Operational Safety Research Articles

High acetone soluble organosolv lignin extraction and its application towards green antifouling and wear-resistant coating

Marine fouling poses significant challenges to the efficiency and longevity of marine engineering equipment. To address this issue, developing effective marine antifouling coatings is critical to ensure the economic viability, environmental sustainability, and safety of offshore operations. In this study, we developed an innovative green antifouling and wear-resistant coating based on lignin, a renewable and sustainable resource. Lignin is considered environmentally friendly because it is abundant, biodegradable, and reduces reliance on petroleum-based materials. The coating was formulated with a controlled hydrophilic-to-hydrophobic ratio of 2:8, leveraging lignin's unique properties. Applying lignin increased the water contact angle by 14.5 %, improving surface hydrophobicity and contributing to the coating's antifouling efficacy. Moreover, the mechanical strength of the coating was enhanced by approximately 200 %, significantly boosting its durability in harsh marine environments. Additionally, the friction coefficient was reduced by about 85 %, further preventing organism adhesion. These results demonstrate that lignin-based coatings offer a greener alternative to traditional antifouling solutions. The results of this study not only help advance antifouling coating technology but are also consistent with the broader goal of promoting environmental responsibility in marine engineering practice.

Effect of particle impact on spatial and temporal erosion characteristics of turboshaft engine compressor

When turboshaft engines operate in dusty environments, particulate matter erodes the compressor blades, which may leads to structural damage and presents a severe threat to the operational reliability and safety of helicopters. The aim of this study is to determine the erosion characteristics of compressor blades and how it changes with time. Particle velocimetry and erosive experiments were conducted to obtain the SiO2 particle velocity and the erosion rate of the Ti-6Al-4V titanium alloy. Based on the measured data, the parameters of the Tabakoff erosion model have been refined, which is critical for establishing a transient erosion model for the 1.5-stage compressor of a turboshaft engine that accounts for the effect of particle erosion time. Simulation results show that the motion behaviour of particles exhibits changing patterns at different time intervals, which leads to variations in the erosion area and erosion rate of the compressor. The erosion area and rate on blades increase nonlinearly with time. In some locations, when erosion time increases, the erosion area and erosion rate also increase. While, in other locations, the erosion area and erosion rate hardly change with time. The maximum erosion rates increased by 27.3%, 28.6%, and 87.2% for the guide blades, 42.9%, 69.6%, and 84.0% for the rotor blades, and 69.0%, 103.6%, and 142.8% for the stator blades at 0.50s, 0.75s, and 1.00s, respectively, in comparison to 0.25s.

Strengthening environmental safety in oil and gas operations: Optimizing health, safety, and environmental (HSE) protocols

The oil and gas industry faces significant environmental challenges, including spills, emissions, and equipment failures, which pose serious risks to ecosystems and human health. This review explores strategies for optimizing Health, Safety, and Environmental (HSE) protocols in oil and gas operations, emphasizing technological advancements, proactive safety measures, and industry-regulator collaboration. It examines how real-time monitoring systems, predictive analytics, and automation improve risk management, while best practices such as comprehensive risk assessments and safety training mitigate potential hazards. The paper also highlights the need for flexible regulatory frameworks and enhanced transparency through data sharing. The findings underscore the importance of refining HSE protocols to ensure environmental safety and operational resilience. Recommendations include expanding the use of advanced technologies, strengthening safety cultures, and fostering closer cooperation between the industry and regulatory bodies. These strategies aim to reduce environmental risks, improve compliance, and enhance sustainability in the sector.

Effects of Operations of Various Water Reservoirs and Dams According to their Safety for the Surroundings Areas

Dams are essential pieces of infrastructure that are required for river navigation, flood risk reduction, agriculture, water security, and the production of clean energy. However, controlling dam operations becomes more complex as a result of these several, frequently incompatible goals. Furthermore, dam infrastructure has been developing into intricate system-ofsystems with several interdependent parts and subsystems that are all vulnerable to a variety of uncertainties. Due to these intricacies and uncertainties, numerous research projects centered on dam systems and reservoir operational safety have been initiated. This work focuses on the latter by doing research-on-research, or meta-research, on previously published studies in order to pinpoint important research gaps and suggest future lines of inquiry. In order to discover and categorize significant and latent issues in the discipline, this research first conducts a quantitative analysis of the relevant literature using text mining and then topic modeling. The highlighted subjects are then critically reviewed using qualitative analysis, which explores the concepts, definitions, modeling methods, and key research trends. The study specifically targeted seven topics: water supply management, flood risk, inflow forecasting, hydropower generation, climate change, optimization models, and risk-based assessment and management. The paper also identifies three primary research needs related to the lack of resilience-guided management of dam operational safety, modeling tool capabilities, and modeling idea constraints. In order to guarantee the resilience of such vital infrastructure, particularly in the era of climate change, this study provides an overview of the existing research on dam and reservoir operational safety, the knowledge gaps related to it, and possible future research avenues.

