APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) TO FORECAST THE TREND OF IGNITION DELAY TIMING IN AN ENGINE USING BIODIESEL FUEL

Abstract

Using the artificial neutral network (ANN) model described in the methodology section, the authors obtained highly promising results. As an example, we illustrate the proposed method by presenting the predictions at compression ratio e = 15 and e = 17, where the ANN model achieved RMSE values of 24.72 (ms) and 32.44 (ms), MSE values of 611.34 and 1052.37, MAPE values of 0.89% and 1.43%, and R2 values of 0.98 and 0.96, respectively. The effectiveness of this new method is further confirmed through rigorous calculations and validation tests on the ANN performance model. The research results from this study not only develop and supplement existing knowledge in the domain but also substantially improve the accuracy of predicting ignition delay times for biodiesel engines. These results can be used to enhance engine performance, optimize the fuel efficiency, and reduce emissions in real-world applications. This paper is novel because it introduces a cutting-edge approach using the ANN model, which outperforms conventional methods and significantly enhances the accuracy of fire delay prediction for engines using biodiesel fuel. The groundbreaking findings presented in this research hold great promise for advancing the field of internal combustion engines and may have broad implications for the automotive industry’s future. Keywords: Artificial Neural Network, Biodiesel Fuel, Diesel Engine, Ignition Delay Times DOI： https://doi.org/10.35741/issn.0258-2724.58.4.53