ArticleACS omega2025
Application of Robust Neural Networks to Modeling Crude Oil Viscosity Reduction Using Nanoparticles.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Viscosity plays a critical role in determining the flow behavior and recoverability of crude oil, particularly in enhanced oil recovery (EOR) processes. Recent advancements in nanotechnology have introduced nanoparticles (NPs) as effective agents for reducing oil viscosity and improving EOR efficiency. In this study, 646 experimental data points were used to model the reduction in crude oil viscosity achieved through the NPs treatment. Four neural network architectures were applied to predict the final-to-initial viscosity ratio: multilayer perceptron (MLP), cascade-forward neural network (CFNN), radial basis function network (RBF), and generalized regression neural network (GRNN). Among them, the CFNN trained using the Levenberg-Marquardt algorithm (CFNN-LM) demonstrated the highest performance, achieving the lowest overall average absolute relative error of 1.83% and the highest coefficient of determination (
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