Operational Factors Research Articles

Mesoscopic Modelling of Li Deposition in Sulfide-Based Solid Electrolyte Using Two-Dimensional Artificial Polycrystal Structure

Abstract Understanding Li nucleation and growth mechanism during charging in solid electrolytes (SEs) is essential in development of all-solid-state batteries with metallic Li, because the Li dendrite could cause internal short-circuit by penetration of SE. However, it is still under debate how the factors including degradation of SE layer, the stacking pressure, and the microstructure affect the Li nucleation and growth in SE. In this study, the coupled current-deposition-stress models using the two-dimensional artificial SE structures have been developed by combination of finite element method and Monte Carlo simulations. The model assumed that Li flux on the SE grain induces an eigen strain in the deposited Li region, and Li grows into the SE layer by breaking grain boundary (GB). Degradation of SE was modelled as the decrease of fracture strength of GB using a coefficient. The effects of these microstructure and operation factors on GB fracture and Li deposition have been evaluated and discussed.

Small-scale farmers' market constraints in Ghana: influential farm-level contingencies

This study sought to identify significant barriers to market access and marketing connections for small-scale rural farmers, evaluating the impact of demographic factors (gender, education level) and operational factors (years in farming, distance from home, type of commodity) on these constraints. The research employed a cross-sectional descriptive and inferential quantitative approach to examine the barriers to market access and marketing connections among small-scale rural farmers in Ghana's Volta area. The conceptual framework is based on a multi-theory approach. The contingency theory elucidates the significance of farm size, resource availability, and geographic location; the human capital theory delineates the impact of education level and years of farming experience; and the logistics and spatial analysis framework examines how transportation infrastructure, geographic location, and commodity type affect market access and connections. The investigation indicated that inadequate road infrastructure and elevated transportation expenses were the primary obstacles for small-scale rural farmers in accessing markets, with notable disparities based on gender and educational attainment. The commodity kind, years of cultivation, and distance from home substantially influenced limitations. The study concentrated on the Volta region, however the results are probably relevant to other regions in Ghana because of comparable agro-ecological zones and rural socio-economic traits. The rigorous research design, data collection techniques, and comprehensive analysis guarantee dependable and valid outcomes. Notwithstanding possible constraints in generalisability, the research provides comprehensive insights into the distinct issues encountered by small-scale farmers. It emphasises socio-economic factors affecting market access, illustrates the impact of farm-level contingencies on market access and linkages, identifies both diversity and uniformity among small-scale farmers, and addresses a gap in the literature by offering empirical evidence on market access constraints specific to the Volta region that may have global relevance. These contributions are significant for both academic comprehension and practical initiatives aimed at assisting small-scale rural farmers in Ghana and analogous environments worldwide.

Static and Dynamic Model Calibration for Upper Thermosphere Determination

AbstractThermospheric density, a crucial factor in spacecraft operations, poses a significant challenge in accurate determination due to the intricate coupling of the thermosphere‐ionosphere system. Despite capturing long‐term trends, the widely used empirical models, which are used for low Earth orbit spacecraft operations, often fail to reproduce small‐scale or short‐term variations in the thermosphere. This work presents a novel approach for model improvement. The background model employed is our recently developed empirical model, which blends numerical simulations and satellite measurements. The model residuals are compiled into longitude and latitude bins, and the corresponding basic modes of describing variabilities are subsequently derived via the PCA method. The attendant amplitudes exhibit significant local time and seasonal dependencies, motivating optimized parameterization, that is, static calibration. Moreover, the present study reveals that the most dominant mode correlates to land‐sea contrasts and manifests global synchronicity upon excluding local time and seasonal dependences. This establishes the real‐time model adjustment foundation by exploiting limited calibration observations, which is referred to as dynamic calibration. The results from the dynamic calibration model indicated considerably improved thermospheric representation, especially for small‐scale or short‐term variations, toward a better thermospheric prediction.

