Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering

Abstract

This study was conducted to investigate the performance of a novel Random Forest (RF) model for predicting variables from an original experimental dataset of a diesel engine adapted to work with both compressed natural gas and diesel fuels. The aim was to develop a reliable framework for diesel engine emissions prediction that could assist designers, engineers, and decision-makers in optimizing engine performance and reducing emissions. The engine was modified to run on compressed natural gas as well as diesel fuel, and five variables were studied. Trials were done on a six-cylinder diesel engine to assess the RF model, employing various factors for improving engine performance and emissions, such as fuel injection angles, air-fuel ratio mixtures, diesel-to-gas exchange rates, and fuel rail pressure. A tree structured Parzen estimator and six feature engineering approaches were used to tune the RF model's parameters. In addition, the Shapley Additive explanation (SHAP) approach adapting a concept coming from game theory is employed to interpret the RF model outputs. The results analysis showed that the RF model correctly predicted the output signals of the diesel engine, with determination coefficient R2 of 0.9811, 0.9276, 0.9516, 0.8842, and 0.8944, respectively, for the studied five output variables. The RF regression model's predictive power can be used to generate an efficient modeling framework, and successfully predicts the output signals of the diesel engine, confirming the viability, effectiveness, and competitive performance.