Operational Safety Research Articles

Multi-channeled halloysite nanotube-blended polybenzimidazole separators for enhancing lithium-ion battery performance

The microscopic transport mode of lithium ions in the separator is crucial for solving the problem of irregular lithium dendrite growth, thereby enhancing the safety of battery operation, and it also has a positive impact on the electrochemical performance of the battery. Furthermore, the heat resistance of the separator is an important indicator for measuring the performance of the separator and the safety of battery usage. In this work, we have introduced sulfonated and lithiated halloysite nanotubes into a high-temperature-resistant OPBI matrix through an easily implementable NIPS, resulting in a high-temperature-resistant separator with multiple lithium-ion micro-transport morphologies. Lithium ions can shuttle through the halloysite nanotube pores, negatively charged sulfonate groups on the surface, and larger finger-like pores formed by NIPS within the separator, creating a high and uniform lithium-ion flux that prevents excessive localized ion flow leading to the aggressive growth of lithium dendrites. According to the characterization data, this separator exhibits relatively satisfactory performance, such as an ionic conductivity of 1.82 mS cm−1, a porosity of 82 %, an electrolyte uptake of 397 %, and no significant physical shrinkage at 200 °C. Additionally, the cells assembled using the OPBI@sHNT-Li20 separator delivers higher discharge capacity (164.92 mAh·g−1), more stable cycle, superior rate performance. So the OPBI@sHNT-Li separator is expected to play an active role in improving the safety and electrochemical performance of lithium-ion batteries.

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

As a key component in the transmission system of high-speed trains, bearings need to withstand certain loads while rotating at high speeds. Once a failure occurs, it can directly affect the safety of train operations. Therefore, it is of great significance to establish a reliable remaining useful life model to ensure train operation safety. Addressing the problems of redundant features in the existing multi-feature fusion process, which affects diagnostic performance, and the spurious fluctuations in the fusion features, which cause inaccurate determination of the start fault time and lead to low prediction accuracy of remaining useful life, we propose a method based on adaptive feature fusion and the autoregressive integrated moving average model for rolling bearing prediction. Firstly, features from the time domain, frequency domain, and entropy are extracted. A feature selection mechanism with minimum redundancy is constructed to screen the optimal sensitive feature set. Secondly, based on adaptive feature fusion, the optimal sensitive feature set is dynamically fused, and the spurious fluctuations of the health index are corrected using linear regression and the 3σ principle. Next, a bottom-up time series segmentation method is employed to divide the health status of the Improved Health Indicators. Finally, a remaining useful life prediction model based on the autoregressive integrated moving average model is established. This study demonstrates that the proposed method effectively identifies features that are most sensitive to degradation trends, accurately determines the initial fault moment of bearings, and achieves effective prediction of the remaining useful life of bearings.

Study on the Prediction of Motion Response of Offshore Platforms Based on ResCNN-LSTM

In the random sea environment, offshore platforms are influenced by factors such as wind, waves, and currents, as well as their interactions, leading to complex motion phenomena that affect the safety of offshore platform operations. Consequently, accurately predicting the motion response of offshore platforms has long been a key focus in the fields of naval architecture and ocean engineering. This paper utilizes STAR-CCM+ to simulate time-history data of offshore platform motion responses under both regular and irregular waves. Furthermore, a predictive model combining residual convolutional neural networks and long short-term memory neural networks using neural network technology is also studied. This model utilizes an autoregressive approach to predict the motion responses of offshore platforms, with its predictive accuracy validated through comprehensive evaluations. Under regular wave conditions, the coefficient of determination (R2) for the platform’s heave and pitch responses consistently exceeds 0.99. Meanwhile, under irregular wave conditions, the R2 values remain generally above 0.4. Additionally, the model exhibits commendable performance in terms of Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. The aim of this study is to present a novel approach to predicting offshore platform motion responses, while providing a more scientific basis for decision-making in offshore platform operations.

The Calibrated Safety Constraints Optimal Power Flow for the Operation of Wind-Integrated Power Systems

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety.

