Applying feature selection and machine learning techniques to estimate the biomass higher heating value.

ABSTRACT: The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson's correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares su

SUBMITTER: Abdollahi SA 

PROVIDER: S-EPMC10522575 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature