Experimental measurement and modeling study for estimation of wax disappearance temperature

Mansourpoor, Mojtaba; Azin, Reza; Osfouri, Shahriar; Izadpanah, Amir Abbas

doi:10.1080/01932691.2018.1461635

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…Daridon et al (2002), Ji et al (2004), Milhet et al (2005), Pauly et al (2007Pauly et al ( , 2010Pauly et al ( , 2012, Ghanaei et al (2007Ghanaei et al ( e 2014 desenvolveram modelos experimentais e termodinâmicos com intuito de prever a temperatura de aparecimento (WAT), de desaparecimento (WDT) e a quantidade de parafina precipitada (m%). Devido aos altos custos e às dificuldades atreladas à mensuração de dados experimentais, além da falta de precisão dos modelos termodinâmicos para preverem a temperatura de desaparecimento da parafina, o desenvolvimento de métodos computacionais tornou-se imprescindível na resolução desse tipo de problema (RAHIMPOUR et al, 2013;JALALNEZHAD et al, 2016;MANSOURPOOR et al, 2019). Métodos de inteligência artificial como rede neural artificial (ou, no inglês, artificial neural network -ANN), máquina de vetor de suporte (ou, no inglês, support vector machine -SVM), algoritmo genético (ou, no inglês, genetic algorithm -GA) e lógica difusa (ou, no inglês, fuzzy logic -FL) encontraram muitas aplicações em problemas de regressão e classificação de forma precisa, sendo ferramentas poderosas e confiáveis para análise de dados em problemas de engenharia (ZHANG et al, 2017;MANSOURPOOR et al, 2019).…”

Section: Introductionunclassified

Prediction of wax disappearance temperature: a review

Corrêa,

Dos Santos,

Simonelli

2024

CLIUM

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Wax deposition in pipelines is a recurring problem in the oil industry. Therefore, several studies have analyzed the phenomenon and predicted the wax disappearance temperature (WDT). This variable represents the exact solid-liquid equilibrium point. Such information is an effective aid in decision-making regarding pipelines and production facilities. However, the study of paraffin deposition is highly dependent on conducting experiments, which are typically costly and can make this type of analysis impractical. The increasing progress of models based on machine learning techniques has been an alternative to experimental and thermodynamic methods to predict this phenomenon. In the present study, a bibliographic review of the main authors on models for predicting kerosene deposition has been compiled.

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Section: Introductionunclassified

Prediction of wax disappearance temperature: a review

Corrêa,

Dos Santos,

Simonelli

2024

CLIUM

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“…It is evident that due to the quaternary system’s complexity, WAT measuring for only five different mixtures of a quaternary system is not sufficient to estimate system behavior. Mansourpoor et al 18 experimentally measured the WATs of 12 samples of Iranian crude oils and condensates using DSC and viscometry methods. They used an artificial neural network (ANN) model to estimate the WDTs of crude oils.…”

Section: Introductionmentioning

confidence: 99%

Experimental Measurement and Thermodynamic Modeling of the Wax Disappearance Temperature (WDT) for a Quaternary System of Normal Paraffins

et al. 2022

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Normal paraffin ( N -alkane)-based wax is well known as a severe problem in petroleum production, transportation, and processing. Implementing suitable solutions for wax-related problems requires vast technical knowledge and investigation of the wax disappearance temperature (WDT) of multicomponent systems in petroleum-dominated systems. In this study, the WDTs of a quaternary system comprising different mixtures of n -undecane + n -tetradecane + n -hexadecane + n -octadecane were measured using a visual-based diagnosis apparatus under atmospheric pressure. On the other hand, the WDTs of the studied systems are predicted by applying a solid solution model without any adjustable parameter. Two approaches namely γ–φ and γ–γ are assessed. In the (γ–φ) approach, perturbed-chain statistical associating fluid theory (PC-SAFT) is applied for liquid phase modeling, while the solid phase is described using different activity coefficient models. In the (γ–γ) approach, nonidealities of both the liquid and solid phases are investigated using different combinations of activity coefficient models such as ideal solution, regular solution theory, predictive Wilson, predictive UNIQUAC, and UNIFAC. Comparison of experimental data and thermodynamic modeling results indicates that applying the predictive UNIQUAC model for describing the nonideality of the solid phase and the regular solution model for the liquid phase is the best combination for the aforementioned system with the average absolute deviation (AAD) of 0.8 K.

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“…After that, with the continuous precipitation of wax crystals, the solid particles will be gradually connected to form a spatial network structure, and the crude oil is wrapped in its pores, which results in a deterioration of flows and eventually causes the blockage of the pipeline. It is concluded that the wax deposition process is influenced by many factors, such as crude oil composition, pipe wall material, temperature, , flow velocity, and flow pattern. , For the wax deposition mechanism, the classical theories include molecular diffusion, ,− shear dispersion, , Brownian motion, , and gravity deposition. , Recently, with the further development of the wax-deposited research, the mechanisms of shear stripping, gelation (emulsified nucleation) − and aging was proposed. ,− Many scholars have performed research regarding those mechanisms. Hoffmann et al and Mehrotra et al found that the carbon number distribution on the surface of wax deposition layer is closer to that of the flowing oil when the flow rate is low and the water content is high.…”

Section: Introductionmentioning

confidence: 99%

“…13,14 For the wax deposition mechanism, the classical theories include molecular diffusion, 5,15−17 shear dispersion, 12,18 Brownian motion, 19,20 and gravity deposition. 15,21 Recently, with the further development of the wax-deposited research, the mechanisms of shear stripping, 22 gelation (emulsified nucleation) 23−25 and aging was proposed. 7,26−28 Many scholars have performed research regarding those mechanisms.…”

Section: ■ Introductionmentioning

confidence: 99%

“…its influence on wax deposition is generally believed to be small and negligible 15,21 shear stripping The shear stress produced by the oil flow on the wax deposition layer will strip off the oil and wax molecules in the crevices of the deposition, which could increase the hardness of the deposition layer and the number of C atoms in the composition 22,44 stabilizes the wax deposition on the wall gelation (emulsification nucleation) the emulsified particles in the low-temperature oil came into contact and combined with each other, and the wax crystal particles are adsorbed on the surface of the dispersed phase of the emulsification system, accumulated, and grown continuously to form a gel network structure and accelerate the wax deposition process 23−25 at high oil temperatures, molecular diffusion plays a important role; at low oil temperatures, gelation plays a leading role rotating disk device, the loop device, and the OSDC device are dynamic apparatuses. 37,48 However, there is no clear definition for the division of the experimental device.…”

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wax and Wax–Hydrate Deposition Characteristics in Single-, Two-, and Three-Phase Pipelines: A Review

Liu

Wang

et al. 2020

Energy Fuels

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In the oil pipeline transportation system, wax deposition will reduce the effective circulation area, weaken the overall pipeline transportation capacity, and even block the pipeline, which seriously threatens the safety of the pipeline flows. In the actual production of oil fields and the transportation of oil products, except for the single-phase waxy crude oil, under most conditions, operations are performed using multiphase mixed transportation, so it is necessary to determine the wax deposition characteristics in those pipeline systems. In this Review, three new wax deposition mechanisms proposed in recent years and several typical experimental devices were summarized, the wax deposition in oil–water and oil–gas flow systems and the wax–hydrate coupling deposition in oil–gas–water flow system flows were concluded; the mathematical models were also categorized based on the three flow systems. Meanwhile, it highlighted the fact that, in the two-phase flow, because of the complexity of the flow pattern and the presence of the second interface on the wax deposition process, independent theoretical research on the mechanism of wax deposition is particularly necessary; based on this, a more-accurate wax deposition prediction model can also be developed. For the coupled deposition of wax-hydrate in the three-phase flow system, experimental instruments such as FBRM, PVM and molecular dynamics simulations could be used to study the distribution characteristics and interaction rules of wax crystals and hydrate particles from a microscopic perspective. On the other hand, the mechanism of wax crystal and hydrate coupling deposition by additives such as antiwax agents, hydrate inhibitors, antipolymerization agents, and pipeline transportation conditions can be investigated experimentally to better control the deposition process during pipeline operation and ensure the safe flow in the transportation system.

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Experimental measurement and modeling study for estimation of wax disappearance temperature

Cited by 9 publications

References 25 publications

Prediction of wax disappearance temperature: a review

Prediction of wax disappearance temperature: a review

Experimental Measurement and Thermodynamic Modeling of the Wax Disappearance Temperature (WDT) for a Quaternary System of Normal Paraffins

Wax and Wax–Hydrate Deposition Characteristics in Single-, Two-, and Three-Phase Pipelines: A Review

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