Substantiation of environmental safety in metro facility operations considering hydrogeological risks

Substantiation of environmental safety in metro facility operations considering hydrogeological risks

THE ART OF DESIGN, OPERATION AND IMPROVEMENT OF TECHNOLOGIES, RECIPES, SAFETY AND QUALITY OF FEEDS. IX -th session of the International School of Feeds

From July 1 to 6, 2024, with the support of the European Federation of Feed Manufacturers, the European Federation of Food Science and Technology, the Association "Union of Feed Manufacturers of Ukraine", Odesa National University of Technological (ONTU), ProAgro Group of Companies and the "Club of Young Scientists" ONUT held the IX -th session of the International School of Feeds "THE ART OF DESIGN, OPERATION AND IMPROVEMENT OF TECHNOLOGIES, RECIPES, SAFETY AND QUALITY OF FEEDS". Despite martial law, air alarms, blackouts and other obstacles, the session took place, and it was successful. The session was attended by scientists and specialists in the field of compound feed production, 3 scientific and project organizations: Interprojekt Gmbh, LLC "S-engineering", GK "Zernova stolitsia", 17 representatives from 8 compound feed enterprises: PrJSC MZVKK, Katerynopilsky elevator, AGROPROGRESS PLUS GRAIN COMPANY, VitagroNutryshyn, Kombifeed, MEGA KORM LLC, Kramar LLC, AGRICON Group, as well as Technik BG Gmbh, JSC ADM Illichivsk, LLC Sok-Trade. The session discussed trends in design, increasing the efficiency of operation and robotization of feed production, features of providing nutrients and biologically active substances, production of feed and regulation of their productive action, trainings in modern scientific laboratories of ONUT.

Mechanical‐performance deterioration of RC segment of shield tunnel under high corrosion degree and the transformation of eccentric‐failure mode

AbstractReinforcement corrosion in shield tunnel segments has an important impact on the structure's bearing capacity and failure mode. Generally, a shield tunnel structure is under small eccentric compression, but if the reinforcement corrosion develops sufficiently, then a transition occurs from small to large eccentric load state, thereby lowering the structural bearing capacity and endangering the safety of tunnel operation. In the study reported here, based on the compressive bending load characteristics of the shield lining structure, corrosion test columns with small eccentric loading were designed to simulate the actual force state of the tunnel lining, and the whole evolution process of their mid‐span strain, ultimate bearing capacity, and cracks under different corrosion degrees was analyzed. With increasing corrosion, the bearing capacity, concrete strain, and depth of the compressive zone of a test column decrease more and more rapidly, and at the maximum corrosion degree of 68.32%, the maximum reduction of bearing capacity is 53.23%. The tests show that the failure mode of a test column with high corrosion degree (≥46.25%) is transformed from small to large eccentric failure. Based on theoretical analysis of the normal section bearing capacity of reinforced concrete flexural members, a theoretical method is proposed for calculating the critical corrosion degree for the tunnel lining structure to transform from small to large eccentric failure. The theoretical calculation method is verified against finite‐element calculations, and the present research results offer theoretical support for the durability design and safety evaluation of shield tunnel lining structures.

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Computational Analysis of Stiffness Reduction Effects on the Dynamic Behaviour of Floating Offshore Wind Turbine Blades

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems.

Evaluation of Operational Safety Risk in Wastewater Treatment Plants Based on WSR and Matter–Element Extension Theory

Wastewater treatment plants are of significant importance in facilitating China’s urbanization process. The fundamental purpose of wastewater treatment plants is to control water pollution and protect the environment. It is therefore essential that they are operated in a safe manner in order to achieve this goal. In order to guarantee a systematic and objective assessment of the operational safety risks inherent to wastewater treatment plants, the Wuli–Shili–Renli (WSR) theory has been introduced. Accordingly, a three-dimensional evaluation index system was developed for the assessment of these risks. A combination of subjective and objective methods utilizing game theory was employed to determine comprehensive risk factor weights. Subsequently, the material–element extension theory model was implemented in a wastewater treatment plant situated in the northern region of the Changchun Development Zone. The assessment classified the plant as Level 1 (safe), although it was determined that improvements in accident prevention are necessary. This study presents a robust framework for operational safety assessment that contributes to enhanced risk management and the long-term reliability of wastewater treatment operations. The results of the sensitivity analyses provide substantial evidence of the reliability and validity of the model.

Progress in carbon dioxide capture, storage and monitoring in geological landform

