugated steel webs

In this study, the characteristics and influencing factors of the early corrosion evolution process of the weathering steel (WS) bridge in northwest China were examined, revealing the seldom-studied corrosion evolution law of the corrugated web of weathering steel. The corrosion losses and average corrosion thickness of WS bridge components were measured and evaluated. The results revealed that compared with traditional flat steel webs, the corrosion of corrugated steel webs was enhanced in the vertical direction and at the wave peak, and resulted in a more complex corrosion pattern. The trend of the rust layer on the inner and outer surfaces of the box girder is related to the stage characteristics of the corrosion products. The appearance of microscopic characteristics of the rust layer is periodic, with the rust layer thickness, colour, and particle size varying with the bridge’s location. The development of the corrosion pit followed a lognormal distribution, and the corrosion rate was balanced in the radial and depth directions. These results can serve as a basis for designers or inspectors to design WS bridge structures, select correct corrosion allowances, and improve corrosion monitoring and detection techniques.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

In Situ Label‐Free Monitoring of Riboflavin Concentration in Shewanella via a Fiber‐Optic Biosensor Modified by Ti3C2‐MXene@ SGNps Composites

AbstractRiboflavin is a common marker of microbial corrosion and can act as an electron shuttle in the solvated state to enhance electron transfer efficiency. In this paper, an in‐situ label‐free fiber‐optic biosensor modified by Ti3C2‐MXene@Square Gold nanoparticles (SGNps) composites and riboflavin kinase is developed, which behaved with high sensitivity for measuring riboflavin. The sensing interface of the sensor is constructed by stepwise assembly of SGNps and riboflavin kinase using Ti3C2‐MXene nanosheets as the supporting substrate. Riboflavin kinase catalyzed the phosphorylation of riboflavin to form flavin mononucleotides in the presence of adenine nucleoside triphosphate and Mg2+. Experimental results showed that the sensitivity of riboflavin measurement is 5.61 nm µm−1 and the limit of detection (LOD) is 115 nmol L−1 (nm) in the concentration range of 0–2.656 µm. In situ label‐free monitoring of riboflavin in Shewanella, the sensitivity of the sensor in bacterial fragmentation fluid is 53.90 nm/OD within the optical density (OD) of 0–0.175, and the LOD is OD = 0.017. The above results verified the feasibility of the sensor for in situ monitoring of riboflavin in microbial corrosive environments, with ultra‐low detection limit and good selectivity, which undoubtedly provided great research value for the application of optical fiber sensors in microbial corrosion monitoring.

Artificial intelligence-based ship hull plate corrosion monitoring using Convolutional Neural Network (CNN): comparison of YOLOv8 and Detectron2 architecture models

ABSTRACT Detecting and monitoring structural damage is crucial to prevent significant damage to ship structures caused by corrosion. This study aims to measure the performance of corrosion detection using Convolutional Neural Networks (CNN) by comparing YOLOv8 and Detectron2 models analyzed at various data augmentation settings. The results show that Detectron2 with the ResNet101 backbone performs better than Detectron2 ResNet50 and YOLOv8 models. The result found that applied augmentation data settings, such as vertical & horizontal flip, rotation, and increasing & decreasing colour saturation, highlight their importance in training robust corrosion detection models. These findings contribute to developing corrosion detection methods, showing that the Detectron2 model with instance segmentation can overcome the complexity and variety of corrosion shapes more effectively than YOLOv8 model. The instance segmentation model has proven to be the more effective CNN model for detecting abstract objects, such as corrosion damage in ship hulls.

Development of an integrated online deposition and corrosion monitoring system in a full-scale solid waste CFB boiler

Solid waste incineration is a clean and sustainable approach for solid waste management. However, ash deposition and corrosion remain a critical issue due to fuel’s inherent enrichment of alkali chlorine. This study develops an integrated online deposition and corrosion monitoring system to enhance the operational safety and efficiency of solid waste incineration boilers. This system combines linear polarization resistance (LPR) for corrosion rate estimation with heat flux measurements for ash deposition analysis. It can offer a novel approach for real-time monitoring of heat exchangers’ safety during solid waste combustion. It was deployed in a full-scale circulating fluidized bed (CFB) boiler that purely combust solid wastes. Key findings demonstrate the system’s capability to deliver continuous, real-time data, crucial for the dynamic control of combustion processes and the maintenance of heat transfer surfaces. Its robust diagnostic capabilities were evident across various scenarios. Specially, initial corrosion rates sharply increase with deposition rates due to the enrichment of alkali chlorine on inner deposit layer, in which chlorine serves as a catalyst, facilitating the rapid penetration and aggravation of corrosion by other agents. As deposit further buildup, the corrosion rate steadily decreases along with surface temperature, highlighting a dynamic interaction. Moreover, measured corrosion rates can quickly response to temperature variations. Such multi-process online monitoring system provide more possibilities to investigate the inherent interaction between deposition and corrosion. Therefore, this work offers insights that could significantly influence operational strategies, maintenance protocols, and the overall reliability of waste-to-energy technologies.

