Modeling Wax Disappearance Temperature Using Advanced Intelligent Frameworks

The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) which is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely Radial Basis Function Neural Network (RBFNN) coupled with Genetic Algorithm (GA) and Artificial Bee Colony (ABC). Besides, accurate and user-friendly correlation was established by implementing Group Method of Data Handling (GMDH). Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error (AARD) value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performances comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic mode...