Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm
Xiaorui Liu, Yang Jiamin, Yuan Longji
Abstract
) larger than 0.91 was achieved when the SSA-SVM model was implemented, and the values of RMSE were also fairly acceptable. The agreement between experimental data and SSA-SVM predicted values demonstrated the high predictive precision of the model. This study provides a reference for the utilization of torrefied biomass in solid fuels and the design of torrefaction facilities.
Topics & Concepts
TorrefactionSupport vector machineBiomass (ecology)SparrowRaw materialKernel (algebra)NitrogenAlgorithmComputer scienceProcess engineeringBiological systemEnvironmental scienceMathematicsPulp and paper industryArtificial intelligenceEngineeringChemistryWaste managementAgronomyCombinatoricsBiologyOrganic chemistryPyrolysisEcologyThermochemical Biomass Conversion ProcessesIron and Steelmaking ProcessesBiofuel production and bioconversion