Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models

Solid scales can cause significant problems in oil production and transmission systems such as oil flow rate reduction. Wax is one of the most critical substances that are highly prone to precipitate and deposit in oil pipelines. This occurrence typically happens in low temperatures, and can extremely affect the flow rate. In the wax deposition process, wax disappearance temperature (WDT) is a crucial factor since it denotes the lowest temperature, at which wax deposits melt and the consequences of wax deposition vanish. In this research, smart models were utilized to predict WDT as a function of molar mass and pressure according to a comprehensive database. The proposed intelligent models are radial basis function (RBF) and multilayer perceptron (MLP) neural networks, adaptive neuro-fuzzy inference system (ANFIS), random forest (RF), extra tree (ET), and decision tree (DT). The MLP networks were trained with Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms, and the ANFIS models were coupled with three optimization algorithms, namely Cultural Algorithm (CA), Biogeography-based Optimization (BBO), and Teaching-Learning-Based Optimization (TLBO). Results demonstrate that the developed RF model could present an outstanding performance and provide predictions for WDT with the lowest average absolute percent relative error (AAPRE = 0.246%), the maximum coefficient of determination (for predicting = 0.993), and minimum root mean squared error (RMSE = 1.01). Trend analysis showed that increasing pressure and molar mass leads to wax disappearance temperature increase. Lastly, outlier discovery was performed using the Leverage approach to recognize the suspected data points. The outlier detection found that only 6 points (out of 346) are located in the upper and lower suspected data zones.

Automated summary

The study utilized intelligent models to predict wax disappearance temperature (WDT) and found that the RF model performed exceptionally well, providing WDT predictions with the lowest average absolute percent relative error (AAPRE = 0.246%). Increasing pressure and molar mass resulted in an increase in wax disappearance temperature. Outlier detection using the Leverage approach revealed only 6 out of 346 points located in the upper and lower suspected data zones.