Effect of post-injection strategies on regulated and unregulated harmful emissions from a heavy-duty diesel engine

Farhan, Sheikh Muhammad; Wang, Pan; Wu, Yan; Yi, Jing; Lei, Lili

doi:10.1177/1468087420980917

Cited by 5 publications

(1 citation statement)

References 40 publications

(47 reference statements)

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“…There are three main pulses in the Cummins diesel engine injection system in this work; Pulse I is pre-injection, Pulse II is the main injection, and Pulse III is post-injection which only occur for limited load areas. Post injection plays a crucial rule in lowering soot emissions production by increasing the soot emissions burn rate [35].…”

Section: Gray-box and Black-box Modelsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Gray-box and Black-box Modelsmentioning

confidence: 99%