Stéfano Corrêa, Luiz Carlos Lobato dos Santos, G. Simonelli
{"title":"Prediction of wax disappearance temperature: a review","authors":"Stéfano Corrêa, Luiz Carlos Lobato dos Santos, G. Simonelli","doi":"10.53660/clm-3218-24g10","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":505714,"journal":{"name":"Concilium","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concilium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53660/clm-3218-24g10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.