Evaluating drivers of PM2.5 air pollution at urban scales using interpretable machine learning

Reducing urban fine particulate matter (PM2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM2.5 will enable the development of targeted strategies to reduce PM2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM2.5 concentrations, achieving a coefficient of determination (R2) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000–2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM2.5 concentrations effectively in each city.

A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines

Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs.

The Influence of Steering Axle Design Parameters and Operational Factors on Tire Steering Resistance Torque

This paper presents the results of the study of the kinematics and dynamics of an elastic steered wheel depending on its state, the kingpin unit design parameters, and the tire characteristics in order to obtain dependencies for calculating the tire steering resistance torque in static state and during movement. It was established that when steering in static state, the locked steered wheel is taken as a complex mechanism, and the tire contact patch is rotated about the kingpin axis–support surface intersection point. When turning the unlocked steered wheel, it was determined that the tire contact patch participates in transport and relative motions. The transport motion center is the projection onto the support surface of the center of rotation of the wheel about the kingpin axis. The relative motion center is within the contact patch and is determined experimentally. For both states of the steered wheel, analytical dependencies were obtained for determining the tire steering resistance torque. The results of the analytical studies were experimentally verified on a bench equipped by components and aggregates of a truck, and included an additional rigid false wheel and a special rim. The false wheel made it possible to experimentally determine the tire contact patch transport motion center, and the special rim—to change the knuckle length in the range of 0.22–0.82 m.

PROPERTIES OF BITUMEN MODIFIED WITH ADHESIVE ADDITIVES BASED ON RENEWABLE RAW MATERIALS

The main quality characteristics of road petroleum bitumen are: adhesive properties that determine the adhesion of binders to mineral materials; rheological properties that determine technological and operational parameters; physical and chemical properties that determine resistance to weather, climate and operational factors during the service life (aging resistance). Bitumen is the main binder of asphalt pavements, so it is the properties of bitumen that determine the performance of asphalt concrete. The adhesion of bitumen to mineral material, such as crushed stone or glass, is one of the main indicators in the design and construction of roads. This indicator determines the strength and service life of the road surface. Whereas a lack of cohesion can lead to significant damage to asphalt concrete. Since most distillation and oxidised bitumen have low adhesive properties, it is necessary to introduce adhesive additives to them. The most common method of improving the adhesion of bitumen binders to stone materials contained in asphalt mixtures is the use of adhesive additives (surfactants): cationic, anionic and non-ionic. The problem of producing high-quality road bitumen is quite acute. There is an increasing number of studies aimed at the production of asphalt pavements from alternative renewable bioresources. That is why, in this paper, it is proposed to modify binding surfactants of plant origin. These surfactants are obtained by amidation of rapeseed oil. Such additives are profitable in terms of production, affordable, environmentally friendly and made from renewable raw materials, which are grown in sufficient quantities in Ukraine. The resulting additives were used to modify the bitumen and determine its adhesion to the surfaces of crushed stone and glass.

Understanding decision making for the use of atraumatic restorative approach based on non-clinical factors by Indian pedodontists - a conjoint analysis.

Atraumatic Restorative Treatment (ART) approach is considered as one of the minimally invasive interventions. The success of restoration depends on various clinical factors like material, patient and operator factor. Conjoint analysis is a technique for measuring individual's preference structures via systematical variations of products attributes in an experimental design. Assess Pedodontists' perception of non-clinical factors and investigate influence of child's age, level of cooperation and vulnerability status on their decisions to perform Atraumatic Restorative Approach (ART) approach using hypothetical patient scenario's and conjoint design. A cross-sectional exploratory survey using a web-based questionnaire was performed among Pedodontists registered as life members under Indian Association of Pedodontic and Preventive Dentistry. Self-designed structured scenario-based questionnaire was prepared and validated. Three non-clinical factors Age of child, Cooperation level and Vulnerability status were considered. Using orthogonal design, SPSS software nine profiles were created randomly along with two holdouts. Final study proforma consisted of three sections with description of eleven clinical scenarios. It was administered to participants using Google forms. Using SPSS conjoint software relative utilities for each factor on decision for using ART was estimated. Four hundred and thirty-two pedodontists had completed the survey (35.9%). Pedodontists considered vulnerability status of the child as the most important factor for their decision to use ART treatment. For the vulnerability status, the factor vulnerable had the greatest utility (-0.364) compared to non-vulnerable factor (0.364). For cooperation level, factor uncooperative had greater utility (-0.343), as compared to moderately cooperative (0.066) and uncooperative (0.277). For the age factor, the age of 4 years had the greatest utility (-0.175) compared with age 6 (-0.013) and age 12 (0.19). The most preferred scenario by pedodontists to consider ART as treatment of choice was child belonging to vulnerable section, being very young and uncooperative.

