A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods

Nguyen Van Tuan; Duong Quang Minh; Nguyen Xuan Khoa; Ocktaeck Lim

doi:10.1021/acsomega.2c07186

A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods

ACS Omega. 2023 Mar 7;8(11):9995-10005. doi: 10.1021/acsomega.2c07186. eCollection 2023 Mar 21.

Authors

Nguyen Van Tuan¹, Duong Quang Minh¹, Nguyen Xuan Khoa², Ocktaeck Lim³

Affiliations

¹ Faculty of Mechanical Engineering, University of Transport Technology, No. 54 Trieu Khuc Street, Thanh Xuan District, Hanoi 100000, Vietnam.
² HaNoi University of Industry, No. 298, Cau Dien Street, Bac Tu Liem District, Ha Noi 100000, Vietnam.
³ School of Mechanical Engineering, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Korea.

Abstract

Over time, machine learning methods have developed, but there have not been many studies comparing how well they predict ignition delays. In this study, a model that forecasts the ignition delay of a diesel engine utilizing diesel fuel and biodiesel fuel was developed using Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques. This work has clarified the problems in designing and training the model. The effectiveness of the ANN and SVM machine learning methods' ignition delay prediction models has been evaluated under various input variable conditions. The authors employed a data set of over 700 input data sets from diesel fuel and biodiesel in the B0 to B60 range for this purpose. To evaluate the accuracy of the models, the authors compared the average accuracy of the overall classification as well as the standard deviation. The results after training and verifying the accuracy of the models show that the SVM model has a better ability to predict the fire ignition delay than the ANN model. Specifically, with the test data set and the SVM model at compression ratio (ε) = 15, RMSE = 34.45 μs, MAPE = 1.30%, MAE = 28.33 μs, MAE = 28.33 μs, and R ² = 0.967, respectively, and the SVM model can predict well. At compression ratio ε = 17, RMSE = 30.18 μs, MAPE = 1.30%, MAE = 23.48 μs, and R ² = 0.908, respectively. With an ANN neural network model, the prediction error value at compression ratio ε = 15 is RMSE = 41.29 μs, MAPE = 1.35%, MAE = 29.68 μs, and R ² = 0.952, respectively; at compression ratio ε = 17, it is RMSE = 30.28 μs, MAPE = 1.25%, MAE = 23.00 μs, and R ² = 0.975, respectively. With this accuracy, the SVM model is fully capable of forecasting the ignition delay combustion time of diesel/biodiesel engines.