Potential Blockages Research Articles

Development of predictive model for determination of paraffin deposition in Kazakh crude oil

Kazakhstan's crude oil contains significant amounts of heavy hydrocarbons that solidify as wax at lower temperatures, leading to reduced flow rates and potential blockages in pipelines. Paraffin, a substantial component of crude oil, crystallizes below its pour point, posing challenges for maintaining efficient crude oil transportation. Addressing wax deposition is essential for optimizing oil flow and meeting global energy demand. This article presents an innovative method for forecasting paraffin deposition in Kazakh crude oil, using its chemical composition and thermobaric conditions. The methodology encompassed data analysis, ASTM-standard laboratory tests, and computations employing modified equations for fusion properties calculation. Outcomes comprise computed temperatures, enthalpy, and heat capacity related to melting, along with revised correlations for melting and pour points specific to Kazakh crude oils. The melting point correlation was modified to fit the properties of Kazakh crude oils, resulting in standard deviation of 0.55 %. Computed pour points for hydrocarbons improved by 17 % respectively. As a result of the research a novel software tool was developed and evaluated, highlighting the project’s contribution in providing an adjusted thermodynamic model for paraffin deposition forecasting. Comparisons between the developed software, PVTSim, and field data showed the wax appearance temperature (WAT) predictions closely aligned, with 0.74 % of error. This numerical tool shows potential in predicting wax deposition, thus aiding in the planning of oil production and refining processes in Kazakhstan. The importance of this work extends to its potential economic impact on companies and the nation, as well as its environmental benefits by facilitating eco-friendly planning of oil production facilities. Future research could expand on these findings to further enhance predictive models of paraffin precipitation across diverse conditions

Machine Learning-based fixed access Network Interface Congestion Prediction

Network interface congestion refers to a situation in which network interfaces or communication channels within a network experience high levels of traffic, leading to performance degradation or potential network failures. In modern networking, this issue remains relevant and can manifest in various contexts due to a surge in data traffic, Internet of Things (IoT) devices can generate substantial data, increased use of VPNs for remote work and when multiple users or applications concurrently access and transfer data in and out of the cloud. Efforts to forecast and address network interface bottlenecks are crucial to sustaining the performance and consistency of modern networks. In the PTCL fixed access network infrastructure, network blocking is a serious problem that necessities to be addressed. The main objective of this study is to overcome network congestion problems that occur in PTCL's fixed access network through the use of machine learning techniques. Despite having real-time monitoring tools such as SolarWinds, there remains a dearth of software capable of predicting imminent bottlenecks in the network interface. The primary aim is to build an analytical model which can predict potential blockages within 30 days and facilitate proactive management strategies for better administration. Top of Form The study classifies the most effective machine learning technique for this purpose and evaluates its performance against current methods. To achieve the goal, a diagnostic dataset was created, leveraging appropriate configurations, software tools, and hardware resources. Data was sourced from SolarWinds, consisting of information from over 23,000 interfaces across 11,000 access network nodes. This data was analyzed in PTCL's Data Warehouse, where a historical aggregated dataset was constructed. Exploratory data analysis was performed using Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. This investigation sets the foundation for proactive network management and enhanced user experiences within PTCL's fixed access network, addressing the critical essential for advanced predictive tools to combat congestion and enhance network competence.

Numerical Study of Wax Deposition from Multiphase Flow in Oil Pipelines with Heat and Mass Transfer

Wax deposition in field‐scale crude oil pipelines poses a significant challenge to the oil and gas industry, leading to reduced flow rates, increased pressure drops, and potential blockages. Understanding the mechanisms governing wax deposition is crucial for developing effective mitigation strategies. This study investigates the impact of multiphase flow conditions, including water‐in‐oil emulsion, wax precipitation kinetics, shear dispersion, and molecular diffusion, on wax deposition in field‐scale crude oil pipelines. A numerical model is developed that employs second‐order semi‐implicit temporal discretization schemes, such as Crank–Nicolson and Adams–Bashforth methods, in conjunction with a bivariate spectral collocation scheme using Chebyshev–Gauss–Lobatto grid points. The impact of various flow parameters, including Reynolds number (Re), mass Grashof number (Gr), Schmidt number (Sc), and Weber number (We), on the flow variables, wall shear stress, and heat and mass fluxes are investigated. The numerical simulations demonstrate that flow parameters significantly influence the flow behavior, wall shear stress, wall heat flux, and wall mass flux in waxy crude oil pipelines. Specifically, the aggregation of wax crystals in the pipeline decreases by at most 2.5% with increasing Reynolds number from 2.2361 to 3.1361. Conversely, it increases by at most 3.4% with increasing mass Grashof number from 5 to 11 and by at most 4.8% with increasing Weber number from 1.0 to 2.5. Furthermore, the Nusselt number increases from 1.9907 to 4.9834 with increasing Reynolds number from 2.2361 to 5.2361 and from 1.9907 to 2.0225 with increasing mass Grashof number from 5 to 20. It also increases from 1.9907 to 2.0434 with increasing Weber number from 1.0 to 2.5. The insights gained from this study can be applied to optimize pipeline design, operational parameters, and wax deposition mitigation strategies, leading to enhanced pipeline performance and reduced operational costs. The numerical model developed in this work serves as a valuable tool for simulating and predicting wax deposition behavior under various operating conditions.

