Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

Saeid Shahpouri, Armin Norouzi, Christopher Hayduk, Reza Rezaei, Mahdi Shahbakhti and Charles Robert Koch
Additional contact information
Saeid Shahpouri: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada
Armin Norouzi: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada
Christopher Hayduk: IAV GmbH, 38518 Gifhorn, Germany
Reza Rezaei: IAV GmbH, 38518 Gifhorn, Germany
Mahdi Shahbakhti: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada
Charles Robert Koch: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada

Energies, 2021, vol. 14, issue 23, 1-25

Abstract: The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R 2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R 2 of 0.97.

Keywords: diesel engines; soot emissions; machine learning; gray-box modeling; data-driven modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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