Treatment of molasses-based alcohol distillery wastewater using Electro-Fenton oxidation: Optimization and model prediction by response surface methodology

In this study, an Electro-Fenton process was employed to treat high-strength alcohol distillery wastewater. The simultaneously removal of chemical oxygen demand (COD), color, and turbidity were examined. The optimum value of the operational parameters including the number of electrodes and their arrangement, electrodes’ interval, initial pH, and electrolyte concentration were determined by one-factor-at-a-time (OFAT). The effect of essential parameters including current density, hydrogen peroxide concentration, and the duration of the process were optimized using response surface methodology (RSM). At fixed defined operational conditions by OFAT as four electrodes in a parallel arrangement with a 1 cm interval, 0.3 mol/L of electrolyte salt, and pH of 4, respectively, the optimal condition by RSM suggested the current density of 24 mA/cm2, 40 g/L of H2O2, and reaction time of 2 h. By adopting optimal condition at defined values of operational factors the removal efficiencies for COD, color, and turbidity were approximately 83%, 99%, and 97%, respectively, using fresh water for dilution. The energy consumption of 1.9 kWh/kg COD removal was achieved. Also, to save water, the treated wastewater (instead of fresh water) was used for the dilution of the raw wastewater, indicating the satisfactory results with removal efficiencies for COD, color, and turbidity of 71%, 94%, and 95%, respectively. Overall, the Electro-Fenton process is introduced as an appropriate process for treating high-strength alcohol distillery wastewater.

Pedestrian crossing behavior in social groups: Exploring cultural contexts through a comparative study

Walking is a common mode of transportation that offers numerous benefits. However, pedestrians are vulnerable road users and account for a significant number of traffic accidents worldwide. Characterizing pedestrian behavior is crucial for enhancing safety and comfort. Several factors influence pedestrian behavior, including personal, situational, social, and cultural aspects. It is essential to consider these factors collectively for accurate predictions. Previous studies have highlighted the importance of cultural and location-specific influences on pedestrian behavior, yet many have focused primarily on risk perception, often neglecting the role of social interactions during street crossings. This study analyzes pedestrian behavior at a crossing in two culturally diverse cities: Bucaramanga in Colombia and Guimarães in Portugal. The research emphasizes the social and operational factors that affect how pedestrians cross streets. Specifically, it explores how cultural and social differences in each city influence group walking speed and mobility. The study examines several factors, including walking speed, the distance between pedestrians (whether in groups or alone), and the impact of group size and gender composition. The results demonstrate that informal rules and cultural context significantly impact pedestrian behavior during various crossing phases. Notable variations were observed in pedestrian speed before crossing and the spatial organization of groups during the crossing. To enhance road safety, interventions should consider these cultural factors and include educational programs that promote safe practices. This study contributes to a better understanding of how social factors influence pedestrian behavior and provides valuable insights for modeling pedestrian behavior, optimizing pedestrian facilities, and developing effective strategies for improving road user safety.

