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

• Published in: Measurement : journal of the International Measurement Confederation 2024-08, Vol.235, p.115018, Article 115018
• Main Authors: Bortnowski, Piotr, Matla, Jędrzej, Sierzputowski, Gustaw, Włostowski, Radosław, Wróbel, Radosław
• Format: Article
• Language: English
• Subjects: Decision trees; Emission prediction; Engine vibration analysis; Spectral uncertainty; Toxic compounds
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.115018

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%). [Display omitted] •Examined engine vibrations’ spectral uncertainty and exhaust toxin emissions.•Developed a decision-tree-based toxic emission prediction model.•Used spectral uncertainty average and engine torque as predictors.•Found fuel type irrelevant for prediction accuracy, enhancing universality.•Achieved satisfactory forecast accuracy — less than 8%.