Operational Safety Research Articles

Analisis Fungsi Inspeksi Unit Apron Movement Control (AMC) dalam Menunjang Kedisiplinan Petugas Apron di Bandar Udara Supadio Pontianak

Apron Movement Control (AMC) Unit is a vital component in airport operations, playing an important role in ensuring the safety and efficiency of flight services. This study aims to determine what obstacles are encountered during inspection activities and how does the Apron Movement Control (AMC) unit overcome these obstacles. This study uses a qualitative approach with field observations and then conducting analysis. The subjects of this study were Apron Movement Control (AMC) Unit officers at Supadio Airport, Pontianak. The research period was 1 month. The results of the study can be concluded that the main obstacles of the AMC unit at Supadio Airport, Pontianak include communication difficulties due to noise, limited visual supervision, and coordination between units. The proposed solutions include improving communication systems, expanding surveillance camera coverage, optimizing personnel, regular training, using new technologies such as drones, and evaluating and improving operational procedures. The implementation of these solutions is expected to improve the effectiveness of the AMC inspection function, support the discipline of apron officers, and improve the safety and smoothness of airport operations..

Dynamic modeling and robust control of a multi-cable payload transfer system for offshore cranes subjected to random waves and wind loads

The payload often experiences unpredictable motion due to the combined effects of random waves and wind loads, significantly impacting the safety and efficiency of offshore operations. To expand the effective working window for offshore transfer operations, this study introduces a multi-cable payload transfer system (MPTS). Using a 45-ton offshore crane mounted on a multipurpose vessel from COSCO Shipping Group as a prototype, this study establishes a dynamic model of MPTS, considering the effects of vessel motion, crane actions, and wind loads. The payload remains in an unstable state due to external disturbances. Additionally, variations in payload specifications and mass lead to considerable uncertainties in the dynamic parameters of MPTS. To address these uncertainties, a second-order linear observer is developed. Subsequently, an improved computed torque controller is proposed, and the boundedness of the tracking errors is proven using the Lyapunov theory. The transfer process is divided into three distinct phases: lifting, slewing, and lowering. The feasibility of the transfer scheme is verified through high-fidelity simulations and prototype experiments. This research provides a solid theoretical foundation for the subsequent engineering application of MPTS.

Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Abstract The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g., low illumination and multiple obstructions), the model can be used for intelligent operation and maintenance in metro tunnel projects.

Mechanism of Wheel-Rail Adhesion Recovery during Large Sliding processes under Water Lubrication condition

The challenge of low adhesion between the wheel and rail constrains the advancement of train braking technologies, resulting in sliding during braking, increased braking distances, and even operational safety incidents. Low adhesion arises from the contamination of the rail surface by a third body, such as water or oil, significantly reducing adhesion. However, experimental studies have found that wheel-rail adhesion coefficient under water lubrication conditions possesses a recovery capability: In scenarios characterized by low adhesion in water lubrication conditions, a secondary rise in adhesion occurs when substantial wheel-rail sliding is present. This paper focuses on numerically modeling the unique phenomenon of adhesion recovery during large-sliding processes under water lubrication conditions. Considering the notable differences in frictional heating during wheel sliding, as opposed to pure rolling conditions, we construct a wheel-rail contact model under mixed lubrication conditions that incorporates the solid-liquid coupled heat transfer effect. This model achieves stable solutions for the wheel-rail contact relationship and the temperature at the wheel-rail interface. The impacts of velocity, axle load, water temperature, and slide ratio on the adhesion recovery phenomenon are discussed, revealing the underlying mechanism of adhesion recovery during large sliding processes. Finally, brake adhesion tests under water lubrication conditions are conducted on a scaled test rig to validate the proposed theoretical model.

Effective passenger flow congestion propagation model for multi-mode rail transit networks

ABSTRACT During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

Forklift Design Integrated Speed Limiter and Position Tracking Through IoT-Based Website

Forklifts, as lifting and transportation equipment, play a crucial role in logistics. However, forklift operators often chase targets, pushing the forklift to perform at higher speeds. Operating a forklift at high speeds not only poses potential dangers to the working conditions but can also lead to a decrease in forklift performance. Operators do not ensure the forklift is running according to procedures due to a lack of supervision. In response to this issue, the author proposes an innovation in the form of a monitoring device capable of recording speed violations committed by operators. With the planned device, it is expected that forklift operators will exercise more caution and take greater responsibility when operating the forklift. The objective of this research is to reduce the likelihood of accidents and forklift damage due to improper use. The study produces a forklift design equipped with supporting components for the implementation of the device and a website as a monitoring platform for forklift operators and workers, providing real-time access to collected data. This system enhances operational safety in operating forklifts. The research significantly contributes to the development of safer and smarter forklift technology, aligning with strict demands for safety standards and risk management in industrial environments.

High-Precision Monitoring during the Installation of Large Steel Structures by UAV Nap-of-the-Object Photogrammetry

Abstract. This paper proposes and validates a method based on UAV nap-of-the-object photogrammetry for refined 3D digital modeling to achieve high-precision monitoring during the installation of large steel structures, offering a comparative analysis with traditional oblique photogrammetry methods. Through field tests and data processing on the steel structure of a sports stadium in Zhejiang Province, the results demonstrate that nap-of-the-object photogrammetry can capture high-resolution images, offering significant advantages in detail representation and modeling of complex structures. Experimental data reveal that the model's root mean square error (RMSE) generated by nap-of-the-object photogrammetry is 14.56mm, markedly lower than the 51.99mm achieved by traditional methods, meeting the Level II accuracy standard specified in the "Technical Specifications for Nap-of-the-Object Photogrammetry." The study further shows that, when combined with optimized flight path planning, nap-of-the-object photogrammetry enhances both the efficiency and accuracy of data collection while improving operational safety. This technique is highly applicable and flexible, making it suitable for data acquisition of complex structures and high-rise buildings. In summary, the proposed method exhibits significant advantages in terms of accuracy, efficiency, safety, and applicability, providing a practical solution for high-precision, comprehensive digital modeling of large steel structures.

Modelling pressure dynamics in a gas pipeline with an in-line sampling

Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure duringstart-up or shutdown and at specific extraction points. This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point. The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

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.