International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Ayam M. Alsaegh, Hussein Al-Gburi *

Power Mechanics Techniques Engineering Department, Al-Musaib Technical College. Al-Furat Al-Awsat Technical University, Kufa, Iraq

 

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ABSTRACT

This study considered the potential of artificial neural networks (ANNs) as analytical tools for modeling the main properties and performance of diesel engines fueled with biodiesel-zinc oxide (ZnO) nanoparticle blends. Experimental datasets were first collected from past researches to estimate kinematic viscosity, calorific value, brake thermal efficiency (BTE), and brake specific fuel consumption (BSFC) across various diesel-biodiesel ratios, ZnO dosages, engine rotational speeds, and loads. While adding biodiesel causes increasing viscosity and reducing heating value, the inclusion of ZnO nanoparticles was found to mitigate these drawbacks by stabilizing viscosity and improving energy content, which translated into improved combustion efficiency and reduced fuel consumption. To capture these complex nonlinear interactions, four independent ANN models were established: two for predicting viscosity and calorific value, and two for engine performance indicators (BTE and BSFC). The models employed feed forward backpropagation networks trained with the Levenberg–Marquardt algorithm. Statistical evaluation confirmed strong predictive capability, with the BTE and BSFC models showing the highest accuracy (R² values of 0.9 and 0.8, respectively), followed by viscosity (R² = 0.8) and calorific value (R² = 0.7). These results highlight that ANN performs best when outputs are strongly sensitive to input and operating conditions, such as in performance metrics, whereas intrinsic chemical properties remain more challenging to predict. Overall, the findings demonstrate that ZnO nanoparticles are effective additives for improving biodiesel–diesel blends and that ANN models provide reliable, computationally efficient alternatives to extensive experimental trials. This work bridges experimental evidence with machine learning, offering predictive tools and decision guidelines for optimizing biodiesel–nanoparticle formulations in diesel engines, while also identifying future research needs related to emissions modeling, dataset diversity, and real-time deployment.


Keywords: Artificial neural network, Biodiesel blends, Diesel engine performance, Fuel properties prediction, Machine learning in combustion, Zinc oxide nanoparticles.


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ARTICLE INFORMATION

Received: 2025-09-22
Revised: 2025-11-12
Accepted: 2025-12-07
Available Online: 2026-01-07


Cite this article:

Alsaegh, A.M., Hussein, A.G., 2025. Prediction of fuel properties and engine performance of biodiesel-ZnO blends using artificial neural networks. International Journal of Applied Science and Engineering, 23, 2025235. https://doi.org/10.6703/IJASE.202603_23(1).006

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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