Machine learning model designed to predict the amount of CO₂ produced by a small pellet boiler

KET, SjF, Žilinská Univerzita v Žiline, Univerzitná 1, 010 26 Žilina

^* Corresponding author: alexander.backa@fstroj.uniza.sk

Abstract

Emissions, including CO₂ emissions, are generated during the combustion process. Perfect combustion of biomass should not lead to the formation of CO, but all carbon should burn perfectly and change to CO₂ by the oxidation process. Under real conditions, complete combustion never occurs and part of the carbon is not burned at all or only imperfectly to form CO. The aim of the work was to create a prediction model of machine learning, which allows to predict in advance the amount of CO₂ generated during the combustion of wood pellets. This model uses machine learning regression methods. The most accurate model (Gaussian process) showed a root-mean-square error, RMSE = 0.55. The resulting mathematical model was subsequently verified on independent measurements, where the ability of the model to correctly predict the amount of CO₂ generated in % was demonstrated. The average deviation of the measured and predicted amount of CO₂ represented a difference of 0.53 %, which is 8.8 % of the total measured range (3.08 - 9.2). Such a model can be modified and used in the prediction of other combustion parameters.