Modeling wax disappearance temperature using robust white-box machine learning

•The WDT was modeled using a robust explicit-based ML approach.•The suggested smart correlations achieved remarkable performance.•The proposed new correlations outperformed the prior models.•The conservation of physical trend of WDT was testified using the trend analysis. Wax deposition is one of th...

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Bibliographic Details
Published in:	Fuel (Guildford) Vol. 376; p. 132703
Main Authors:	Nait Amar, Menad, Zeraibi, Noureddine, Benamara, Chahrazed, Djema, Hakim, Saifi, Redha, Gareche, Mourad
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15-11-2024
Subjects:	Gene expression programming Machine learning Petroleum production system Wax Wax disappearance temperature Petroleum production system Machine learning Wax Wax disappearance temperature Gene expression programming
Online Access:	Get full text
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Summary:	•The WDT was modeled using a robust explicit-based ML approach.•The suggested smart correlations achieved remarkable performance.•The proposed new correlations outperformed the prior models.•The conservation of physical trend of WDT was testified using the trend analysis. Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.
ISSN:	0016-2361
DOI:	10.1016/j.fuel.2024.132703