Corrosion evolution and quantitative corrosion monitoring of Q355 steel for offshore wind turbines in multiple marine corrosion zones

Offshore wind turbines operating in harsh marine environments are at risk of corrosion. Thickness loss and perforation caused by corrosion can lead to component failure or structure collapse. An environmental test setup was constructed to simulate various marine corrosion zones. Several experimental studies were conducted on Q355 steel, commonly used in offshore wind turbines. The mass loss and electrochemical properties of the steel specimens were analyzed, including electrochemical impedance spectra and polarization curves. The results indicate that corrosion remains stable in the immersion and tidal zones, whereas corrosion in the splash zone is the most severe, with a corrosion rate as high as 0.70 mm/a. A significant macroscopic corrosion cell effect was observed on the vertical steel strip, accelerating corrosion in the splash zone and at the low tide line. The corrosion product compositions and corrosion morphology of each corrosion zone were characterized and analyzed. Furthermore, a corrosion monitoring sensor based on electromechanical impedance was proposed. A prediction model for the mass loss rate of vertical steel strips was developed based on the conductance peak frequency. This study elucidates the corrosion evolution of wind turbine structural steel and proposes a comprehensive steel corrosion monitoring solution.

Durability of Alkali activated concrete made using multiple precursors as primary binders

Alkali-activated binders (AABs) have been and continue to be extensively explored as an alternative to ordinary Portland cement (OPC), addressing environmental and sustainability issues. A large number of studies that were conducted earlier focused mainly on the mechanical properties and some durability aspects of industrial waste-based AABs. However, chloride migration and diffusion causing reinforcement corrosion in AAB-based concrete (AAC) have not been thoroughly investigated. The present study focuses on the durability of AAC produced using multiple precursors that included industrial waste materials and natural minerals such as limestone powder (LSP), red mud (RM), silicomanganese fume (SMF), and natural pozzolan (NP) activated by alkali. Firstly, the dosages of all four precursors were optimized through trials and then NP was partially replaced by 10, 20 and 30% OPC to enhance the performance of the AABs. The AAC mixtures were prepared and cured under different conditions and tested for compressive strength and durability characteristics related to chloride permeability, chloride migration, chloride diffusion, and chloride-induced reinforcement corrosion to evaluate the performances of the mixtures. The results show that the composition of the AABs and curing regimes significantly influenced the performance of the AAC. The inclusion of the OPC resulted in a very significant enhancement of the strength and durability characteristics of the AAC. In addition, there is a sharp increase in the performance of the AAC mixtures when the OPC content of the precursor was increased from 10 to 20%, irrespective of the curing method. Therefore, an optimum dosage of the OPC in the precursor can be considered as 20%, allowing utilization of 80% waste materials and natural minerals as the precursor with much higher strength and durability characteristics than traditional OPC concrete, thus saving energy and reducing environmental pollution leading to a cleaner production of concrete. Lastly, electrochemically measured corrosion potential (Ecorr ) and corrosion current density (Icorr ) values for the reinforcing bars (rebars) embedded in AAC mixtures were found to be misleading, as confirmed by visual inspection of the extracted rebars in addition to gravimetric test results. Hence, there is a need for further research to develop corrective measures before the utilization of the electrochemical-reinforced corrosion monitoring methods in the case of AACs.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Monitoring the Corrosion Resistance of Cold-Spray Coatings with Membrane-Based Sensors

