Diesel Engine Power Prediction Based on Fuel Blends Using Neural Network

Authors

  • Rizqi Fitri Naryanto Mechanical Engineering Department, Faculty of Engineering, Universitas Negeri Semarang, Indonesia
  • Hadromi Hadromi Automotive Engineering Education, Faculty of Engineering, Universitas Negeri Semarang Indonesia
  • Ari Dwi Nur Indriawan Musyono Mechanical Engineering Department, Faculty of Engineering, Universitas Negeri Semarang, Indonesia
  • Rizki Setiadi Mechanical Engineering Department, Faculty of Engineering, Universitas Negeri Semarang, Indonesia
  • Wahyu Caesarendra Department of Mechanical and Mechatronics Engineering, Curtin University Malaysia

DOI:

https://doi.org/10.31181/rme492

Keywords:

Engine Diesel Performance, Machine Learning, Neural Network, Fuel Blends

Abstract

Diesel engines are extensively used across various sectors—including transportation, petrochemicals, power generation, military, and heavy machinery—with particularly widespread application in the automotive industry. An internal combustion engine burns a mixture of fuel and air to efficiently generate mechanical energy. Engine performance, measured by power output, torque, and fuel efficiently enhances with improved combustion efficiency. To predict diesel engine performance, this study employs a neural network model. The objective is to analyze engine behavior across various fuel blends and identify the most accurate machine learning-based prediction model. The best performance of the biodiesel fuel blends with diesel is in MIX-5, which is 69.8 kW in 4900 rpm. Based on testing on experimental data, the best neural network topology is obtained with three hidden layers. In this neural network topology, training is carried out on the engine performance and the regression value is 0,98416.

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Published

2025-10-25

Issue

Vol. 6 No. 1 (2025): Reports in Mechanical Engineering

Section

Articles

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How to Cite

Diesel Engine Power Prediction Based on Fuel Blends Using Neural Network. (2025). Reports in Mechanical Engineering, 6(1), 127-135. https://doi.org/10.31181/rme492