Carbon Capture and Storage (CCS) is recognized as a potent strategy for managing the accumulation of human-generated CO2 in the atmosphere, helping to alleviate climate change’s effects. The CO2 gas is captured from the point source through methods such as pre-treating fossil fuels, oxy-fuel combustion, or post-combustion capture; thereafter; it is transported to a storage location and injected into geological formations. This article provides an overview of carbon dioxide capture and sequestration, focusing on its key principles, technologies, associated risks, and challenges. Direct Air Capture (DAC) and Scalable Modelling, Artificial intelligence (Al), Rapid Theoretical calculations SMART technologies are detailed as emerging and promising approaches to CO2 capture. Numerous pilot and commercial projects commissioned to manage carbon dioxide emissions are presented. Additionally, the paper explores approaches combining geological, geophysical, geochemical, and environmental monitoring techniques to ensure the secure and sustainable storage of CO2 underground. These are essential to address uncertainties, minimize risks, and build public confidence in CCS as a viable climate mitigation strategy. The successful deployment of these technologies on a global scale will require continued innovation, particularly in the areas of monitoring, risk management, and public engagement. Emerging technologies such as AI and SMART systems could play a crucial role in enhancing the efficiency and safety of CCS operations. However, the integration of these advancements with existing infrastructure and regulatory frameworks remains a challenge. Ultimately, a multi-disciplinary approach, combining technological, economic, and regulatory perspectives, will be vital to realizing the full potential of CCS in combating climate change.

Efficient Fault Warning Model Using Improved Red Deer Algorithm and Attention-Enhanced Bidirectional Long Short-Term Memory Network

The rapid advancement of industrial processes makes ensuring the stability of industrial equipment a critical factor in improving production efficiency and safeguarding operational safety. Fault warning systems, as a key technological means to enhance equipment stability, are increasingly gaining attention across industries. However, as equipment structures and functions become increasingly complex, traditional fault warning methods face challenges such as limited prediction accuracy and difficulties in meeting real-time requirements. To address these challenges, this paper proposes an innovative hybrid fault warning method. The proposed approach integrates a multi-strategy improved red deer optimization algorithm (MIRDA), attention mechanism, and bidirectional long short-term memory network (BiLSTM). Firstly, the red deer optimization algorithm (RDA) is enhanced through improvements in population initialization strategy, adaptive optimal guidance strategy, chaos regulation factor, and double-sided mirror reflection theory, thereby enhancing its optimization performance. Subsequently, the MIRDA is employed to optimize the hyperparameters of the BiLSTM model incorporating an attention mechanism. A predictive model is then constructed based on the optimized Attention-BiLSTM, which, combined with a sliding window approach, provides robust support for fault threshold identification. The proposed algorithm’s efficacy is demonstrated through its application to real-world gas-fired power plant equipment fault cases. Comparative analyses with other advanced algorithms reveal its superior robustness and accuracy in efficiently issuing fault warnings. This research not only provides a more reliable safeguard for the stable operation of industrial equipment but also pioneers a new avenue for the application of metaheuristic algorithms.

Fully Printed and Sweat-Activated Micro-Batteries with Lattice-Match Zn/MoS2 Anode for Long-Duration Wearables.

Aqueous zinc-ion batteries with superior operational safety have great promise to serve as wearable energy storage devices. However, the poor cycling stability and low output voltage limited their practical applications. Here, fully printable Zn/MoS2-MnO2 micro-batteries are developed and demonstrated significantly enhanced cycling stability with sweat activation. 2D MoS2 is utilized to enable lattice-matching with Zn powders to realize printed Zn anodes with desirable stability and promote electron/ion transfer. Interestingly, the mild acid epidermal sweat also contributed to eliminating the MnO2 cathode by-products and compensating for the hydrogel electrolytes' water loss. The Zn/MoS2-MnO2 micro-batteries achieve a high specific capacity of 318.9µAhcm-2 at the current density of 0.16mAcm-2, and an energy density of 424.6µWhcm-2, with remarkable cycle stability of ≈90% after 250 cycles. In-battery electrochromic display of capacity level and feasible electronics charging are demonstrated. The as-printed micro-batteries with innovative sweat activation would inspire the advances of sustainable power supply for wearables.

Anomaly detection and confidence interval‐based replacement in decay state coefficient of ship power system

AbstractThe anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

Initial Characteristics of Submillimeter Metal Particles in GIS under Impact Vibration

As a critical component within power systems, Gas Insulated Switchgear (GIS) is notably susceptible to insulation degradation due to submillimeter metal particles, compromising its operational safety and stability. Moreover, impact vibrations induced by circuit breaker operations can dislodge particles that typically adhere to the interior walls, causing them to become airborne and thereby intensifying their movement and discharge activities. To investigate this phenomenon, this study establishes a testing platform where alternating current (AC) voltage is superimposed with impact vibration. A camera system, interfaced with an upper computer, is used to capture the real-time motion behavior of the particles. This study focuses on characterizing the initial movement of submillimeter metal particles of varying sizes under the influence of impact vibration by analyzing critical voltages, such as the initial lift-off voltage, stable jumping voltage, and extinction voltage. Furthermore, the effects of introducing large, centimeter-scale spherical metal and rubber particles on these initial characteristics are examined. The findings provide crucial insights into the initial behavior of submillimeter metal particles in GIS, particularly in relation to circuit breaker operations.