Ensuring the management of gas transportation infrastructure requires the monitoring of internal corrosion within pipelines. The utilization of membrane-based electrochemical sensors is a promising advancement in corrosion risk monitoring. These sensors demonstrate an ability to operate within humidified gas environments, previously inaccessible to conventional electrochemical monitoring methods. Simultaneously, ongoing research is delving into novel sacrificial coatings and application methods to prolong the lifespan of existing pipeline structures and minimize associated repair costs. In this context, we broaden the application of membrane-based electrochemical sensors to investigate the response of electrodes equipped with cold-spray coatings. This examination encompasses a range of fluids exhibiting varying levels of corrosivity pertinent to natural gas pipelines. Multiple sensors with 316 stainless steel working electrodes were coated with cold spray sacrificial coating then exposed to humidified gases with differing relative humidities (RH) (5 %, 50 %, and 95 %) and condensed phases with differing salt content (deionized water and 0.1 mol kg-1 NaCl solution). Three electrochemical techniques (potentiometry, impedance spectroscopy and potentiodynamic scans) were used to monitor the corrosive environment and probe the extent of coating coverage. For the conditions studied, impedance values were extremely sensitive to changes in water content through changes in the membrane resistance (from ~1 kΩ in deionized water to ~5 MΩ with 5 % RH). Polarization resistances (Rp) consistently decreased with increasing corrosivity (~3 kΩ in deionized water to 15 GΩ with 5 % RH). Scanning electron microscopy and energy dispersive X-ray spectroscopy were used to identify the extent to which corrosion products permeated the membrane after major corrosion events.

Corrosion monitoring for transmission line based on electromechanical impedance instrumented circular piezoelectric corrosion sensor

Corrosion-induced thickness loss is a common form structural damage in transmission lines. In our previous work, a novel circular piezoelectric-metal transducer for corrosion sensing using the electromechanical impedance (EMI) technique was proposed. This transducer was fabricated by attaching a lead zirconate titanate (PZT) patch to a metal plate. In the present study, the piezoelectric-metal transducer was encapsulated and enhanced, and two types of corrosion sensors were developed: piezoelectric-steel and piezoelectric-zinc sensors. To assess the practical performance of the corrosion sensors, the piezoelectric-steel and piezoelectric-zinc sensors were connected to the angle steels and the steel strands, which are highly prone to corrosion in transmission lines. Corrosion monitoring experiments were conducted in the laboratory. The results demonstrate that the peak frequencies in the conductance signatures decrease linearly with increasing corrosion-induced thickness loss. Furthermore, it was found that piezoelectric-steel sensors are effective for quantitative monitoring of metallic corrosion, whereas piezoelectric-zinc sensors are better suited for early-stage corrosion monitoring and warning.

Early Detection and Monitoring of Corrosion in Reinforced Concrete Structures Using Coupled Time-Frequency Analysis

Early Detection and Monitoring of Corrosion in Reinforced Concrete Structures Using Coupled Time-Frequency Analysis

Interaction of aluminum alloys with MKPC and Portland-based cements on the metal-matrix interface

Cementitious matrices are considered for the immobilisation of radioactive aluminium. The processes occurring at the interface between both materials are relevant for the long-term stability of the repository. The present study compares Ordinary Portland cement (OPC), CEM I-type, with alternative Portland blended cement (CEM IV and CEM I + 50% silica fume) and magnesium potassium phosphate cement (MKPC). Al A1050 and Al AA5754, with 3.5% Mg, have been used as reactive metal alloys. Two exposure conditions were employed: (1) water immersion and (2) isolated in sealed plastic films. Long-term corrosion monitoring (Ecorr, icorr and Vcorr) was evaluated to understand the effect of Al reactivity in the matrix. The associated H2 release was quantified to understand the changes at the metal/matrix interface. Furthermore, pore ion concentrations and the pore microstructure were evaluated. The metal/matrix interface alterations were analysed at the end of the tests. The study revealed that after 300 days of water immersion, the CEM I + 50% of silica fume matrix had reduced the pore solution pH down to 10.5 compared to CEM I, which remained high alkaline (pH 12.9), and CEM IV with no significant decreases (pH 12.3). In contrast, MKPC showed the lowest pH (7–9.8). Low Al corrosion rates were found with MKPC, followed by CEM I + 50% SF according to their lower pH. A corrosion product layer of 90-50 μm thickness was observed at the Al/CEM I matrix interface constituted of aluminium and oxygen. Furthermore, enrichment in Al was detected in the matrix (around 1 mm depth), causing the formation of ettringite nodules. Voids were detected at the interface level associated with the high volume of H2 released. The MKPC matrix showed no alteration at the metal/matrix interface due to the lower reactivity of the matrix and lower H2 release. A homogeneous and dense region (30 μm) rich in phosphorous, potassium and magnesium was observed near the metal surface.