A review of the mathematical models for predicting the heating value of biomass materials
By J. M. Vargas-Moreno, Á. Callejón-Ferre, José Pérez-Alonso, B. Velázquez‐Martí. 2012
265 citations recorded by the source corpus.
Following the discovery of fire, biomass became the main source of energy used by mankind. Advanced societies have largely replaced the use of biomass with the use of fossil fuels, but our dependence on these ever scarcer resources, plus the need to reduce CO2 emissions in the face of climate change, is forcing us to make use of renewable sources of energy, including biomass. The exploitation of this resource often requires that its heating value be known. This can be determined either directly (though not cheaply) or by the use of models that predict it using a number of easily and economically determined variables. The present review gathers together the most recent models for predicting the heating value of biomass, assesses their areas of application, and highlights errors that have been made in their formulation, transcription, and in the references made to them. Different models have relied upon elemental, proximal, structural, physical and chemical analyses to determine the values of necessary variables, although those relying on the results of the first two types of analysis have been the most popular. The simplest models and those with the widest range of applications are those most often referred to in the literature. The frequency with which important information has been left unconsidered in some studies, which has led to errors in the expressions presented, as well as errors of transcription and referencing, suggest that future work should be undertaken with greater diligence.
Subfields: Biomedical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/b4430f356be276221f9751c214f8545eaa8b53a7
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Citations in the corpus
- Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value (2020, background): “The coefficient of determination values were very close to parity (1).”