Analysis of Pressure Drop in Gas–Solids Cyclone Separators: An Experimental Approach

In industrial operations, cyclone separators are essential for removing particles from gas streams. The performance and energy efficiency of these devices are greatly impacted by the pressure drop across them. Through experimental investigation, this paper examines the pressure decrease in gas-solids cyclone separators. The effect of many operational factors on the pressure drop was investigated, including gas velocity, particle size, and input design.The findings demonstrated that the pressure drop across the cyclone increases significantly with increasing inflow velocity and reduces with increasing cyclone size. Regardless of cyclone size, input velocity, or particle size, a strong direct correlation was found between the pressure drop for dusty air and that for clean air. Operating at moderate gas velocities was shown to significantly reduce pressure drop without compromising separation performance, whereas higher velocities necessitate advanced cyclone designs or secondary systems to mitigate the associated pressure increases

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety.

A LESSON IN MILITARY DOCTRINAL AND OPERATIONAL FAILURES: THE BATTLE OF HOSTOMEL AND THE RUSSIAN MILITARY’S FAILURE TO CAPTURE KYIV.

Abstract: This paper will examine the underlying doctrinal and operational factors that led to the failure of the Russian military to capture Kyiv, and its subsequent military implications that led to its (re)assessment of military strategy. Firstly, this paper will examine the historical lead-up to the 2022 Russo-Ukrainian War, detailing the relative political and strategic factors that impacted upon President Putin’s decision to launch the ‘Special Military Operation’. Secondly, this paper will go into detail into the relative military and tactical landscape of Russia’s attempt to capture Kyiv, in particular, its failure to seize Hostomel airport, thereby leading to strategic failure. Thirdly, this paper will contend that the failure to capture Hostomel was based in part on operational factors but also upon the inability of Russian military planners to fully predict the Ukrainian resolve, as well as the limitations found in its doctrinal approach to warfare.

Illegitimate Websites Detection Using Deep Learning Framework

Phishing is a crime involving robbery of confidential user data. The phishing websites are aimed at individuals, businesses, and cloud storage and government websites. Hardware- based anti-phishing methods are generally used, but software- based approaches are favored because of costs and operational factors. There is no solution to the problem such as zero-day phishing attacks from current phishing detection approaches. A three-phase attack detection called the Phishing Attack Detector based on Web Crawler was proposed to resolve these problems and precisely detect phishing incidences using recurrent neural network. It includes the input features Web traffic, web content and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

Productivity and costs of a tracked excavator-based processor at the cable yarder landing in the North-western Italian Alps

ABSTRACT Excavator-based processors are a valuable machine to process trees at cable yarder landings. This paper contributes to the studies on productivity and time consumption of excavator-based processors at a landing in the North-western Italian Alps. The study analyzed the time consumption and productivity of an excavator-based processor in a spruce forest, focusing on operational factors influencing processing time and employing statistical modeling. Results showed that processing (21%) and stacking (19%) accounted for a significant portion of the total cycle time (254.2 s), with delays (48%) dominating due to waiting times for incoming loads. Regression analysis revealed that tree diameter, height, and number of logs significantly influenced cycle time. Productivity (25.7 m3 PMH−1) increased with tree volume, although larger trees required longer cycle times. Obtaining two logs per tree, when the tree volume increased by 0.1 m3, productivity increased by about 2.5 m3 PMH−1, while when obtaining five logs per tree, the increment in productivity for the same increase in tree volume was about 1.6 m3 PMH−1. Overall, the study demonstrates the feasibility and efficiency of using excavator-based processors in forest operations in the Northwestern Italian Alps. The findings provide valuable insights into operational dynamics and economic considerations, highlighting the potential of this equipment as a cost-effective alternative.

Model benchmarking for PEM Water Electrolyzer for energy management purposes

This research conducts a comprehensive evaluation of various Proton Exchange Membrane Water Electrolyzer (PEMWE) models through a test bench, optimizing parameters and comparing obtained models against real-world data. Key operational factors such as reversible potential, activation overpotential, ohmic overpotential, and concentration overpotential are examined through experimental data. This study addresses critical gaps in current PEMWE research by reviewing modelling approaches, introducing a novel classification of models, and proposing an integrated approach that combines experimental validation with comprehensive model analysis. A novel, systematic methodology for model and submodel selection is presented, enabling practitioners to identify models that balance computational efficiency and predictive accuracy tailored to specific energy management and power allocation needs. This approach bridges the gap between complex modelling and industrial applications, enhancing the practical implementation of PEMWE systems in sustainable hydrogen production. Enhances model reliability for operational and manufacturing differences, provides invaluable guidance for improving the design and operation of these systems, and promotes a more robust and efficient hydrogen energy infrastructure.

Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance in terms of MAPE and MLSE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. SHAP effectively assesses model impacts, accounting for variable interdependencies, and offers visualization in high-dimensional contexts. Given the correlation and dimensionality of variables predicting calcine temperature, SHAP was preferred over Feature Importance or PDP for the analysis. By incorporating seven out of twenty operational factors like burner fuel and reductant feed rate, combustion conditions inside of the rotary kiln and RPM, the calcine temperature increased from 840 °C in 2023 to 910 °C by October 2024, concurrently reducing the electricity unit consumption of the electric furnace by 7.8%. Enhancements to the CatBoost algorithm will enable the provision of guidance values after optimizing key variables. It is expected that managing the rotary kiln’s calcine temperature according to the predictive model’s guidance values will allow for autonomous operation of the rotary kiln through inputting guidance values to the PLC.

Nanocomposite of cellulose nanofibers/alumina for effective adsorption of brilliant blue and ethyl orange dyes: Equilibrium, kinetic and mechanism studies

Our study investigated the performance of a nanocomposite prepared from TEMPO- oxidized cellulose nanofibers (CNF) and alumina (Al2O3) on two models of anionic dyes namely Brilliant blue FCF (B.B) and Ethyl orange (E.O) dyes. Here the Al2O3 was prepared using the solution combustion (SC) method. Cellulose nanofibers were prepared from local agriculture waste (bagasse) and mixed with Al2O3 nanoparticles for preparing composite films through the casting technique. Various analytical tools have been used to characterize cellulose nanofibers and cellulose nanofibers/ Al2O3 nanocomposite, including, FTIR, FE- SEM, TEM, and XRD. In the conducted batch experiments aimed at optimizing various operational factors, the study of adsorption isotherms revealed that the data closely align with the freundlich adsorption isotherm model, demonstrating multilayer adsorption. Furthermore, it was found that, the pseudo-second-order model fit the data effectively. Thus, CNF/ Al2O3 nanocomposite was found to be an active adsorbent for handling solutions containing the anionic dyes, which shows its hopeful tolerable of being a highly reusable, cost-effective sustainable and adsorbent for dyeing wastewater treatment.

Electrocoagulation as a Remedial Approach for Phosphorus Removal from Onsite Wastewater: A Review

Onsite wastewater treatment systems (OWTSs), although essential for managing domestic sewage in areas without centralized sewerage treatment plants, often release phosphorus (P) into the environment due to inadequate treatment. This unregulated P discharge exacerbates water quality degradation and jeopardizes aquatic habitats and human health. Among different treatment technologies, electrocoagulation (EC) demonstrates considerable potential for addressing this challenge by efficiently removing P from OWTSs and thus protecting water resources and ecological integrity. Through electrochemical reactions, EC destabilizes and aggregates P-bearing particles, facilitating their removal through precipitation. Compared to conventional treatment approaches, i.e., chemical and biological methods, EC offers several advantages, including high efficiency, minimal chemical usage, and adaptability to varying wastewater compositions. This review underscores the urgent need for mitigating P discharge from OWTSs and the efficacy of EC as a sustainable solution for P removal, offering insights into its mechanisms, reactor design considerations, important operational factors, performance, and potential applications in OWTSs as well as providing future research directions.