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

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 R2 = 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 R2 = 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 R2 = 0.952, respectively; at compression ratio ε = 17, it is RMSE = 30.28 μs, MAPE = 1.25%, MAE = 23.00 μs, and R2 = 0.975, respectively. With this accuracy, the SVM model is fully capable of forecasting the ignition delay combustion time of diesel/biodiesel engines.

[1]  Yuchao Yan,et al.  An Artificial Neural Network Model to Predict Efficiency and Emissions of a Gasoline Engine , 2022, Processes.

[2]  O. Lim,et al.  A Study the Effect of Biodiesel Blends and the Injection Timing on Performance and Emissions of Common Rail Diesel Engines , 2021, Energies.

[3]  Ruomiao Yang,et al.  Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine , 2021, Applied Thermal Engineering.

[4]  O. Lim,et al.  A Study on the Effect of Ignition Timing on Residual Gas, Effective Release Energy, and Engine Emissions of a V-Twin Engine , 2021, Energies.

[5]  Tuan Ngo Nguyen,et al.  The Correlation of Biodiesel Blends with the Common Rail Diesel Engine’s Performance and Emission Characteristics , 2021, Energies.

[6]  O. Onukwuli,et al.  Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine , 2021, Advances in Mechanical Engineering.

[7]  Charles Robert Koch,et al.  Machine Learning-based Diesel Engine-Out NOx Reduction Using a plug-in PD-type Iterative Learning Control , 2020, 2020 IEEE Conference on Control Technology and Applications (CCTA).

[8]  C. Koch,et al.  A correlation-based model order reduction approach for a diesel engine NOx and brake mean effective pressure dynamic model using machine learning , 2020 .

[9]  Sibendu Som,et al.  Time-sequenced flow field prediction in an optical spark-ignition direct-injection engine using bidirectional recurrent neural network (bi-RNN) with long short-term memory , 2020, Applied Thermal Engineering.

[10]  Omar I. Awad,et al.  NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis , 2020 .

[11]  Jesús Casanova,et al.  Real-world fuel efficiency and emissions from an urban diesel bus engine under transient operating conditions , 2020 .

[12]  Tuan Anh Pham,et al.  Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections , 2019, Applied Sciences.

[13]  Qing-song Zuo,et al.  An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends , 2018 .

[14]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[15]  Nick Molden,et al.  Engine maps of fuel use and emissions from transient driving cycles , 2016 .

[16]  Feng Lin,et al.  A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine , 2016 .

[17]  Mehmet Sevkli,et al.  Fuel Consumption Models Applied to Automobiles Using Real-time Data: A Comparison of Statistical Models , 2016, ANT/SEIT.

[18]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[19]  Roger Sierens,et al.  Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks , 2014 .

[20]  Wenming Yang,et al.  Performance, combustion and emission characteristics of biodiesel derived from waste cooking oils , 2013 .

[21]  Medhat A. Nemitallah,et al.  Experimental investigations of ignition delay period and performance of a diesel engine operated with Jatropha oil biodiesel , 2013 .

[22]  Haji Hassan Masjuki,et al.  Ignition delay, combustion and emission characteristics of diesel engine fueled with biodiesel , 2013 .

[23]  D. Rothamer,et al.  Systematic study of ignition delay for jet fuels and diesel fuel in a heavy-duty diesel engine , 2013 .

[24]  Yongsheng He,et al.  Design of Engine-Out Virtual NO x Sensor Using Neural Networks and Dynamic System Identification , 2011 .

[25]  Roger Sierens,et al.  Ignition delay in a palm oil and rapeseed oil biodiesel fuelled engine and predictive correlations for the ignition delay period , 2011 .

[26]  Mustafa Inalli,et al.  Performance prediction of a ground-coupled heat pump system using artificial neural networks , 2008, Expert Syst. Appl..

[27]  Mustafa Inalli,et al.  Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system , 2008 .

[28]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[29]  Yetis Sazi Murat,et al.  Use of artificial neural networks for transport energy demand modeling , 2006 .

[30]  Soteris A. Kalogirou,et al.  Artificial neural networks in energy applications in buildings , 2006 .

[31]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[32]  Fabian Mauss,et al.  Modelling a Dual-Fuelled Multi-Cylinder HCCI Engine Using a PDF Based Engine Cycle Simulator , 2004 .

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[34]  Scott B. Fiveland,et al.  A Predictive Ignition Delay Correlation Under Steady-State and Transient Operation of a Direct Injection Diesel Engine , 2003 .

[35]  Y. Hung,et al.  Use of artificial neural networks , 1995 .