Data Mining Algorithms for Operating Pressure Forecasting of Crude Oil Distribution Pipelines to Identify Potential Blockages

The implementation of data mining has become very popular in many fields recently, including in the petroleum industry. It is widely used to help in decision-making processes in order to minimize oil losses during operations. One of the major causes of loss is oil flow blockages during transport to the gathering facility, known as the congeal phenomenon. To overcome this situation, real-time surveillance is used to monitor the oil flow condition inside pipes. However, this system is not able to forecast the pipeline pressure on the next several days. The objective of this study is to forecast the pressure several days in advance using real-time pressure data, as well as external factor data recorded by nearby weather stations, such as ambient temperature and precipitation. Three machine learning algorithms—multi-layer perceptron (MLP), long short-term memory (LSTM), and nonlinear autoregressive exogenous model (NARX)—are evaluated and compared with each other using standard regression evaluation metrics, including a steady-state model. As a result, with proper hyperparameters, in the proposed method of NARX with MLP as a regressor, the NARX algorithm showed the best performance among the evaluated algorithms, indicated by the highest values of R2 and lowest values of RMSE. This algorithm is capable of forecasting the pressure with high correlation to actual field data. By forecasting the pressure several days ahead, system owners may take pre-emptive actions to prevent congealing.

Leveraging water-wastewater data interdependencies to understand infrastructure systems’ behaviors during COVID-19 pandemic

Social distancing policies (SDPs) implemented worldwide in response to COVID-19 pandemic have led to spatiotemporal variations in water demand and wastewater flow, creating potential operational and service-related quality issues in water-sector infrastructure. Understanding water-demand variations is especially challenging in contexts with limited availability of smart meter infrastructure, hindering utilities' ability to respond in real time to identified system vulnerabilities. Leveraging water and wastewater infrastructures' interdependencies, this study proposes the use of high-granular wastewater-flow data as a proxy to understand both water and wastewater systems’ behaviors during active SDPs. Enabled by a random-effects model of wastewater flow in an urban metropolitan city in Texas, we explore the impacts of various SDPs (e.g., stay home-work safe, reopening phases) using daily flow data gathered between March 19, 2019, and December 31, 2020. Results indicate an increase in residential flow that offset a decrease in nonresidential flow, demonstrating a spatial redistribution of wastewater flow during the stay home-work safe period. Our results show that the three reopening phases had statistically significant relationships to wastewater flow. While this yielded only marginal net effects on overall wastewater flow, it serves as an indicator of behavioral changes in water demand at sub-system spatial scales given demand-flow interdependencies. Our assessment should enable utilities without smart meters in their water system to proactively target their operational response during pandemics, such as (1) monitoring wastewater-flow velocity to alleviate potential blockages in sewer pipes in case of decreased flows, and (2) closely investigating any consequential water-quality problems due to decreased demands.

On the Physical Layer Security of Untrusted Millimeter Wave Relaying Networks: A Stochastic Geometry Approach

