An overview of slagging and fouling indicators and their applicability to biomass fuels

Abstract

Slagging and fouling are common problems associated with biomass firing. The different nature of the mineral and phase composition of biomass ash makes the vast experience with coal firing insufficient for its translation to biomass fuels, especially when it comes to slagging and fouling behavior. Biomass tends to have lower ash content than coals; however, it is often rich in volatile alkalis. The mineral deposits found on boiler walls and superheater tubes are often comprised of alkali compounds. Numerous studies on ash melting and particle sticking behavior have been conducted. Laboratory observed ash fusion temperatures are commonly used to evaluate the slagging and fouling propensity of fuels. The tests are often time consuming, therefore several predictive indices have been developed to estimate the propensity based on the ash composition alone. Thermodynamic models as well as neural networks have also been applied to this end. However, for practical in the field purposes, the ash fusion tests and predictive indices are preferred because of their convenience. An overview of these indices is presented in this work. A sizeable dataset has been collected in order to statistically evaluate the applicability of the indices and of several AFT prediction formulas. General trends in ash composition on this extensive dataset have also been illustrated. Finally, a more convenient graphical solution is presented for preliminary slagging and fouling predictions.Slagging and fouling are common problems associated with biomass firing. The different nature of the mineral and phase composition of biomass ash makes the vast experience with coal firing insufficient for its translation to biomass fuels, especially when it comes to slagging and fouling behavior. Biomass tends to have lower ash content than coals; however, it is often rich in volatile alkalis. The mineral deposits found on boiler walls and superheater tubes are often comprised of alkali compounds. Numerous studies on ash melting and particle sticking behavior have been conducted. Laboratory observed ash fusion temperatures are commonly used to evaluate the slagging and fouling propensity of fuels. The tests are often time consuming, therefore several predictive indices have been developed to estimate the propensity based on the ash composition alone. Thermodynamic models as well as neural networks have also been applied to this end. However, for practical in the field purposes, the ash fusion tests and predictive indices are preferred because of their convenience. An overview of these indices is presented in this work. A sizeable dataset has been collected in order to statistically evaluate the applicability of the indices and of several AFT prediction formulas. General trends in ash composition on this extensive dataset have also been illustrated. Finally, a more convenient graphical solution is presented for preliminary slagging and fouling predictions.