Publication | Peer reviewed papers | Verbrennung

Prediction of slag related problems during fixed bed combustion of biomass by application of a multivariate statistical approach on fuel properties and burner technology

Rebbling A, Näzelius IL, Schwabl M, Feldmeier S, Schön C, Dahl J, Haslinger W, Boström D, Öhman M, Boman C.

Published 2020

Citation: Rebbling A, Näzelius IL, Schwabl M, Feldmeier S, Schön C, Dahl J, Haslinger W, Boström D, Öhman M, Boman C. Prediction of slag related problems during fixed bed combustion of biomass by application of a multivariate statistical approach on fuel properties and burner technology. Biomass and Bioenergy 2020.137:105557.

Abstract

Slag is related to the melting properties of ash and is affected by both the chemical composition of the fuel ash and the combustion parameters. Chemical analysis of slag from fixed bed combustion of phosphorus-poor biomass show that the main constituents are Si, Ca, K, O (and some Mg, Al, and Na), which indicates that the slag consists of different silicates. Earlier research also points out viscosity and fraction of the ash that melts, as crucial parameters for slag formation. To the authors’ knowledge, very few of the papers published to this day discuss slagging problems of different pelletized fuels combusted in multiple combustion appliances. Furthermore, no comprehensive classification of both burner technology and fuel ash parameters has been presented in the literature so far. The objective of the present paper was therefore to give a first description of a qualitative model where ash content, concentrations of main ash forming elements in the fuel and type of combustion appliance are related to slagging behaviour and potential operational problems of a biomass fuel in different small- and medium scale fixed bed appliances.

Based on the results from the combustion of a wide range of pelletized biomass fuels in nine different burners, a model is presented for amount of slag formed and expected severity of operational problems. The model was validated by data collected from extensive combustion experiments and it can be concluded that the model predicts qualitative results.

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