The physical layer security (PLS) of millimeter wave (mmWave) communication systems is investigated, where the secure source-to-destination communication is assisted by an untrusted relay selected from a group of them and there are also several passive eavesdroppers (Eves) in the network. In the considered system model, while the distributions of the untrusted relays and Eves follow a homogeneous Poisson Point Process (PPP). To maximize the instantaneous secrecy rate, a novel joint relay selection and power allocation (JRP) method is developed where the destination and source aim for jamming the reception of both the untrusted relays and passive Eves. New expressions of the optimal power allocation (OPA) are derived for both non-colluding Eves (NCE) and colluding Eves (CE). Subsequently, by considering the impact of potential blockages, new closed-form equations are derived for analyzing the system’s ergodic secrecy rate (ESR) and secrecy outage probability (SOP) for transmission over fading mmWave channels. Finally, numerical examples are provided for demonstrating the superiority of our proposed JRP method over the relevant benchmarks found in the literature. Interestingly, the ESR increases with the density of untrusted relays for both the NCE and CE scenarios, which is a benefit of the improved probability of selecting a relay with a stronger second-hop channel. Furthermore, in the low transmit power regime, employing relatively low mmWave frequencies achieves better ESR, while in the high transmit power regime, high mmWave frequencies provide higher ESR.

EvacuSafe: A real-time model for building evacuation based on Dijkstra's algorithm

Building evacuation strategies directly affect the success of an emergency response plan in reaction to a disastrous event in a building. Evacuation alternatives result from applying time delays or altering path planning in the evacuation strategy. The best strategy for evacuation depends on the characteristics of the building, circumstances of the particular emergency, and reactions of the evacuees during the emergency. A real-time evacuation management model that identifies the fire hazard, contemplates possibilities, foresees consequences, and eventually proposes the best strategy of evacuation during the emergency can reduce the risk of a failed evacuation. In this paper, a real-time model is proposed, whose main goal is to discover the safest strategy of path-planning at every instant of time during an evacuation. The model focuses on fire emergencies, as they are the dominant cause of fatalities in buildings compared to other types of natural and manmade disasters. The proposed model first defines a risk factor for each compartment based on the location of the fire and any potential blockages caused by structural collapse or door malfunction. The lowest risk path is then calculated based on a modified Dijkstra's algorithm. The model is developed and implemented such that it conforms to the Active Dynamic Signage System (ADSS). Herein, a model is designed to monitor the building in real-time and in case of any unexpected event, changes to the evacuation strategy are communicated to occupants using ADSS. The case study shows that the proposed model for real-time evacuation management can significantly enhance the safety level of evacuation compared to the conventional shortest path evacuation plan.

Degradation of Fat by a Bioaugmentation Product Comprising of Bacillus spp. Before and After the Addition of a Pseudomonas sp

A bioaugmentation product, BFL, comprising strains of the genus Bacillus, is evaluated for its ability to degrade fat in laboratory‐scale experiments. Addition of a Pseudomonas putida strain CP1 to the commercial mixed population (BFL‐CP1) is tested for optimization of fat degradation. Experiments are carried out in aerobic batch culture, at 30 °C and 150 rpm for 13 days incubation. A minimal medium (MM) and an enriched nutrient medium (ENM) are investigated supplemented with 1% (w/v) butter. Fat removal is determined gravimetrically and the lipid content is analyzed using thin layer chromatography (TLC) and gas chromatography (GC). No degradation of butter by the product is recorded after 13 days of incubation, while up to 97% degradation is observed by BFL‐CP1. All the Bacillus isolates produced lipase but not the Pseudomonas putida. TLC and GC results suggested that while the Bacillus spp. hydrolyzed the fat to fatty acids and glycerol, complete metabolism of the breakdown products only took place in the presence of the Pseudomonad sp. A citrate buffer is used to investigate fat removal by BFL‐CP1in low and stable pH using citrated minimal buffer. Similar fat removal is observed. The use of citrated minimal buffer caused flocculation of the mixed culture, a phenomenon desirable for fats, oils, and grease (FOGs) degradation in grease traps.Practical Applications: The bioaugmentation product, BFL, comprising strains of the genus Bacillus, only promoted high fat removal in a few days incubation after the addition of a Pseudomonas putida strain CP1. Analysis of the remaining fat suggested a cooperative activity between the Bacillus spp., which hydrolyzed the fat to fatty acids and glycerol, and the Pseudomonas putida CP1, which assimilated the released fatty acids. Formation of flocs were observed when the inoculum was tested under different environmental conditions with low pH. This phenomenon is desirable and along with the high degradative ability of the that new inoculum, it showed good potential for use in the treatment of FOG in grease traps.Fats, oils, and greases (FOGs) are generated in high amounts from food facilities presenting potential blockages and wastewater management problems. FOG may be intercepted at source using grease traps and may be treated biologically in situ using bioaugmentation, an environmentally desirable approach that involves the introduction of suitable microorganisms. In this study, combination of Gram‐positive and Gram‐negative bacteria successfully cooperated and degraded the FOG breaking down the lipids to fatty acids and glycerol by the activity of lipases, and also assimilating the produced fatty acids to carbon dioxide and water through the β‐oxidation process.

