Woody and agricultural wastes are important fuels in many countries, and have the potential of being even more important in the future. The main problems of plant biomass combustion are low ash melting temperatures and increased emissions. The most widely used treatment to solve the problem of low ash melting point is blending a fuel with an additive. In this work, pellets were produced from wheat straw containing wood sawdust and paper sludge in the following proportions 40:40:20 and 45:45:10 (straw/sawdust/paper sludge). The purpose of this work was to study the influence of sludge and dendromass on the straw pellet parameters and combustion process. The highest calorific value of 15.71 MJ kg−1 was registered for a sample with a 10% paper sludge concentration. The effectiveness of paper sludge was proved, and the ash melting temperature was increased from 1025 to 1328 °C for the same sample.

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.

Solid fuel combustion accompanies particulate matter production. These particles negatively affect human health, so ways of capturing them are being sought. Local heat sources are a major producer, with this article focusing on the flue gas tract of this heat source. A baffle has been placed in the flue gas tract, the position of which is changing. It is observed the impact of particulate matter flowing through this baffle with a focus on the settling areas. Particles are trapped in the settling areas and do not flow further through the flue gas tract. Particulate matter flowing was investigated by the visualization method called Particle image velocimetry (PIV). Visualizing the flow allows to get a figure of the flow and a dynamic record of the state of the object. The result of the visualization is the assessment of the monitored flow, the determination of its trajectory and streamlines.

The standard detection of the emission and power parameters of the combustion process can be time and money consuming due to the performance of experimental measurements with the necessary measuring instruments. Alternatives to describe the combustion process are mathematical models. Their variability is high and therefore, depending on the need for use, an analytical mathematical model was chosen. Among the computationally faster and less time-consuming models is the analytical model, which can not only describe some parameters of the combustion process (power, emissions and others) but also predict the parameters of the combustion process according to the selected input values. This work is focused on the prediction of selected emission parameters through an analytical model for 100 % straw and 100 % wood pellets.

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