Classification of vessel types using the visual geometry group based convolutional neural network

This study explores the application of Convolutional Neural Networks (CNNs) for the classification of vessel types within Nigerian pilotage districts, focusing on the Visual Geometry Group (VGG) architecture. The research methodology encompasses data collection, preprocessing, model selection, training, and evaluation, resulting in a robust dataset of 15,760 images representing 394 different vessel types. The VGG model achieved a validation accuracy of 94%, alongside a precision of 92%, recall of 90%, and an F1-score of 91%. These metrics indicate strong classification capabilities, yet also reveal potential overfitting, as evidenced by a plateau in validation accuracy after 50 epochs. The confusion matrix highlights the model's challenges in accurately classifying certain vessel types, suggesting a need for further refinement. Overall, this work contributes to the growing body of knowledge in maritime Artificial Intelligence (AI) applications, with implications for improved operational efficiency and safety in port management.

Advancing process safety management systems in the oil and gas industry: Strategies for risk mitigation

Process Safety Management (PSM) is critical for mitigating operational risks and enhancing worker safety in the oil and gas industry. This paper explores the essential components of PSM systems, such as hazard analysis, incident investigation, and management of change, highlighting the role of safety culture, leadership, and accountability. Key challenges in implementing effective PSM, including aging infrastructure and compliance issues, and lessons learned from industry incidents are examined. The paper presents strategies for improving risk mitigation through advanced technologies like digital tools, predictive maintenance, real-time monitoring, and continuous workforce training and engagement. The findings underscore the need for a balanced approach that integrates human oversight with technology to ensure more effective risk management. Recommendations include further investments in digital transformation, stronger leadership commitment to safety, and collaborative efforts with regulatory bodies to improve industry standards. The oil and gas sector can significantly reduce risks and improve operational safety by adopting these strategies.

Analisis Faktor Yang Mempengaruhi Keselamatan Operasional Kapal di Pelabuhan Merak

The study, titled "Analysis of Factors Affecting Safety at Merak Port," aims to identify and analyze key factors influencing operational safety at Merak Port. This port is a strategic route linking Java and Sumatra, serving millions of passengers and vehicles annually. However, high activity levels and operational complexity present significant safety challenges. This research employs a qualitative case study approach, combining interviews, field observations, and document analysis. Quantitative data were also collected via questionnaires distributed to operators, workers, and passengers. Findings reveal that human factors, weather conditions, and infrastructure are critical to safety. Incidents in 2023, such as the KMP Royce 1 fire and a crane accident, underscore the need for enhanced risk management and infrastructure maintenance. The study concludes that gaps in safety training and compliance with safety procedures are key challenges. Additionally, extreme weather and suboptimal heavy equipment conditions increase operational risks. Recommendations include improving safety management practices, enhancing infrastructure maintenance, and providing regular training for workers to strengthen operational safety at Merak Port.

Full-Scale Testing of Corrosion Resistant Alloy-Mechanically Lined Pipes for Submarine Pipelines

Abstract This study evaluates the fatigue performance of Corrosion Resistant Alloy–Mechanically Lined Pipes (CRA-MLPs), focusing on the critical triple point, where the CRA liner, weld overlay, and backing steel pipe intersect. Through Full-Scale Bending Tests (FSBT) and Full-Scale Resonance Fatigue Tests (FSFT), real-world operational and installation conditions were simulated to assess fatigue resistance, particularly at the triple point–an area not adequately addressed in current industry standards. The results reveal that the triple point exhibits excellent fatigue performance, exceeding the criteria of existing guidelines, despite the lack of specific provisions for this critical region. This study provides valuable data to inform updates to industry standards, enhancing the safety and reliability of subsea pipeline operations.

Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR

In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks.

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

Acoustic Tunnel Lining Void Detection: Modeling and Instrument System Development

The detachment of railway tunnel lining constitutes a grave danger to train operation safety and drastically curtails the tunnel’s service life. This study endeavors to efficiently detect the void defects in railway tunnel lining by creating a finite element model of tunnel lining structures. Utilizing this model, the study simulates the nonlinear acoustic wave propagation cloud maps for three representative tunnel lining structures: void-free, air void, and water void. This facilitates a thorough examination of the acoustic signal characteristics in the wavefield, time domain, and frequency domain. To satisfy the precision and efficiency demands of tunnel lining void detection, this study has devised and developed a portable acoustic detector that incorporates automatic analysis and processing capabilities and is furnished with a high-performance rare-earth magneto-strictive acoustic excitation device. This detection system can swiftly detect and assess typical void defects in tunnel lining. To further validate the effectiveness of this system, this study conducted lining defect detection in the Pingdao Railway Tunnel in the eastern Qinling Mountains. The test results show that the detection rate of this system for both air-filled and water-filled voids with a width of 1 m reached 100%, demonstrating its extremely high application value.

