Evaluation of slagging characteristics for microalgae and lignocellulose: A comparison of aggregative index and model

Abstract

Biomass combustion can generate the slagging problem in the power generation boiler, which reduces the efficiency and safety of the boiler. Therefore, it is necessary to evaluate the slagging tendency of biomass to reduce the slagging degree. In this study, six sample groups (three microalgae and three lignocellulose groups) were ashed, and the ash was analyzed by x-ray fluorescence. Microalgae contain more phosphorus than bagasse and other lignocelluloses, which leads to a heavier slagging tendency. After washing pretreatment, smaller and more separated ash particles were observed and the slagging tendencies were shallower in the washing groups. The weight value for six common single indices [acidic compounds ratio (B/A), silica ratio (G), silica to aluminous compounds ratio (S/A), alkaline index (AI), fouling index, and slag index] were calculated by the entropy weight method, and AI (weight value w = 0.2655) was the most important index affecting the slagging tendency. An aggregative index Rs was obtained by the multiple regression analysis method based on the six single indices, which covered all ash compositions. An artificial neural networks (ANN) model was established to predict the slagging tendency of biomass. The slagging tendencies of microalgae, bagasse, and 45 other kinds of lignocelluloses were estimated by the aggregative index and ANN method, and the results agreed well with the experiment slagging results. The aggregative index and model may serve to roughly estimate the combustion behavior of microalgae, lignocellulose, and fuels rich in Ca, P, or Si. The results have verified the correctness of the aggregative index and model, and provided a new reference for biomass slagging trend estimation based on ash composition.