A Comparative Evaluation of Thermodynamic Models for Prediction of Wax Deposition

2021 ◽  
Author(s):  
Jamilyam Ismailova ◽  
Aibek Abdukarimov ◽  
Bagdat Mombekov ◽  
Dinara Delikesheva ◽  
Luis E. Zerpa ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Prediction Models ◽  
Petroleum Industry ◽  
Mitigation Strategies ◽  
Thermodynamic Models ◽  
Wax Deposition ◽  
Cold Environments ◽  
Ideal Solutions ◽  
Solid Phases ◽  
Assurance Problem

Abstract Wax deposition on inner surfaces of pipelines is a costly problem for the petroleum industry. This flow assurance problem is of particular interest during the production and transportation of waxy oils in cold environments. An understanding of known mechanisms and available thermodynamic models will be useful for the management and planning of mitigation strategies for wax deposition. This paper presents a critical review of wax prediction models used for estimation of wax deposition based on chemical hydrocarbon compositions and thermobaric condition. The comparative analysis is applied to highlight the effective mechanisms guiding the wax deposition, and how this knowledge can be used to model and provide solutions to reducing wax deposition issues. One group of thermodynamic models assume that the precipitated wax is a solid solution. These models are divided into two categories: ideal (Erickson and Pedersen models) and non-ideal solutions (Won and Coutinho models). In the other group of models, the wax phase consists of many solid phases (Lira-Galeana model). The authors summarized the limitations of the models, evaluated, and identified ways to represent the overview of existing thermodynamical models for predicting wax precipitation. Within the strong demand from industry, the results of this manuscript can aid to aspire engineers and researcher.

scholarly journals Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 55
Author(s):  
Gary L. Achtemeier ◽  
Scott L. Goodrick
Keyword(s):  
Real Time ◽  
Short Range ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Density Currents ◽  
Gust Fronts ◽  
Gust Front ◽  
Range Forecasts

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.

The Influence of Rheological Parameters in Wax Deposition in Channel Flow

2010 ◽  
Author(s):  
L. R. Minchola ◽  
L. F. A. Azevedo ◽  
A. O. Nieckele
Keyword(s):  
Rheological Properties ◽  
Molecular Diffusion ◽  
Rectangular Channel ◽  
Rheological Parameters ◽  
Efficient Design ◽  
Wax Deposition ◽  
Deposition Mechanism ◽  
Cold Environments ◽  
Operational Problem ◽  
Newtonian Behavior

Wax deposition is a critical operational problem in crude oil transportation through pipelines in cold environments. Accurate prediction of the wax deposition is crucial for the efficient design of subsea lines. Wax deposition is a complex process for which the basic mechanisms are still not fully understood. Although Fick’s molecular diffusion model is considered by several authors as the leading deposition mechanism, it is shown that it does not represent well the wax deposition thickness, measured during the transient regime, in a simple experiment, in a rectangular channel, with a laboratory oil-wax mixture. Another important wax deposition mechanism identified is associated with the rheological properties of the fluid, since oil-paraffin mixtures shows a non-Newtonian behavior at temperatures below the fluid Wax Appearance Temperature. The mixture can be modeled as a Bingham fluid, with a dependence of the yield stress on wax concentration, temperature and rate of cooling. The present paper presents a numerical model for predicting wax deposition in channel flows considering the influence of rheological properties combined with a diffusion-based deposition mechanism. To determine the amount of deposit, the conservation equations of mass, momentum, energy and wax concentration in the mixture were numerically solved with the finite volume method. A nonorthogonal moving coordinate system that adapts to the wax interface deposit geometry was employed. The results demonstrated that additional deposition is obtained as a result of the non Newtonian behavior of the fluid. This trend is in agreement with experimental observation conducted in previous studies.

scholarly journals Estimating COVID-19 hospital demand using a non-parametric model: a case study in Galicia (Spain)

2020 ◽  
Author(s):  
Ana López-Cheda ◽  
María Amalia Jácome ◽  
Ricardo Cao ◽  
Pablo Martínez de Salazar
Keyword(s):  
Length Of Stay ◽  
Prediction Models ◽  
Current Knowledge ◽  
Mitigation Strategies ◽  
Hospital Ward ◽  
Monte Carlo Algorithm ◽  
Time To Event ◽  
Mixture Cure Model ◽  
Lengths Of Stay ◽  
Kaplan Meier

Understanding the demand for hospital beds for COVID-19 patients is key for decision-making and planning mitigation strategies, as overwhelming healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the length-of-stay in the ICU, requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, like the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients hospitalized, without parametric priors and adjusting for individual covariates. We applied a nonparametric Mixture Cure Model and compared its performance in estimating hospital ward/ICU lengths-of-stay to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and hospital ward length-of-stay estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting ICU occupancy, as well as discharge or death outcomes.

Traffic impact assessment and mitigation strategies for disruptions

2018 ◽  
Author(s):  
◽  
Yohan Chang
Keyword(s):  
Prediction Models ◽  
Management Strategies ◽  
Mitigation Strategies ◽  
Work Zone ◽  
Learning Approaches ◽  
Work Zones ◽  
Work Activity ◽  
Traffic Conditions ◽  
Traffic Impact ◽  
The Impact

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation research focuses on modeling traffic conditions affected by disruptive events such as work zones, incidents, and hurricanes. Using a combination of field data and simulation experiments, this research tried to address the relationship between disruptive events and their impact on traffic conditions and driver behavior. The first half of the dissertation assesses the impact of work zones. First, a data-driven assessment of the traffic impact of work zones using different data sources was conducted. A tool was developed for practitioners to estimate the delay and travel times of planned work zones. Second, traffic flow and speed prediction models were developed for work zones in order to assist with the better scheduling of work activity. Machine learning approaches were used to develop the prediction models. In addition to work zone effects, the effects of another special event, baseball gameday conditions, were also studied and traffic prediction models were developed. Third, using naturalistic driving study data, classification algorithms categorized work zone events into crashes, nearcrashes, and baseline conditions. In the second half of the dissertation, the focus shifts to the effect of emergency on evacuation. Two chapters in this section present the results of different traffic management strategies -- 1) contraflow crossover and ramp closure optimization and 2) reservation-based intersection control in connected and autonomous vehicle environment.

Study on the Pipeline Wax Deposition Mechanism and Influencing Factors

2012 ◽  
Vol 516-517 ◽  
pp. 1018-1021 ◽  
Author(s):  
Rong Ge Xiao ◽  
Bing Qian Wei ◽  
Pei Fen Yao ◽  
Dong Rui Yi
Keyword(s):  
Crude Oil ◽  
Influencing Factors ◽  
Petroleum Industry ◽  
Oil Production ◽  
Oil Exploration ◽  
Wax Deposition ◽  
Safe Operation ◽  
Deposition Mechanism ◽  
Crude Oil Production

The phenomenon of wax deposition is an extremely important issue in the petroleum industry. Researching it is of great help for the oil exploration, gathering, and transportation. This paper described the phenomenon of wax deposition, discussed the mechanism, analyzed the influencing factors and proposed the positive significance of the phenomenon of wax deposition on crude oil production and safe operation in oil gathering and transportation pipeline.

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