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.

中文翻译：

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评估微藻和木质纤维素的结渣特性：综合指数和模型的比较

生物质燃烧会在发电锅炉中产生结渣问题，从而降低锅炉的效率和安全性。因此，有必要评估生物质的结渣趋势以降低结渣程度。在这项研究中，将六个样品组（三个微藻和三个木质纤维素组）灰化，并通过X射线荧光分析灰分。微藻类所含的磷比甘蔗渣和其他木质纤维素要多，这会导致更严重的结渣趋势。在洗涤预处理之后，观察到洗涤组中的灰分较小且分离得更多，并且结渣趋势较浅。六种常见单一指标的重量值[酸性化合物比（B / A），二氧化硅比（G），通过熵权法计算二氧化硅与铝化合物的比率（S / A），碱性指数（AI），结垢指数和矿渣指数]，而AI（重量值w  = 0.2655）是影响硅铝含量的最重要指标。结渣趋势。综合指数Rs通过基于六个单一指标的多元回归分析方法获得了该值，该指标涵盖了所有灰分成分。建立了人工神经网络（ANN）模型来预测生物质的结渣趋势。用综合指数和人工神经网络方法估算了微藻，甘蔗渣和其他45种木质纤维素的结渣趋势，其结果与实验结渣结果吻合良好。综合指数和模型可用于粗略估计微藻，木质纤维素和富含Ca，P或Si的燃料的燃烧行为。结果验证了综合指标和模型的正确性，为灰分组成的生物质结渣趋势估算提供了新的参考。