Simone Ferrero
Neural Network Algorithm for Water Dew Point Detection in Diesel Passenger Car's Exhaust Pipeline.
Rel. Ezio Spessa, Daniela Anna Misul. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2024
Abstract
In this work a research problem is going to be discussed. The hosting company is Bosch, one of the main software and ECU supplier of the world, being in particular the reference for all the main Italian OEMs. Bosch engineering team supports the customers, providing calibration services for different software functions that ranges from thermal models to rail and engine components government. This project has the goal of developing a proof of concept model, based on Machine Leaning (ML) architecture, showing the capabilities of the technology in improving the performances of the actual software function chosen for this study. The name of the function is "Dew Point Detection", which has the aim of predicting the presence of condensed liquid water inside the exhaust pipeline, fundamental to ensure the proper operation of the pollutant sensors (NOx, PM) mounted along the ATS (After Treatment System) exhaust pipeline.
The basic concept is that every combustion process produces discharge gasses, among which vapor water (steam) is a relevant fraction
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