View in EDS

Modeling Dynamic Viscosity of Pure Fatty Acid Methyl Esters via Robust Machine Learning Approaches.

Saved in:
Bibliographic Details
Title: Modeling Dynamic Viscosity of Pure Fatty Acid Methyl Esters via Robust Machine Learning Approaches.
Authors: Abu‐Shareha, Ahmad Adel1 (AUTHOR), Zaki, Magdi E. A.2 (AUTHOR), Alfilh, Raed3 (AUTHOR), Sudhamsu, Gadug4 (AUTHOR), Sahu, Prabhat Kumar5 (AUTHOR), Kamalesh, Murari Devakannan6 (AUTHOR), Sharma, Sumit7 (AUTHOR), Gomha, Sobhi M.8 (AUTHOR) smgomha@iu.edu.sa, Hussaini, Soraya9 (AUTHOR) soraya.hussaini1995@outlook.com
Source: Chemical Engineering & Technology. Apr2026, Vol. 49 Issue 4, p1-20. 20p.
Subjects: Dynamic viscosity, Machine learning, Biomass energy, Fatty acid methyl esters, Pressure measurement, Thermodynamics
Abstract: Fatty acid methyl esters (FAMEs) are renewable, biodegradable biofuels. This study built machine learning models (DT, AdaBoost, EL, K‐nearest neighboring (KNN), random forest (RF), EN, CNN, SVR, and MLP‐ANN) to predict their dynamic viscosity using 488 literature data points. Inputs: temperature, pressure, molar mass, and C/H/O fractions. Models used five‐fold cross‐validation (90% training/validation, 10% testing). Metrics: R2, mean squared error (MSE), AARE%. MLP‐ANN performed best (R2 = 0.9987, MSE = 0.0038, and AARE = 1.68%). DT and AdaBoost showed higher errors; elastic net was weakest (R2 = 0.8486, AARE ≈ 25.78%). Pressure was the most impactful parameter, followed by temperature. SHapley Additive exPlanations (SHAP) analysis confirmed pressure as dominant. The framework is robust, accurate, and cost‐effective across broad thermodynamic conditions. [ABSTRACT FROM AUTHOR]
Copyright of Chemical Engineering & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Fatty acid methyl esters (FAMEs) are renewable, biodegradable biofuels. This study built machine learning models (DT, AdaBoost, EL, K‐nearest neighboring (KNN), random forest (RF), EN, CNN, SVR, and MLP‐ANN) to predict their dynamic viscosity using 488 literature data points. Inputs: temperature, pressure, molar mass, and C/H/O fractions. Models used five‐fold cross‐validation (90% training/validation, 10% testing). Metrics: R2, mean squared error (MSE), AARE%. MLP‐ANN performed best (R2 = 0.9987, MSE = 0.0038, and AARE = 1.68%). DT and AdaBoost showed higher errors; elastic net was weakest (R2 = 0.8486, AARE ≈ 25.78%). Pressure was the most impactful parameter, followed by temperature. SHapley Additive exPlanations (SHAP) analysis confirmed pressure as dominant. The framework is robust, accurate, and cost‐effective across broad thermodynamic conditions. [ABSTRACT FROM AUTHOR]
ISSN:09307516
DOI:10.1002/ceat.70220