Quantitative Detection Method for Surface Angled Cracks Based on Laser Ultrasonic Full-Field Scanning Data

Surface angled cracks on critical components in high-speed machinery can lead to fractures under stress and pressure, posing a significant threat to the operational safety of equipment. To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data.” By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. This study investigates the propagation behavior of ultrasonic waves and their interaction with surface angled cracks through theoretical calculations. The crack width is solved by analyzing the distribution of Rayleigh waves in the full-field scanning data. This paper also discusses the differences in ultrasonic wave propagation between near-field and far-field detection and identifies the critical point between these regions. Different computational methods are employed to calculate the inclination angle and the crack endpoint at various scan positions. Four sets of experiments were conducted to validate the proposed method, with results showing that the errors in determining the width, angle, and length of the surface angled cracks were all within 5%. This confirms the feasibility of the method for detecting surface angled cracks. The quantitative detection of surface angled cracks on critical components using this method allows for a comprehensive assessment of the component’s condition, aiding in the prediction of service life and the mitigation of operational risks. This method shows promising application potential in areas such as aircraft engine blade inspection and gear inspection.

An Ionic Sieve‐Integrated Conductive Interfacial Design to Simultaneously Regulate the Zn2+ Flux and Interfacial Resistance for Advancing Zinc‐Ion Batteries

AbstractZinc‐ion batteries possess operation safety, high energy density, production flexibility and affordability, making them attractive for scalable energy storage. While Zn anodes face significant challenges from rampant dendrite growth and electrolyte‐related side‐reactions in a complex interfacial microenvironment. The growing interfacial resistance further degrades the battery performance. An integrated anode interfacial design is reported to regulate simultaneously the Zn2+ flux and interfacial resistance through in situ confinement growth of Zn2+ sieve, that is, 2D CuBDC metal–organic framework in mesoporous carbonaceous host. CuBDC with sub‐nanometer channels is selected for efficient dehydration and directional Zn2+ flux transport, and lowering the nucleation barrier by zincophilic Cu(II) and N sites. Conductive meso‐carbon reduces the interfacial resistance and blocks the interfacial side‐reactions. Resultantly, the modified Zn anodes demonstrate improved cycling stability with lower voltage polarization, supported by operando optical microscopy and ex situ analysis. This work provides a feasible strategy improving aqueous Zn anodes and new interfacial insights on designing advancing zinc batteries.

The Impact of Employee Appreciation Approaches on Job Satisfaction: Empirical Evidence from Air Traffic Controllers of Sri Lanka to Enhance Human

Understanding the dynamics of job satisfaction among Air Traffic Controllers (ATCs) is imperative for ensuring the human performance, productivity, efficiency and safety of aviation operations. This study investigates the impact of various employee appreciation methods on the job satisfaction levels to enhance the performance of ATCs in Sri Lanka. Drawing upon existing literature and analysis of the data collected from the current employees, the research is aimed at filling a critical knowledge gap regarding the relationship between employee appreciation and job satisfaction within the ATC sector. The research problem centres on the need to comprehend how different forms of appreciation, ranging from verbal acknowledgements to tangible rewards, influence ATCs' job satisfaction. Through a structured survey utilizing a Likert scale, the study measured the effects of seven appreciation methods on job satisfaction, namely verbal expressions in one-on-one and public settings, electronic notes, written communications, tangible items, monetary bonuses, and the absence of gratitude. Key research questions addressed the specific impacts of each appreciation method on the job satisfaction of ATCs'. By analysing responses from ATCs in Sri Lanka, the study aimed to provide insights for supervisors and managers to tailor appreciation strategies effectively. The findings hold significance in enhancing the work environment, retaining talent, and promoting organisational productivity within the aviation industry. Acknowledging potential limitations such as participant biases and organisational policies, the research adopts measures to ensure data integrity and confidentiality. By leveraging a comprehensive approach to data collection and analysis, it enhanced the reliability and credibility of the findings of the study.

Dynamics Modelling and Adaptive Identification: Towards Improved Human-Robot Interaction in Collaborative Systems

This article presents a novel approach to enhancing human-robot collaboration and safety through advanced dynamic modelling and adaptive identification techniques. We introduce a comprehensive methodology that integrates motion trajectory design with real-time torque detection, addressing the critical limitations of conventional systems that rely on costly joint torque sensors. By simultaneously identifying friction forces in an integrated joint and a simplified two-bar mechanism, our approach leverages existing kinematic and dynamic models to achieve precise dynamic parameter identification. The proposed method significantly advances the fields of drag-teaching and collision detection by eliminating the need for force sensors, thus making it more feasible for mass-produced robotic systems. Our findings demonstrate that accurate dynamic modelling is essential for effective zero-force control, particularly in high-speed drag-teaching scenarios, where inertia and friction present substantial challenges. Experimental validation confirms the efficacy of our dynamic feed-forward controller design and the adaptability of drag-teaching parameters, leading to improved operational flexibility and safety in collaborative environments. This research contributes a critical framework for future developments in intelligent robotic systems, providing a robust basis for integrating advanced human-robot interactions in industrial applications.