Wireless Power Transfer in Millimeter Wave Tactical Networks

Wireless power transfer may enable remotely powered operation of low-energy devices in challenging environments such as tactical networks. Millimeter wave (mmWave) is a possible candidate for wireless power transfer, thanks to the use of directional antenna arrays. This letter presents a feasibility study of using mmWave for wireless power transfer in a large-scale network consisting of power beacons and energy harvesters. Using stochastic geometry, system performance is characterized while treating the network nodes as potential blockages to mmWave signals. A link-level metric (energy coverage probability) and a network-level metric (overall success probability) are considered. The former captures whether a typical energy transfer link provides the requisite energy, while the latter also takes the network load into account. Numerical simulations suggest that network densification helps improve the performance, despite an increase in the blockage density. For a given density, deploying an optimal fraction of nodes as power beacons maximizes the number of successful energy harvesters. Finally, mmWave outperforms lower frequency solutions despite blockages.

Random Beamforming in Millimeter-Wave NOMA Networks

This paper investigates the coexistence between two key enabling technologies for the fifth generation (5G) mobile networks, non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communications. Particularly, the application of random beamforming to the addressed mmWave-NOMA scenario is considered in this paper, in order to avoid the requirement that the base station knows all the users' channel state information. Stochastic geometry is used to characterize the performance of the proposed mmWave-NOMA transmission scheme, by using the key features of mmWave systems, e.g., mmWave transmission is highly directional and potential blockages will thin the user distribution. Two random beamforming approaches which can further reduce the system overhead are also proposed to the addressed mmWave-NOMA communication scenario, where their performance is studied by developing analytical results about sum rates and outage probabilities. Simulation results are also provided to demonstrate the performance of the proposed mmWave-NOMA transmission schemes and verify the accuracy of the developed analytical results.

Policing in austerity: time to go lean?

Purpose The purpose of this paper is to consider the way in which the police service in England and Wales may be able to deal with significant cuts in government funding. The concept of “lean”, as developed in Japanese manufacturing in the 1950s, is proposed as a method by which waste can be reduced at the same time as improvements being made in policing outcomes. Characteristics of police culture and leadership are presented as potential blockages to the successful implementation of lean. Design/methodology/approach This paper reviews literature within the sphere of operations management, policing and organisational behaviour to make appropriate recommendations. A case study, considering the performance challenges facing the Metropolitan Police Service in London, is provided to aid understanding and act as a catalyst for further discussion and research. Findings This paper argues that the simplistic approach to managing austerity so far has been short sighted – rather than considering the longer term development of policing and how a methodology such as lean may be better placed to deliver genuine improvements in public service, whilst also meeting unprecedented fiscal challenges. Originality/value Consideration of lean within public sector management has received recent scrutiny, but very little is offered in terms of the opportunities that lean thinking can offer within policing.

Random Beamforming in Millimeter-Wave NOMA Networks

This paper investigates the coexistence between two key enabling technologies for the fifth generation (5G) mobile networks, non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communications. Particularly, the application of random beamforming to the addressed mmWave-NOMA scenario is considered in this paper, in order to avoid the requirement that the base station knows all the users' channel state information. Stochastic geometry is used to characterize the performance of the proposed mmWave-NOMA transmission scheme, by using the key features of mmWave systems, e.g., mmWave transmission is highly directional and potential blockages will thin the user distribution. Two random beamforming approaches which can further reduce the system overhead are also proposed to the addressed mmWave-NOMA communication scenario, where their performance is studied by developing analytical results about sum rates and outage probabilities. Simulation results are also provided to demonstrate the performance of the proposed mmWave-NOMA transmission schemes and verify the accuracy of the developed analytical results.

Scenario Reduction for Probabilistic Robot Path Planning in the Presence of Preferences on Hidden States

In practical robot motion planning, robots usually do not have a full model of their surrounding, and hence no complete and correct plan can be prepared in advance. In other words, in real-world scenarios a mobile robot operates in a partially known environment, meaning that there exists incomplete information about the true state of several ‘hidden’ variables, such as open/closed doors, which may represent potential blockages of the path of the robot. Consequently, a robot may have a probability distribution estimation of the states of the hidden variables and a preference over the possible values of such hidden variables. In this paper, to deal with the problem of choosing optimal policies for planning in off-line mode, a linear programming model is developed that incorporates the probability distribution of hidden variables. Furthermore, a heuristic method is proposed for planning in the presence of numerous hidden variables which relies on optimistic assumptive planning and produces near-optimal policies.

