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 R2 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 R2 of 0.97.
Highlights
Around the world, Compression Ignition (CI) engines power most heavy duty vehicles such as trucks and public buses
Bayesian optimization [39] is used for Regression Tree (RT), Support Vector Machine (SVM), and Ensemble of Regression Trees (ERT) models hyperparameters optimization while grid search [36] method is used for Neural Network (NN)-based models such as Artificial Neural Network (ANN) and Bayesian Neural Network (BNN)
To predict soot emissions for a compression ignition engine, physical, black-box, and gray-box modeling were used in this study
Summary
Compression Ignition (CI) engines power most heavy duty vehicles such as trucks and public buses. An alternative way for choosing the input feature set is using ML feature selection methods which was the main focus of our last study [23], where a new gray-box mechanism and black-box emission model for a different diesel engine was developed. For the post-processing stage, a systematic way including a K-means algorithm is applied in order to divide different methods and feature sets into groups based on their accuracy, complexity, timing, etc. Previous studies used conventional ML methods such as SVM and ANN and GPR with fixed input feature set for soot emissions modeling. Two K-means clustering algorithms that perform as filters are used to select the best soot emissions models This method could be used for other engine modeling studies.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
