Tuan Nguyen Van, Minh Duong Quang, Khoa Nguyen Xuan, Lim Ocktaeck
Faculty of Mechanical Engineering, University of Transport Technology, No. 54 Trieu Khuc Street, Thanh Xuan District, Hanoi 100000, Vietnam.
HaNoi University of Industry, No. 298, Cau Dien Street, Bac Tu Liem District, Ha Noi 100000, Vietnam.
ACS Omega. 2023 Mar 7;8(11):9995-10005. doi: 10.1021/acsomega.2c07186. eCollection 2023 Mar 21.
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 = 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 = 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 = 0.952, respectively; at compression ratio ε = 17, it is RMSE = 30.28 μs, MAPE = 1.25%, MAE = 23.00 μs, and = 0.975, respectively. With this accuracy, the SVM model is fully capable of forecasting the ignition delay combustion time of diesel/biodiesel engines.
随着时间的推移，机器学习方法不断发展，但比较它们预测着火延迟效果的研究并不多。在本研究中，利用人工神经网络（ANN）和支持向量机（SVM）机器学习技术开发了一个预测使用柴油和生物柴油的柴油发动机着火延迟的模型。这项工作阐明了模型设计和训练中的问题。在各种输入变量条件下评估了ANN和SVM机器学习方法着火延迟预测模型的有效性。为此，作者采用了一组超过700个来自B0至B60范围内柴油和生物柴油的输入数据集。为了评估模型的准确性，作者比较了总体分类的平均准确率以及标准差。对模型准确性进行训练和验证后的结果表明，SVM模型比ANN模型具有更好的预测着火延迟的能力。具体而言，对于测试数据集和压缩比（ε）= 15时的SVM模型，均方根误差（RMSE）= 34.45微秒，平均绝对百分比误差（MAPE）= 1.30%，平均绝对误差（MAE）= 28.33微秒，相关系数（ ）= 0.967，SVM模型能够很好地进行预测。在压缩比ε = 17时，RMSE = 30.18微秒，MAPE = 1.30%，MAE = 23.48微秒， = 0.908。对于ANN神经网络模型，在压缩比ε = 15时的预测误差值分别为RMSE = 41.29微秒，MAPE = 1.35%，MAE = 29.68微秒， = 0.952；在压缩比ε = 17时，分别为RMSE = 30.28微秒，MAPE = 1.25%，MAE = 23.00微秒， = 0.975。基于这样的准确性，SVM模型完全能够预测柴油/生物柴油发动机的着火延迟燃烧时间。