Study on stress wave propagation and failure characteristics of key parts in tunnel under blasting load

To investigate the propagation mechanisms of stress waves and the characteristics of crack distribution in tunnel structures subjected to explosive effects, an experimental model simulating rock mass using cement mortar was employed. Blasting experiments were conducted at various vertical locations relative to the tunnel. Utilizing ultra-dynamic strain monitoring alongside high-speed digital image recognition, we captured in real time the dynamic evolution of stress waves as well as the precise initiation and expansion paths of cracks. A comprehensive analysis was performed on both stress wave propagation and damage patterns within the refuge structure. Furthermore, the reliability of our numerical simulation algorithm was validated through an examination of fluid-structure coupling algorithms. The results indicated that peak strains at monitoring points within the tunnel increased as detonation points approached it, leading to heightened structural damage. Numerical simulations demonstrated a strong correlation between observed peak strains at critical locations and corresponding damage data from our experimental model. Additionally, it was found that decreasing height between detonation points and the tunnel resulted in increased dynamic response parameters—such as overpressure, velocity, and acceleration—at monitoring sites within the tunnel, thereby exacerbating damage to key areas including vaults and footwall structures. To mitigate potential structural instability within refuges, a full-section molded concrete lining support system was implemented along with supplementary anchor (mesh) spraying in critical regions to ensure long-term operational safety.

Configuration of fast/slow charging piles for multiple microgrids considering climbing costs and load fluctuations

This paper presents a two-layer optimal configuration model for EVs' fast/slow charging stations within a multi-microgrid system. The model considers costs related to climbing and netload fluctuations, aiming to meet EVs' charging demands while ensuring grid safety and economy. The upper layer focuses on minimizing investment and operational costs for the microgrid, accounting for EVs' charging characteristics and interdependencies among microgrids. It develops an optimal configuration model for charging stations across multiple microgrids and implements differentiated electricity pricing in various zones to promote orderly charging. The lower layer aims to minimize EVs' charging costs. Using differentiated pricing, it analyzes the spatiotemporal distribution of EVs' charging loads through the Monte Carlo method. This analysis informs the upper layer for further optimization of charging station configurations. An analysis of three scenarios shows that the proposed approach reduces EVs' charging costs by 44.3% compared to uncoordinated charging. It also mitigates the impact of EVs' charging loads on the microgrid, enhances operational safety and contributes to increased social welfare.

Research on the Application of Stewart Platform for Wave Compensation

In the field of ocean engineering, the efficiency and safety of ship operation are significantly affected by waves. Faced with the instability and safety risks of ship crane operation under complex sea conditions, a design and research method of efficient wave compensation device based on Stewart platform is proposed in this paper, aiming at developing a compensation system that can respond in real time and effectively counteract the effects of waves, so as to improve the safety and efficiency of offshore operations. Aiming at the limitation of traditional wave compensation technology, this paper adopts the BP neural network PID control algorithm combined with Stewart platform. Utilizing the Stewart platform's six-degree-of-freedom motion capability, fast compensation of wave motion is achieved. On this basis, the BP neural network PID control strategy is introduced to optimize the response speed and compensation accuracy of the system, and realize the efficient and accurate compensation of wave motion under complex sea conditions.

Internet of things (IoT) for safety and efficiency in construction building site operations

Internet of Things (IoT) technologies present transformative opportunities through connectivity of intelligent devices, environmental sensors, and integrated management systems. This study aims to investigate the benefits and impact of IoT implementation on construction sites by analyzing relationships between key factors and outcomes for safety and efficiency. Hypotheses were developed proposing positive correlations between each factor and effective IoT adoption on construction sites. Structural equation modeling analysis on survey data from construction professionals and site reports strongly validated the research hypotheses. Positive path coefficients and high statistical significance confirmed environmental monitoring (0.38), equipment management (0.343), predictive analytics and maintenance (0.222) and safety monitoring (0.369) as crucial enablers for successful IoT integration leading to safer and more productive construction operations. The findings highlight imperative focus areas and provide actionable insights for construction stakeholders on strategies to effectively leverage IoT capabilities.

Impact of Situation Awareness Variations on Multimodal Physiological Responses in High-Speed Train Driving.

In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety.