Operational Factors Research Articles

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.

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.

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.

Heterogeneity Analysis of Economic Policy Uncertainty on Corporate Investment Based on Panel Threshold Regression

As the global landscape evolves rapidly, economic policies must constantly adapt to the volatile economic situation, leading to economic policy uncertainty (EPU). Firms, as microeconomic entities, need to continuously adjust their strategies in response to these changes. Given that corporate investment is one of the most crucial factors in business operations, it is of paramount importance to investigate how the characteristics of corporate investment are influenced by EPU. Using the panel threshold regression method, we delve into the nonlinear impact of EPU on corporate investment. By collecting data from 2000 to 2023 for A-share listed companies in Shanghai and Shenzhen, as well as macroeconomic data, we empirically find that EPU exerts significant and differentiated effects on corporate investment across four dimensions: leverage level, company size, profitability, and growth potential.

Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R2, and AAPRE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings.

Correction: Chow et al. Anaerobic Co-Digestion of Wastewater Sludge: A Review of Potential Co-Substrates and Operating Factors for Improved Methane Yield. Processes 2020, 8, 39

The journal is publishing this correction to update the name of the Academic Editor listed on this published paper [...]

Operative Factors Associated with Severe Hypotension in the Postoperative Setting in Patients with Carotid Artery Endarterectomy

Background: Carotid endarterectomy is a recognized method for preventing stroke for both symptomatic and asymptomatic hemodynamically carotid artery stenosis. Hemodynamic depression is more frequently associated with carotid artery stenting, while postoperative hypertension and cerebral hyperperfusion syndrome are known frequent complications after carotid endarterectomy. Severe hypotension after carotid revascularization is associated with a higher risk of perioperative stroke, myocardial infarction, and death, with limited data existing regarding risk factors. This study aims to investigate intraoperative risk factors for severe hypotension after carotid endarterectomy. Methods: A total of 160 patients who underwent carotid endarterectomy were included in this study. Patients with other cardiac conditions that required concomitant cardiac surgery, patients with incomplete medical records, and patients considered high risk for surgery were excluded. Results: The incidence of severe hypotension was 30.6%. Patients with severe hypotension after carotid endarterectomy had a higher incidence of diabetes, moderate mitral valve regurgitation, a history of percutaneous coronary intervention, and higher operative times. Using logistic regression, temporary shunt insertion was independently associated with severe postoperative hypotension (OR = 2.26, 95% CI = 1.09–4.71, p = 0.029). Conclusions: This result favors the selective shunting strategy when performing carotid endarterectomies, especially for those patients with comorbidities predisposing to postoperative complications.

Efficient multi-objective optimization approach for solving optimal DG placement and sizing problem in distribution systems

Incorporating distributed generations (DGs) like wind turbines (WTs) and solar photovoltaic (SPV) arrays into radial distribution systems (RDSs) has become increasingly important due to their benefits in enhancing system performance. This requires determining appropriate DG locations and their optimal power output when injected into the RDS. To this end, this paper introduces a novel application of a recent multi-objective optimization technique called multi-objective grey wolf optimization (MOGWO) to allocate multiple DG units into the RDS optimally. The primary objectives are to minimize total real power losses (RPL), reduce voltage deviation (VD), and enhance the voltage stability index (VSI) across the entire system while adhering to operational constraints. Various DG operational power factor (PF) scenarios are considered, including unity-PF, fixed-PF, and optimal-PF. The proposed approach generates a Pareto-optimal set of solutions to address the multi-objective problem, after which a fuzzy decision-making method is adopted to select the best trade-off solution from the set. The technique has been implemented on 69-bus and the actual Portuguese 94-bus RDS. Numerical results are compared with other recent well-known algorithms from the literature, highlighting the efficacy of the suggested MOGWO method in handling multi-objective problems. Comparatively speaking, it provides superior solutions than other methods in selecting appropriate control parameters.

Obesity is highly associated with a non-home discharge following total ankle arthroplasty

Studies have shown that non-home discharge following orthopedic procedures is associated with a higher risk of 30-day complications and significantly increases medical costs. The purpose of this study was to identify risk factors for being discharged to a non-home destination following total ankle arthroplasty (TAA).We included patients undergoing TAA from the American College of Surgeons National Surgical Quality Improvement Program database (NSQIP) between 2014 and 2019. TAA was identified using CPT codes 27702, 27703 and 27704. Logistic regression models were used to assess the association between discharge location (home versus non-home) and a series of exposure variables (e.g., patient demographics, patient health characteristics, and operative factors).A total of 1,704 patients were included, experiencing a 3.6% short term complication rate (61/1,704). 8.5% of the population were discharged to a non-home destination. In the final adjusted model, patients who were older [OR 1.11; 95%CI 1.08, 1.13], female [OR 2.94; 95%CI 2.04, 4.34], obese [OR 1.93; 95%CI 1.29, 2.89], had surgery in an inpatient setting [OR 5.73; 95%CI 1.78, 18.46], and ASA class IV [OR 10.65; 95%CI 1.03, 110.61] were at greater risk for a non-home discharge.People living with obesity experienced a nearly 2x greater likelihood of being discharged to a non-home destination after TAA despite their preoperative functional (e.g., ASA class) and metabolic status (i.e., diabetes). Opportunities to mitigate this risk will be needed to lessen the financial burden of TAA surgery as a growing number of obese patients become eligible for TAA in the US.

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.

Correlation between severity of preoperative low back pain and postoperative outcomes in lumbar disc herniation surgery: A retrospective cohort study.

BACKGROUND CONTEXTLow back pain (LBP) frequently occurs in patients with lumbar disc herniation (LDH), however the extent to which discectomy ameliorates it and how preoperative LBP influences postoperative outcomes remains unclear. PURPOSETo evaluate the improvement in LBP and its impact on postoperative patient-reported outcome measures (PROMs). STUDY DESIGN/SETTINGMulticenter retrospective observational study. PATIENT SAMPLEPatients undergoing discectomy for LDH at eight hospitals from April 2017 to March 2021. OUTCOME MEASURESData were collected on patients’ backgrounds, operative factors, and PROMs, including the Numeric Rating Scale (NRS) for pain in the low back, buttock, or leg, EuroQol 5 Dimension (EQ-5D), Oswestry Disability Index (ODI) scores, and postoperative satisfaction. METHODSPatients were categorized into mild (NRS 0–3), moderate (NRS 4–7), or severe (NRS 8–10) LBP groups. Inverse probability weighting with propensity scores was used to adjust for demographic and clinical differences between groups. Chi-square tests and one-way analysis of variance were conducted to compare background data and clinical characteristics. Additionally, multivariate logistic regression was performed to identify risk factors for less than 50% improvement in LBP. RESULTSThis study included 928 patients: 270 mild, 343 moderate, and 315 severe. After adjustment, preoperative NRS scores were significantly different across groups, with scores of 1.6 (SD 1.3), 5.6 (SD 1.0), and 8.8 (SD 0.9), respectively. Postoperative NRS scores also varied significantly, with worse outcomes observed in the severe group compared to the mild group, yet similar to the moderate group. A total of 46.8% of the mild group, 86.6% of the moderate group, and 72.9% of the severe group achieved a 50% decrease in NRS scores of LBP. Preoperative EQ-5D and ODI scores were significantly worse in the severe group compared to the mild or moderate groups, but postoperative scores were not significantly different between the severe and moderate groups. No significant differences in postoperative satisfaction were observed among the groups. Being female was a significant risk factor for less than 50% improvement in LBP (odds ratio = 1.56, p = 0.022). CONCLUSIONSDiscectomy significantly improved LBP in patients with LDH, including those with moderate or severe LBP. Patients with severe LBP showed similar improvements in PROMs as those with moderate LBP. Female gender emerged as a significant risk factor for less than optimal improvement in LBP.

State Surveillance and Fault Diagnosis of Distillation Columns Using Residual Network-Based Passive Acoustic Monitoring

The operational state of distillation columns significantly impacts product quality and production efficiency. However, due to the complex operation and diverse influencing factors, ensuring the safety and efficient operation of the distillation columns becomes paramount. This research combines passive acoustic monitoring with artificial intelligence techniques, proposed a technology based on residual network (ResNet), which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states. The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform. Ultimately, this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns. Through this approach, the operational states of distillation columns were monitored. Various faults, including flooding, entrainment, dry-tray, etc., were diagnosed with an accuracy of 98.91%. Moreover, an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method. Under the transitional state, the acoustic signals achieved an accuracy of 97.85% on the ResNet, which enables early warnings before faults occur, enhancing the safety of chemical production processes. The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment, particularly distillation columns, ensuring the safety and efficiency.

Optimizing Attended Home Delivery: Multiple recovery options and customer availability profiles to face synchronization failures

In the growing sector of Attended Home Delivery, unsynchronized deliveries between couriers and recipients affect both customers’ satisfaction and companies’ costs. Hence, reducing such failures improves companies’ service quality and logistics efficiency. To address this issue, we study an Attended Home Delivery Problem with Recovery Options (AHDPRO) in which customers specify their probability of being at home during different timeslots of the day and their preferred recovery option in case of a synchronization failure. The options include leaving the package in a predefined safe location, bringing it to a generic collection point, or scheduling a second delivery attempt. Each alternative involves different costs and, in most cases, additional operational decisions. The AHDPRO aims to complete all customer deliveries while minimizing overall routing times as well as the overall penalty due to the recovery actions implemented and weighted by the probability of a synchronization failure to occur. We propose a branch-and-cut algorithm, including valid inequalities and heuristic procedures, to solve a Mixed-Integer Linear Programming model based on an expanded graph. Using the developed method as a tool for evaluating costs and operations, we conduct an experimental campaign on scenarios adapted from the literature involving lexicographic-based optimization procedures able to address the multiple attributes of the solutions. The results obtained allow us to assess the impact of the different recovery options on the optimal solutions and their values. Additionally, the results yield several managerial insights for companies operating in the Attended Home Delivery sector, such as the timeslot length, perceived service quality, and other key operational factors contributing to efficient planning and improved customer satisfaction.

ПЛАНИРОВАНИЕ ЭКСПЕРИМЕНТА ДЛЯ ОПРЕДЕЛЕНИЯ ЗАВИСИМОСТИ ИЗМЕНЕНИЯ ЩЕЛОЧНОГО ЧИСЛА МОТОРНОГО МАСЛА ОТ ЭКСПЛУАТАЦИОННЫХ ФАКТОРОВ В ПРЕДЕЛАХ ОДНОГО СРОКА ЗАМЕНЫ

Timely replacement of oil in engines of modern agricultural machinery is economically feasible, since it allows to fully exhaust the resource of engine oil and ensure the safety of the internal combustion engine. One of the ways to determine the moment of engine oil replacement with sufficient accuracy is to use a digital twin, for the creation of which data on the influence of operational factors on oil degradation obtained experimentally are required. Features of conducting experimental studies during engine bench tests are analyzed in the paper. An algorithm for planning an experiment based on probabilistic methods has been developed. It is shown that a bench experiment to collect data for building a digital twin can be carried out within one period of engine oil replacement, it should be planned in several stages and consist of several series of experiments, since the moment of reaching the critical value of the base number is not known in advance, and when planning the main experiment covering 250 engine hours in two series of 10 experiments, the duration of each experiment is determined from the condition of equality of the error in determining the change in the base number. The duration of the first series of experiments was 62.7 engine hours, and the second — 187.3 engine hours. The developed algorithm allowed to build a plan of a bench experiment consisting of four series of experiments with sequential uniform filling of the factor space without repeating the engine operating modes. The resulting experimental plan allows to determine the data with the planned accuracy, minimizes the number of experiments and uniformly fills the factor space at any duration of the experiment.

Building a theoretical framework for financial risk management in emerging markets: Applying global best practices to the oil and gas industry in developing economies

Financial risk management is crucial for the sustainability and growth of the oil and gas industry in developing economies, where volatility and regulatory uncertainties present significant challenges. This abstract explores the construction of a theoretical framework for financial risk management, adapting global best practices to the specific contexts of emerging markets within the oil and gas sector. In developing economies, the oil and gas industry faces unique risks such as geopolitical instability, currency fluctuations, and regulatory changes. Effective financial risk management mitigates these risks through proactive strategies that balance risk exposure with strategic objectives. Adopting global best practices involves the integration of comprehensive risk assessment methodologies, scenario planning, and hedging techniques tailored to local market conditions. Key components of the theoretical framework include risk identification through comprehensive analysis of market, operational, and financial factors. This approach enables oil and gas companies to anticipate and mitigate risks before they escalate, ensuring financial stability and resilience in volatile environments. Furthermore, the application of global best practices in financial risk management fosters transparency and accountability, enhancing investor confidence and facilitating access to capital. By aligning risk management strategies with international standards and benchmarks, developing economy oil and gas firms can navigate uncertainty more effectively, thereby optimizing operational performance and sustaining long-term growth. However, challenges such as limited access to sophisticated financial instruments and expertise may hinder effective implementation. Overcoming these barriers requires capacity building, collaboration with experienced partners, and continuous adaptation of strategies to evolving market dynamics. In conclusion, building a theoretical framework for financial risk management in the oil and gas industry in developing economies is essential for mitigating volatility and maximizing opportunities. By applying global best practices tailored to local contexts, companies can strengthen their resilience, attract investment, and drive sustainable development in emerging markets.