Prediction of higher heating value of biochars using proximate analysis by artificial neural network
| Autor: | Saba A. Gheni, Gülce Çakman, Selim Ceylan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Renewable Energy
Sustainability and the Environment business.industry 020209 energy Biomass 02 engineering and technology 010501 environmental sciences Solid fuel 01 natural sciences Biofuel Linear regression Biochar 0202 electrical engineering electronic engineering information engineering Coal Heat of combustion business Biological system Pyrolysis 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Biomass Conversion and Biorefinery. |
| ISSN: | 2190-6823 2190-6815 |
| DOI: | 10.1007/s13399-021-01358-4 |
| Popis: | The biochars obtained from the pyrolysis of biomass at different conditions have the potential to be used as biofuels. Thus, as a critical fuel property, the higher heating value (HHV) of biochars must be determined to decide on their application area. However, oxygen bomb calorimeters that are employed for HHV determination are expensive. Also, analysis is time-consuming, needs specialists, and can suffer from experimental errors. Although some model equations are available for solid fuels (biomass, coal, etc.) to calculate HHV, biochar has different properties, and a new model is required. This study aims to form an artificial neural network (ANN) model in order to estimate HHV of biochars by using simple proximate analysis data of 129 different biochars. The experimental and the predicted model results showed good agreement that the ANN model presented the highest regression coefficient of 0.9651 and the lowest mean absolute deviation of 0.5569 among all models previously reported in the literature. |
| Databáze: | OpenAIRE |
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