Prediction of toxic compounds emissions in exhaust gases based on engine vibration and Bayesian optimized decision trees

Emission control is vital for environmental and health protection. Traditional methods to assess toxic substance emissions from combustion engines are generally expensive and time-consuming. This paper proposes a new approach using indirect methods that predict emissions based on current engine operational parameters. The focus is on analyzing the engine's vibrational signal. Extensive research was conducted in a laboratory setting, using an diesel engine, fueled by various types of fuel. The study linked the spectral uncertainty of engine vibrations with the emission levels of toxic substances. A emission prediction model was developed, utilizing a decision tree system. This model's hyperparameters were finely tuned using Bayesian optimization techniques. The model for a single predictor in the form of spectral uncertainty already showed satisfactory accuracy in predicting the emission of several harmful substances (10%). However, supplementing it with a second predictor in the form of engine torque allows for even smaller errors (8%).