A Cooperative Control Method for Wide-Range Maneuvering of Autonomous Aerial Refueling Controllable Drogue

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions.

A comprehensive ergonomic validation of workspaces design employing digital and physical mock-ups in the case of light armored vehicle

The study aims to evaluate the compatibility of the proposed design workspace dimensions of light armored vehicle (LAV) with the targeted populations of Ethiopian army personnel using both digital and physical ergonomic evaluations. The dimensional compatibility was tested using digital mock-up interfaced with human manikin representing the anthropometries of 5th percentile female and 95th percentile male values for Ethiopian army. CATIA V5 software was utilized for developing digital human and vehicle models, while the physical mock-up was tested through minimum possible user trials (to avoid redundancy of testing) that involved 13 users sufficient to represent the target population. The intended vehicular workspaces, in terms of space adequacy, view field, reaching, and manipulative needs were found to be ideally compatible with the target users, accommodating 90% (for adjustable units) and 95% (for non-adjustable units) of them. This confirms that the proposed basic design dimensions of LAV workspaces for driver, gunner, commander, and other infantry troops are compatible with the target users in both evaluation techniques. Hence, our findings indicate that the newly proposed design dimensions can be utilized as design standards primarily for fabricating Ethiopian LAVs, as they ensure occupational compatibility for the users, which can improve crew operational performance, efficiency, and safety.

Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections

In dual-vehicle interactive scenarios, drivers often encounter decision-making dilemmas, even with connected information, thus posing significant risk challenges to traffic operations. This study explored the impact of such dilemmas on decision-making and traffic operations for drivers involved in straight-going (SG) and left-turning (LT) vehicles at uncontrolled intersections. Employing driving simulation technology, a decision-making experiment incorporating preemptive (P) and yielding (Y) strategies was conducted with 72 S G and 72 L T drivers under connected information provision. By analyzing joint decisions (P-P, Y-P, and Y-Y) made by drivers on both sides, we investigated implications for traffic operational safety and efficiency. Furthermore, recognizing the diversity in individual characteristics and time pressure, we developed a random parameters multinomial logit model to examine the relationship between potential traffic operational performance (both safety and efficiency) and various contributing factors. Moreover, we calculated the marginal effects of these contributing factors. By assessing the probability change of Y-P joint decisions balancing safety and efficiency, we identified reference values for each contributing factor that promote effective interactive operations. The findings suggest that: (1) Regarding interactive dilemmas conditions, Conditions where the SG vehicle had higher speed and farther distance than the LT vehicle were more likely to result in heterogeneous Y-P joint decisions, in which one driver chooses to yield and the other party passes quickly, thereby improving traffic performance. (2) Time pressure increased the likelihood of heterogeneous Y-P joint decisions but also increased P-P joint decisions. (3) heterogeneous individual attributes did not always lead to higher heterogeneous decisions. Overall, this study provides insights into factors influencing joint decisions and traffic operational performance under connected information provision, considering individual heterogeneity and time pressure, aiming to the advancement of more efficient and safer vehicle-connected transportation systems.

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics.

Research on Underwater Constant High-Voltage DC Switching Technology for MCSEM.

The marine controlled source electromagnetic (MCSEM) transmitter can transmit high currents near the seabed to detect the electrical structure of the seafloor. The use of three-phase alternating current (AC) transmission can lead to three-phase imbalance, which results in an excessive current in one phase's power line and affects the safety of the tow cable. This paper proposes an MCSEM underwater constant high-voltage direct-current (DC) switching scheme that replaces AC transmission with DC transmission. This scheme can fundamentally avoid three-phase imbalance and the AC loss caused by inductance. After establishing a simulation model to analyze the effect of the scheme, the relevant hardware units were designed. The hardware unit mainly consists of three parts: a DC switching inverter unit, a filter unit, and a step-down rectification unit. The DC inverter unit controls six insulated gate bipolar transistor (IGBT) modules with sinusoidal pulse width modulation (SPWM) signals to convert DC to three-phase AC power; the filter unit filters out extra harmonic components; and the step-down rectification unit converts high-voltage three-phase AC to low-voltage DC. The scheme ultimately achieved an adjustable DC output of 48.3-73.4 V under a constant DC input voltage of 3000 V and effectively reduced the current on the cable. This scheme has the potential to replace the previous AC transmission, reducing the risk of tow cable burnout and enhancing the safety of MCSEM operations.