Prediction of ash induced agglomeration in biomass fired fluidized b 2015 Fu

Energy crops and biogeneous residues offer the highest potential for future growth in biomass utilization. Traditional forest grown wood types, along with their consistent combustion characteristics, will thus be replaced by fuels with highly heterogeneous composition. Reliable prediction of their combustion characteristics and in particular of their ash behavior is essential for plant designers and operators trying to harvest this potential for energy conversion. In fluidized bed combustion, the fuel–ash induced agglomeration of the bed materials is one such behavior that needs to be described. In this paper a black-box model for agglomeration prediction was created through multivariate regression modeling using R-statistics v3.0.2. It based on the input variables bed ash concentration, particle size, fluidization velocity and fuel ash composition and predicts the maximum operable agglomeration-free temperature. Three linear and nine non-linear modeling algorithms have been applied to the data, optimized and validated in independent subsets. This validation was performed on results of controlled agglomeration tests, partly performed on our own test reactors and partly derived from literature. The final data set comprises 350 test results, covering 83 different fuels tested in seven different reactors. The validation revealed good predictive performance of the regression models, in particular of non-linear ensemble algorithms such as random forests, or cubist.