EPIGONE: the argus on the daily operation of throttle structures

This study presents the development of an Early Warning System (EWS) called EPIGONE focusing on the detection of dry weather overflows in the vicinity of throttle structures in sewer systems. Throttle structures are considered as vital parts of a sewer system as they are control sections limiting flow rates to a designed operational value. Because these structures are by definition prone to potential clogging or blockages, a close follow-up of the daily operation by an EWS facilitates increased vigilance or even alarm. Primary goal of EPIGONE is to alert operators and thus allow fast intervention in case of suspected failures of these structures within a settled timeframe. EPIGONE combines overflow water level measurements with rainfall radar information to determine Combined Sewer Overflow (CSO) activity during dry weather as this dual condition will indicate malfunctioning. This combination of measurements was found to be the most cost effective set-up to deploy on a large scale. Water level data are recorded and logged on-site and sent to a central controller via GSM/GPRS, where an algorithm determines dry weather overflow conditions. Rainfall radar data are used as criterion to decide on dry weather conditions. From there on alarms are sent out to multiple recipients via e-mail and/or text messages (SMS). Next to this, it is obvious that this system can also be used for ‘regular’ wet weather CSO monitoring.

Successful Laboratory Testing of Practical Methods of Hydraulic-Control-System Failure Diagnosis Using Valve Signatures

Summary The two hydraulic-diagnostic methods detailed in this paper can monitor the "health" of a subsea hydraulic-control system, diagnose or predict a system problem (particularly leaking or clogging problems), and locate its source by using readily available valve-signature data. The methods were tested in a laboratory environment using a hydraulic system with simulated clogging and leaking mechanisms. Leak detection was performed using both signature-matching and mass-balance methods. The results showed a mean accuracy of 93% for mass-balance method and 82% for the signature-matching method. Clog detection by signature matching showed high accuracy in the controlled laboratory condition. The difference between the simulated and actual clogging is within 1%. The methods provide a monitoring tool for detecting and locating potential leaks, blockages, and other system changes or anomalies, which could be used to estimate the potential high cost of intervention, possible downtime, and lost revenue.

Using video image analysis to count hens in cages and reduce egg breakage on collection belts

Stock people working in modern cage layer sheds spend more than half their daily work time directly checking hens to monitor health and welfare. In addition, mechanical egg collection belts must be checked for potential blockages that may result in cracked or broken eggs during the collection process. These are important tasks in the profitable management of modern multi-tier cage systems. However, where the upper tiers of cages are above stockperson eye level, the effectiveness of humans to perform these tasks accurately may be questioned. We investigated whether video image analysis (VIA, the ability of a computer to ‘see’) could automatically perform two common tasks – that of counting the number of hens per cage and scanning the egg collection belt to identify foreign (non-egg) objects. Cameras were attached to the robotic feeder that moved along the front of the cages. Views of the interior of the cages and the egg collection belt were recorded on digital video as the robotic feeder moved. Two VIA prototypes were evaluated, initially at the research institute and subsequently at a commercial farm. Using the respective automatic detection algorithms that were developed for the research, 79% of targets (hen legs) in cages were correctly counted, while 95% of foreign objects on the egg collection belt were detected. The results demonstrate that VIA can be used to monitor egg belts for potential blockages, and has potential as technology to count hens.

Design of a High-Speed Word-Switched Transport Station

A MININET acts as a user concentrator into the MININET word-switched network where each word (of up to 16 bits) entering a network port is mapped into a single packet for immediate transmission through the network. The stations maintain highly transparent connections between user devices. Among the important requirements for MININET are that, under saturation conditions, the communication resources of the network be shared fairly between the user devices and that the network possesses very short end-to-end propagation delays. The design of a station meeting these requirements, capable of processing over one million packets per second, is described. Its architecture is based on special-purpose processors which are functionally distributed. The biggest design challenge was to minimize the processing delay in the node while avoiding potential blockages and remaining fair. The final solution has been to use a master arbiter, which looks at the readiness of the ports and channels in conjunction with connection, routing, and flow control information. The station management entities reside in a general-purpose microcomputer. These monitor and modify the operation of the high-speed processors, establish virtual connections, and support the routing algorithm.