Assesment of the effects of chemical and physical parameters in the fluidization of biomass and sand binary mixtures through statistical analysis

• Published in: Energy (Oxford) 2020-01, Vol.190, p.116401, Article 116401
• Main Authors: Ocanha, Enzo Schlottfeldt, Zinani, Flávia Schwarz Franceschini, Modolo, Regina Celia Espinosa, Santos, Fernando Almeida
• Format: Article
• Language: English
• Subjects: Angle of repose; Bagasse; Binary mixtures; Biomass; Branches; Bulk density; Correlation analysis; Design parameters; Diameters; Empirical analysis; Eucalyptus; Fluidization; Fluidization. biomass. renewable energy sources. multiple linear regression; Fluidizing; Fruit trees; Hardwoods; Lignocellulose; Morphology; Organic chemistry; Physical properties; Physicochemical properties; Regression analysis; Sand; Statistical analysis; Sugarcane
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.116401

Empirical correlations of fluidization design parameters such as characteristic velocity are based on properties such as particle mean diameter, sphericity and true density. These properties are difficult to measure for particles with morphologies typical of lignocellulosic biomasses. Thus, these correlations generally show limited practical applicability. The aim of the present work was to verify through an innovative multiple linear regression if other physical and physicochemical properties would be more appropriate for characteristic fluidization velocities prediction. The methodology involved evaluating sugarcane straw and bagasse, eucalyptus wood, rice husk and apple tree branches in binary mixtures with sand in the proportions of 20, 40, 60 and 80% of biomass volume fraction. The multiple linear regression analysis indicated that the mixture bulk density and repose angle were the most relevant parameters in the prediction of characteristic fluidization velocities. Two correlations were developed employing these properties and attained suitable results: the adjusted R2 coefficient obtained for Umf prediction was 0.772 while for Ucf prediction was 0.855. In 90% of mixtures, the relative error found for Umf was smaller than 24.3% while for Ucf this error was smaller than 22.5%. •Fluidization is essential for thermal energy conversions in a low carbon scenario.•Empirical fluidization correlations generally show limited practical applicability.•Multiple linear regression was used to assess relevant fluidization parameters.•Biomasses evaluated were sugarcane, eucalyptus, rice husk and apple tree branches.•Biomass densities and repose angles presented highlighted fluidization relevance.