ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Rigorous data-driven scrutinization of viscosity for pure fatty acid methyl esters

Ahmad Adel Abu-Shareha, Magdi E. A. Zaki, Raed Alfilh, Gadug Sudhamsu, Prabhat Kumar Sahu, Murari Devakannan Kamalesh, Sumit Sharma, Sobhi M. Gomha, and Soraya Hussaini

Department of Data Science and Artificial Intelligence, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

E-mail: smgomha@iu.edu.sa

Received: 9 February 2026  Accepted: 23 April 2026

Abstract:

Precisely determining the dynamic viscosity of pure fatty acid methyl esters (FAMEs) is a fundamental requirement for various energy and chemical sectors, particularly in the design and processing of biofuels. The present research leverages advanced machine learning frameworks to estimate FAME viscosity by incorporating crucial input features, specifically temperature, pressure, molar mass, and elemental composition including carbon (wt. %), hydrogen (wt. %), and oxygen (wt. %). To accomplish this, Gradient Boosting Decision Tree (GBDT) networks were structured and tested against a robust dataset containing \(488\) experimental observation points gathered from seven different types of FAMEs. The modeling phase utilized four distinct tuning strategies: Batch Bayesian Optimization (BBO), Evolution Strategies (ES), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). To quantify the precision of the developed models, statistical metrics such as the coefficient of determination (\({R}^{2}\)), Mean Squared Error (\(MSE\)), and Average Absolute Relative Error (\(AARE\text{\%}\)) were utilized. Through a rigorous comparative analysis, the GBDT-BPI approach was identified as the most highly accurate formulation, delivering an outstanding overall \({R}^{2}\) of \(0.998716\) coupled with an \(MSE\) of \(0.003416\). Additionally, feature importance evaluations using the SHAP methodology indicated that system pressure and temperature act as the dominant driving forces dictating viscosity behavior, followed subsequently by hydrogen content, oxygen content, molar mass, and carbon content. The investigation also juxtaposed the computational demands of the applied optimization protocols. While BPI yielded superior accuracy, the GPO algorithm proved to be the most computationally economical, completing its execution in merely \(182.9\) seconds. Ultimately, these outcomes underline the profound capability of data-driven computational tools to rapidly and accurately evaluate the viscometric properties of FAMEs, offering an indispensable theoretical foundation for industrial operations seeking to maximize process efficiency and cost-effectiveness.

Keywords: Biodiesel properties; Fatty acid methyl esters; Metaheuristic optimization; SHAP value analysis

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-026-04952-w

Chemical Papers 80 (8) 9275–9294 (2026)